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huggingface/transformers

Generate: general TF XLA constrastive search are now slow tests

# What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

gante

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

0f78529f982eceb79c5855d0466c287ec8a18df1

Generate: general TF XLA constrastive search are now slow tests. # What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

./tests/models/qdqbert/__init__.py

huggingface/transformers

Generate: general TF XLA constrastive search are now slow tests

# What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

gante

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

0f78529f982eceb79c5855d0466c287ec8a18df1

Generate: general TF XLA constrastive search are now slow tests. # What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

./src/transformers/models/marian/configuration_marian.py

# coding=utf-8 # Copyright 2021 The Marian Team Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Marian model configuration""" from collections import OrderedDict from typing import Any, Mapping, Optional from ... import PreTrainedTokenizer from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast from ...onnx.utils import compute_effective_axis_dimension from ...utils import TensorType, is_torch_available, logging logger = logging.get_logger(__name__) MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP = { "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json", # See all Marian models at https://huggingface.co/models?filter=marian } class MarianConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MarianModel`]. It is used to instantiate an Marian model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Marian [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 58101): Vocabulary size of the Marian model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MarianModel`] or [`TFMarianModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models) forced_eos_token_id (`int`, *optional*, defaults to 0): The id of the token to force as the last generated token when `max_length` is reached. Usually set to `eos_token_id`. Examples: ```python >>> from transformers import MarianModel, MarianConfig >>> # Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration >>> configuration = MarianConfig() >>> # Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration >>> model = MarianModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "marian" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=58101, decoder_vocab_size=None, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function="gelu", d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=58100, classifier_dropout=0.0, scale_embedding=False, pad_token_id=58100, eos_token_id=0, forced_eos_token_id=0, share_encoder_decoder_embeddings=True, **kwargs ): self.vocab_size = vocab_size self.decoder_vocab_size = decoder_vocab_size or vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.share_encoder_decoder_embeddings = share_encoder_decoder_embeddings super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs, ) class MarianOnnxConfig(OnnxSeq2SeqConfigWithPast): @property # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task in ["default", "seq2seq-lm"]: common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ] ) if self.use_past: common_inputs["decoder_input_ids"] = {0: "batch"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"} else: common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"} if self.use_past: self.fill_with_past_key_values_(common_inputs, direction="inputs") elif self.task == "causal-lm": # TODO: figure this case out. common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ] ) if self.use_past: num_encoder_layers, _ = self.num_layers for i in range(num_encoder_layers): common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"} common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"} else: common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}), ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}), ] ) return common_inputs @property # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs def outputs(self) -> Mapping[str, Mapping[int, str]]: if self.task in ["default", "seq2seq-lm"]: common_outputs = super().outputs else: common_outputs = super(OnnxConfigWithPast, self).outputs if self.use_past: num_encoder_layers, _ = self.num_layers for i in range(num_encoder_layers): common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"} common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"} return common_outputs def _generate_dummy_inputs_for_default_and_seq2seq_lm( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: encoder_inputs = self._generate_dummy_inputs_for_encoder_and_decoder( tokenizer, batch_size, seq_length, is_pair, framework ) # Generate decoder inputs decoder_seq_length = seq_length if not self.use_past else 1 decoder_inputs = self._generate_dummy_inputs_for_encoder_and_decoder( tokenizer, batch_size, decoder_seq_length, is_pair, framework ) decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()} common_inputs = dict(**encoder_inputs, **decoder_inputs) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch, encoder_seq_length = common_inputs["input_ids"].shape decoder_seq_length = common_inputs["decoder_input_ids"].shape[1] num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads encoder_shape = ( batch, num_encoder_attention_heads, encoder_seq_length, self._config.hidden_size // num_encoder_attention_heads, ) decoder_past_length = decoder_seq_length + 3 decoder_shape = ( batch, num_decoder_attention_heads, decoder_past_length, self._config.hidden_size // num_decoder_attention_heads, ) common_inputs["decoder_attention_mask"] = torch.cat( [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1 ) common_inputs["past_key_values"] = [] # If the number of encoder and decoder layers are present in the model configuration, both are considered num_encoder_layers, num_decoder_layers = self.num_layers min_num_layers = min(num_encoder_layers, num_decoder_layers) max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder" for _ in range(min_num_layers): common_inputs["past_key_values"].append( ( torch.zeros(decoder_shape), torch.zeros(decoder_shape), torch.zeros(encoder_shape), torch.zeros(encoder_shape), ) ) # TODO: test this. shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape for _ in range(min_num_layers, max_num_layers): common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape))) return common_inputs def _generate_dummy_inputs_for_causal_lm( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: common_inputs = self._generate_dummy_inputs_for_encoder_and_decoder( tokenizer, batch_size, seq_length, is_pair, framework ) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch, seqlen = common_inputs["input_ids"].shape # Not using the same length for past_key_values past_key_values_length = seqlen + 2 num_encoder_layers, _ = self.num_layers num_encoder_attention_heads, _ = self.num_attention_heads past_shape = ( batch, num_encoder_attention_heads, past_key_values_length, self._config.hidden_size // num_encoder_attention_heads, ) mask_dtype = common_inputs["attention_mask"].dtype common_inputs["attention_mask"] = torch.cat( [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1 ) common_inputs["past_key_values"] = [ (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers) ] return common_inputs # Copied from BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering # We renamed this function because Marian models do not have a sequence classification or question answering head def _generate_dummy_inputs_for_encoder_and_decoder( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: # Copied from OnnxConfig.generate_dummy_inputs # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity. # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension( batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 ) # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX token_to_add = tokenizer.num_special_tokens_to_add(is_pair) seq_length = compute_effective_axis_dimension( seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add ) # Generate dummy inputs according to compute batch and sequence dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size common_inputs = dict(tokenizer(dummy_input, return_tensors=framework)) return common_inputs def generate_dummy_inputs( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: if self.task in ["default", "seq2seq-lm"]: common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) else: common_inputs = self._generate_dummy_inputs_for_causal_lm( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) return common_inputs # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._flatten_past_key_values_ def _flatten_past_key_values_(self, flattened_output, name, idx, t): if self.task in ["default", "seq2seq-lm"]: flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t) else: flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_( flattened_output, name, idx, t ) @property def atol_for_validation(self) -> float: return 1e-4

# coding=utf-8 # Copyright 2021 The Marian Team Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Marian model configuration""" from collections import OrderedDict from typing import Any, Mapping, Optional from ... import PreTrainedTokenizer from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast from ...onnx.utils import compute_effective_axis_dimension from ...utils import TensorType, is_torch_available, logging logger = logging.get_logger(__name__) MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP = { "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json", # See all Marian models at https://huggingface.co/models?filter=marian } class MarianConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MarianModel`]. It is used to instantiate an Marian model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Marian [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 58101): Vocabulary size of the Marian model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MarianModel`] or [`TFMarianModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models) forced_eos_token_id (`int`, *optional*, defaults to 0): The id of the token to force as the last generated token when `max_length` is reached. Usually set to `eos_token_id`. Examples: ```python >>> from transformers import MarianModel, MarianConfig >>> # Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration >>> configuration = MarianConfig() >>> # Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration >>> model = MarianModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "marian" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=58101, decoder_vocab_size=None, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function="gelu", d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=58100, classifier_dropout=0.0, scale_embedding=False, pad_token_id=58100, eos_token_id=0, forced_eos_token_id=0, share_encoder_decoder_embeddings=True, **kwargs ): self.vocab_size = vocab_size self.decoder_vocab_size = decoder_vocab_size or vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.share_encoder_decoder_embeddings = share_encoder_decoder_embeddings super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs, ) class MarianOnnxConfig(OnnxSeq2SeqConfigWithPast): @property # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task in ["default", "seq2seq-lm"]: common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ] ) if self.use_past: common_inputs["decoder_input_ids"] = {0: "batch"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"} else: common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"} if self.use_past: self.fill_with_past_key_values_(common_inputs, direction="inputs") elif self.task == "causal-lm": # TODO: figure this case out. common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ] ) if self.use_past: num_encoder_layers, _ = self.num_layers for i in range(num_encoder_layers): common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"} common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"} else: common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}), ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}), ] ) return common_inputs @property # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs def outputs(self) -> Mapping[str, Mapping[int, str]]: if self.task in ["default", "seq2seq-lm"]: common_outputs = super().outputs else: common_outputs = super(OnnxConfigWithPast, self).outputs if self.use_past: num_encoder_layers, _ = self.num_layers for i in range(num_encoder_layers): common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"} common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"} return common_outputs def _generate_dummy_inputs_for_default_and_seq2seq_lm( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: encoder_inputs = self._generate_dummy_inputs_for_encoder_and_decoder( tokenizer, batch_size, seq_length, is_pair, framework ) # Generate decoder inputs decoder_seq_length = seq_length if not self.use_past else 1 decoder_inputs = self._generate_dummy_inputs_for_encoder_and_decoder( tokenizer, batch_size, decoder_seq_length, is_pair, framework ) decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()} common_inputs = dict(**encoder_inputs, **decoder_inputs) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch, encoder_seq_length = common_inputs["input_ids"].shape decoder_seq_length = common_inputs["decoder_input_ids"].shape[1] num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads encoder_shape = ( batch, num_encoder_attention_heads, encoder_seq_length, self._config.hidden_size // num_encoder_attention_heads, ) decoder_past_length = decoder_seq_length + 3 decoder_shape = ( batch, num_decoder_attention_heads, decoder_past_length, self._config.hidden_size // num_decoder_attention_heads, ) common_inputs["decoder_attention_mask"] = torch.cat( [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1 ) common_inputs["past_key_values"] = [] # If the number of encoder and decoder layers are present in the model configuration, both are considered num_encoder_layers, num_decoder_layers = self.num_layers min_num_layers = min(num_encoder_layers, num_decoder_layers) max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder" for _ in range(min_num_layers): common_inputs["past_key_values"].append( ( torch.zeros(decoder_shape), torch.zeros(decoder_shape), torch.zeros(encoder_shape), torch.zeros(encoder_shape), ) ) # TODO: test this. shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape for _ in range(min_num_layers, max_num_layers): common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape))) return common_inputs def _generate_dummy_inputs_for_causal_lm( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: common_inputs = self._generate_dummy_inputs_for_encoder_and_decoder( tokenizer, batch_size, seq_length, is_pair, framework ) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch, seqlen = common_inputs["input_ids"].shape # Not using the same length for past_key_values past_key_values_length = seqlen + 2 num_encoder_layers, _ = self.num_layers num_encoder_attention_heads, _ = self.num_attention_heads past_shape = ( batch, num_encoder_attention_heads, past_key_values_length, self._config.hidden_size // num_encoder_attention_heads, ) mask_dtype = common_inputs["attention_mask"].dtype common_inputs["attention_mask"] = torch.cat( [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1 ) common_inputs["past_key_values"] = [ (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers) ] return common_inputs # Copied from BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering # We renamed this function because Marian models do not have a sequence classification or question answering head def _generate_dummy_inputs_for_encoder_and_decoder( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: # Copied from OnnxConfig.generate_dummy_inputs # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity. # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension( batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 ) # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX token_to_add = tokenizer.num_special_tokens_to_add(is_pair) seq_length = compute_effective_axis_dimension( seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add ) # Generate dummy inputs according to compute batch and sequence dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size common_inputs = dict(tokenizer(dummy_input, return_tensors=framework)) return common_inputs def generate_dummy_inputs( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: if self.task in ["default", "seq2seq-lm"]: common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) else: common_inputs = self._generate_dummy_inputs_for_causal_lm( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) return common_inputs # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._flatten_past_key_values_ def _flatten_past_key_values_(self, flattened_output, name, idx, t): if self.task in ["default", "seq2seq-lm"]: flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t) else: flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_( flattened_output, name, idx, t ) @property def atol_for_validation(self) -> float: return 1e-4

huggingface/transformers

Generate: general TF XLA constrastive search are now slow tests

# What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

gante

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

0f78529f982eceb79c5855d0466c287ec8a18df1

Generate: general TF XLA constrastive search are now slow tests. # What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

./src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py

# coding=utf-8 # Copyright 2022 HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Classes to support TF Vision-Encoder-Text-Decoder architectures""" import gc import os import tempfile import warnings from typing import Optional import tensorflow as tf from ...configuration_utils import PretrainedConfig from ...modeling_tf_outputs import TFBaseModelOutput, TFSeq2SeqLMOutput from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, get_initializer, unpack_inputs from ...tf_utils import shape_list from ...utils import ( DUMMY_INPUTS, ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from ..auto.configuration_auto import AutoConfig from ..auto.modeling_tf_auto import TFAutoModel, TFAutoModelForCausalLM from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VisionEncoderDecoderConfig" DEPRECATION_WARNING = ( "Version v4.17.0 introduces a better way to train encoder-decoder models by computing the loss inside the" " encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if" " fine-tuning a model trained with versions anterior to 4.17.0. The decoder_input_ids are now created based on the" " labels, no need to pass them yourself anymore." ) VISION_ENCODER_DECODER_START_DOCSTRING = r""" This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via [`~TFAutoModel.from_pretrained`] function and the decoder is loaded via [`~TFAutoModelForCausalLM.from_pretrained`] function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, like image captioning. The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical character recognition (OCR) yields a significant performance improvement. After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. Parameters: config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r""" Args: pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using the vision's model's image processor. For example, using [`ViTImageProcessor`]. See [`ViTImageProcessor.__call__`] for details. decoder_input_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). Provide for sequence to sequence training to the decoder. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. decoder_attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. encoder_outputs (`tuple(tuple(tf.Tensor)`, *optional*): This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` (`tf.Tensor` of shape `({0}, hidden_size)`) is a tensor of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(tf.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `({0})`. decoder_inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. labels (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). kwargs (*optional*): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors: - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function. - With a *decoder_* prefix which will be input as `**decoder_kwargs` for the decoder forward function. """ # Copied from transformers.models.encoder_decoder.modeling_tf_encoder_decoder.shift_tokens_right def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int): if pad_token_id is None: raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.") pad_token_id = tf.cast(pad_token_id, input_ids.dtype) if decoder_start_token_id is None: raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.") decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype) start_tokens = tf.fill((shape_list(input_ids)[0], 1), decoder_start_token_id) shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = tf.where( shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids ) # "Verify that `labels` has only positive values and -100" assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype)) # Make sure the assertion op is called by wrapping the result in an identity no-op with tf.control_dependencies([assert_gte0]): shifted_input_ids = tf.identity(shifted_input_ids) return shifted_input_ids @add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING) class TFVisionEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss): r""" [`TFVisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with one of the base vision model classes of the library as encoder and another one of the base model classes as decoder when created with the [`~TFAutoModel.from_pretrained`] class method for the encoder and [`~TFAutoModelForCausalLM.from_pretrained`] class method for the decoder. """ config_class = VisionEncoderDecoderConfig base_model_prefix = "vision_encoder_decoder" load_weight_prefix = "tf_vision_encoder_decoder_model" main_input_name = "pixel_values" def __init__( self, config: Optional[PretrainedConfig] = None, encoder: Optional[TFPreTrainedModel] = None, decoder: Optional[TFPreTrainedModel] = None, ): if config is None and (encoder is None or decoder is None): raise ValueError("Either a configuration or an encoder and a decoder has to be provided.") if config is None: config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config) else: if not isinstance(config, self.config_class): raise ValueError(f"config: {config} has to be of type {self.config_class}") if config.decoder.cross_attention_hidden_size is not None: if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size: raise ValueError( "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal" f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for" f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for" " `config.encoder.hidden_size`." ) # initialize with config super().__init__(config) if encoder is None: encoder = TFAutoModel.from_config(config.encoder, name="encoder") if decoder is None: decoder = TFAutoModelForCausalLM.from_config(config.decoder, name="decoder") self.encoder = encoder self.decoder = decoder if self.encoder.config.to_dict() != self.config.encoder.to_dict(): logger.warning( f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:" f" {self.config.encoder}" ) if self.decoder.config.to_dict() != self.config.decoder.to_dict(): logger.warning( f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:" f" {self.config.decoder}" ) # make sure that the individual model's config refers to the shared config # so that the updates to the config will be synced self.encoder.config = self.config.encoder self.decoder.config = self.config.decoder # encoder outputs might need to be projected to different dimension for decoder if ( self.encoder.config.hidden_size != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None ): self.enc_to_dec_proj = tf.keras.layers.Dense( units=self.decoder.config.hidden_size, kernel_initializer=get_initializer(config.encoder.initializer_range), name="enc_to_dec_proj", ) if self.encoder.get_output_embeddings() is not None: raise ValueError( f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head" ) @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: `Dict[str, tf.Tensor]`: The dummy inputs. """ decoder_input_ids = tf.constant(DUMMY_INPUTS) batch_size, seq_len = decoder_input_ids.shape VISION_DUMMY_INPUTS = tf.random.uniform( shape=( batch_size, self.config.encoder.num_channels, self.config.encoder.image_size, self.config.encoder.image_size, ), dtype=tf.float32, ) pixel_values = tf.constant(VISION_DUMMY_INPUTS) # Add `decoder_input_ids` because `self.decoder` requires it. dummy = {"pixel_values": pixel_values, "decoder_input_ids": decoder_input_ids} return dummy def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def get_input_embeddings(self): return self.encoder.get_input_embeddings() def get_output_embeddings(self): return self.decoder.get_output_embeddings() def set_output_embeddings(self, new_embeddings): return self.decoder.set_output_embeddings(new_embeddings) @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): r""" Example: ```python >>> from transformers import TFVisionEncoderDecoderModel, ViTImageProcessor, GPT2Tokenizer >>> from PIL import Image >>> import requests >>> image_processor = ViTImageProcessor.from_pretrained("ydshieh/vit-gpt2-coco-en") >>> decoder_tokenizer = GPT2Tokenizer.from_pretrained("ydshieh/vit-gpt2-coco-en") >>> model = TFVisionEncoderDecoderModel.from_pretrained("ydshieh/vit-gpt2-coco-en") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> img = Image.open(requests.get(url, stream=True).raw) >>> pixel_values = image_processor(images=img, return_tensors="tf").pixel_values # Batch size 1 >>> output_ids = model.generate( ... pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True ... ).sequences >>> preds = decoder_tokenizer.batch_decode(output_ids, skip_special_tokens=True) >>> preds = [pred.strip() for pred in preds] >>> assert preds == ["a cat laying on top of a couch next to another cat"] ```""" from_pt = kwargs.pop("from_pt", False) if from_pt: import torch from transformers import VisionEncoderDecoderModel # a workaround to load from pytorch checkpoint _model = VisionEncoderDecoderModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) config = _model.config with tempfile.TemporaryDirectory() as tmpdirname: encoder_dir = os.path.join(tmpdirname, "encoder") decoder_dir = os.path.join(tmpdirname, "decoder") _model.encoder.save_pretrained(encoder_dir) _model.decoder.save_pretrained(decoder_dir) if hasattr(_model, "enc_to_dec_proj"): enc_to_dec_proj_kernel = tf.transpose( tf.constant(_model.enc_to_dec_proj.weight.detach().to("cpu").numpy()), perm=(1, 0) ) enc_to_dec_proj_bias = tf.constant(_model.enc_to_dec_proj.bias.detach().to("cpu").numpy()) del _model gc.collect() torch.cuda.empty_cache() model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( encoder_dir, decoder_dir, encoder_from_pt=True, decoder_from_pt=True ) # This is only for copying some specific attributes of this particular model. model.config = config if hasattr(model, "enc_to_dec_proj"): model(model.dummy_inputs) model.enc_to_dec_proj.kernel.assign(enc_to_dec_proj_kernel) model.enc_to_dec_proj.bias.assign(enc_to_dec_proj_bias) return model return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) @classmethod def from_encoder_decoder_pretrained( cls, encoder_pretrained_model_name_or_path: str = None, decoder_pretrained_model_name_or_path: str = None, *model_args, **kwargs ) -> TFPreTrainedModel: r""" Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model checkpoints. Params: encoder_pretrained_model_name_or_path (`str`, *optional*): Information necessary to initiate the encoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An example is `google/vit-base-patch16-224-in21k`. - A path to a *directory* containing model weights saved using [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *pytorch index checkpoint file* (e.g, `./pt_model/`). In this case, `encoder_from_pt` should be set to `True`. decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to *None*): Information necessary to initiate the decoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *pytorch checkpoint file* (e.g, `./pt_model/`). In this case, `decoder_from_pt` should be set to `True`. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter. - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import TFVisionEncoderDecoderModel >>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized >>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "bert-base-uncased" ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./vit-bert") >>> # load fine-tuned model >>> model = TFVisionEncoderDecoderModel.from_pretrained("./vit-bert") ```""" kwargs_encoder = { argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_") } kwargs_decoder = { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } # remove encoder, decoder kwargs from kwargs for key in kwargs_encoder.keys(): del kwargs["encoder_" + key] for key in kwargs_decoder.keys(): del kwargs["decoder_" + key] # Load and initialize the encoder and decoder # The distinction between encoder and decoder at the model level is made # by the value of the flag `is_decoder` that we need to set correctly. encoder = kwargs_encoder.pop("model", None) if encoder is None: if encoder_pretrained_model_name_or_path is None: raise ValueError( "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_encoder: encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path) if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: logger.info( f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model " "from a decoder model. Cross-attention and casual mask are disabled." ) encoder_config.is_decoder = False encoder_config.add_cross_attention = False kwargs_encoder["config"] = encoder_config kwargs_encoder["name"] = "encoder" kwargs_encoder["load_weight_prefix"] = cls.load_weight_prefix encoder = TFAutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder) # Necessary to make `save_pretrained -> from_pretrained` work correctly for the converted PT -> TF model. # See https://github.com/huggingface/transformers/pull/14016#issuecomment-944046313 if kwargs_encoder.get("from_pt", None): del kwargs_encoder["from_pt"] with tempfile.TemporaryDirectory() as tmp_dirname: encoder.save_pretrained(tmp_dirname) del encoder encoder = TFAutoModel.from_pretrained(tmp_dirname, *model_args, **kwargs_encoder) decoder = kwargs_decoder.pop("model", None) if decoder is None: if decoder_pretrained_model_name_or_path is None: raise ValueError( "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_decoder: decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path) if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False: logger.info( f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention" f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if" f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers." ) decoder_config.is_decoder = True decoder_config.add_cross_attention = True kwargs_decoder["config"] = decoder_config if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: logger.warning( f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. " f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, " "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` " "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a " "`decoder_config` to `.from_encoder_decoder_pretrained(...)`" ) kwargs_decoder["name"] = "decoder" kwargs_decoder["load_weight_prefix"] = cls.load_weight_prefix decoder = TFAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder) # Necessary to make `save_pretrained -> from_pretrained` work correctly for the converted PT -> TF model. # See https://github.com/huggingface/transformers/pull/14016#issuecomment-944046313 if kwargs_decoder.get("from_pt", None): del kwargs_decoder["from_pt"] with tempfile.TemporaryDirectory() as tmp_dirname: decoder.save_pretrained(tmp_dirname) del decoder decoder = TFAutoModelForCausalLM.from_pretrained(tmp_dirname, **kwargs_decoder) # Make sure these 2 `tf.keras.Model` have fixed names so `from_pretrained` could load model weights correctly. if encoder.name != "encoder": raise ValueError("encoder model must be created with the name `encoder`.") if decoder.name != "decoder": raise ValueError("decoder model must be created with the name `decoder`.") # instantiate config with corresponding kwargs config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs) return cls(encoder=encoder, decoder=decoder, config=config) @unpack_inputs @add_start_docstrings_to_model_forward( VISION_ENCODER_DECODER_INPUTS_DOCSTRING.format("batch_size, sequence_length") ) @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_outputs=None, past_key_values=None, decoder_inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs, ): r""" Returns: Examples: ```python >>> from transformers import AutoImageProcessor, AutoTokenizer, TFVisionEncoderDecoderModel >>> from PIL import Image >>> import requests >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") >>> decoder_tokenizer = AutoTokenizer.from_pretrained("gpt2") >>> # initialize a bert2gpt2 from a pretrained BERT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "gpt2" ... ) >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> img = Image.open(requests.get(url, stream=True).raw) >>> # forward >>> pixel_values = image_processor(images=img, return_tensors="tf").pixel_values # Batch size 1 >>> decoder_input_ids = decoder_tokenizer("Linda Davis", return_tensors="tf").input_ids # Batch size 1 >>> outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids) >>> # training >>> outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, labels=decoder_input_ids) >>> loss, logits = outputs.loss, outputs.logits >>> # save and load from pretrained >>> model.save_pretrained("vit-gpt2") >>> model = TFVisionEncoderDecoderModel.from_pretrained("vit-gpt2") >>> # generation >>> generated = model.generate(pixel_values, decoder_start_token_id=model.config.decoder.bos_token_id) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")} kwargs_decoder = { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } # Let the user be responsible for the expected format. if encoder_outputs is not None: if return_dict and not isinstance(encoder_outputs, ModelOutput): raise ValueError( "If `return_dict=True` and `encoder_outputs` is provided, it should be an instance of " f"`ModelOutput`. Got an instance {type(encoder_outputs)} for `encoder_outputs`." ) if encoder_outputs is None: encoder_inputs = { "input_ids": pixel_values, "output_attentions": output_attentions, "output_hidden_states": output_hidden_states, "return_dict": return_dict, "training": training, } # Add arguments to encoder from `kwargs_encoder` encoder_inputs.update(kwargs_encoder) if "input_ids" in encoder_inputs: encoder_inputs["pixel_values"] = encoder_inputs.pop("input_ids") if encoder_inputs["pixel_values"] is None: raise ValueError("You have to specify pixel_values") # Handle the case where the inputs are passed as a single dict which contains `labels`. # The `labels` shouldn't be passed to `self.encoder` below, because it is a based model without this # parameter (otherwise, an error occurs when `input_processing` is called inside `self.encoder.call()`). if "labels" in encoder_inputs: labels = encoder_inputs.pop("labels") # handle the init case where `dummy_inputs` returns a dict containing `decoder_input_ids`. if "decoder_input_ids" in encoder_inputs: decoder_input_ids = encoder_inputs.pop("decoder_input_ids") # handle the init case where `dummy_inputs` returns a dict containing `decoder_input_ids`. if "decoder_attention_mask" in encoder_inputs: decoder_attention_mask = encoder_inputs.pop("decoder_attention_mask") encoder_outputs = self.encoder(**encoder_inputs) encoder_hidden_states = encoder_outputs[0] # optionally project encoder_hidden_states if ( self.encoder.config.hidden_size != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None ): encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None): decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) batch_size, sequence_length = shape_list(encoder_hidden_states)[:2] encoder_attention_mask = tf.ones(shape=(batch_size, sequence_length), dtype=tf.int32) decoder_inputs = { "input_ids": decoder_input_ids, "attention_mask": decoder_attention_mask, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, "inputs_embeds": decoder_inputs_embeds, "output_attentions": output_attentions, "output_hidden_states": output_hidden_states, "use_cache": use_cache, "past_key_values": past_key_values, "return_dict": return_dict, "training": training, } # Add arguments to decoder from `kwargs_decoder` decoder_inputs.update(kwargs_decoder) decoder_outputs = self.decoder(**decoder_inputs) logits = decoder_outputs[0] # Compute loss independent from decoder (as some shift the logits inside them) loss = None if labels is not None: warnings.warn(DEPRECATION_WARNING, FutureWarning) loss = self.hf_compute_loss(labels, logits) if not return_dict: past_key_values = None if use_cache: past_key_values = decoder_outputs[1] # The starting index of the remaining elements in `decoder_outputs` start_index = sum([1 if x is not None else 0 for x in (loss, logits, past_key_values)]) if not isinstance(encoder_outputs, tuple): encoder_outputs = encoder_outputs.to_tuple() output = (loss, logits, past_key_values) + decoder_outputs[start_index:] + encoder_outputs output = tuple([x for x in output if x is not None]) return output return TFSeq2SeqLMOutput( loss=loss, logits=decoder_outputs.logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) def serving_output(self, output): pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None cross_attns = ( tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions and output.cross_attentions is not None else None ) return TFSeq2SeqLMOutput( logits=output.logits, past_key_values=pkv, decoder_hidden_states=dec_hs, decoder_attentions=dec_attns, encoder_last_hidden_state=output.encoder_last_hidden_state, encoder_hidden_states=enc_hs, encoder_attentions=enc_attns, cross_attentions=cross_attns, ) def prepare_inputs_for_generation( self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs ): decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past=past) decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None past_key_values = decoder_inputs.get("past_key_values") if past_key_values is None: past_key_values = decoder_inputs.get("past") # e.g. on TF GPT2 input_dict = { "pixel_values": None, # needs to be passed to make Keras.layer.__call__ happy "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "decoder_input_ids": decoder_inputs["input_ids"], # TODO (joao): the `TFBaseModelOutput` wrapper should not be needed after the generate refactor is complete "encoder_outputs": TFBaseModelOutput(last_hidden_state=encoder_outputs[0]), "past_key_values": past_key_values, "use_cache": use_cache, } return input_dict def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor): return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) def resize_token_embeddings(self, *args, **kwargs): raise NotImplementedError( "Resizing the embedding layers via the TFVisionEncoderDecoderModel directly is not supported." "Please use the respective methods of the wrapped objects (model.decoder.resize_token_embeddings(...))" ) def _reorder_cache(self, past, beam_idx): # apply decoder cache reordering here return self.decoder._reorder_cache(past, beam_idx)

# coding=utf-8 # Copyright 2022 HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Classes to support TF Vision-Encoder-Text-Decoder architectures""" import gc import os import tempfile import warnings from typing import Optional import tensorflow as tf from ...configuration_utils import PretrainedConfig from ...modeling_tf_outputs import TFBaseModelOutput, TFSeq2SeqLMOutput from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, get_initializer, unpack_inputs from ...tf_utils import shape_list from ...utils import ( DUMMY_INPUTS, ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from ..auto.configuration_auto import AutoConfig from ..auto.modeling_tf_auto import TFAutoModel, TFAutoModelForCausalLM from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VisionEncoderDecoderConfig" DEPRECATION_WARNING = ( "Version v4.17.0 introduces a better way to train encoder-decoder models by computing the loss inside the" " encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if" " fine-tuning a model trained with versions anterior to 4.17.0. The decoder_input_ids are now created based on the" " labels, no need to pass them yourself anymore." ) VISION_ENCODER_DECODER_START_DOCSTRING = r""" This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via [`~TFAutoModel.from_pretrained`] function and the decoder is loaded via [`~TFAutoModelForCausalLM.from_pretrained`] function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, like image captioning. The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical character recognition (OCR) yields a significant performance improvement. After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. Parameters: config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r""" Args: pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using the vision's model's image processor. For example, using [`ViTImageProcessor`]. See [`ViTImageProcessor.__call__`] for details. decoder_input_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). Provide for sequence to sequence training to the decoder. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. decoder_attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. encoder_outputs (`tuple(tuple(tf.Tensor)`, *optional*): This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` (`tf.Tensor` of shape `({0}, hidden_size)`) is a tensor of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(tf.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `({0})`. decoder_inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. labels (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). kwargs (*optional*): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors: - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function. - With a *decoder_* prefix which will be input as `**decoder_kwargs` for the decoder forward function. """ # Copied from transformers.models.encoder_decoder.modeling_tf_encoder_decoder.shift_tokens_right def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int): if pad_token_id is None: raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.") pad_token_id = tf.cast(pad_token_id, input_ids.dtype) if decoder_start_token_id is None: raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.") decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype) start_tokens = tf.fill((shape_list(input_ids)[0], 1), decoder_start_token_id) shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = tf.where( shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids ) # "Verify that `labels` has only positive values and -100" assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype)) # Make sure the assertion op is called by wrapping the result in an identity no-op with tf.control_dependencies([assert_gte0]): shifted_input_ids = tf.identity(shifted_input_ids) return shifted_input_ids @add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING) class TFVisionEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss): r""" [`TFVisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with one of the base vision model classes of the library as encoder and another one of the base model classes as decoder when created with the [`~TFAutoModel.from_pretrained`] class method for the encoder and [`~TFAutoModelForCausalLM.from_pretrained`] class method for the decoder. """ config_class = VisionEncoderDecoderConfig base_model_prefix = "vision_encoder_decoder" load_weight_prefix = "tf_vision_encoder_decoder_model" main_input_name = "pixel_values" def __init__( self, config: Optional[PretrainedConfig] = None, encoder: Optional[TFPreTrainedModel] = None, decoder: Optional[TFPreTrainedModel] = None, ): if config is None and (encoder is None or decoder is None): raise ValueError("Either a configuration or an encoder and a decoder has to be provided.") if config is None: config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config) else: if not isinstance(config, self.config_class): raise ValueError(f"config: {config} has to be of type {self.config_class}") if config.decoder.cross_attention_hidden_size is not None: if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size: raise ValueError( "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal" f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for" f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for" " `config.encoder.hidden_size`." ) # initialize with config super().__init__(config) if encoder is None: encoder = TFAutoModel.from_config(config.encoder, name="encoder") if decoder is None: decoder = TFAutoModelForCausalLM.from_config(config.decoder, name="decoder") self.encoder = encoder self.decoder = decoder if self.encoder.config.to_dict() != self.config.encoder.to_dict(): logger.warning( f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:" f" {self.config.encoder}" ) if self.decoder.config.to_dict() != self.config.decoder.to_dict(): logger.warning( f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:" f" {self.config.decoder}" ) # make sure that the individual model's config refers to the shared config # so that the updates to the config will be synced self.encoder.config = self.config.encoder self.decoder.config = self.config.decoder # encoder outputs might need to be projected to different dimension for decoder if ( self.encoder.config.hidden_size != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None ): self.enc_to_dec_proj = tf.keras.layers.Dense( units=self.decoder.config.hidden_size, kernel_initializer=get_initializer(config.encoder.initializer_range), name="enc_to_dec_proj", ) if self.encoder.get_output_embeddings() is not None: raise ValueError( f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head" ) @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: `Dict[str, tf.Tensor]`: The dummy inputs. """ decoder_input_ids = tf.constant(DUMMY_INPUTS) batch_size, seq_len = decoder_input_ids.shape VISION_DUMMY_INPUTS = tf.random.uniform( shape=( batch_size, self.config.encoder.num_channels, self.config.encoder.image_size, self.config.encoder.image_size, ), dtype=tf.float32, ) pixel_values = tf.constant(VISION_DUMMY_INPUTS) # Add `decoder_input_ids` because `self.decoder` requires it. dummy = {"pixel_values": pixel_values, "decoder_input_ids": decoder_input_ids} return dummy def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def get_input_embeddings(self): return self.encoder.get_input_embeddings() def get_output_embeddings(self): return self.decoder.get_output_embeddings() def set_output_embeddings(self, new_embeddings): return self.decoder.set_output_embeddings(new_embeddings) @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): r""" Example: ```python >>> from transformers import TFVisionEncoderDecoderModel, ViTImageProcessor, GPT2Tokenizer >>> from PIL import Image >>> import requests >>> image_processor = ViTImageProcessor.from_pretrained("ydshieh/vit-gpt2-coco-en") >>> decoder_tokenizer = GPT2Tokenizer.from_pretrained("ydshieh/vit-gpt2-coco-en") >>> model = TFVisionEncoderDecoderModel.from_pretrained("ydshieh/vit-gpt2-coco-en") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> img = Image.open(requests.get(url, stream=True).raw) >>> pixel_values = image_processor(images=img, return_tensors="tf").pixel_values # Batch size 1 >>> output_ids = model.generate( ... pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True ... ).sequences >>> preds = decoder_tokenizer.batch_decode(output_ids, skip_special_tokens=True) >>> preds = [pred.strip() for pred in preds] >>> assert preds == ["a cat laying on top of a couch next to another cat"] ```""" from_pt = kwargs.pop("from_pt", False) if from_pt: import torch from transformers import VisionEncoderDecoderModel # a workaround to load from pytorch checkpoint _model = VisionEncoderDecoderModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) config = _model.config with tempfile.TemporaryDirectory() as tmpdirname: encoder_dir = os.path.join(tmpdirname, "encoder") decoder_dir = os.path.join(tmpdirname, "decoder") _model.encoder.save_pretrained(encoder_dir) _model.decoder.save_pretrained(decoder_dir) if hasattr(_model, "enc_to_dec_proj"): enc_to_dec_proj_kernel = tf.transpose( tf.constant(_model.enc_to_dec_proj.weight.detach().to("cpu").numpy()), perm=(1, 0) ) enc_to_dec_proj_bias = tf.constant(_model.enc_to_dec_proj.bias.detach().to("cpu").numpy()) del _model gc.collect() torch.cuda.empty_cache() model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( encoder_dir, decoder_dir, encoder_from_pt=True, decoder_from_pt=True ) # This is only for copying some specific attributes of this particular model. model.config = config if hasattr(model, "enc_to_dec_proj"): model(model.dummy_inputs) model.enc_to_dec_proj.kernel.assign(enc_to_dec_proj_kernel) model.enc_to_dec_proj.bias.assign(enc_to_dec_proj_bias) return model return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) @classmethod def from_encoder_decoder_pretrained( cls, encoder_pretrained_model_name_or_path: str = None, decoder_pretrained_model_name_or_path: str = None, *model_args, **kwargs ) -> TFPreTrainedModel: r""" Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model checkpoints. Params: encoder_pretrained_model_name_or_path (`str`, *optional*): Information necessary to initiate the encoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An example is `google/vit-base-patch16-224-in21k`. - A path to a *directory* containing model weights saved using [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *pytorch index checkpoint file* (e.g, `./pt_model/`). In this case, `encoder_from_pt` should be set to `True`. decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to *None*): Information necessary to initiate the decoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *pytorch checkpoint file* (e.g, `./pt_model/`). In this case, `decoder_from_pt` should be set to `True`. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter. - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import TFVisionEncoderDecoderModel >>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized >>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "bert-base-uncased" ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./vit-bert") >>> # load fine-tuned model >>> model = TFVisionEncoderDecoderModel.from_pretrained("./vit-bert") ```""" kwargs_encoder = { argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_") } kwargs_decoder = { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } # remove encoder, decoder kwargs from kwargs for key in kwargs_encoder.keys(): del kwargs["encoder_" + key] for key in kwargs_decoder.keys(): del kwargs["decoder_" + key] # Load and initialize the encoder and decoder # The distinction between encoder and decoder at the model level is made # by the value of the flag `is_decoder` that we need to set correctly. encoder = kwargs_encoder.pop("model", None) if encoder is None: if encoder_pretrained_model_name_or_path is None: raise ValueError( "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_encoder: encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path) if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: logger.info( f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model " "from a decoder model. Cross-attention and casual mask are disabled." ) encoder_config.is_decoder = False encoder_config.add_cross_attention = False kwargs_encoder["config"] = encoder_config kwargs_encoder["name"] = "encoder" kwargs_encoder["load_weight_prefix"] = cls.load_weight_prefix encoder = TFAutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder) # Necessary to make `save_pretrained -> from_pretrained` work correctly for the converted PT -> TF model. # See https://github.com/huggingface/transformers/pull/14016#issuecomment-944046313 if kwargs_encoder.get("from_pt", None): del kwargs_encoder["from_pt"] with tempfile.TemporaryDirectory() as tmp_dirname: encoder.save_pretrained(tmp_dirname) del encoder encoder = TFAutoModel.from_pretrained(tmp_dirname, *model_args, **kwargs_encoder) decoder = kwargs_decoder.pop("model", None) if decoder is None: if decoder_pretrained_model_name_or_path is None: raise ValueError( "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_decoder: decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path) if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False: logger.info( f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention" f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if" f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers." ) decoder_config.is_decoder = True decoder_config.add_cross_attention = True kwargs_decoder["config"] = decoder_config if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: logger.warning( f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. " f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, " "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` " "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a " "`decoder_config` to `.from_encoder_decoder_pretrained(...)`" ) kwargs_decoder["name"] = "decoder" kwargs_decoder["load_weight_prefix"] = cls.load_weight_prefix decoder = TFAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder) # Necessary to make `save_pretrained -> from_pretrained` work correctly for the converted PT -> TF model. # See https://github.com/huggingface/transformers/pull/14016#issuecomment-944046313 if kwargs_decoder.get("from_pt", None): del kwargs_decoder["from_pt"] with tempfile.TemporaryDirectory() as tmp_dirname: decoder.save_pretrained(tmp_dirname) del decoder decoder = TFAutoModelForCausalLM.from_pretrained(tmp_dirname, **kwargs_decoder) # Make sure these 2 `tf.keras.Model` have fixed names so `from_pretrained` could load model weights correctly. if encoder.name != "encoder": raise ValueError("encoder model must be created with the name `encoder`.") if decoder.name != "decoder": raise ValueError("decoder model must be created with the name `decoder`.") # instantiate config with corresponding kwargs config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs) return cls(encoder=encoder, decoder=decoder, config=config) @unpack_inputs @add_start_docstrings_to_model_forward( VISION_ENCODER_DECODER_INPUTS_DOCSTRING.format("batch_size, sequence_length") ) @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_outputs=None, past_key_values=None, decoder_inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs, ): r""" Returns: Examples: ```python >>> from transformers import AutoImageProcessor, AutoTokenizer, TFVisionEncoderDecoderModel >>> from PIL import Image >>> import requests >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") >>> decoder_tokenizer = AutoTokenizer.from_pretrained("gpt2") >>> # initialize a bert2gpt2 from a pretrained BERT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "gpt2" ... ) >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> img = Image.open(requests.get(url, stream=True).raw) >>> # forward >>> pixel_values = image_processor(images=img, return_tensors="tf").pixel_values # Batch size 1 >>> decoder_input_ids = decoder_tokenizer("Linda Davis", return_tensors="tf").input_ids # Batch size 1 >>> outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids) >>> # training >>> outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, labels=decoder_input_ids) >>> loss, logits = outputs.loss, outputs.logits >>> # save and load from pretrained >>> model.save_pretrained("vit-gpt2") >>> model = TFVisionEncoderDecoderModel.from_pretrained("vit-gpt2") >>> # generation >>> generated = model.generate(pixel_values, decoder_start_token_id=model.config.decoder.bos_token_id) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")} kwargs_decoder = { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } # Let the user be responsible for the expected format. if encoder_outputs is not None: if return_dict and not isinstance(encoder_outputs, ModelOutput): raise ValueError( "If `return_dict=True` and `encoder_outputs` is provided, it should be an instance of " f"`ModelOutput`. Got an instance {type(encoder_outputs)} for `encoder_outputs`." ) if encoder_outputs is None: encoder_inputs = { "input_ids": pixel_values, "output_attentions": output_attentions, "output_hidden_states": output_hidden_states, "return_dict": return_dict, "training": training, } # Add arguments to encoder from `kwargs_encoder` encoder_inputs.update(kwargs_encoder) if "input_ids" in encoder_inputs: encoder_inputs["pixel_values"] = encoder_inputs.pop("input_ids") if encoder_inputs["pixel_values"] is None: raise ValueError("You have to specify pixel_values") # Handle the case where the inputs are passed as a single dict which contains `labels`. # The `labels` shouldn't be passed to `self.encoder` below, because it is a based model without this # parameter (otherwise, an error occurs when `input_processing` is called inside `self.encoder.call()`). if "labels" in encoder_inputs: labels = encoder_inputs.pop("labels") # handle the init case where `dummy_inputs` returns a dict containing `decoder_input_ids`. if "decoder_input_ids" in encoder_inputs: decoder_input_ids = encoder_inputs.pop("decoder_input_ids") # handle the init case where `dummy_inputs` returns a dict containing `decoder_input_ids`. if "decoder_attention_mask" in encoder_inputs: decoder_attention_mask = encoder_inputs.pop("decoder_attention_mask") encoder_outputs = self.encoder(**encoder_inputs) encoder_hidden_states = encoder_outputs[0] # optionally project encoder_hidden_states if ( self.encoder.config.hidden_size != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None ): encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None): decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) batch_size, sequence_length = shape_list(encoder_hidden_states)[:2] encoder_attention_mask = tf.ones(shape=(batch_size, sequence_length), dtype=tf.int32) decoder_inputs = { "input_ids": decoder_input_ids, "attention_mask": decoder_attention_mask, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, "inputs_embeds": decoder_inputs_embeds, "output_attentions": output_attentions, "output_hidden_states": output_hidden_states, "use_cache": use_cache, "past_key_values": past_key_values, "return_dict": return_dict, "training": training, } # Add arguments to decoder from `kwargs_decoder` decoder_inputs.update(kwargs_decoder) decoder_outputs = self.decoder(**decoder_inputs) logits = decoder_outputs[0] # Compute loss independent from decoder (as some shift the logits inside them) loss = None if labels is not None: warnings.warn(DEPRECATION_WARNING, FutureWarning) loss = self.hf_compute_loss(labels, logits) if not return_dict: past_key_values = None if use_cache: past_key_values = decoder_outputs[1] # The starting index of the remaining elements in `decoder_outputs` start_index = sum([1 if x is not None else 0 for x in (loss, logits, past_key_values)]) if not isinstance(encoder_outputs, tuple): encoder_outputs = encoder_outputs.to_tuple() output = (loss, logits, past_key_values) + decoder_outputs[start_index:] + encoder_outputs output = tuple([x for x in output if x is not None]) return output return TFSeq2SeqLMOutput( loss=loss, logits=decoder_outputs.logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) def serving_output(self, output): pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None cross_attns = ( tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions and output.cross_attentions is not None else None ) return TFSeq2SeqLMOutput( logits=output.logits, past_key_values=pkv, decoder_hidden_states=dec_hs, decoder_attentions=dec_attns, encoder_last_hidden_state=output.encoder_last_hidden_state, encoder_hidden_states=enc_hs, encoder_attentions=enc_attns, cross_attentions=cross_attns, ) def prepare_inputs_for_generation( self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs ): decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past=past) decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None past_key_values = decoder_inputs.get("past_key_values") if past_key_values is None: past_key_values = decoder_inputs.get("past") # e.g. on TF GPT2 input_dict = { "pixel_values": None, # needs to be passed to make Keras.layer.__call__ happy "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "decoder_input_ids": decoder_inputs["input_ids"], # TODO (joao): the `TFBaseModelOutput` wrapper should not be needed after the generate refactor is complete "encoder_outputs": TFBaseModelOutput(last_hidden_state=encoder_outputs[0]), "past_key_values": past_key_values, "use_cache": use_cache, } return input_dict def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor): return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) def resize_token_embeddings(self, *args, **kwargs): raise NotImplementedError( "Resizing the embedding layers via the TFVisionEncoderDecoderModel directly is not supported." "Please use the respective methods of the wrapped objects (model.decoder.resize_token_embeddings(...))" ) def _reorder_cache(self, past, beam_idx): # apply decoder cache reordering here return self.decoder._reorder_cache(past, beam_idx)

huggingface/transformers

Generate: general TF XLA constrastive search are now slow tests

# What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

gante

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

0f78529f982eceb79c5855d0466c287ec8a18df1

Generate: general TF XLA constrastive search are now slow tests. # What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

./docs/source/en/migration.mdx

<!--- Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Migrating from previous packages ## Migrating from transformers `v3.x` to `v4.x` A couple of changes were introduced when the switch from version 3 to version 4 was done. Below is a summary of the expected changes: #### 1. AutoTokenizers and pipelines now use fast (rust) tokenizers by default. The python and rust tokenizers have roughly the same API, but the rust tokenizers have a more complete feature set. This introduces two breaking changes: - The handling of overflowing tokens between the python and rust tokenizers is different. - The rust tokenizers do not accept integers in the encoding methods. ##### How to obtain the same behavior as v3.x in v4.x - The pipelines now contain additional features out of the box. See the [token-classification pipeline with the `grouped_entities` flag](main_classes/pipelines#transformers.TokenClassificationPipeline). - The auto-tokenizers now return rust tokenizers. In order to obtain the python tokenizers instead, the user may use the `use_fast` flag by setting it to `False`: In version `v3.x`: ```py from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") ``` to obtain the same in version `v4.x`: ```py from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False) ``` #### 2. SentencePiece is removed from the required dependencies The requirement on the SentencePiece dependency has been lifted from the `setup.py`. This is done so that we may have a channel on anaconda cloud without relying on `conda-forge`. This means that the tokenizers that depend on the SentencePiece library will not be available with a standard `transformers` installation. This includes the **slow** versions of: - `XLNetTokenizer` - `AlbertTokenizer` - `CamembertTokenizer` - `MBartTokenizer` - `PegasusTokenizer` - `T5Tokenizer` - `ReformerTokenizer` - `XLMRobertaTokenizer` ##### How to obtain the same behavior as v3.x in v4.x In order to obtain the same behavior as version `v3.x`, you should install `sentencepiece` additionally: In version `v3.x`: ```bash pip install transformers ``` to obtain the same in version `v4.x`: ```bash pip install transformers[sentencepiece] ``` or ```bash pip install transformers sentencepiece ``` #### 3. The architecture of the repo has been updated so that each model resides in its folder The past and foreseeable addition of new models means that the number of files in the directory `src/transformers` keeps growing and becomes harder to navigate and understand. We made the choice to put each model and the files accompanying it in their own sub-directories. This is a breaking change as importing intermediary layers using a model's module directly needs to be done via a different path. ##### How to obtain the same behavior as v3.x in v4.x In order to obtain the same behavior as version `v3.x`, you should update the path used to access the layers. In version `v3.x`: ```bash from transformers.modeling_bert import BertLayer ``` to obtain the same in version `v4.x`: ```bash from transformers.models.bert.modeling_bert import BertLayer ``` #### 4. Switching the `return_dict` argument to `True` by default The [`return_dict` argument](main_classes/output) enables the return of dict-like python objects containing the model outputs, instead of the standard tuples. This object is self-documented as keys can be used to retrieve values, while also behaving as a tuple as users may retrieve objects by index or by slice. This is a breaking change as the limitation of that tuple is that it cannot be unpacked: `value0, value1 = outputs` will not work. ##### How to obtain the same behavior as v3.x in v4.x In order to obtain the same behavior as version `v3.x`, you should specify the `return_dict` argument to `False`, either in the model configuration or during the forward pass. In version `v3.x`: ```bash model = BertModel.from_pretrained("bert-base-cased") outputs = model(**inputs) ``` to obtain the same in version `v4.x`: ```bash model = BertModel.from_pretrained("bert-base-cased") outputs = model(**inputs, return_dict=False) ``` or ```bash model = BertModel.from_pretrained("bert-base-cased", return_dict=False) outputs = model(**inputs) ``` #### 5. Removed some deprecated attributes Attributes that were deprecated have been removed if they had been deprecated for at least a month. The full list of deprecated attributes can be found in [#8604](https://github.com/huggingface/transformers/pull/8604). Here is a list of these attributes/methods/arguments and what their replacements should be: In several models, the labels become consistent with the other models: - `masked_lm_labels` becomes `labels` in `AlbertForMaskedLM` and `AlbertForPreTraining`. - `masked_lm_labels` becomes `labels` in `BertForMaskedLM` and `BertForPreTraining`. - `masked_lm_labels` becomes `labels` in `DistilBertForMaskedLM`. - `masked_lm_labels` becomes `labels` in `ElectraForMaskedLM`. - `masked_lm_labels` becomes `labels` in `LongformerForMaskedLM`. - `masked_lm_labels` becomes `labels` in `MobileBertForMaskedLM`. - `masked_lm_labels` becomes `labels` in `RobertaForMaskedLM`. - `lm_labels` becomes `labels` in `BartForConditionalGeneration`. - `lm_labels` becomes `labels` in `GPT2DoubleHeadsModel`. - `lm_labels` becomes `labels` in `OpenAIGPTDoubleHeadsModel`. - `lm_labels` becomes `labels` in `T5ForConditionalGeneration`. In several models, the caching mechanism becomes consistent with the other models: - `decoder_cached_states` becomes `past_key_values` in all BART-like, FSMT and T5 models. - `decoder_past_key_values` becomes `past_key_values` in all BART-like, FSMT and T5 models. - `past` becomes `past_key_values` in all CTRL models. - `past` becomes `past_key_values` in all GPT-2 models. Regarding the tokenizer classes: - The tokenizer attribute `max_len` becomes `model_max_length`. - The tokenizer attribute `return_lengths` becomes `return_length`. - The tokenizer encoding argument `is_pretokenized` becomes `is_split_into_words`. Regarding the `Trainer` class: - The `Trainer` argument `tb_writer` is removed in favor of the callback `TensorBoardCallback(tb_writer=...)`. - The `Trainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`. - The `Trainer` attribute `data_collator` should be a callable. - The `Trainer` method `_log` is deprecated in favor of `log`. - The `Trainer` method `_training_step` is deprecated in favor of `training_step`. - The `Trainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`. - The `Trainer` method `is_local_master` is deprecated in favor of `is_local_process_zero`. - The `Trainer` method `is_world_master` is deprecated in favor of `is_world_process_zero`. Regarding the `TFTrainer` class: - The `TFTrainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`. - The `Trainer` method `_log` is deprecated in favor of `log`. - The `TFTrainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`. - The `TFTrainer` method `_setup_wandb` is deprecated in favor of `setup_wandb`. - The `TFTrainer` method `_run_model` is deprecated in favor of `run_model`. Regarding the `TrainingArguments` class: - The `TrainingArguments` argument `evaluate_during_training` is deprecated in favor of `evaluation_strategy`. Regarding the Transfo-XL model: - The Transfo-XL configuration attribute `tie_weight` becomes `tie_words_embeddings`. - The Transfo-XL modeling method `reset_length` becomes `reset_memory_length`. Regarding pipelines: - The `FillMaskPipeline` argument `topk` becomes `top_k`. ## Migrating from pytorch-transformers to 🤗 Transformers Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to 🤗 Transformers. ### Positional order of some models' keywords inputs (`attention_mask`, `token_type_ids`...) changed To be able to use Torchscript (see #1010, #1204 and #1195) the specific order of some models **keywords inputs** (`attention_mask`, `token_type_ids`...) has been changed. If you used to call the models with keyword names for keyword arguments, e.g. `model(inputs_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)`, this should not cause any change. If you used to call the models with positional inputs for keyword arguments, e.g. `model(inputs_ids, attention_mask, token_type_ids)`, you may have to double check the exact order of input arguments. ## Migrating from pytorch-pretrained-bert Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to 🤗 Transformers ### Models always output `tuples` The main breaking change when migrating from `pytorch-pretrained-bert` to 🤗 Transformers is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters. The exact content of the tuples for each model are detailed in the models' docstrings and the [documentation](https://huggingface.co/transformers/). In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`. Here is a `pytorch-pretrained-bert` to 🤗 Transformers conversion example for a `BertForSequenceClassification` classification model: ```python # Let's load our model model = BertForSequenceClassification.from_pretrained("bert-base-uncased") # If you used to have this line in pytorch-pretrained-bert: loss = model(input_ids, labels=labels) # Now just use this line in 🤗 Transformers to extract the loss from the output tuple: outputs = model(input_ids, labels=labels) loss = outputs[0] # In 🤗 Transformers you can also have access to the logits: loss, logits = outputs[:2] # And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation) model = BertForSequenceClassification.from_pretrained("bert-base-uncased", output_attentions=True) outputs = model(input_ids, labels=labels) loss, logits, attentions = outputs ``` ### Serialization Breaking change in the `from_pretrained()`method: 1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them don't forget to set them back in training mode (`model.train()`) to activate the dropout modules. 2. The additional `*inputs` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute first which can break derived model classes build based on the previous `BertForSequenceClassification` examples. More precisely, the positional arguments `*inputs` provided to `from_pretrained()` are directly forwarded the model `__init__()` method while the keyword arguments `**kwargs` (i) which match configuration class attributes are used to update said attributes (ii) which don't match any configuration class attributes are forwarded to the model `__init__()` method. Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before. Here is an example: ```python ### Let's load a model and tokenizer model = BertForSequenceClassification.from_pretrained("bert-base-uncased") tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") ### Do some stuff to our model and tokenizer # Ex: add new tokens to the vocabulary and embeddings of our model tokenizer.add_tokens(["[SPECIAL_TOKEN_1]", "[SPECIAL_TOKEN_2]"]) model.resize_token_embeddings(len(tokenizer)) # Train our model train(model) ### Now let's save our model and tokenizer to a directory model.save_pretrained("./my_saved_model_directory/") tokenizer.save_pretrained("./my_saved_model_directory/") ### Reload the model and the tokenizer model = BertForSequenceClassification.from_pretrained("./my_saved_model_directory/") tokenizer = BertTokenizer.from_pretrained("./my_saved_model_directory/") ``` ### Optimizers: BertAdam & OpenAIAdam are now AdamW, schedules are standard PyTorch schedules The two optimizers previously included, `BertAdam` and `OpenAIAdam`, have been replaced by a single `AdamW` optimizer which has a few differences: - it only implements weights decay correction, - schedules are now externals (see below), - gradient clipping is now also external (see below). The new optimizer `AdamW` matches PyTorch `Adam` optimizer API and let you use standard PyTorch or apex methods for the schedule and clipping. The schedules are now standard [PyTorch learning rate schedulers](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) and not part of the optimizer anymore. Here is a conversion examples from `BertAdam` with a linear warmup and decay schedule to `AdamW` and the same schedule: ```python # Parameters: lr = 1e-3 max_grad_norm = 1.0 num_training_steps = 1000 num_warmup_steps = 100 warmup_proportion = float(num_warmup_steps) / float(num_training_steps) # 0.1 ### Previously BertAdam optimizer was instantiated like this: optimizer = BertAdam( model.parameters(), lr=lr, schedule="warmup_linear", warmup=warmup_proportion, num_training_steps=num_training_steps, ) ### and used like this: for batch in train_data: loss = model(batch) loss.backward() optimizer.step() ### In 🤗 Transformers, optimizer and schedules are split and instantiated like this: optimizer = AdamW( model.parameters(), lr=lr, correct_bias=False ) # To reproduce BertAdam specific behavior set correct_bias=False scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps ) # PyTorch scheduler ### and used like this: for batch in train_data: loss = model(batch) loss.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), max_grad_norm ) # Gradient clipping is not in AdamW anymore (so you can use amp without issue) optimizer.step() scheduler.step() ```

<!--- Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Migrating from previous packages ## Migrating from transformers `v3.x` to `v4.x` A couple of changes were introduced when the switch from version 3 to version 4 was done. Below is a summary of the expected changes: #### 1. AutoTokenizers and pipelines now use fast (rust) tokenizers by default. The python and rust tokenizers have roughly the same API, but the rust tokenizers have a more complete feature set. This introduces two breaking changes: - The handling of overflowing tokens between the python and rust tokenizers is different. - The rust tokenizers do not accept integers in the encoding methods. ##### How to obtain the same behavior as v3.x in v4.x - The pipelines now contain additional features out of the box. See the [token-classification pipeline with the `grouped_entities` flag](main_classes/pipelines#transformers.TokenClassificationPipeline). - The auto-tokenizers now return rust tokenizers. In order to obtain the python tokenizers instead, the user may use the `use_fast` flag by setting it to `False`: In version `v3.x`: ```py from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") ``` to obtain the same in version `v4.x`: ```py from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False) ``` #### 2. SentencePiece is removed from the required dependencies The requirement on the SentencePiece dependency has been lifted from the `setup.py`. This is done so that we may have a channel on anaconda cloud without relying on `conda-forge`. This means that the tokenizers that depend on the SentencePiece library will not be available with a standard `transformers` installation. This includes the **slow** versions of: - `XLNetTokenizer` - `AlbertTokenizer` - `CamembertTokenizer` - `MBartTokenizer` - `PegasusTokenizer` - `T5Tokenizer` - `ReformerTokenizer` - `XLMRobertaTokenizer` ##### How to obtain the same behavior as v3.x in v4.x In order to obtain the same behavior as version `v3.x`, you should install `sentencepiece` additionally: In version `v3.x`: ```bash pip install transformers ``` to obtain the same in version `v4.x`: ```bash pip install transformers[sentencepiece] ``` or ```bash pip install transformers sentencepiece ``` #### 3. The architecture of the repo has been updated so that each model resides in its folder The past and foreseeable addition of new models means that the number of files in the directory `src/transformers` keeps growing and becomes harder to navigate and understand. We made the choice to put each model and the files accompanying it in their own sub-directories. This is a breaking change as importing intermediary layers using a model's module directly needs to be done via a different path. ##### How to obtain the same behavior as v3.x in v4.x In order to obtain the same behavior as version `v3.x`, you should update the path used to access the layers. In version `v3.x`: ```bash from transformers.modeling_bert import BertLayer ``` to obtain the same in version `v4.x`: ```bash from transformers.models.bert.modeling_bert import BertLayer ``` #### 4. Switching the `return_dict` argument to `True` by default The [`return_dict` argument](main_classes/output) enables the return of dict-like python objects containing the model outputs, instead of the standard tuples. This object is self-documented as keys can be used to retrieve values, while also behaving as a tuple as users may retrieve objects by index or by slice. This is a breaking change as the limitation of that tuple is that it cannot be unpacked: `value0, value1 = outputs` will not work. ##### How to obtain the same behavior as v3.x in v4.x In order to obtain the same behavior as version `v3.x`, you should specify the `return_dict` argument to `False`, either in the model configuration or during the forward pass. In version `v3.x`: ```bash model = BertModel.from_pretrained("bert-base-cased") outputs = model(**inputs) ``` to obtain the same in version `v4.x`: ```bash model = BertModel.from_pretrained("bert-base-cased") outputs = model(**inputs, return_dict=False) ``` or ```bash model = BertModel.from_pretrained("bert-base-cased", return_dict=False) outputs = model(**inputs) ``` #### 5. Removed some deprecated attributes Attributes that were deprecated have been removed if they had been deprecated for at least a month. The full list of deprecated attributes can be found in [#8604](https://github.com/huggingface/transformers/pull/8604). Here is a list of these attributes/methods/arguments and what their replacements should be: In several models, the labels become consistent with the other models: - `masked_lm_labels` becomes `labels` in `AlbertForMaskedLM` and `AlbertForPreTraining`. - `masked_lm_labels` becomes `labels` in `BertForMaskedLM` and `BertForPreTraining`. - `masked_lm_labels` becomes `labels` in `DistilBertForMaskedLM`. - `masked_lm_labels` becomes `labels` in `ElectraForMaskedLM`. - `masked_lm_labels` becomes `labels` in `LongformerForMaskedLM`. - `masked_lm_labels` becomes `labels` in `MobileBertForMaskedLM`. - `masked_lm_labels` becomes `labels` in `RobertaForMaskedLM`. - `lm_labels` becomes `labels` in `BartForConditionalGeneration`. - `lm_labels` becomes `labels` in `GPT2DoubleHeadsModel`. - `lm_labels` becomes `labels` in `OpenAIGPTDoubleHeadsModel`. - `lm_labels` becomes `labels` in `T5ForConditionalGeneration`. In several models, the caching mechanism becomes consistent with the other models: - `decoder_cached_states` becomes `past_key_values` in all BART-like, FSMT and T5 models. - `decoder_past_key_values` becomes `past_key_values` in all BART-like, FSMT and T5 models. - `past` becomes `past_key_values` in all CTRL models. - `past` becomes `past_key_values` in all GPT-2 models. Regarding the tokenizer classes: - The tokenizer attribute `max_len` becomes `model_max_length`. - The tokenizer attribute `return_lengths` becomes `return_length`. - The tokenizer encoding argument `is_pretokenized` becomes `is_split_into_words`. Regarding the `Trainer` class: - The `Trainer` argument `tb_writer` is removed in favor of the callback `TensorBoardCallback(tb_writer=...)`. - The `Trainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`. - The `Trainer` attribute `data_collator` should be a callable. - The `Trainer` method `_log` is deprecated in favor of `log`. - The `Trainer` method `_training_step` is deprecated in favor of `training_step`. - The `Trainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`. - The `Trainer` method `is_local_master` is deprecated in favor of `is_local_process_zero`. - The `Trainer` method `is_world_master` is deprecated in favor of `is_world_process_zero`. Regarding the `TFTrainer` class: - The `TFTrainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`. - The `Trainer` method `_log` is deprecated in favor of `log`. - The `TFTrainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`. - The `TFTrainer` method `_setup_wandb` is deprecated in favor of `setup_wandb`. - The `TFTrainer` method `_run_model` is deprecated in favor of `run_model`. Regarding the `TrainingArguments` class: - The `TrainingArguments` argument `evaluate_during_training` is deprecated in favor of `evaluation_strategy`. Regarding the Transfo-XL model: - The Transfo-XL configuration attribute `tie_weight` becomes `tie_words_embeddings`. - The Transfo-XL modeling method `reset_length` becomes `reset_memory_length`. Regarding pipelines: - The `FillMaskPipeline` argument `topk` becomes `top_k`. ## Migrating from pytorch-transformers to 🤗 Transformers Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to 🤗 Transformers. ### Positional order of some models' keywords inputs (`attention_mask`, `token_type_ids`...) changed To be able to use Torchscript (see #1010, #1204 and #1195) the specific order of some models **keywords inputs** (`attention_mask`, `token_type_ids`...) has been changed. If you used to call the models with keyword names for keyword arguments, e.g. `model(inputs_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)`, this should not cause any change. If you used to call the models with positional inputs for keyword arguments, e.g. `model(inputs_ids, attention_mask, token_type_ids)`, you may have to double check the exact order of input arguments. ## Migrating from pytorch-pretrained-bert Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to 🤗 Transformers ### Models always output `tuples` The main breaking change when migrating from `pytorch-pretrained-bert` to 🤗 Transformers is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters. The exact content of the tuples for each model are detailed in the models' docstrings and the [documentation](https://huggingface.co/transformers/). In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`. Here is a `pytorch-pretrained-bert` to 🤗 Transformers conversion example for a `BertForSequenceClassification` classification model: ```python # Let's load our model model = BertForSequenceClassification.from_pretrained("bert-base-uncased") # If you used to have this line in pytorch-pretrained-bert: loss = model(input_ids, labels=labels) # Now just use this line in 🤗 Transformers to extract the loss from the output tuple: outputs = model(input_ids, labels=labels) loss = outputs[0] # In 🤗 Transformers you can also have access to the logits: loss, logits = outputs[:2] # And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation) model = BertForSequenceClassification.from_pretrained("bert-base-uncased", output_attentions=True) outputs = model(input_ids, labels=labels) loss, logits, attentions = outputs ``` ### Serialization Breaking change in the `from_pretrained()`method: 1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them don't forget to set them back in training mode (`model.train()`) to activate the dropout modules. 2. The additional `*inputs` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute first which can break derived model classes build based on the previous `BertForSequenceClassification` examples. More precisely, the positional arguments `*inputs` provided to `from_pretrained()` are directly forwarded the model `__init__()` method while the keyword arguments `**kwargs` (i) which match configuration class attributes are used to update said attributes (ii) which don't match any configuration class attributes are forwarded to the model `__init__()` method. Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before. Here is an example: ```python ### Let's load a model and tokenizer model = BertForSequenceClassification.from_pretrained("bert-base-uncased") tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") ### Do some stuff to our model and tokenizer # Ex: add new tokens to the vocabulary and embeddings of our model tokenizer.add_tokens(["[SPECIAL_TOKEN_1]", "[SPECIAL_TOKEN_2]"]) model.resize_token_embeddings(len(tokenizer)) # Train our model train(model) ### Now let's save our model and tokenizer to a directory model.save_pretrained("./my_saved_model_directory/") tokenizer.save_pretrained("./my_saved_model_directory/") ### Reload the model and the tokenizer model = BertForSequenceClassification.from_pretrained("./my_saved_model_directory/") tokenizer = BertTokenizer.from_pretrained("./my_saved_model_directory/") ``` ### Optimizers: BertAdam & OpenAIAdam are now AdamW, schedules are standard PyTorch schedules The two optimizers previously included, `BertAdam` and `OpenAIAdam`, have been replaced by a single `AdamW` optimizer which has a few differences: - it only implements weights decay correction, - schedules are now externals (see below), - gradient clipping is now also external (see below). The new optimizer `AdamW` matches PyTorch `Adam` optimizer API and let you use standard PyTorch or apex methods for the schedule and clipping. The schedules are now standard [PyTorch learning rate schedulers](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) and not part of the optimizer anymore. Here is a conversion examples from `BertAdam` with a linear warmup and decay schedule to `AdamW` and the same schedule: ```python # Parameters: lr = 1e-3 max_grad_norm = 1.0 num_training_steps = 1000 num_warmup_steps = 100 warmup_proportion = float(num_warmup_steps) / float(num_training_steps) # 0.1 ### Previously BertAdam optimizer was instantiated like this: optimizer = BertAdam( model.parameters(), lr=lr, schedule="warmup_linear", warmup=warmup_proportion, num_training_steps=num_training_steps, ) ### and used like this: for batch in train_data: loss = model(batch) loss.backward() optimizer.step() ### In 🤗 Transformers, optimizer and schedules are split and instantiated like this: optimizer = AdamW( model.parameters(), lr=lr, correct_bias=False ) # To reproduce BertAdam specific behavior set correct_bias=False scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps ) # PyTorch scheduler ### and used like this: for batch in train_data: loss = model(batch) loss.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), max_grad_norm ) # Gradient clipping is not in AdamW anymore (so you can use amp without issue) optimizer.step() scheduler.step() ```

huggingface/transformers

Generate: general TF XLA constrastive search are now slow tests

# What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

gante

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

0f78529f982eceb79c5855d0466c287ec8a18df1

Generate: general TF XLA constrastive search are now slow tests. # What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

./src/transformers/convert_slow_tokenizer.py

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities to convert slow tokenizers in their fast tokenizers counterparts. All the conversions are grouped here to gather SentencePiece dependencies outside of the fast tokenizers files and allow to make our dependency on SentencePiece optional. """ import warnings from typing import Dict, List, Tuple from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors from tokenizers.models import BPE, Unigram, WordPiece from .utils import requires_backends class SentencePieceExtractor: """ Extractor implementation for SentencePiece trained models. https://github.com/google/sentencepiece """ def __init__(self, model: str): requires_backends(self, "sentencepiece") from sentencepiece import SentencePieceProcessor self.sp = SentencePieceProcessor() self.sp.Load(model) def extract(self) -> Tuple[Dict[str, int], List[Tuple]]: sp = self.sp vocab = {sp.id_to_piece(index): index for index in range(sp.GetPieceSize())} # Merges merges = [] for piece_l in vocab.keys(): for piece_r in vocab.keys(): merge = f"{piece_l}{piece_r}" piece_id = vocab.get(merge, None) if piece_id: merges += [(piece_l, piece_r, piece_id)] merges = sorted(merges, key=lambda val: val[2]) merges = [(val[0], val[1]) for val in merges] return vocab, merges def check_number_comma(piece: str) -> bool: return len(piece) < 2 or piece[-1] != "," or not piece[-2].isdigit() class Converter: def __init__(self, original_tokenizer): self.original_tokenizer = original_tokenizer def converted(self) -> Tokenizer: raise NotImplementedError() class BertConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class SplinterConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) question = str(self.original_tokenizer.question_token) dot = "." cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id question_token_id = self.original_tokenizer.question_token_id dot_token_id = self.original_tokenizer.convert_tokens_to_ids(".") if self.original_tokenizer.padding_side == "right": pair = f"{cls}:0 $A:0 {question} {dot} {sep}:0 $B:1 {sep}:1" else: pair = f"{cls}:0 $A:0 {sep}:0 $B:1 {question} {dot} {sep}:1" tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=pair, special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), (question, question_token_id), (dot, dot_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class FunnelConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:2 $A:0 {sep}:0", # token_type_id is 2 for Funnel transformer pair=f"{cls}:2 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class MPNetConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 {sep}:0 $B:1 {sep}:1", # MPNet uses two [SEP] tokens special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class OpenAIGPTConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) unk_token = self.original_tokenizer.unk_token tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, unk_token=str(unk_token), end_of_word_suffix="</w>", fuse_unk=False, ) ) if tokenizer.token_to_id(str(unk_token)) is not None: tokenizer.add_special_tokens([str(unk_token)]) tokenizer.normalizer = normalizers.BertNormalizer(lowercase=True) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() tokenizer.decoder = decoders.BPEDecoder(suffix="</w>") return tokenizer class GPT2Converter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=self.original_tokenizer.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() if self.original_tokenizer.add_bos_token: bos = self.original_tokenizer.bos_token bos_token_id = self.original_tokenizer.bos_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{bos}:0 $A:0", # token_type_id is 2 for Funnel transformer pair=f"{bos}:0 $A:0 $B:1", special_tokens=[ (bos, bos_token_id), ], ) else: # XXX trim_offsets=False actually means this post_processor doesn't # really do anything. tokenizer.post_processor = processors.ByteLevel(trim_offsets=False) return tokenizer class HerbertConverter(Converter): def converted(self) -> Tokenizer: tokenizer_info_str = "#version:" token_suffix = "</w>" vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) if tokenizer_info_str in merges[0][0]: merges = merges[1:] tokenizer = Tokenizer( BPE( vocab, merges, dropout=None, unk_token=self.original_tokenizer.unk_token, end_of_word_suffix=token_suffix, ) ) tokenizer.normalizer = normalizers.BertNormalizer(lowercase=False, strip_accents=False) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() tokenizer.decoder = decoders.BPEDecoder(suffix=token_suffix) tokenizer.post_processor = processors.BertProcessing( sep=(self.original_tokenizer.sep_token, self.original_tokenizer.sep_token_id), cls=(self.original_tokenizer.cls_token, self.original_tokenizer.cls_token_id), ) return tokenizer class RobertaConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.RobertaProcessing( sep=(ot.sep_token, ot.sep_token_id), cls=(ot.cls_token, ot.cls_token_id), add_prefix_space=ot.add_prefix_space, trim_offsets=True, # True by default on Roberta (historical) ) return tokenizer class RoFormerConverter(Converter): def converted(self) -> Tokenizer: from .models.roformer.tokenization_utils import JiebaPreTokenizer vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=False, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.PreTokenizer.custom(JiebaPreTokenizer(vocab)) cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class DebertaConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) return tokenizer class SpmConverter(Converter): def __init__(self, *args): requires_backends(self, "protobuf") super().__init__(*args) from .utils import sentencepiece_model_pb2 as model_pb2 m = model_pb2.ModelProto() with open(self.original_tokenizer.vocab_file, "rb") as f: m.ParseFromString(f.read()) self.proto = m if self.proto.trainer_spec.byte_fallback: warnings.warn( "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option" " which is not implemented in the fast tokenizers. In practice this means that the fast version of the" " tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these " "unknown tokens into a sequence of byte tokens matching the original piece of text." ) def vocab(self, proto): return [(piece.piece, piece.score) for piece in proto.pieces] def unk_id(self, proto): return proto.trainer_spec.unk_id def tokenizer(self, proto): model_type = proto.trainer_spec.model_type vocab = self.vocab(proto) unk_id = self.unk_id(proto) if model_type == 1: tokenizer = Tokenizer(Unigram(vocab, unk_id)) elif model_type == 2: _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract() bpe_vocab = {word: i for i, (word, score) in enumerate(vocab)} tokenizer = Tokenizer( BPE( bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, ) ) else: raise Exception( "You're trying to run a `Unigram` model but you're file was trained with a different algorithm" ) return tokenizer def normalizer(self, proto): precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap if not precompiled_charsmap: return normalizers.Sequence([normalizers.Replace(Regex(" {2,}"), " ")]) else: return normalizers.Sequence( [normalizers.Precompiled(precompiled_charsmap), normalizers.Replace(Regex(" {2,}"), " ")] ) def pre_tokenizer(self, replacement, add_prefix_space): return pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space) def post_processor(self): return None def converted(self) -> Tokenizer: tokenizer = self.tokenizer(self.proto) # Tokenizer assemble tokenizer.normalizer = self.normalizer(self.proto) replacement = "▁" add_prefix_space = True tokenizer.pre_tokenizer = self.pre_tokenizer(replacement, add_prefix_space) tokenizer.decoder = decoders.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space) post_processor = self.post_processor() if post_processor: tokenizer.post_processor = post_processor return tokenizer class AlbertConverter(SpmConverter): def vocab(self, proto): return [ (piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100) for piece in proto.pieces ] def normalizer(self, proto): list_normalizers = [ normalizers.Replace("``", '"'), normalizers.Replace("''", '"'), ] if not self.original_tokenizer.keep_accents: list_normalizers.append(normalizers.NFKD()) list_normalizers.append(normalizers.StripAccents()) if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " ")) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class BarthezConverter(SpmConverter): def unk_id(self, proto): unk_id = 3 return unk_id def post_processor(self): return processors.TemplateProcessing( single="<s> $A </s>", pair="<s> $A </s> </s> $B </s>", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class CamembertConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>NOTUSED", 0.0), ("<pad>", 0.0), ("</s>NOTUSED", 0.0), ("<unk>", 0.0), ("<unk>NOTUSED", -100), ] # We down-grade the original SentencePiece by -100 to avoid using it and use our added token instead vocab += [(piece.piece, piece.score) for piece in proto.pieces[1:]] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): # See vocab unk position return 3 def post_processor(self): return processors.TemplateProcessing( single="<s> $A </s>", pair="<s> $A </s> </s> $B </s>", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class DebertaV2Converter(SpmConverter): def pre_tokenizer(self, replacement, add_prefix_space): list_pretokenizers = [] if self.original_tokenizer.split_by_punct: list_pretokenizers.append(pre_tokenizers.Punctuation(behavior="isolated")) list_pretokenizers.append(pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space)) return pre_tokenizers.Sequence(list_pretokenizers) def normalizer(self, proto): list_normalizers = [] if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) list_normalizers.append(normalizers.Strip()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap if precompiled_charsmap: list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " ")) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class MBartConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] vocab += [ ("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0), ("ro_RO", 0.0), ("ru_RU", 0.0), ("si_LK", 0.0), ("tr_TR", 0.0), ("vi_VN", 0.0), ("zh_CN", 0.0), ] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): return 3 def post_processor(self): return processors.TemplateProcessing( single="$A </s> en_XX", pair="$A $B </s> en_XX", special_tokens=[ ("en_XX", self.original_tokenizer.convert_tokens_to_ids("en_XX")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class MBart50Converter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] # fmt: off vocab += [("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0), ("ro_RO", 0.0), ("ru_RU", 0.0), ("si_LK", 0.0), ("tr_TR", 0.0), ("vi_VN", 0.0), ("zh_CN", 0.0), ("af_ZA", 0.0), ("az_AZ", 0.0), ("bn_IN", 0.0), ("fa_IR", 0.0), ("he_IL", 0.0), ("hr_HR", 0.0), ("id_ID", 0.0), ("ka_GE", 0.0), ("km_KH", 0.0), ("mk_MK", 0.0), ("ml_IN", 0.0), ("mn_MN", 0.0), ("mr_IN", 0.0), ("pl_PL", 0.0), ("ps_AF", 0.0), ("pt_XX", 0.0), ("sv_SE", 0.0), ("sw_KE", 0.0), ("ta_IN", 0.0), ("te_IN", 0.0), ("th_TH", 0.0), ("tl_XX", 0.0), ("uk_UA", 0.0), ("ur_PK", 0.0), ("xh_ZA", 0.0), ("gl_ES", 0.0), ("sl_SI", 0.0)] # fmt: on vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): return 3 def post_processor(self): return processors.TemplateProcessing( single="en_XX $A </s>", pair="en_XX $A $B </s>", special_tokens=[ ("en_XX", self.original_tokenizer.convert_tokens_to_ids("en_XX")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class NllbConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] vocab += [ # fmt: off ('ace_Arab', 0.0), ('ace_Latn', 0.0), ('acm_Arab', 0.0), ('acq_Arab', 0.0), ('aeb_Arab', 0.0), ('afr_Latn', 0.0), ('ajp_Arab', 0.0), ('aka_Latn', 0.0), ('amh_Ethi', 0.0), ('apc_Arab', 0.0), ('arb_Arab', 0.0), ('ars_Arab', 0.0), ('ary_Arab', 0.0), ('arz_Arab', 0.0), ('asm_Beng', 0.0), ('ast_Latn', 0.0), ('awa_Deva', 0.0), ('ayr_Latn', 0.0), ('azb_Arab', 0.0), ('azj_Latn', 0.0), ('bak_Cyrl', 0.0), ('bam_Latn', 0.0), ('ban_Latn', 0.0), ('bel_Cyrl', 0.0), ('bem_Latn', 0.0), ('ben_Beng', 0.0), ('bho_Deva', 0.0), ('bjn_Arab', 0.0), ('bjn_Latn', 0.0), ('bod_Tibt', 0.0), ('bos_Latn', 0.0), ('bug_Latn', 0.0), ('bul_Cyrl', 0.0), ('cat_Latn', 0.0), ('ceb_Latn', 0.0), ('ces_Latn', 0.0), ('cjk_Latn', 0.0), ('ckb_Arab', 0.0), ('crh_Latn', 0.0), ('cym_Latn', 0.0), ('dan_Latn', 0.0), ('deu_Latn', 0.0), ('dik_Latn', 0.0), ('dyu_Latn', 0.0), ('dzo_Tibt', 0.0), ('ell_Grek', 0.0), ('eng_Latn', 0.0), ('epo_Latn', 0.0), ('est_Latn', 0.0), ('eus_Latn', 0.0), ('ewe_Latn', 0.0), ('fao_Latn', 0.0), ('pes_Arab', 0.0), ('fij_Latn', 0.0), ('fin_Latn', 0.0), ('fon_Latn', 0.0), ('fra_Latn', 0.0), ('fur_Latn', 0.0), ('fuv_Latn', 0.0), ('gla_Latn', 0.0), ('gle_Latn', 0.0), ('glg_Latn', 0.0), ('grn_Latn', 0.0), ('guj_Gujr', 0.0), ('hat_Latn', 0.0), ('hau_Latn', 0.0), ('heb_Hebr', 0.0), ('hin_Deva', 0.0), ('hne_Deva', 0.0), ('hrv_Latn', 0.0), ('hun_Latn', 0.0), ('hye_Armn', 0.0), ('ibo_Latn', 0.0), ('ilo_Latn', 0.0), ('ind_Latn', 0.0), ('isl_Latn', 0.0), ('ita_Latn', 0.0), ('jav_Latn', 0.0), ('jpn_Jpan', 0.0), ('kab_Latn', 0.0), ('kac_Latn', 0.0), ('kam_Latn', 0.0), ('kan_Knda', 0.0), ('kas_Arab', 0.0), ('kas_Deva', 0.0), ('kat_Geor', 0.0), ('knc_Arab', 0.0), ('knc_Latn', 0.0), ('kaz_Cyrl', 0.0), ('kbp_Latn', 0.0), ('kea_Latn', 0.0), ('khm_Khmr', 0.0), ('kik_Latn', 0.0), ('kin_Latn', 0.0), ('kir_Cyrl', 0.0), ('kmb_Latn', 0.0), ('kon_Latn', 0.0), ('kor_Hang', 0.0), ('kmr_Latn', 0.0), ('lao_Laoo', 0.0), ('lvs_Latn', 0.0), ('lij_Latn', 0.0), ('lim_Latn', 0.0), ('lin_Latn', 0.0), ('lit_Latn', 0.0), ('lmo_Latn', 0.0), ('ltg_Latn', 0.0), ('ltz_Latn', 0.0), ('lua_Latn', 0.0), ('lug_Latn', 0.0), ('luo_Latn', 0.0), ('lus_Latn', 0.0), ('mag_Deva', 0.0), ('mai_Deva', 0.0), ('mal_Mlym', 0.0), ('mar_Deva', 0.0), ('min_Latn', 0.0), ('mkd_Cyrl', 0.0), ('plt_Latn', 0.0), ('mlt_Latn', 0.0), ('mni_Beng', 0.0), ('khk_Cyrl', 0.0), ('mos_Latn', 0.0), ('mri_Latn', 0.0), ('zsm_Latn', 0.0), ('mya_Mymr', 0.0), ('nld_Latn', 0.0), ('nno_Latn', 0.0), ('nob_Latn', 0.0), ('npi_Deva', 0.0), ('nso_Latn', 0.0), ('nus_Latn', 0.0), ('nya_Latn', 0.0), ('oci_Latn', 0.0), ('gaz_Latn', 0.0), ('ory_Orya', 0.0), ('pag_Latn', 0.0), ('pan_Guru', 0.0), ('pap_Latn', 0.0), ('pol_Latn', 0.0), ('por_Latn', 0.0), ('prs_Arab', 0.0), ('pbt_Arab', 0.0), ('quy_Latn', 0.0), ('ron_Latn', 0.0), ('run_Latn', 0.0), ('rus_Cyrl', 0.0), ('sag_Latn', 0.0), ('san_Deva', 0.0), ('sat_Beng', 0.0), ('scn_Latn', 0.0), ('shn_Mymr', 0.0), ('sin_Sinh', 0.0), ('slk_Latn', 0.0), ('slv_Latn', 0.0), ('smo_Latn', 0.0), ('sna_Latn', 0.0), ('snd_Arab', 0.0), ('som_Latn', 0.0), ('sot_Latn', 0.0), ('spa_Latn', 0.0), ('als_Latn', 0.0), ('srd_Latn', 0.0), ('srp_Cyrl', 0.0), ('ssw_Latn', 0.0), ('sun_Latn', 0.0), ('swe_Latn', 0.0), ('swh_Latn', 0.0), ('szl_Latn', 0.0), ('tam_Taml', 0.0), ('tat_Cyrl', 0.0), ('tel_Telu', 0.0), ('tgk_Cyrl', 0.0), ('tgl_Latn', 0.0), ('tha_Thai', 0.0), ('tir_Ethi', 0.0), ('taq_Latn', 0.0), ('taq_Tfng', 0.0), ('tpi_Latn', 0.0), ('tsn_Latn', 0.0), ('tso_Latn', 0.0), ('tuk_Latn', 0.0), ('tum_Latn', 0.0), ('tur_Latn', 0.0), ('twi_Latn', 0.0), ('tzm_Tfng', 0.0), ('uig_Arab', 0.0), ('ukr_Cyrl', 0.0), ('umb_Latn', 0.0), ('urd_Arab', 0.0), ('uzn_Latn', 0.0), ('vec_Latn', 0.0), ('vie_Latn', 0.0), ('war_Latn', 0.0), ('wol_Latn', 0.0), ('xho_Latn', 0.0), ('ydd_Hebr', 0.0), ('yor_Latn', 0.0), ('yue_Hant', 0.0), ('zho_Hans', 0.0), ('zho_Hant', 0.0), ('zul_Latn', 0.0) # fmt: on ] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): return 3 def post_processor(self): return processors.TemplateProcessing( single="eng_Latn $A </s>", pair="eng_Latn $A $B </s>", special_tokens=[ ("eng_Latn", self.original_tokenizer.convert_tokens_to_ids("eng_Latn")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class XLMRobertaConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): unk_id = 3 return unk_id def post_processor(self): return processors.TemplateProcessing( single="<s> $A </s>", pair="<s> $A </s> </s> $B </s>", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class XLNetConverter(SpmConverter): def vocab(self, proto): return [ (piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100) for piece in proto.pieces ] def normalizer(self, proto): list_normalizers = [ normalizers.Replace("``", '"'), normalizers.Replace("''", '"'), ] if not self.original_tokenizer.keep_accents: list_normalizers.append(normalizers.NFKD()) list_normalizers.append(normalizers.StripAccents()) if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " ")) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="$A:0 <sep>:0 <cls>:2", pair="$A:0 <sep>:0 $B:1 <sep>:1 <cls>:2", special_tokens=[ ("<sep>", self.original_tokenizer.convert_tokens_to_ids("<sep>")), ("<cls>", self.original_tokenizer.convert_tokens_to_ids("<cls>")), ], ) class ReformerConverter(SpmConverter): pass class RemBertConverter(SpmConverter): # Inspired from AlbertConverter def normalizer(self, proto): list_normalizers = [ normalizers.Replace("``", '"'), normalizers.Replace("''", '"'), normalizers.Replace(Regex(" {2,}"), " "), ] if not self.original_tokenizer.keep_accents: list_normalizers.append(normalizers.NFKD()) list_normalizers.append(normalizers.StripAccents()) if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class BertGenerationConverter(SpmConverter): pass class PegasusConverter(SpmConverter): def vocab(self, proto): vocab = [ (self.original_tokenizer.pad_token, 0.0), (self.original_tokenizer.eos_token, 0.0), ] if self.original_tokenizer.mask_token_sent is not None: vocab += [(self.original_tokenizer.mask_token_sent, 0.0)] if ( self.original_tokenizer.mask_token is not None and self.original_tokenizer.mask_token_id < self.original_tokenizer.offset ): vocab += [(self.original_tokenizer.mask_token, 0.0)] vocab += [(f"<unk_{i}>", -100.0) for i in range(2, self.original_tokenizer.offset)] vocab += [(piece.piece, piece.score) for piece in proto.pieces[2:]] return vocab def unk_id(self, proto): return proto.trainer_spec.unk_id + self.original_tokenizer.offset def pre_tokenizer(self, replacement, add_prefix_space): return pre_tokenizers.Sequence( [ pre_tokenizers.WhitespaceSplit(), pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space), ] ) def post_processor(self): eos = self.original_tokenizer.eos_token special_tokens = [ (eos, self.original_tokenizer.eos_token_id), ] return processors.TemplateProcessing(single=["$A", eos], pair=["$A", "$B", eos], special_tokens=special_tokens) class T5Converter(SpmConverter): def vocab(self, proto): num_extra_ids = self.original_tokenizer._extra_ids vocab = [(piece.piece, piece.score) for piece in proto.pieces] vocab += [(f"<extra_id_{i}>", 0.0) for i in range(num_extra_ids - 1, -1, -1)] return vocab def post_processor(self): return processors.TemplateProcessing( single=["$A", "</s>"], pair=["$A", "</s>", "$B", "</s>"], special_tokens=[ ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class BigBirdConverter(SpmConverter): def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class CLIPConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) unk_token = self.original_tokenizer.unk_token tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="</w>", fuse_unk=False, unk_token=str(unk_token), ) ) tokenizer.normalizer = normalizers.Sequence( [normalizers.NFC(), normalizers.Replace(Regex(r"\s+"), " "), normalizers.Lowercase()] ) tokenizer.pre_tokenizer = pre_tokenizers.Sequence( [ pre_tokenizers.Split( Regex(r"""'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+"""), behavior="removed", invert=True, ), pre_tokenizers.ByteLevel(add_prefix_space=False), ] ) tokenizer.decoder = decoders.ByteLevel() # Hack to have a ByteLevel and TemplaceProcessor tokenizer.post_processor = processors.RobertaProcessing( sep=(self.original_tokenizer.eos_token, self.original_tokenizer.eos_token_id), cls=(self.original_tokenizer.bos_token, self.original_tokenizer.bos_token_id), add_prefix_space=False, trim_offsets=False, ) return tokenizer class LayoutLMv2Converter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = True if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class BlenderbotConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.TemplateProcessing( single=f"$A:0 {ot.eos_token}:0", special_tokens=[ (ot.eos_token, ot.eos_token_id), ], ) return tokenizer class XGLMConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] # fmt: off vocab += [("<madeupword0>", 0.0), ("<madeupword1>", 0.0), ("<madeupword2>", 0.0), ("<madeupword3>", 0.0), ("<madeupword4>", 0.0), ("<madeupword5>", 0.0), ("<madeupword6>", 0.0)] # fmt: on return vocab def unk_id(self, proto): unk_id = 3 return unk_id def post_processor(self): return processors.TemplateProcessing( single="</s> $A", pair="</s> $A </s> </s> $B", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class MarkupLMConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, unk_token=self.original_tokenizer.unk_token, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls} $A {sep}", pair=f"{cls} $A {sep} $B {sep}", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) return tokenizer SLOW_TO_FAST_CONVERTERS = { "AlbertTokenizer": AlbertConverter, "BartTokenizer": RobertaConverter, "BarthezTokenizer": BarthezConverter, "BertTokenizer": BertConverter, "BigBirdTokenizer": BigBirdConverter, "BlenderbotTokenizer": BlenderbotConverter, "CamembertTokenizer": CamembertConverter, "CLIPTokenizer": CLIPConverter, "CodeGenTokenizer": GPT2Converter, "ConvBertTokenizer": BertConverter, "DebertaTokenizer": DebertaConverter, "DebertaV2Tokenizer": DebertaV2Converter, "DistilBertTokenizer": BertConverter, "DPRReaderTokenizer": BertConverter, "DPRQuestionEncoderTokenizer": BertConverter, "DPRContextEncoderTokenizer": BertConverter, "ElectraTokenizer": BertConverter, "FNetTokenizer": AlbertConverter, "FunnelTokenizer": FunnelConverter, "GPT2Tokenizer": GPT2Converter, "HerbertTokenizer": HerbertConverter, "LayoutLMTokenizer": BertConverter, "LayoutLMv2Tokenizer": BertConverter, "LayoutLMv3Tokenizer": RobertaConverter, "LayoutXLMTokenizer": XLMRobertaConverter, "LongformerTokenizer": RobertaConverter, "LEDTokenizer": RobertaConverter, "LxmertTokenizer": BertConverter, "MarkupLMTokenizer": MarkupLMConverter, "MBartTokenizer": MBartConverter, "MBart50Tokenizer": MBart50Converter, "MPNetTokenizer": MPNetConverter, "MobileBertTokenizer": BertConverter, "MvpTokenizer": RobertaConverter, "NllbTokenizer": NllbConverter, "OpenAIGPTTokenizer": OpenAIGPTConverter, "PegasusTokenizer": PegasusConverter, "RealmTokenizer": BertConverter, "ReformerTokenizer": ReformerConverter, "RemBertTokenizer": RemBertConverter, "RetriBertTokenizer": BertConverter, "RobertaTokenizer": RobertaConverter, "RoFormerTokenizer": RoFormerConverter, "SqueezeBertTokenizer": BertConverter, "T5Tokenizer": T5Converter, "XLMRobertaTokenizer": XLMRobertaConverter, "XLNetTokenizer": XLNetConverter, "SplinterTokenizer": SplinterConverter, "XGLMTokenizer": XGLMConverter, } def convert_slow_tokenizer(transformer_tokenizer) -> Tokenizer: """ Utilities to convert a slow tokenizer instance in a fast tokenizer instance. Args: transformer_tokenizer ([`~tokenization_utils_base.PreTrainedTokenizer`]): Instance of a slow tokenizer to convert in the backend tokenizer for [`~tokenization_utils_base.PreTrainedTokenizerFast`]. Return: A instance of [`~tokenizers.Tokenizer`] to be used as the backend tokenizer of a [`~tokenization_utils_base.PreTrainedTokenizerFast`] """ tokenizer_class_name = transformer_tokenizer.__class__.__name__ if tokenizer_class_name not in SLOW_TO_FAST_CONVERTERS: raise ValueError( f"An instance of tokenizer class {tokenizer_class_name} cannot be converted in a Fast tokenizer instance." " No converter was found. Currently available slow->fast convertors:" f" {list(SLOW_TO_FAST_CONVERTERS.keys())}" ) converter_class = SLOW_TO_FAST_CONVERTERS[tokenizer_class_name] return converter_class(transformer_tokenizer).converted()

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities to convert slow tokenizers in their fast tokenizers counterparts. All the conversions are grouped here to gather SentencePiece dependencies outside of the fast tokenizers files and allow to make our dependency on SentencePiece optional. """ import warnings from typing import Dict, List, Tuple from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors from tokenizers.models import BPE, Unigram, WordPiece from .utils import requires_backends class SentencePieceExtractor: """ Extractor implementation for SentencePiece trained models. https://github.com/google/sentencepiece """ def __init__(self, model: str): requires_backends(self, "sentencepiece") from sentencepiece import SentencePieceProcessor self.sp = SentencePieceProcessor() self.sp.Load(model) def extract(self) -> Tuple[Dict[str, int], List[Tuple]]: sp = self.sp vocab = {sp.id_to_piece(index): index for index in range(sp.GetPieceSize())} # Merges merges = [] for piece_l in vocab.keys(): for piece_r in vocab.keys(): merge = f"{piece_l}{piece_r}" piece_id = vocab.get(merge, None) if piece_id: merges += [(piece_l, piece_r, piece_id)] merges = sorted(merges, key=lambda val: val[2]) merges = [(val[0], val[1]) for val in merges] return vocab, merges def check_number_comma(piece: str) -> bool: return len(piece) < 2 or piece[-1] != "," or not piece[-2].isdigit() class Converter: def __init__(self, original_tokenizer): self.original_tokenizer = original_tokenizer def converted(self) -> Tokenizer: raise NotImplementedError() class BertConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class SplinterConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) question = str(self.original_tokenizer.question_token) dot = "." cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id question_token_id = self.original_tokenizer.question_token_id dot_token_id = self.original_tokenizer.convert_tokens_to_ids(".") if self.original_tokenizer.padding_side == "right": pair = f"{cls}:0 $A:0 {question} {dot} {sep}:0 $B:1 {sep}:1" else: pair = f"{cls}:0 $A:0 {sep}:0 $B:1 {question} {dot} {sep}:1" tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=pair, special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), (question, question_token_id), (dot, dot_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class FunnelConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:2 $A:0 {sep}:0", # token_type_id is 2 for Funnel transformer pair=f"{cls}:2 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class MPNetConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 {sep}:0 $B:1 {sep}:1", # MPNet uses two [SEP] tokens special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class OpenAIGPTConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) unk_token = self.original_tokenizer.unk_token tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, unk_token=str(unk_token), end_of_word_suffix="</w>", fuse_unk=False, ) ) if tokenizer.token_to_id(str(unk_token)) is not None: tokenizer.add_special_tokens([str(unk_token)]) tokenizer.normalizer = normalizers.BertNormalizer(lowercase=True) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() tokenizer.decoder = decoders.BPEDecoder(suffix="</w>") return tokenizer class GPT2Converter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=self.original_tokenizer.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() if self.original_tokenizer.add_bos_token: bos = self.original_tokenizer.bos_token bos_token_id = self.original_tokenizer.bos_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{bos}:0 $A:0", # token_type_id is 2 for Funnel transformer pair=f"{bos}:0 $A:0 $B:1", special_tokens=[ (bos, bos_token_id), ], ) else: # XXX trim_offsets=False actually means this post_processor doesn't # really do anything. tokenizer.post_processor = processors.ByteLevel(trim_offsets=False) return tokenizer class HerbertConverter(Converter): def converted(self) -> Tokenizer: tokenizer_info_str = "#version:" token_suffix = "</w>" vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) if tokenizer_info_str in merges[0][0]: merges = merges[1:] tokenizer = Tokenizer( BPE( vocab, merges, dropout=None, unk_token=self.original_tokenizer.unk_token, end_of_word_suffix=token_suffix, ) ) tokenizer.normalizer = normalizers.BertNormalizer(lowercase=False, strip_accents=False) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() tokenizer.decoder = decoders.BPEDecoder(suffix=token_suffix) tokenizer.post_processor = processors.BertProcessing( sep=(self.original_tokenizer.sep_token, self.original_tokenizer.sep_token_id), cls=(self.original_tokenizer.cls_token, self.original_tokenizer.cls_token_id), ) return tokenizer class RobertaConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.RobertaProcessing( sep=(ot.sep_token, ot.sep_token_id), cls=(ot.cls_token, ot.cls_token_id), add_prefix_space=ot.add_prefix_space, trim_offsets=True, # True by default on Roberta (historical) ) return tokenizer class RoFormerConverter(Converter): def converted(self) -> Tokenizer: from .models.roformer.tokenization_utils import JiebaPreTokenizer vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=False, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.PreTokenizer.custom(JiebaPreTokenizer(vocab)) cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class DebertaConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) return tokenizer class SpmConverter(Converter): def __init__(self, *args): requires_backends(self, "protobuf") super().__init__(*args) from .utils import sentencepiece_model_pb2 as model_pb2 m = model_pb2.ModelProto() with open(self.original_tokenizer.vocab_file, "rb") as f: m.ParseFromString(f.read()) self.proto = m if self.proto.trainer_spec.byte_fallback: warnings.warn( "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option" " which is not implemented in the fast tokenizers. In practice this means that the fast version of the" " tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these " "unknown tokens into a sequence of byte tokens matching the original piece of text." ) def vocab(self, proto): return [(piece.piece, piece.score) for piece in proto.pieces] def unk_id(self, proto): return proto.trainer_spec.unk_id def tokenizer(self, proto): model_type = proto.trainer_spec.model_type vocab = self.vocab(proto) unk_id = self.unk_id(proto) if model_type == 1: tokenizer = Tokenizer(Unigram(vocab, unk_id)) elif model_type == 2: _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract() bpe_vocab = {word: i for i, (word, score) in enumerate(vocab)} tokenizer = Tokenizer( BPE( bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, ) ) else: raise Exception( "You're trying to run a `Unigram` model but you're file was trained with a different algorithm" ) return tokenizer def normalizer(self, proto): precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap if not precompiled_charsmap: return normalizers.Sequence([normalizers.Replace(Regex(" {2,}"), " ")]) else: return normalizers.Sequence( [normalizers.Precompiled(precompiled_charsmap), normalizers.Replace(Regex(" {2,}"), " ")] ) def pre_tokenizer(self, replacement, add_prefix_space): return pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space) def post_processor(self): return None def converted(self) -> Tokenizer: tokenizer = self.tokenizer(self.proto) # Tokenizer assemble tokenizer.normalizer = self.normalizer(self.proto) replacement = "▁" add_prefix_space = True tokenizer.pre_tokenizer = self.pre_tokenizer(replacement, add_prefix_space) tokenizer.decoder = decoders.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space) post_processor = self.post_processor() if post_processor: tokenizer.post_processor = post_processor return tokenizer class AlbertConverter(SpmConverter): def vocab(self, proto): return [ (piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100) for piece in proto.pieces ] def normalizer(self, proto): list_normalizers = [ normalizers.Replace("``", '"'), normalizers.Replace("''", '"'), ] if not self.original_tokenizer.keep_accents: list_normalizers.append(normalizers.NFKD()) list_normalizers.append(normalizers.StripAccents()) if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " ")) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class BarthezConverter(SpmConverter): def unk_id(self, proto): unk_id = 3 return unk_id def post_processor(self): return processors.TemplateProcessing( single="<s> $A </s>", pair="<s> $A </s> </s> $B </s>", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class CamembertConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>NOTUSED", 0.0), ("<pad>", 0.0), ("</s>NOTUSED", 0.0), ("<unk>", 0.0), ("<unk>NOTUSED", -100), ] # We down-grade the original SentencePiece by -100 to avoid using it and use our added token instead vocab += [(piece.piece, piece.score) for piece in proto.pieces[1:]] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): # See vocab unk position return 3 def post_processor(self): return processors.TemplateProcessing( single="<s> $A </s>", pair="<s> $A </s> </s> $B </s>", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class DebertaV2Converter(SpmConverter): def pre_tokenizer(self, replacement, add_prefix_space): list_pretokenizers = [] if self.original_tokenizer.split_by_punct: list_pretokenizers.append(pre_tokenizers.Punctuation(behavior="isolated")) list_pretokenizers.append(pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space)) return pre_tokenizers.Sequence(list_pretokenizers) def normalizer(self, proto): list_normalizers = [] if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) list_normalizers.append(normalizers.Strip()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap if precompiled_charsmap: list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " ")) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class MBartConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] vocab += [ ("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0), ("ro_RO", 0.0), ("ru_RU", 0.0), ("si_LK", 0.0), ("tr_TR", 0.0), ("vi_VN", 0.0), ("zh_CN", 0.0), ] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): return 3 def post_processor(self): return processors.TemplateProcessing( single="$A </s> en_XX", pair="$A $B </s> en_XX", special_tokens=[ ("en_XX", self.original_tokenizer.convert_tokens_to_ids("en_XX")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class MBart50Converter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] # fmt: off vocab += [("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0), ("ro_RO", 0.0), ("ru_RU", 0.0), ("si_LK", 0.0), ("tr_TR", 0.0), ("vi_VN", 0.0), ("zh_CN", 0.0), ("af_ZA", 0.0), ("az_AZ", 0.0), ("bn_IN", 0.0), ("fa_IR", 0.0), ("he_IL", 0.0), ("hr_HR", 0.0), ("id_ID", 0.0), ("ka_GE", 0.0), ("km_KH", 0.0), ("mk_MK", 0.0), ("ml_IN", 0.0), ("mn_MN", 0.0), ("mr_IN", 0.0), ("pl_PL", 0.0), ("ps_AF", 0.0), ("pt_XX", 0.0), ("sv_SE", 0.0), ("sw_KE", 0.0), ("ta_IN", 0.0), ("te_IN", 0.0), ("th_TH", 0.0), ("tl_XX", 0.0), ("uk_UA", 0.0), ("ur_PK", 0.0), ("xh_ZA", 0.0), ("gl_ES", 0.0), ("sl_SI", 0.0)] # fmt: on vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): return 3 def post_processor(self): return processors.TemplateProcessing( single="en_XX $A </s>", pair="en_XX $A $B </s>", special_tokens=[ ("en_XX", self.original_tokenizer.convert_tokens_to_ids("en_XX")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class NllbConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] vocab += [ # fmt: off ('ace_Arab', 0.0), ('ace_Latn', 0.0), ('acm_Arab', 0.0), ('acq_Arab', 0.0), ('aeb_Arab', 0.0), ('afr_Latn', 0.0), ('ajp_Arab', 0.0), ('aka_Latn', 0.0), ('amh_Ethi', 0.0), ('apc_Arab', 0.0), ('arb_Arab', 0.0), ('ars_Arab', 0.0), ('ary_Arab', 0.0), ('arz_Arab', 0.0), ('asm_Beng', 0.0), ('ast_Latn', 0.0), ('awa_Deva', 0.0), ('ayr_Latn', 0.0), ('azb_Arab', 0.0), ('azj_Latn', 0.0), ('bak_Cyrl', 0.0), ('bam_Latn', 0.0), ('ban_Latn', 0.0), ('bel_Cyrl', 0.0), ('bem_Latn', 0.0), ('ben_Beng', 0.0), ('bho_Deva', 0.0), ('bjn_Arab', 0.0), ('bjn_Latn', 0.0), ('bod_Tibt', 0.0), ('bos_Latn', 0.0), ('bug_Latn', 0.0), ('bul_Cyrl', 0.0), ('cat_Latn', 0.0), ('ceb_Latn', 0.0), ('ces_Latn', 0.0), ('cjk_Latn', 0.0), ('ckb_Arab', 0.0), ('crh_Latn', 0.0), ('cym_Latn', 0.0), ('dan_Latn', 0.0), ('deu_Latn', 0.0), ('dik_Latn', 0.0), ('dyu_Latn', 0.0), ('dzo_Tibt', 0.0), ('ell_Grek', 0.0), ('eng_Latn', 0.0), ('epo_Latn', 0.0), ('est_Latn', 0.0), ('eus_Latn', 0.0), ('ewe_Latn', 0.0), ('fao_Latn', 0.0), ('pes_Arab', 0.0), ('fij_Latn', 0.0), ('fin_Latn', 0.0), ('fon_Latn', 0.0), ('fra_Latn', 0.0), ('fur_Latn', 0.0), ('fuv_Latn', 0.0), ('gla_Latn', 0.0), ('gle_Latn', 0.0), ('glg_Latn', 0.0), ('grn_Latn', 0.0), ('guj_Gujr', 0.0), ('hat_Latn', 0.0), ('hau_Latn', 0.0), ('heb_Hebr', 0.0), ('hin_Deva', 0.0), ('hne_Deva', 0.0), ('hrv_Latn', 0.0), ('hun_Latn', 0.0), ('hye_Armn', 0.0), ('ibo_Latn', 0.0), ('ilo_Latn', 0.0), ('ind_Latn', 0.0), ('isl_Latn', 0.0), ('ita_Latn', 0.0), ('jav_Latn', 0.0), ('jpn_Jpan', 0.0), ('kab_Latn', 0.0), ('kac_Latn', 0.0), ('kam_Latn', 0.0), ('kan_Knda', 0.0), ('kas_Arab', 0.0), ('kas_Deva', 0.0), ('kat_Geor', 0.0), ('knc_Arab', 0.0), ('knc_Latn', 0.0), ('kaz_Cyrl', 0.0), ('kbp_Latn', 0.0), ('kea_Latn', 0.0), ('khm_Khmr', 0.0), ('kik_Latn', 0.0), ('kin_Latn', 0.0), ('kir_Cyrl', 0.0), ('kmb_Latn', 0.0), ('kon_Latn', 0.0), ('kor_Hang', 0.0), ('kmr_Latn', 0.0), ('lao_Laoo', 0.0), ('lvs_Latn', 0.0), ('lij_Latn', 0.0), ('lim_Latn', 0.0), ('lin_Latn', 0.0), ('lit_Latn', 0.0), ('lmo_Latn', 0.0), ('ltg_Latn', 0.0), ('ltz_Latn', 0.0), ('lua_Latn', 0.0), ('lug_Latn', 0.0), ('luo_Latn', 0.0), ('lus_Latn', 0.0), ('mag_Deva', 0.0), ('mai_Deva', 0.0), ('mal_Mlym', 0.0), ('mar_Deva', 0.0), ('min_Latn', 0.0), ('mkd_Cyrl', 0.0), ('plt_Latn', 0.0), ('mlt_Latn', 0.0), ('mni_Beng', 0.0), ('khk_Cyrl', 0.0), ('mos_Latn', 0.0), ('mri_Latn', 0.0), ('zsm_Latn', 0.0), ('mya_Mymr', 0.0), ('nld_Latn', 0.0), ('nno_Latn', 0.0), ('nob_Latn', 0.0), ('npi_Deva', 0.0), ('nso_Latn', 0.0), ('nus_Latn', 0.0), ('nya_Latn', 0.0), ('oci_Latn', 0.0), ('gaz_Latn', 0.0), ('ory_Orya', 0.0), ('pag_Latn', 0.0), ('pan_Guru', 0.0), ('pap_Latn', 0.0), ('pol_Latn', 0.0), ('por_Latn', 0.0), ('prs_Arab', 0.0), ('pbt_Arab', 0.0), ('quy_Latn', 0.0), ('ron_Latn', 0.0), ('run_Latn', 0.0), ('rus_Cyrl', 0.0), ('sag_Latn', 0.0), ('san_Deva', 0.0), ('sat_Beng', 0.0), ('scn_Latn', 0.0), ('shn_Mymr', 0.0), ('sin_Sinh', 0.0), ('slk_Latn', 0.0), ('slv_Latn', 0.0), ('smo_Latn', 0.0), ('sna_Latn', 0.0), ('snd_Arab', 0.0), ('som_Latn', 0.0), ('sot_Latn', 0.0), ('spa_Latn', 0.0), ('als_Latn', 0.0), ('srd_Latn', 0.0), ('srp_Cyrl', 0.0), ('ssw_Latn', 0.0), ('sun_Latn', 0.0), ('swe_Latn', 0.0), ('swh_Latn', 0.0), ('szl_Latn', 0.0), ('tam_Taml', 0.0), ('tat_Cyrl', 0.0), ('tel_Telu', 0.0), ('tgk_Cyrl', 0.0), ('tgl_Latn', 0.0), ('tha_Thai', 0.0), ('tir_Ethi', 0.0), ('taq_Latn', 0.0), ('taq_Tfng', 0.0), ('tpi_Latn', 0.0), ('tsn_Latn', 0.0), ('tso_Latn', 0.0), ('tuk_Latn', 0.0), ('tum_Latn', 0.0), ('tur_Latn', 0.0), ('twi_Latn', 0.0), ('tzm_Tfng', 0.0), ('uig_Arab', 0.0), ('ukr_Cyrl', 0.0), ('umb_Latn', 0.0), ('urd_Arab', 0.0), ('uzn_Latn', 0.0), ('vec_Latn', 0.0), ('vie_Latn', 0.0), ('war_Latn', 0.0), ('wol_Latn', 0.0), ('xho_Latn', 0.0), ('ydd_Hebr', 0.0), ('yor_Latn', 0.0), ('yue_Hant', 0.0), ('zho_Hans', 0.0), ('zho_Hant', 0.0), ('zul_Latn', 0.0) # fmt: on ] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): return 3 def post_processor(self): return processors.TemplateProcessing( single="eng_Latn $A </s>", pair="eng_Latn $A $B </s>", special_tokens=[ ("eng_Latn", self.original_tokenizer.convert_tokens_to_ids("eng_Latn")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class XLMRobertaConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): unk_id = 3 return unk_id def post_processor(self): return processors.TemplateProcessing( single="<s> $A </s>", pair="<s> $A </s> </s> $B </s>", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class XLNetConverter(SpmConverter): def vocab(self, proto): return [ (piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100) for piece in proto.pieces ] def normalizer(self, proto): list_normalizers = [ normalizers.Replace("``", '"'), normalizers.Replace("''", '"'), ] if not self.original_tokenizer.keep_accents: list_normalizers.append(normalizers.NFKD()) list_normalizers.append(normalizers.StripAccents()) if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " ")) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="$A:0 <sep>:0 <cls>:2", pair="$A:0 <sep>:0 $B:1 <sep>:1 <cls>:2", special_tokens=[ ("<sep>", self.original_tokenizer.convert_tokens_to_ids("<sep>")), ("<cls>", self.original_tokenizer.convert_tokens_to_ids("<cls>")), ], ) class ReformerConverter(SpmConverter): pass class RemBertConverter(SpmConverter): # Inspired from AlbertConverter def normalizer(self, proto): list_normalizers = [ normalizers.Replace("``", '"'), normalizers.Replace("''", '"'), normalizers.Replace(Regex(" {2,}"), " "), ] if not self.original_tokenizer.keep_accents: list_normalizers.append(normalizers.NFKD()) list_normalizers.append(normalizers.StripAccents()) if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class BertGenerationConverter(SpmConverter): pass class PegasusConverter(SpmConverter): def vocab(self, proto): vocab = [ (self.original_tokenizer.pad_token, 0.0), (self.original_tokenizer.eos_token, 0.0), ] if self.original_tokenizer.mask_token_sent is not None: vocab += [(self.original_tokenizer.mask_token_sent, 0.0)] if ( self.original_tokenizer.mask_token is not None and self.original_tokenizer.mask_token_id < self.original_tokenizer.offset ): vocab += [(self.original_tokenizer.mask_token, 0.0)] vocab += [(f"<unk_{i}>", -100.0) for i in range(2, self.original_tokenizer.offset)] vocab += [(piece.piece, piece.score) for piece in proto.pieces[2:]] return vocab def unk_id(self, proto): return proto.trainer_spec.unk_id + self.original_tokenizer.offset def pre_tokenizer(self, replacement, add_prefix_space): return pre_tokenizers.Sequence( [ pre_tokenizers.WhitespaceSplit(), pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space), ] ) def post_processor(self): eos = self.original_tokenizer.eos_token special_tokens = [ (eos, self.original_tokenizer.eos_token_id), ] return processors.TemplateProcessing(single=["$A", eos], pair=["$A", "$B", eos], special_tokens=special_tokens) class T5Converter(SpmConverter): def vocab(self, proto): num_extra_ids = self.original_tokenizer._extra_ids vocab = [(piece.piece, piece.score) for piece in proto.pieces] vocab += [(f"<extra_id_{i}>", 0.0) for i in range(num_extra_ids - 1, -1, -1)] return vocab def post_processor(self): return processors.TemplateProcessing( single=["$A", "</s>"], pair=["$A", "</s>", "$B", "</s>"], special_tokens=[ ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class BigBirdConverter(SpmConverter): def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class CLIPConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) unk_token = self.original_tokenizer.unk_token tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="</w>", fuse_unk=False, unk_token=str(unk_token), ) ) tokenizer.normalizer = normalizers.Sequence( [normalizers.NFC(), normalizers.Replace(Regex(r"\s+"), " "), normalizers.Lowercase()] ) tokenizer.pre_tokenizer = pre_tokenizers.Sequence( [ pre_tokenizers.Split( Regex(r"""'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+"""), behavior="removed", invert=True, ), pre_tokenizers.ByteLevel(add_prefix_space=False), ] ) tokenizer.decoder = decoders.ByteLevel() # Hack to have a ByteLevel and TemplaceProcessor tokenizer.post_processor = processors.RobertaProcessing( sep=(self.original_tokenizer.eos_token, self.original_tokenizer.eos_token_id), cls=(self.original_tokenizer.bos_token, self.original_tokenizer.bos_token_id), add_prefix_space=False, trim_offsets=False, ) return tokenizer class LayoutLMv2Converter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = True if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class BlenderbotConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.TemplateProcessing( single=f"$A:0 {ot.eos_token}:0", special_tokens=[ (ot.eos_token, ot.eos_token_id), ], ) return tokenizer class XGLMConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] # fmt: off vocab += [("<madeupword0>", 0.0), ("<madeupword1>", 0.0), ("<madeupword2>", 0.0), ("<madeupword3>", 0.0), ("<madeupword4>", 0.0), ("<madeupword5>", 0.0), ("<madeupword6>", 0.0)] # fmt: on return vocab def unk_id(self, proto): unk_id = 3 return unk_id def post_processor(self): return processors.TemplateProcessing( single="</s> $A", pair="</s> $A </s> </s> $B", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class MarkupLMConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, unk_token=self.original_tokenizer.unk_token, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls} $A {sep}", pair=f"{cls} $A {sep} $B {sep}", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) return tokenizer SLOW_TO_FAST_CONVERTERS = { "AlbertTokenizer": AlbertConverter, "BartTokenizer": RobertaConverter, "BarthezTokenizer": BarthezConverter, "BertTokenizer": BertConverter, "BigBirdTokenizer": BigBirdConverter, "BlenderbotTokenizer": BlenderbotConverter, "CamembertTokenizer": CamembertConverter, "CLIPTokenizer": CLIPConverter, "CodeGenTokenizer": GPT2Converter, "ConvBertTokenizer": BertConverter, "DebertaTokenizer": DebertaConverter, "DebertaV2Tokenizer": DebertaV2Converter, "DistilBertTokenizer": BertConverter, "DPRReaderTokenizer": BertConverter, "DPRQuestionEncoderTokenizer": BertConverter, "DPRContextEncoderTokenizer": BertConverter, "ElectraTokenizer": BertConverter, "FNetTokenizer": AlbertConverter, "FunnelTokenizer": FunnelConverter, "GPT2Tokenizer": GPT2Converter, "HerbertTokenizer": HerbertConverter, "LayoutLMTokenizer": BertConverter, "LayoutLMv2Tokenizer": BertConverter, "LayoutLMv3Tokenizer": RobertaConverter, "LayoutXLMTokenizer": XLMRobertaConverter, "LongformerTokenizer": RobertaConverter, "LEDTokenizer": RobertaConverter, "LxmertTokenizer": BertConverter, "MarkupLMTokenizer": MarkupLMConverter, "MBartTokenizer": MBartConverter, "MBart50Tokenizer": MBart50Converter, "MPNetTokenizer": MPNetConverter, "MobileBertTokenizer": BertConverter, "MvpTokenizer": RobertaConverter, "NllbTokenizer": NllbConverter, "OpenAIGPTTokenizer": OpenAIGPTConverter, "PegasusTokenizer": PegasusConverter, "RealmTokenizer": BertConverter, "ReformerTokenizer": ReformerConverter, "RemBertTokenizer": RemBertConverter, "RetriBertTokenizer": BertConverter, "RobertaTokenizer": RobertaConverter, "RoFormerTokenizer": RoFormerConverter, "SqueezeBertTokenizer": BertConverter, "T5Tokenizer": T5Converter, "XLMRobertaTokenizer": XLMRobertaConverter, "XLNetTokenizer": XLNetConverter, "SplinterTokenizer": SplinterConverter, "XGLMTokenizer": XGLMConverter, } def convert_slow_tokenizer(transformer_tokenizer) -> Tokenizer: """ Utilities to convert a slow tokenizer instance in a fast tokenizer instance. Args: transformer_tokenizer ([`~tokenization_utils_base.PreTrainedTokenizer`]): Instance of a slow tokenizer to convert in the backend tokenizer for [`~tokenization_utils_base.PreTrainedTokenizerFast`]. Return: A instance of [`~tokenizers.Tokenizer`] to be used as the backend tokenizer of a [`~tokenization_utils_base.PreTrainedTokenizerFast`] """ tokenizer_class_name = transformer_tokenizer.__class__.__name__ if tokenizer_class_name not in SLOW_TO_FAST_CONVERTERS: raise ValueError( f"An instance of tokenizer class {tokenizer_class_name} cannot be converted in a Fast tokenizer instance." " No converter was found. Currently available slow->fast convertors:" f" {list(SLOW_TO_FAST_CONVERTERS.keys())}" ) converter_class = SLOW_TO_FAST_CONVERTERS[tokenizer_class_name] return converter_class(transformer_tokenizer).converted()

huggingface/transformers

Generate: general TF XLA constrastive search are now slow tests

# What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

gante

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

0f78529f982eceb79c5855d0466c287ec8a18df1

Generate: general TF XLA constrastive search are now slow tests. # What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

./tests/generation/test_logits_process.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a clone of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, torch_device from ..test_modeling_common import ids_tensor if is_torch_available(): import torch from torch import nn from transformers.generation import ( EncoderNoRepeatNGramLogitsProcessor, ExponentialDecayLengthPenalty, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitNormalization, LogitsProcessorList, MinLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper, ) @require_torch class LogitsProcessorTest(unittest.TestCase): def _get_uniform_logits(self, batch_size: int, length: int): scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length return scores def test_min_length_dist_processor(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 min_dist_processor = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id) # check that min length is applied at length 5 input_ids = ids_tensor((batch_size, 5), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_min_length = min_dist_processor(input_ids, scores) self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(), 4 * [-float("inf")]) # check that min length is not applied anymore at length 15 input_ids = ids_tensor((batch_size, 15), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_min_length = min_dist_processor(input_ids, scores) self.assertFalse(torch.isinf(scores_before_min_length).any()) def test_temperature_dist_warper(self): input_ids = None length = 20 scores = self._get_uniform_logits(batch_size=2, length=length) # tweak scores to not be uniform anymore scores[1, 5] = (1 / length) + 0.1 # peak, 1st batch scores[1, 10] = (1 / length) - 0.4 # valley, 1st batch # compute softmax probs = nn.functional.softmax(scores, dim=-1) temp_dist_warper_sharper = TemperatureLogitsWarper(temperature=0.5) temp_dist_warper_smoother = TemperatureLogitsWarper(temperature=1.3) warped_prob_sharp = nn.functional.softmax(temp_dist_warper_sharper(input_ids, scores.clone()), dim=-1) warped_prob_smooth = nn.functional.softmax(temp_dist_warper_smoother(input_ids, scores.clone()), dim=-1) # uniform distribution stays uniform self.assertTrue(torch.allclose(probs[0, :], warped_prob_sharp[0, :], atol=1e-3)) self.assertTrue(torch.allclose(probs[0, :], warped_prob_smooth[0, :], atol=1e-3)) # sharp peaks get higher, valleys get lower self.assertLess(probs[1, :].max(), warped_prob_sharp[1, :].max()) self.assertGreater(probs[1, :].min(), warped_prob_sharp[1, :].min()) # smooth peaks get lower, valleys get higher self.assertGreater(probs[1, :].max(), warped_prob_smooth[1, :].max()) self.assertLess(probs[1, :].min(), warped_prob_smooth[1, :].min()) def test_repetition_penalty_dist_process(self): input_ids = torch.tensor([[0, 1], [5, 0]], device=torch_device, dtype=torch.long) vocab_size = 10 scores = self._get_uniform_logits(batch_size=2, length=vocab_size) # give values special values scores[0, 0] = -(1 / vocab_size) scores[1, 5] = 4 / vocab_size rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0) scores = rep_penalty_proc(input_ids, scores.clone()) # check that values were correctly changed self.assertAlmostEqual(scores[0, 0].item(), -(1 / vocab_size) * 2) self.assertAlmostEqual(scores[0, 1].item(), (1 / vocab_size) / 2) self.assertAlmostEqual(scores[1, 0].item(), (1 / vocab_size) / 2) self.assertAlmostEqual(scores[1, 5].item(), (4 / vocab_size) / 2) def test_top_k_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create ramp distribution ramp_logits = ( torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1) ) ramp_logits[1:, : vocab_size // 2] = ramp_logits[1:, : vocab_size // 2] + vocab_size top_k_warp = TopKLogitsWarper(3) scores = top_k_warp(input_ids, ramp_logits) # check that correct tokens are filtered self.assertListEqual(torch.isinf(scores[0]).tolist(), 7 * [True] + 3 * [False]) self.assertListEqual(torch.isinf(scores[1]).tolist(), 2 * [True] + 3 * [False] + 5 * [True]) # check special cases length = 5 logits = self._get_uniform_logits(batch_size=batch_size, length=length) top_k_warp_safety_check = TopKLogitsWarper(top_k=1, filter_value=0.0, min_tokens_to_keep=3) scores = top_k_warp_safety_check(input_ids, logits) # uniform dist is not changed self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0]) ramp_logits = torch.arange(length, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1) scores = top_k_warp_safety_check(input_ids, ramp_logits) # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2]) def test_top_p_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper) dist = torch.log( torch.tensor([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float) ) top_p_warp = TopPLogitsWarper(0.8) filtered_dist = torch.exp(top_p_warp(input_ids, dist)) # dist should be filtered to keep min num values so that sum is >= top_p # exp (-inf) => 0 EXPECTED_FILTERED_DIST = torch.tensor( [[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float ) self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3)) # check edge cases with negative and extreme logits ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat( batch_size, 1 ) - (vocab_size // 2) # make ramp_logits more extreme ramp_logits[1] = ramp_logits[1] * 100.0 # make sure at least 2 tokens are kept top_p_warp = TopPLogitsWarper(0.9, min_tokens_to_keep=2, filter_value=0.0) filtered_dist = top_p_warp(input_ids, ramp_logits) # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2. self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [3, 2]) def test_typical_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper) dist = torch.log( torch.tensor([[0.97, 0.01, 0.01, 0.01], [0.4, 0.2, 0.2, 0.2]], device=torch_device, dtype=torch.float) ) typical_warp = TypicalLogitsWarper(0.5) filtered_dist = torch.exp(typical_warp(input_ids, dist)) # dist should be filtered to keep min num values so that sum is >= 0.7 # exp (-inf) => 0 EXPECTED_FILTERED_DIST = torch.tensor( [[0.97, 0.0, 0.0, 0.0], [0.0, 0.2, 0.2, 0.2]], device=torch_device, dtype=torch.float ) self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3)) # check special cases length = 5 logits = self._get_uniform_logits(batch_size=batch_size, length=length) typical_warp_safety_check = TypicalLogitsWarper(mass=0.5, filter_value=0.0, min_tokens_to_keep=3) scores = typical_warp_safety_check(input_ids, logits) # uniform dist is not changed self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0]) # check edge cases with negative and extreme logits ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat( batch_size, 1 ) - (vocab_size // 2) # make ramp_logits more extreme ramp_logits[1] = ramp_logits[1] * 100.0 # make sure at least 2 tokens are kept typical_warp = TypicalLogitsWarper(0.7, min_tokens_to_keep=2, filter_value=0.0) filtered_dist = typical_warp(input_ids, ramp_logits) # first batch should keep two tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2. self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2]) def test_no_repeat_ngram_dist_processor(self): vocab_size = 3 batch_size = 2 input_ids = torch.tensor([[1, 1, 2, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size, vocab_size) no_repeat_proc_2_gram = NoRepeatNGramLogitsProcessor(2) no_repeat_proc_3_gram = NoRepeatNGramLogitsProcessor(3) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2-gram would forbid 2nd and 3rd token (1,2) at 1st batch and 1st token (0) at 2nd batch self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [True, False, False]]) # 3-gram would forbid no token at 1st batch and 1st token (0) at 2nd batch self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, False, False], [True, False, False]] ) def test_encoder_no_repeat_ngram_dist_processor(self): vocab_size = 3 num_beams = 2 batch_size = 1 encoder_input_ids = torch.tensor([1, 2, 1, 1], device=torch_device, dtype=torch.long) input_ids = torch.tensor([[1, 2, 1], [8, 0, 2]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size * num_beams, vocab_size) no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids) no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2-gram would forbid 1st and 2nd token at 1st beam and 1st token (0) at 2nd beam self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False]]) # 3-gram would forbid 1st token at 1st beam and no token at 2nd beam self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False]] ) # Batched input vocab_size = 3 num_beams = 2 batch_size = 2 encoder_input_ids = torch.tensor([[1, 2, 1, 1], [0, 0, 2, 1]], device=torch_device, dtype=torch.long) input_ids = torch.tensor([[1, 2, 1], [1, 0, 2], [0, 0, 0], [0, 2, 2]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size * num_beams, vocab_size) no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids) no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2gram # Batch 1 # - Beam 1: tokens (1, 2) forbidden # - Beam 2: tokens (1) forbidden # Batch 2 # - Beam 1: tokens (0, 2) forbidden # - Beam 2: tokens (1) forbidden self.assertListEqual( torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False], [True, False, True], [False, True, False]], ) # Batch 1 # - Beam 1: tokens (1) forbidden # - Beam 2: tokens () forbidden # Batch 2 # - Beam 1: tokens (2) forbidden # - Beam 2: tokens () forbidden self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False], [False, False, True], [False, False, False]], ) def test_no_bad_words_dist_processor(self): vocab_size = 5 batch_size = 2 eos_token_id = 4 input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) bad_word_tokens = [[1], [4], [1, 0], [0, 1, 2], [1, 3, 1, 3]] scores = self._get_uniform_logits(batch_size, vocab_size) no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=bad_word_tokens, eos_token_id=eos_token_id) filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone()) # batch 1: 1st, 2nd, and 4th (0, 1, 3) token are forbidden # batch 2: 1st, 2nd, and 3rd (0, 1, 2) token are forbidden # Note that 5th element cannot be forbidden as it is EOS token self.assertListEqual( torch.isinf(filtered_scores).tolist(), [[True, True, False, True, False], [True, True, True, False, False]] ) # check edge case no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[4]], eos_token_id=eos_token_id) filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone()) self.assertTrue(torch.allclose(scores, filtered_scores, atol=1e-3)) def test_processor_list(self): batch_size = 4 sequence_length = 10 vocab_size = 15 eos_token_id = 0 # dummy input_ids and scores input_ids = ids_tensor((batch_size, sequence_length), vocab_size) input_ids_comp = input_ids.clone() scores = self._get_uniform_logits(batch_size, vocab_size) scores_comp = scores.clone() # instantiate all dist processors min_dist_proc = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id) temp_dist_warp = TemperatureLogitsWarper(temperature=0.5) rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0) top_k_warp = TopKLogitsWarper(3) top_p_warp = TopPLogitsWarper(0.8) no_repeat_proc = NoRepeatNGramLogitsProcessor(2) no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[1]], eos_token_id=eos_token_id) # no processor list scores = min_dist_proc(input_ids, scores) scores = temp_dist_warp(input_ids, scores) scores = rep_penalty_proc(input_ids, scores) scores = top_k_warp(input_ids, scores) scores = top_p_warp(input_ids, scores) scores = no_repeat_proc(input_ids, scores) scores = no_bad_words_dist_proc(input_ids, scores) # with processor list processor = LogitsProcessorList( [ min_dist_proc, temp_dist_warp, rep_penalty_proc, top_k_warp, top_p_warp, no_repeat_proc, no_bad_words_dist_proc, ] ) scores_comp = processor(input_ids, scores_comp) # scores should be equal self.assertTrue(torch.allclose(scores, scores_comp, atol=1e-3)) # input_ids should never be changed self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist()) def test_prefix_constrained_logits_processor(self): vocab_size = 5 batch_size = 2 input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size, vocab_size) def prefix_allowed_tokens_fn(batch_id, inputs_ids): return [[0, 1], [2, 3]][batch_id] prefix_constrained_logits_proc = PrefixConstrainedLogitsProcessor(prefix_allowed_tokens_fn, 1) filtered_scores = prefix_constrained_logits_proc(input_ids, scores.clone()) # batch 1: 1st, 2nd (0, 1) token are allowed # batch 2: 3rd, 4th (2, 3) token are allowed self.assertListEqual( torch.isinf(filtered_scores).tolist(), [[False, False, True, True, True], [True, True, False, False, True]] ) def test_hamming_diversity(self): vocab_size = 4 num_beams = 2 num_beam_groups = 2 scores = self._get_uniform_logits(num_beams, vocab_size) # batch_idx = 0 -> index batch_idx * num_beam_groups -> idx = 0 * 2 = 0 -> penalises tokens 1 # batch_idx = 1 -> index batch_idx * num_beam_groups -> idx = 1 * 2 = 2 -> penalises tokens 1 current_tokens = torch.tensor([0, 3, 1, 2], device=torch_device, dtype=torch.long) diversity_logits_processor = HammingDiversityLogitsProcessor( diversity_penalty=1.0, num_beams=num_beams, num_beam_groups=num_beam_groups ) processed_scores = diversity_logits_processor(None, scores, current_tokens, 1) self.assertTrue( torch.allclose( processed_scores[0], torch.tensor([-0.7500, 0.2500, 0.2500, 0.2500], device=torch_device), atol=1e-3 ) ) self.assertTrue( torch.allclose( processed_scores[1], torch.tensor([0.2500, -0.7500, 0.2500, 0.2500], device=torch_device), atol=1e-3 ) ) def test_forced_bos_token_logits_processor(self): vocab_size = 20 batch_size = 4 bos_token_id = 0 logits_processor = ForcedBOSTokenLogitsProcessor(bos_token_id=bos_token_id) # check that all scores are -inf except the bos_token_id score input_ids = ids_tensor((batch_size, 1), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertTrue(torch.isneginf(scores[:, bos_token_id + 1 :]).all()) self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0]) # score for bos_token_id shold be zero # check that bos_token_id is not forced if current length is greater than 1 input_ids = ids_tensor((batch_size, 4), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertFalse(torch.isinf(scores).any()) def test_forced_eos_token_logits_processor(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 max_length = 5 logits_processor = ForcedEOSTokenLogitsProcessor(max_length=max_length, eos_token_id=eos_token_id) # check that all scores are -inf except the eos_token_id when max_length-1 is reached input_ids = ids_tensor((batch_size, 4), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertTrue(torch.isneginf(scores[:, eos_token_id + 1 :]).all()) self.assertListEqual(scores[:, eos_token_id].tolist(), 4 * [0]) # score for eos_token_id should be zero # check that eos_token_id is not forced if max_length-1 is not reached input_ids = ids_tensor((batch_size, 3), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertFalse(torch.isinf(scores).any()) def test_remove_nan_inf_logits_processor(self): scores = torch.tensor( [[0.0, 0.7, 0.8, float("nan")], [0.1, float("inf"), 0.3, float("-inf")]], device=torch_device ) input_ids = ids_tensor((2, 4), vocab_size=20) logits_processor = InfNanRemoveLogitsProcessor() scores = logits_processor(input_ids, scores) self.assertTrue( torch.allclose( scores, torch.tensor( [[0.0, 0.7, 0.8, 0.0], [0.1, torch.finfo(scores.dtype).max, 0.3, float("-inf")]], device=torch_device, ), atol=1e-6, ) ) def test_exponential_decay_length_penalty(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 penalty_start = 5 penalty_factor = 1.1 input_ids = ids_tensor((batch_size, 2), vocab_size=vocab_size) input_ids_seq_length = input_ids.shape[-1] length_decay_processor = ExponentialDecayLengthPenalty( exponential_decay_length_penalty=(penalty_start, penalty_factor), eos_token_id=eos_token_id, input_ids_seq_length=input_ids_seq_length, ) # check that penalty is not applied before start scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_start = length_decay_processor(input_ids, scores) self.assertListEqual(scores_before_start[:, eos_token_id].tolist(), scores[:, eos_token_id].tolist()) # check that penalty is applied after start input_ids = ids_tensor((batch_size, 20), vocab_size=vocab_size) scores = self._get_uniform_logits(batch_size, vocab_size) scores_after_start = length_decay_processor(input_ids, scores) self.assertTrue( torch.gt( scores_after_start[penalty_start + 1 :, eos_token_id], scores[penalty_start + 1 :, eos_token_id] ).all() ) def test_normalization(self): input_ids = None scores = torch.tensor( [[-23.18, -29.96, -43.54, 47.77], [-33.58, -26.87, -32.96, 22.51]], device=torch_device, dtype=torch.float ) logit_normalization = LogitNormalization() normalized_scores = logit_normalization(input_ids, scores).exp() ones = torch.ones(scores.shape[0], device=torch_device, dtype=torch.float) self.assertTrue(normalized_scores.sum(dim=-1).allclose(ones)) self.assertTrue(normalized_scores.allclose(scores.softmax(dim=-1)))

# coding=utf-8 # Copyright 2020 The HuggingFace Team Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a clone of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, torch_device from ..test_modeling_common import ids_tensor if is_torch_available(): import torch from torch import nn from transformers.generation import ( EncoderNoRepeatNGramLogitsProcessor, ExponentialDecayLengthPenalty, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitNormalization, LogitsProcessorList, MinLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper, ) @require_torch class LogitsProcessorTest(unittest.TestCase): def _get_uniform_logits(self, batch_size: int, length: int): scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length return scores def test_min_length_dist_processor(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 min_dist_processor = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id) # check that min length is applied at length 5 input_ids = ids_tensor((batch_size, 5), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_min_length = min_dist_processor(input_ids, scores) self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(), 4 * [-float("inf")]) # check that min length is not applied anymore at length 15 input_ids = ids_tensor((batch_size, 15), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_min_length = min_dist_processor(input_ids, scores) self.assertFalse(torch.isinf(scores_before_min_length).any()) def test_temperature_dist_warper(self): input_ids = None length = 20 scores = self._get_uniform_logits(batch_size=2, length=length) # tweak scores to not be uniform anymore scores[1, 5] = (1 / length) + 0.1 # peak, 1st batch scores[1, 10] = (1 / length) - 0.4 # valley, 1st batch # compute softmax probs = nn.functional.softmax(scores, dim=-1) temp_dist_warper_sharper = TemperatureLogitsWarper(temperature=0.5) temp_dist_warper_smoother = TemperatureLogitsWarper(temperature=1.3) warped_prob_sharp = nn.functional.softmax(temp_dist_warper_sharper(input_ids, scores.clone()), dim=-1) warped_prob_smooth = nn.functional.softmax(temp_dist_warper_smoother(input_ids, scores.clone()), dim=-1) # uniform distribution stays uniform self.assertTrue(torch.allclose(probs[0, :], warped_prob_sharp[0, :], atol=1e-3)) self.assertTrue(torch.allclose(probs[0, :], warped_prob_smooth[0, :], atol=1e-3)) # sharp peaks get higher, valleys get lower self.assertLess(probs[1, :].max(), warped_prob_sharp[1, :].max()) self.assertGreater(probs[1, :].min(), warped_prob_sharp[1, :].min()) # smooth peaks get lower, valleys get higher self.assertGreater(probs[1, :].max(), warped_prob_smooth[1, :].max()) self.assertLess(probs[1, :].min(), warped_prob_smooth[1, :].min()) def test_repetition_penalty_dist_process(self): input_ids = torch.tensor([[0, 1], [5, 0]], device=torch_device, dtype=torch.long) vocab_size = 10 scores = self._get_uniform_logits(batch_size=2, length=vocab_size) # give values special values scores[0, 0] = -(1 / vocab_size) scores[1, 5] = 4 / vocab_size rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0) scores = rep_penalty_proc(input_ids, scores.clone()) # check that values were correctly changed self.assertAlmostEqual(scores[0, 0].item(), -(1 / vocab_size) * 2) self.assertAlmostEqual(scores[0, 1].item(), (1 / vocab_size) / 2) self.assertAlmostEqual(scores[1, 0].item(), (1 / vocab_size) / 2) self.assertAlmostEqual(scores[1, 5].item(), (4 / vocab_size) / 2) def test_top_k_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create ramp distribution ramp_logits = ( torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1) ) ramp_logits[1:, : vocab_size // 2] = ramp_logits[1:, : vocab_size // 2] + vocab_size top_k_warp = TopKLogitsWarper(3) scores = top_k_warp(input_ids, ramp_logits) # check that correct tokens are filtered self.assertListEqual(torch.isinf(scores[0]).tolist(), 7 * [True] + 3 * [False]) self.assertListEqual(torch.isinf(scores[1]).tolist(), 2 * [True] + 3 * [False] + 5 * [True]) # check special cases length = 5 logits = self._get_uniform_logits(batch_size=batch_size, length=length) top_k_warp_safety_check = TopKLogitsWarper(top_k=1, filter_value=0.0, min_tokens_to_keep=3) scores = top_k_warp_safety_check(input_ids, logits) # uniform dist is not changed self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0]) ramp_logits = torch.arange(length, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1) scores = top_k_warp_safety_check(input_ids, ramp_logits) # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2]) def test_top_p_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper) dist = torch.log( torch.tensor([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float) ) top_p_warp = TopPLogitsWarper(0.8) filtered_dist = torch.exp(top_p_warp(input_ids, dist)) # dist should be filtered to keep min num values so that sum is >= top_p # exp (-inf) => 0 EXPECTED_FILTERED_DIST = torch.tensor( [[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float ) self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3)) # check edge cases with negative and extreme logits ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat( batch_size, 1 ) - (vocab_size // 2) # make ramp_logits more extreme ramp_logits[1] = ramp_logits[1] * 100.0 # make sure at least 2 tokens are kept top_p_warp = TopPLogitsWarper(0.9, min_tokens_to_keep=2, filter_value=0.0) filtered_dist = top_p_warp(input_ids, ramp_logits) # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2. self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [3, 2]) def test_typical_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper) dist = torch.log( torch.tensor([[0.97, 0.01, 0.01, 0.01], [0.4, 0.2, 0.2, 0.2]], device=torch_device, dtype=torch.float) ) typical_warp = TypicalLogitsWarper(0.5) filtered_dist = torch.exp(typical_warp(input_ids, dist)) # dist should be filtered to keep min num values so that sum is >= 0.7 # exp (-inf) => 0 EXPECTED_FILTERED_DIST = torch.tensor( [[0.97, 0.0, 0.0, 0.0], [0.0, 0.2, 0.2, 0.2]], device=torch_device, dtype=torch.float ) self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3)) # check special cases length = 5 logits = self._get_uniform_logits(batch_size=batch_size, length=length) typical_warp_safety_check = TypicalLogitsWarper(mass=0.5, filter_value=0.0, min_tokens_to_keep=3) scores = typical_warp_safety_check(input_ids, logits) # uniform dist is not changed self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0]) # check edge cases with negative and extreme logits ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat( batch_size, 1 ) - (vocab_size // 2) # make ramp_logits more extreme ramp_logits[1] = ramp_logits[1] * 100.0 # make sure at least 2 tokens are kept typical_warp = TypicalLogitsWarper(0.7, min_tokens_to_keep=2, filter_value=0.0) filtered_dist = typical_warp(input_ids, ramp_logits) # first batch should keep two tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2. self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2]) def test_no_repeat_ngram_dist_processor(self): vocab_size = 3 batch_size = 2 input_ids = torch.tensor([[1, 1, 2, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size, vocab_size) no_repeat_proc_2_gram = NoRepeatNGramLogitsProcessor(2) no_repeat_proc_3_gram = NoRepeatNGramLogitsProcessor(3) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2-gram would forbid 2nd and 3rd token (1,2) at 1st batch and 1st token (0) at 2nd batch self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [True, False, False]]) # 3-gram would forbid no token at 1st batch and 1st token (0) at 2nd batch self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, False, False], [True, False, False]] ) def test_encoder_no_repeat_ngram_dist_processor(self): vocab_size = 3 num_beams = 2 batch_size = 1 encoder_input_ids = torch.tensor([1, 2, 1, 1], device=torch_device, dtype=torch.long) input_ids = torch.tensor([[1, 2, 1], [8, 0, 2]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size * num_beams, vocab_size) no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids) no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2-gram would forbid 1st and 2nd token at 1st beam and 1st token (0) at 2nd beam self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False]]) # 3-gram would forbid 1st token at 1st beam and no token at 2nd beam self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False]] ) # Batched input vocab_size = 3 num_beams = 2 batch_size = 2 encoder_input_ids = torch.tensor([[1, 2, 1, 1], [0, 0, 2, 1]], device=torch_device, dtype=torch.long) input_ids = torch.tensor([[1, 2, 1], [1, 0, 2], [0, 0, 0], [0, 2, 2]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size * num_beams, vocab_size) no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids) no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2gram # Batch 1 # - Beam 1: tokens (1, 2) forbidden # - Beam 2: tokens (1) forbidden # Batch 2 # - Beam 1: tokens (0, 2) forbidden # - Beam 2: tokens (1) forbidden self.assertListEqual( torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False], [True, False, True], [False, True, False]], ) # Batch 1 # - Beam 1: tokens (1) forbidden # - Beam 2: tokens () forbidden # Batch 2 # - Beam 1: tokens (2) forbidden # - Beam 2: tokens () forbidden self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False], [False, False, True], [False, False, False]], ) def test_no_bad_words_dist_processor(self): vocab_size = 5 batch_size = 2 eos_token_id = 4 input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) bad_word_tokens = [[1], [4], [1, 0], [0, 1, 2], [1, 3, 1, 3]] scores = self._get_uniform_logits(batch_size, vocab_size) no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=bad_word_tokens, eos_token_id=eos_token_id) filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone()) # batch 1: 1st, 2nd, and 4th (0, 1, 3) token are forbidden # batch 2: 1st, 2nd, and 3rd (0, 1, 2) token are forbidden # Note that 5th element cannot be forbidden as it is EOS token self.assertListEqual( torch.isinf(filtered_scores).tolist(), [[True, True, False, True, False], [True, True, True, False, False]] ) # check edge case no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[4]], eos_token_id=eos_token_id) filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone()) self.assertTrue(torch.allclose(scores, filtered_scores, atol=1e-3)) def test_processor_list(self): batch_size = 4 sequence_length = 10 vocab_size = 15 eos_token_id = 0 # dummy input_ids and scores input_ids = ids_tensor((batch_size, sequence_length), vocab_size) input_ids_comp = input_ids.clone() scores = self._get_uniform_logits(batch_size, vocab_size) scores_comp = scores.clone() # instantiate all dist processors min_dist_proc = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id) temp_dist_warp = TemperatureLogitsWarper(temperature=0.5) rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0) top_k_warp = TopKLogitsWarper(3) top_p_warp = TopPLogitsWarper(0.8) no_repeat_proc = NoRepeatNGramLogitsProcessor(2) no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[1]], eos_token_id=eos_token_id) # no processor list scores = min_dist_proc(input_ids, scores) scores = temp_dist_warp(input_ids, scores) scores = rep_penalty_proc(input_ids, scores) scores = top_k_warp(input_ids, scores) scores = top_p_warp(input_ids, scores) scores = no_repeat_proc(input_ids, scores) scores = no_bad_words_dist_proc(input_ids, scores) # with processor list processor = LogitsProcessorList( [ min_dist_proc, temp_dist_warp, rep_penalty_proc, top_k_warp, top_p_warp, no_repeat_proc, no_bad_words_dist_proc, ] ) scores_comp = processor(input_ids, scores_comp) # scores should be equal self.assertTrue(torch.allclose(scores, scores_comp, atol=1e-3)) # input_ids should never be changed self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist()) def test_prefix_constrained_logits_processor(self): vocab_size = 5 batch_size = 2 input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size, vocab_size) def prefix_allowed_tokens_fn(batch_id, inputs_ids): return [[0, 1], [2, 3]][batch_id] prefix_constrained_logits_proc = PrefixConstrainedLogitsProcessor(prefix_allowed_tokens_fn, 1) filtered_scores = prefix_constrained_logits_proc(input_ids, scores.clone()) # batch 1: 1st, 2nd (0, 1) token are allowed # batch 2: 3rd, 4th (2, 3) token are allowed self.assertListEqual( torch.isinf(filtered_scores).tolist(), [[False, False, True, True, True], [True, True, False, False, True]] ) def test_hamming_diversity(self): vocab_size = 4 num_beams = 2 num_beam_groups = 2 scores = self._get_uniform_logits(num_beams, vocab_size) # batch_idx = 0 -> index batch_idx * num_beam_groups -> idx = 0 * 2 = 0 -> penalises tokens 1 # batch_idx = 1 -> index batch_idx * num_beam_groups -> idx = 1 * 2 = 2 -> penalises tokens 1 current_tokens = torch.tensor([0, 3, 1, 2], device=torch_device, dtype=torch.long) diversity_logits_processor = HammingDiversityLogitsProcessor( diversity_penalty=1.0, num_beams=num_beams, num_beam_groups=num_beam_groups ) processed_scores = diversity_logits_processor(None, scores, current_tokens, 1) self.assertTrue( torch.allclose( processed_scores[0], torch.tensor([-0.7500, 0.2500, 0.2500, 0.2500], device=torch_device), atol=1e-3 ) ) self.assertTrue( torch.allclose( processed_scores[1], torch.tensor([0.2500, -0.7500, 0.2500, 0.2500], device=torch_device), atol=1e-3 ) ) def test_forced_bos_token_logits_processor(self): vocab_size = 20 batch_size = 4 bos_token_id = 0 logits_processor = ForcedBOSTokenLogitsProcessor(bos_token_id=bos_token_id) # check that all scores are -inf except the bos_token_id score input_ids = ids_tensor((batch_size, 1), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertTrue(torch.isneginf(scores[:, bos_token_id + 1 :]).all()) self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0]) # score for bos_token_id shold be zero # check that bos_token_id is not forced if current length is greater than 1 input_ids = ids_tensor((batch_size, 4), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertFalse(torch.isinf(scores).any()) def test_forced_eos_token_logits_processor(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 max_length = 5 logits_processor = ForcedEOSTokenLogitsProcessor(max_length=max_length, eos_token_id=eos_token_id) # check that all scores are -inf except the eos_token_id when max_length-1 is reached input_ids = ids_tensor((batch_size, 4), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertTrue(torch.isneginf(scores[:, eos_token_id + 1 :]).all()) self.assertListEqual(scores[:, eos_token_id].tolist(), 4 * [0]) # score for eos_token_id should be zero # check that eos_token_id is not forced if max_length-1 is not reached input_ids = ids_tensor((batch_size, 3), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertFalse(torch.isinf(scores).any()) def test_remove_nan_inf_logits_processor(self): scores = torch.tensor( [[0.0, 0.7, 0.8, float("nan")], [0.1, float("inf"), 0.3, float("-inf")]], device=torch_device ) input_ids = ids_tensor((2, 4), vocab_size=20) logits_processor = InfNanRemoveLogitsProcessor() scores = logits_processor(input_ids, scores) self.assertTrue( torch.allclose( scores, torch.tensor( [[0.0, 0.7, 0.8, 0.0], [0.1, torch.finfo(scores.dtype).max, 0.3, float("-inf")]], device=torch_device, ), atol=1e-6, ) ) def test_exponential_decay_length_penalty(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 penalty_start = 5 penalty_factor = 1.1 input_ids = ids_tensor((batch_size, 2), vocab_size=vocab_size) input_ids_seq_length = input_ids.shape[-1] length_decay_processor = ExponentialDecayLengthPenalty( exponential_decay_length_penalty=(penalty_start, penalty_factor), eos_token_id=eos_token_id, input_ids_seq_length=input_ids_seq_length, ) # check that penalty is not applied before start scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_start = length_decay_processor(input_ids, scores) self.assertListEqual(scores_before_start[:, eos_token_id].tolist(), scores[:, eos_token_id].tolist()) # check that penalty is applied after start input_ids = ids_tensor((batch_size, 20), vocab_size=vocab_size) scores = self._get_uniform_logits(batch_size, vocab_size) scores_after_start = length_decay_processor(input_ids, scores) self.assertTrue( torch.gt( scores_after_start[penalty_start + 1 :, eos_token_id], scores[penalty_start + 1 :, eos_token_id] ).all() ) def test_normalization(self): input_ids = None scores = torch.tensor( [[-23.18, -29.96, -43.54, 47.77], [-33.58, -26.87, -32.96, 22.51]], device=torch_device, dtype=torch.float ) logit_normalization = LogitNormalization() normalized_scores = logit_normalization(input_ids, scores).exp() ones = torch.ones(scores.shape[0], device=torch_device, dtype=torch.float) self.assertTrue(normalized_scores.sum(dim=-1).allclose(ones)) self.assertTrue(normalized_scores.allclose(scores.softmax(dim=-1)))

huggingface/transformers

Generate: general TF XLA constrastive search are now slow tests

# What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

gante

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

0f78529f982eceb79c5855d0466c287ec8a18df1

Generate: general TF XLA constrastive search are now slow tests. # What does this PR do? TF's XLA contrastive search tests were time-consuming because of the conversion to XLA, so this PR moves them to more powerful slow tests. Making these tests faster would imply creating smaller model configs for each model, which seems like overkill.

./examples/research_projects/rag/finetune_rag.sh

# Add parent directory to python path to access lightning_base.py export PYTHONPATH="../":"${PYTHONPATH}" # A sample finetuning run, you need to specify data_dir, output_dir and model_name_or_path # run ./examples/rag/finetune_rag.sh --help to see all the possible options python examples/rag/finetune_rag.py \ --data_dir $DATA_DIR \ --output_dir $OUTPUT_DIR \ --model_name_or_path $MODEL_NAME_OR_PATH \ --model_type rag_sequence \ --fp16 \ --gpus 8 \ --profile \ --do_train \ --do_predict \ --n_val -1 \ --train_batch_size 8 \ --eval_batch_size 1 \ --max_source_length 128 \ --max_target_length 25 \ --val_max_target_length 25 \ --test_max_target_length 25 \ --label_smoothing 0.1 \ --dropout 0.1 \ --attention_dropout 0.1 \ --weight_decay 0.001 \ --adam_epsilon 1e-08 \ --max_grad_norm 0.1 \ --lr_scheduler polynomial \ --learning_rate 3e-05 \ --num_train_epochs 100 \ --warmup_steps 500 \ --gradient_accumulation_steps 1 \

# Add parent directory to python path to access lightning_base.py export PYTHONPATH="../":"${PYTHONPATH}" # A sample finetuning run, you need to specify data_dir, output_dir and model_name_or_path # run ./examples/rag/finetune_rag.sh --help to see all the possible options python examples/rag/finetune_rag.py \ --data_dir $DATA_DIR \ --output_dir $OUTPUT_DIR \ --model_name_or_path $MODEL_NAME_OR_PATH \ --model_type rag_sequence \ --fp16 \ --gpus 8 \ --profile \ --do_train \ --do_predict \ --n_val -1 \ --train_batch_size 8 \ --eval_batch_size 1 \ --max_source_length 128 \ --max_target_length 25 \ --val_max_target_length 25 \ --test_max_target_length 25 \ --label_smoothing 0.1 \ --dropout 0.1 \ --attention_dropout 0.1 \ --weight_decay 0.001 \ --adam_epsilon 1e-08 \ --max_grad_norm 0.1 \ --lr_scheduler polynomial \ --learning_rate 3e-05 \ --num_train_epochs 100 \ --warmup_steps 500 \ --gradient_accumulation_steps 1 \

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/image-classification/run_image_classification_no_trainer.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Finetuning any 🤗 Transformers model for image classification leveraging 🤗 Accelerate.""" import argparse import json import logging import math import os from pathlib import Path import datasets import torch from datasets import load_dataset from torch.utils.data import DataLoader from torchvision.transforms import ( CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor, ) from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForImageClassification, SchedulerType, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt") def parse_args(): parser = argparse.ArgumentParser(description="Fine-tune a Transformers model on an image classification dataset") parser.add_argument( "--dataset_name", type=str, default="cifar10", help=( "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," " dataset)." ), ) parser.add_argument("--train_dir", type=str, default=None, help="A folder containing the training data.") parser.add_argument("--validation_dir", type=str, default=None, help="A folder containing the validation data.") parser.add_argument( "--max_train_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ), ) parser.add_argument( "--max_eval_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ), ) parser.add_argument( "--train_val_split", type=float, default=0.15, help="Percent to split off of train for validation", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", default="google/vit-base-patch16-224-in21k", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) parser.add_argument( "--ignore_mismatched_sizes", action="store_true", help="Whether or not to enable to load a pretrained model whose head dimensions are different.", ) args = parser.parse_args() # Sanity checks if args.dataset_name is None and args.train_dir is None and args.validation_dir is None: raise ValueError("Need either a dataset name or a training/validation folder.") if args.push_to_hub or args.with_tracking: if args.output_dir is None: raise ValueError( "Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified." ) if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_image_classification_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) logger.info(accelerator.state) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own training and evaluation files (see below) # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset(args.dataset_name, task="image-classification") else: data_files = {} if args.train_dir is not None: data_files["train"] = os.path.join(args.train_dir, "**") if args.validation_dir is not None: data_files["validation"] = os.path.join(args.validation_dir, "**") dataset = load_dataset( "imagefolder", data_files=data_files, cache_dir=args.cache_dir, task="image-classification", ) # See more about loading custom images at # https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder. # If we don't have a validation split, split off a percentage of train as validation. args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split if isinstance(args.train_val_split, float) and args.train_val_split > 0.0: split = dataset["train"].train_test_split(args.train_val_split) dataset["train"] = split["train"] dataset["validation"] = split["test"] # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. labels = dataset["train"].features["labels"].names label2id = {label: str(i) for i, label in enumerate(labels)} id2label = {str(i): label for i, label in enumerate(labels)} # Load pretrained model and feature extractor # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config = AutoConfig.from_pretrained( args.model_name_or_path, num_labels=len(labels), i2label=id2label, label2id=label2id, finetuning_task="image-classification", ) feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_name_or_path) model = AutoModelForImageClassification.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ignore_mismatched_sizes=args.ignore_mismatched_sizes, ) # Preprocessing the datasets # Define torchvision transforms to be applied to each image. if "shortest_edge" in feature_extractor.size: size = feature_extractor.size["shortest_edge"] else: size = (feature_extractor.size["height"], feature_extractor.size["width"]) normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std) train_transforms = Compose( [ RandomResizedCrop(size), RandomHorizontalFlip(), ToTensor(), normalize, ] ) val_transforms = Compose( [ Resize(size), CenterCrop(size), ToTensor(), normalize, ] ) def preprocess_train(example_batch): """Apply _train_transforms across a batch.""" example_batch["pixel_values"] = [train_transforms(image.convert("RGB")) for image in example_batch["image"]] return example_batch def preprocess_val(example_batch): """Apply _val_transforms across a batch.""" example_batch["pixel_values"] = [val_transforms(image.convert("RGB")) for image in example_batch["image"]] return example_batch with accelerator.main_process_first(): if args.max_train_samples is not None: dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) # Set the training transforms train_dataset = dataset["train"].with_transform(preprocess_train) if args.max_eval_samples is not None: dataset["validation"] = dataset["validation"].shuffle(seed=args.seed).select(range(args.max_eval_samples)) # Set the validation transforms eval_dataset = dataset["validation"].with_transform(preprocess_val) # DataLoaders creation: def collate_fn(examples): pixel_values = torch.stack([example["pixel_values"] for example in examples]) labels = torch.tensor([example["labels"] for example in examples]) return {"pixel_values": pixel_values, "labels": labels} train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=collate_fn, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("image_classification_no_trainer", experiment_config) # Get the metric function metric = evaluate.load("accuracy") # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress {completed_steps} steps", blocking=False, auto_lfs_prune=True, ) if completed_steps >= args.max_train_steps: break model.eval() for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"])) metric.add_batch( predictions=predictions, references=references, ) eval_metric = metric.compute() logger.info(f"epoch {epoch}: {eval_metric}") if args.with_tracking: accelerator.log( { "accuracy": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) if args.output_dir is not None: with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump({"eval_accuracy": eval_metric["accuracy"]}, f) if __name__ == "__main__": main()

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Finetuning any 🤗 Transformers model for image classification leveraging 🤗 Accelerate.""" import argparse import json import logging import math import os from pathlib import Path import datasets import torch from datasets import load_dataset from torch.utils.data import DataLoader from torchvision.transforms import ( CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor, ) from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForImageClassification, SchedulerType, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt") def parse_args(): parser = argparse.ArgumentParser(description="Fine-tune a Transformers model on an image classification dataset") parser.add_argument( "--dataset_name", type=str, default="cifar10", help=( "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," " dataset)." ), ) parser.add_argument("--train_dir", type=str, default=None, help="A folder containing the training data.") parser.add_argument("--validation_dir", type=str, default=None, help="A folder containing the validation data.") parser.add_argument( "--max_train_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ), ) parser.add_argument( "--max_eval_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ), ) parser.add_argument( "--train_val_split", type=float, default=0.15, help="Percent to split off of train for validation", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", default="google/vit-base-patch16-224-in21k", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) parser.add_argument( "--ignore_mismatched_sizes", action="store_true", help="Whether or not to enable to load a pretrained model whose head dimensions are different.", ) args = parser.parse_args() # Sanity checks if args.dataset_name is None and args.train_dir is None and args.validation_dir is None: raise ValueError("Need either a dataset name or a training/validation folder.") if args.push_to_hub or args.with_tracking: if args.output_dir is None: raise ValueError( "Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified." ) if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_image_classification_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) logger.info(accelerator.state) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own training and evaluation files (see below) # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset(args.dataset_name, task="image-classification") else: data_files = {} if args.train_dir is not None: data_files["train"] = os.path.join(args.train_dir, "**") if args.validation_dir is not None: data_files["validation"] = os.path.join(args.validation_dir, "**") dataset = load_dataset( "imagefolder", data_files=data_files, cache_dir=args.cache_dir, task="image-classification", ) # See more about loading custom images at # https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder. # If we don't have a validation split, split off a percentage of train as validation. args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split if isinstance(args.train_val_split, float) and args.train_val_split > 0.0: split = dataset["train"].train_test_split(args.train_val_split) dataset["train"] = split["train"] dataset["validation"] = split["test"] # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. labels = dataset["train"].features["labels"].names label2id = {label: str(i) for i, label in enumerate(labels)} id2label = {str(i): label for i, label in enumerate(labels)} # Load pretrained model and feature extractor # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config = AutoConfig.from_pretrained( args.model_name_or_path, num_labels=len(labels), i2label=id2label, label2id=label2id, finetuning_task="image-classification", ) feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_name_or_path) model = AutoModelForImageClassification.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ignore_mismatched_sizes=args.ignore_mismatched_sizes, ) # Preprocessing the datasets # Define torchvision transforms to be applied to each image. if "shortest_edge" in feature_extractor.size: size = feature_extractor.size["shortest_edge"] else: size = (feature_extractor.size["height"], feature_extractor.size["width"]) normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std) train_transforms = Compose( [ RandomResizedCrop(size), RandomHorizontalFlip(), ToTensor(), normalize, ] ) val_transforms = Compose( [ Resize(size), CenterCrop(size), ToTensor(), normalize, ] ) def preprocess_train(example_batch): """Apply _train_transforms across a batch.""" example_batch["pixel_values"] = [train_transforms(image.convert("RGB")) for image in example_batch["image"]] return example_batch def preprocess_val(example_batch): """Apply _val_transforms across a batch.""" example_batch["pixel_values"] = [val_transforms(image.convert("RGB")) for image in example_batch["image"]] return example_batch with accelerator.main_process_first(): if args.max_train_samples is not None: dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) # Set the training transforms train_dataset = dataset["train"].with_transform(preprocess_train) if args.max_eval_samples is not None: dataset["validation"] = dataset["validation"].shuffle(seed=args.seed).select(range(args.max_eval_samples)) # Set the validation transforms eval_dataset = dataset["validation"].with_transform(preprocess_val) # DataLoaders creation: def collate_fn(examples): pixel_values = torch.stack([example["pixel_values"] for example in examples]) labels = torch.tensor([example["labels"] for example in examples]) return {"pixel_values": pixel_values, "labels": labels} train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=collate_fn, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("image_classification_no_trainer", experiment_config) # Get the metric function metric = evaluate.load("accuracy") # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress {completed_steps} steps", blocking=False, auto_lfs_prune=True, ) if completed_steps >= args.max_train_steps: break model.eval() for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"])) metric.add_batch( predictions=predictions, references=references, ) eval_metric = metric.compute() logger.info(f"epoch {epoch}: {eval_metric}") if args.with_tracking: accelerator.log( { "accuracy": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) all_results = {f"eval_{k}": v for k, v in eval_metric.items()} with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump(all_results, f) if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/language-modeling/run_clm_no_trainer.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset without using HuggingFace Trainer. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=text-generation """ # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. import argparse import json import logging import math import os import random from itertools import chain from pathlib import Path import datasets import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import transformers from accelerate import Accelerator, DistributedType from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForCausalLM, AutoTokenizer, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a causal language modeling task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--validation_split_percentage", default=5, help="The percentage of the train set used as validation set in case there's no validation split", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument( "--block_size", type=int, default=None, help=( "Optional input sequence length after tokenization. The training dataset will be truncated in block of" " this size for training. Default to the model max input length for single sentence inputs (take into" " account special tokens)." ), ) parser.add_argument( "--preprocessing_num_workers", type=int, default=None, help="The number of processes to use for the preprocessing.", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument( "--no_keep_linebreaks", action="store_true", help="Do not keep line breaks when using TXT files." ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if args.dataset_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_clm_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( args.dataset_name, args.dataset_config_name, split=f"train[:{args.validation_split_percentage}%]", ) raw_datasets["train"] = load_dataset( args.dataset_name, args.dataset_config_name, split=f"train[{args.validation_split_percentage}%:]", ) else: data_files = {} dataset_args = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = args.train_file.split(".")[-1] if extension == "txt": extension = "text" dataset_args["keep_linebreaks"] = not args.no_keep_linebreaks raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args) # If no validation data is there, validation_split_percentage will be used to divide the dataset. if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( extension, data_files=data_files, split=f"train[:{args.validation_split_percentage}%]", **dataset_args, ) raw_datasets["train"] = load_dataset( extension, data_files=data_files, split=f"train[{args.validation_split_percentage}%:]", **dataset_args, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.config_name) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer) elif args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name_or_path: model = AutoModelForCausalLM.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ) else: logger.info("Training new model from scratch") model = AutoModelForCausalLM.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # Preprocessing the datasets. # First we tokenize all the texts. column_names = raw_datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] def tokenize_function(examples): return tokenizer(examples[text_column_name]) with accelerator.main_process_first(): tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on dataset", ) if args.block_size is None: block_size = tokenizer.model_max_length if block_size > 1024: logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --block_size xxx." ) block_size = 1024 else: if args.block_size > tokenizer.model_max_length: logger.warning( f"The block_size passed ({args.block_size}) is larger than the maximum length for the model" f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}." ) block_size = min(args.block_size, tokenizer.model_max_length) # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size. def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= block_size: total_length = (total_length // block_size) * block_size # Split by chunks of max_len. result = { k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items() } result["labels"] = result["input_ids"].copy() return result # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower # to preprocess. # # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map with accelerator.main_process_first(): lm_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=args.preprocessing_num_workers, load_from_cache_file=not args.overwrite_cache, desc=f"Grouping texts in chunks of {block_size}", ) train_dataset = lm_datasets["train"] eval_dataset = lm_datasets["validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader( eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size ) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "layer_norm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties. if accelerator.distributed_type == DistributedType.TPU: model.tie_weights() # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("clm_no_trainer", experiment_config) # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: # need to multiply `gradient_accumulation_steps` to reflect real steps resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) # update the progress_bar if load from checkpoint progress_bar.update(starting_epoch * num_update_steps_per_epoch) completed_steps = starting_epoch * num_update_steps_per_epoch for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: if step % args.gradient_accumulation_steps == 0: progress_bar.update(1) completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() losses = [] for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) loss = outputs.loss losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size))) losses = torch.cat(losses) try: eval_loss = torch.mean(losses) perplexity = math.exp(eval_loss) except OverflowError: perplexity = float("inf") logger.info(f"epoch {epoch}: perplexity: {perplexity} eval_loss: {eval_loss}") if args.with_tracking: accelerator.log( { "perplexity": perplexity, "eval_loss": eval_loss, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump({"perplexity": perplexity}, f) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset without using HuggingFace Trainer. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=text-generation """ # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. import argparse import json import logging import math import os import random from itertools import chain from pathlib import Path import datasets import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import transformers from accelerate import Accelerator, DistributedType from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForCausalLM, AutoTokenizer, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a causal language modeling task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--validation_split_percentage", default=5, help="The percentage of the train set used as validation set in case there's no validation split", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument( "--block_size", type=int, default=None, help=( "Optional input sequence length after tokenization. The training dataset will be truncated in block of" " this size for training. Default to the model max input length for single sentence inputs (take into" " account special tokens)." ), ) parser.add_argument( "--preprocessing_num_workers", type=int, default=None, help="The number of processes to use for the preprocessing.", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument( "--no_keep_linebreaks", action="store_true", help="Do not keep line breaks when using TXT files." ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if args.dataset_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_clm_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( args.dataset_name, args.dataset_config_name, split=f"train[:{args.validation_split_percentage}%]", ) raw_datasets["train"] = load_dataset( args.dataset_name, args.dataset_config_name, split=f"train[{args.validation_split_percentage}%:]", ) else: data_files = {} dataset_args = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = args.train_file.split(".")[-1] if extension == "txt": extension = "text" dataset_args["keep_linebreaks"] = not args.no_keep_linebreaks raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args) # If no validation data is there, validation_split_percentage will be used to divide the dataset. if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( extension, data_files=data_files, split=f"train[:{args.validation_split_percentage}%]", **dataset_args, ) raw_datasets["train"] = load_dataset( extension, data_files=data_files, split=f"train[{args.validation_split_percentage}%:]", **dataset_args, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.config_name) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer) elif args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name_or_path: model = AutoModelForCausalLM.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ) else: logger.info("Training new model from scratch") model = AutoModelForCausalLM.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # Preprocessing the datasets. # First we tokenize all the texts. column_names = raw_datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] def tokenize_function(examples): return tokenizer(examples[text_column_name]) with accelerator.main_process_first(): tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on dataset", ) if args.block_size is None: block_size = tokenizer.model_max_length if block_size > 1024: logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --block_size xxx." ) block_size = 1024 else: if args.block_size > tokenizer.model_max_length: logger.warning( f"The block_size passed ({args.block_size}) is larger than the maximum length for the model" f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}." ) block_size = min(args.block_size, tokenizer.model_max_length) # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size. def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= block_size: total_length = (total_length // block_size) * block_size # Split by chunks of max_len. result = { k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items() } result["labels"] = result["input_ids"].copy() return result # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower # to preprocess. # # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map with accelerator.main_process_first(): lm_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=args.preprocessing_num_workers, load_from_cache_file=not args.overwrite_cache, desc=f"Grouping texts in chunks of {block_size}", ) train_dataset = lm_datasets["train"] eval_dataset = lm_datasets["validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader( eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size ) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "layer_norm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties. if accelerator.distributed_type == DistributedType.TPU: model.tie_weights() # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("clm_no_trainer", experiment_config) # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: # need to multiply `gradient_accumulation_steps` to reflect real steps resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) # update the progress_bar if load from checkpoint progress_bar.update(starting_epoch * num_update_steps_per_epoch) completed_steps = starting_epoch * num_update_steps_per_epoch for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: if step % args.gradient_accumulation_steps == 0: progress_bar.update(1) completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() losses = [] for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) loss = outputs.loss losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size))) losses = torch.cat(losses) try: eval_loss = torch.mean(losses) perplexity = math.exp(eval_loss) except OverflowError: perplexity = float("inf") logger.info(f"epoch {epoch}: perplexity: {perplexity} eval_loss: {eval_loss}") if args.with_tracking: accelerator.log( { "perplexity": perplexity, "eval_loss": eval_loss, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump({"perplexity": perplexity}, f) if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/language-modeling/run_mlm_no_trainer.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) on a text file or a dataset without using HuggingFace Trainer. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=fill-mask """ # You can also adapt this script on your own mlm task. Pointers for this are left as comments. import argparse import json import logging import math import os import random from itertools import chain from pathlib import Path import datasets import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import transformers from accelerate import Accelerator, DistributedType from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForLanguageModeling, SchedulerType, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a Masked Language Modeling task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--validation_split_percentage", default=5, help="The percentage of the train set used as validation set in case there's no validation split", ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument( "--max_seq_length", type=int, default=None, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated." ), ) parser.add_argument( "--line_by_line", type=bool, default=False, help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.", ) parser.add_argument( "--preprocessing_num_workers", type=int, default=None, help="The number of processes to use for the preprocessing.", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument( "--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss" ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if args.dataset_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] if extension not in ["csv", "json", "txt"]: raise ValueError("`train_file` should be a csv, json or txt file.") if args.validation_file is not None: extension = args.validation_file.split(".")[-1] if extension not in ["csv", "json", "txt"]: raise ValueError("`validation_file` should be a csv, json or txt file.") if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_mlm_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( args.dataset_name, args.dataset_config_name, split=f"train[:{args.validation_split_percentage}%]", ) raw_datasets["train"] = load_dataset( args.dataset_name, args.dataset_config_name, split=f"train[{args.validation_split_percentage}%:]", ) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = args.train_file.split(".")[-1] if extension == "txt": extension = "text" raw_datasets = load_dataset(extension, data_files=data_files) # If no validation data is there, validation_split_percentage will be used to divide the dataset. if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( extension, data_files=data_files, split=f"train[:{args.validation_split_percentage}%]", ) raw_datasets["train"] = load_dataset( extension, data_files=data_files, split=f"train[{args.validation_split_percentage}%:]", ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.config_name) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer) elif args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name_or_path: model = AutoModelForMaskedLM.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ) else: logger.info("Training new model from scratch") model = AutoModelForMaskedLM.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # Preprocessing the datasets. # First we tokenize all the texts. column_names = raw_datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] if args.max_seq_length is None: max_seq_length = tokenizer.model_max_length if max_seq_length > 1024: logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx." ) max_seq_length = 1024 else: if args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) max_seq_length = min(args.max_seq_length, tokenizer.model_max_length) if args.line_by_line: # When using line_by_line, we just tokenize each nonempty line. padding = "max_length" if args.pad_to_max_length else False def tokenize_function(examples): # Remove empty lines examples[text_column_name] = [ line for line in examples[text_column_name] if len(line) > 0 and not line.isspace() ] return tokenizer( examples[text_column_name], padding=padding, truncation=True, max_length=max_seq_length, # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it # receives the `special_tokens_mask`. return_special_tokens_mask=True, ) with accelerator.main_process_first(): tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=[text_column_name], load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on dataset line_by_line", ) else: # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more # efficient when it receives the `special_tokens_mask`. def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) with accelerator.main_process_first(): tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on every text in dataset", ) # Main data processing function that will concatenate all texts from our dataset and generate chunks of # max_seq_length. def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= max_seq_length: total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value # might be slower to preprocess. # # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map with accelerator.main_process_first(): tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=args.preprocessing_num_workers, load_from_cache_file=not args.overwrite_cache, desc=f"Grouping texts in chunks of {max_seq_length}", ) train_dataset = tokenized_datasets["train"] eval_dataset = tokenized_datasets["validation"] # Conditional for small test subsets if len(train_dataset) > 3: # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # Data collator # This one will take care of randomly masking the tokens. data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=args.mlm_probability) # DataLoaders creation: train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be # shorter in multiprocess) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties. if accelerator.distributed_type == DistributedType.TPU: model.tie_weights() # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("mlm_no_trainer", experiment_config) # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: # need to multiply `gradient_accumulation_steps` to reflect real steps resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) # update the progress_bar if load from checkpoint progress_bar.update(starting_epoch * num_update_steps_per_epoch) completed_steps = starting_epoch * num_update_steps_per_epoch for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: if step % args.gradient_accumulation_steps == 0: progress_bar.update(1) completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() losses = [] for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) loss = outputs.loss losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size))) losses = torch.cat(losses) try: eval_loss = torch.mean(losses) perplexity = math.exp(eval_loss) except OverflowError: perplexity = float("inf") logger.info(f"epoch {epoch}: perplexity: {perplexity}") if args.with_tracking: accelerator.log( { "perplexity": perplexity, "eval_loss": eval_loss, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump({"perplexity": perplexity}, f) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) on a text file or a dataset without using HuggingFace Trainer. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=fill-mask """ # You can also adapt this script on your own mlm task. Pointers for this are left as comments. import argparse import json import logging import math import os import random from itertools import chain from pathlib import Path import datasets import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import transformers from accelerate import Accelerator, DistributedType from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForLanguageModeling, SchedulerType, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a Masked Language Modeling task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--validation_split_percentage", default=5, help="The percentage of the train set used as validation set in case there's no validation split", ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument( "--max_seq_length", type=int, default=None, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated." ), ) parser.add_argument( "--line_by_line", type=bool, default=False, help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.", ) parser.add_argument( "--preprocessing_num_workers", type=int, default=None, help="The number of processes to use for the preprocessing.", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument( "--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss" ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if args.dataset_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] if extension not in ["csv", "json", "txt"]: raise ValueError("`train_file` should be a csv, json or txt file.") if args.validation_file is not None: extension = args.validation_file.split(".")[-1] if extension not in ["csv", "json", "txt"]: raise ValueError("`validation_file` should be a csv, json or txt file.") if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_mlm_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( args.dataset_name, args.dataset_config_name, split=f"train[:{args.validation_split_percentage}%]", ) raw_datasets["train"] = load_dataset( args.dataset_name, args.dataset_config_name, split=f"train[{args.validation_split_percentage}%:]", ) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = args.train_file.split(".")[-1] if extension == "txt": extension = "text" raw_datasets = load_dataset(extension, data_files=data_files) # If no validation data is there, validation_split_percentage will be used to divide the dataset. if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( extension, data_files=data_files, split=f"train[:{args.validation_split_percentage}%]", ) raw_datasets["train"] = load_dataset( extension, data_files=data_files, split=f"train[{args.validation_split_percentage}%:]", ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.config_name) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer) elif args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name_or_path: model = AutoModelForMaskedLM.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ) else: logger.info("Training new model from scratch") model = AutoModelForMaskedLM.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # Preprocessing the datasets. # First we tokenize all the texts. column_names = raw_datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] if args.max_seq_length is None: max_seq_length = tokenizer.model_max_length if max_seq_length > 1024: logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx." ) max_seq_length = 1024 else: if args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) max_seq_length = min(args.max_seq_length, tokenizer.model_max_length) if args.line_by_line: # When using line_by_line, we just tokenize each nonempty line. padding = "max_length" if args.pad_to_max_length else False def tokenize_function(examples): # Remove empty lines examples[text_column_name] = [ line for line in examples[text_column_name] if len(line) > 0 and not line.isspace() ] return tokenizer( examples[text_column_name], padding=padding, truncation=True, max_length=max_seq_length, # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it # receives the `special_tokens_mask`. return_special_tokens_mask=True, ) with accelerator.main_process_first(): tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=[text_column_name], load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on dataset line_by_line", ) else: # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more # efficient when it receives the `special_tokens_mask`. def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) with accelerator.main_process_first(): tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on every text in dataset", ) # Main data processing function that will concatenate all texts from our dataset and generate chunks of # max_seq_length. def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= max_seq_length: total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value # might be slower to preprocess. # # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map with accelerator.main_process_first(): tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=args.preprocessing_num_workers, load_from_cache_file=not args.overwrite_cache, desc=f"Grouping texts in chunks of {max_seq_length}", ) train_dataset = tokenized_datasets["train"] eval_dataset = tokenized_datasets["validation"] # Conditional for small test subsets if len(train_dataset) > 3: # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # Data collator # This one will take care of randomly masking the tokens. data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=args.mlm_probability) # DataLoaders creation: train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be # shorter in multiprocess) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties. if accelerator.distributed_type == DistributedType.TPU: model.tie_weights() # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("mlm_no_trainer", experiment_config) # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: # need to multiply `gradient_accumulation_steps` to reflect real steps resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) # update the progress_bar if load from checkpoint progress_bar.update(starting_epoch * num_update_steps_per_epoch) completed_steps = starting_epoch * num_update_steps_per_epoch for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: if step % args.gradient_accumulation_steps == 0: progress_bar.update(1) completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() losses = [] for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) loss = outputs.loss losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size))) losses = torch.cat(losses) try: eval_loss = torch.mean(losses) perplexity = math.exp(eval_loss) except OverflowError: perplexity = float("inf") logger.info(f"epoch {epoch}: perplexity: {perplexity}") if args.with_tracking: accelerator.log( { "perplexity": perplexity, "eval_loss": eval_loss, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump({"perplexity": perplexity}, f) if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/multiple-choice/run_swag_no_trainer.py

#!/usr/bin/env python # coding=utf-8 # Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning a 🤗 Transformers model on multiple choice relying on the accelerate library without using a Trainer. """ # You can also adapt this script on your own multiple choice task. Pointers for this are left as comments. import argparse import json import logging import math import os import random from dataclasses import dataclass from itertools import chain from pathlib import Path from typing import Optional, Union import datasets import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, PreTrainedTokenizerBase, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import PaddingStrategy, check_min_version, get_full_repo_name, send_example_telemetry # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) # You should update this to your particular problem to have better documentation of `model_type` MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a multiple choice task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--max_length", type=int, default=128, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," " sequences shorter will be padded if `--pad_to_max_lengh` is passed." ), ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument( "--debug", action="store_true", help="Activate debug mode and run training only with a subset of data.", ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args @dataclass class DataCollatorForMultipleChoice: """ Data collator that will dynamically pad the inputs for multiple choice received. Args: tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]): The tokenizer used for encoding the data. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ tokenizer: PreTrainedTokenizerBase padding: Union[bool, str, PaddingStrategy] = True max_length: Optional[int] = None pad_to_multiple_of: Optional[int] = None def __call__(self, features): label_name = "label" if "label" in features[0].keys() else "labels" labels = [feature.pop(label_name) for feature in features] batch_size = len(features) num_choices = len(features[0]["input_ids"]) flattened_features = [ [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features ] flattened_features = list(chain(*flattened_features)) batch = self.tokenizer.pad( flattened_features, padding=self.padding, max_length=self.max_length, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) # Un-flatten batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()} # Add back labels batch["labels"] = torch.tensor(labels, dtype=torch.int64) return batch def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_swag_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = args.train_file.split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files) # Trim a number of training examples if args.debug: for split in raw_datasets.keys(): raw_datasets[split] = raw_datasets[split].select(range(100)) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. if raw_datasets["train"] is not None: column_names = raw_datasets["train"].column_names else: column_names = raw_datasets["validation"].column_names # When using your own dataset or a different dataset from swag, you will probably need to change this. ending_names = [f"ending{i}" for i in range(4)] context_name = "sent1" question_header_name = "sent2" label_column_name = "label" if "label" in column_names else "labels" # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.model_name_or_path) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer) elif args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name_or_path: model = AutoModelForMultipleChoice.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ) else: logger.info("Training new model from scratch") model = AutoModelForMultipleChoice.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # Preprocessing the datasets. # First we tokenize all the texts. padding = "max_length" if args.pad_to_max_length else False def preprocess_function(examples): first_sentences = [[context] * 4 for context in examples[context_name]] question_headers = examples[question_header_name] second_sentences = [ [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers) ] labels = examples[label_column_name] # Flatten out first_sentences = list(chain(*first_sentences)) second_sentences = list(chain(*second_sentences)) # Tokenize tokenized_examples = tokenizer( first_sentences, second_sentences, max_length=args.max_length, padding=padding, truncation=True, ) # Un-flatten tokenized_inputs = {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()} tokenized_inputs["labels"] = labels return tokenized_inputs with accelerator.main_process_first(): processed_datasets = raw_datasets.map( preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names ) train_dataset = processed_datasets["train"] eval_dataset = processed_datasets["validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: if args.pad_to_max_length: # If padding was already done ot max length, we use the default data collator that will just convert everything # to tensors. data_collator = default_data_collator else: # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). data_collator = DataCollatorForMultipleChoice( tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None) ) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Use the device given by the `accelerator` object. device = accelerator.device model.to(device) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("swag_no_trainer", experiment_config) # Metrics metric = evaluate.load("accuracy") # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"])) metric.add_batch( predictions=predictions, references=references, ) eval_metric = metric.compute() accelerator.print(f"epoch {epoch}: {eval_metric}") if args.with_tracking: accelerator.log( { "accuracy": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump({"eval_accuracy": eval_metric["accuracy"]}, f) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning a 🤗 Transformers model on multiple choice relying on the accelerate library without using a Trainer. """ # You can also adapt this script on your own multiple choice task. Pointers for this are left as comments. import argparse import json import logging import math import os import random from dataclasses import dataclass from itertools import chain from pathlib import Path from typing import Optional, Union import datasets import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, PreTrainedTokenizerBase, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import PaddingStrategy, check_min_version, get_full_repo_name, send_example_telemetry # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) # You should update this to your particular problem to have better documentation of `model_type` MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a multiple choice task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--max_seq_length", type=int, default=128, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," " sequences shorter will be padded if `--pad_to_max_lengh` is passed." ), ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument( "--debug", action="store_true", help="Activate debug mode and run training only with a subset of data.", ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args @dataclass class DataCollatorForMultipleChoice: """ Data collator that will dynamically pad the inputs for multiple choice received. Args: tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]): The tokenizer used for encoding the data. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ tokenizer: PreTrainedTokenizerBase padding: Union[bool, str, PaddingStrategy] = True max_length: Optional[int] = None pad_to_multiple_of: Optional[int] = None def __call__(self, features): label_name = "label" if "label" in features[0].keys() else "labels" labels = [feature.pop(label_name) for feature in features] batch_size = len(features) num_choices = len(features[0]["input_ids"]) flattened_features = [ [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features ] flattened_features = list(chain(*flattened_features)) batch = self.tokenizer.pad( flattened_features, padding=self.padding, max_length=self.max_length, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) # Un-flatten batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()} # Add back labels batch["labels"] = torch.tensor(labels, dtype=torch.int64) return batch def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_swag_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = args.train_file.split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files) # Trim a number of training examples if args.debug: for split in raw_datasets.keys(): raw_datasets[split] = raw_datasets[split].select(range(100)) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. if raw_datasets["train"] is not None: column_names = raw_datasets["train"].column_names else: column_names = raw_datasets["validation"].column_names # When using your own dataset or a different dataset from swag, you will probably need to change this. ending_names = [f"ending{i}" for i in range(4)] context_name = "sent1" question_header_name = "sent2" label_column_name = "label" if "label" in column_names else "labels" # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.model_name_or_path) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer) elif args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name_or_path: model = AutoModelForMultipleChoice.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ) else: logger.info("Training new model from scratch") model = AutoModelForMultipleChoice.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # Preprocessing the datasets. # First we tokenize all the texts. padding = "max_length" if args.pad_to_max_length else False def preprocess_function(examples): first_sentences = [[context] * 4 for context in examples[context_name]] question_headers = examples[question_header_name] second_sentences = [ [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers) ] labels = examples[label_column_name] # Flatten out first_sentences = list(chain(*first_sentences)) second_sentences = list(chain(*second_sentences)) # Tokenize tokenized_examples = tokenizer( first_sentences, second_sentences, max_length=args.max_seq_length, padding=padding, truncation=True, ) # Un-flatten tokenized_inputs = {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()} tokenized_inputs["labels"] = labels return tokenized_inputs with accelerator.main_process_first(): processed_datasets = raw_datasets.map( preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names ) train_dataset = processed_datasets["train"] eval_dataset = processed_datasets["validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: if args.pad_to_max_length: # If padding was already done ot max length, we use the default data collator that will just convert everything # to tensors. data_collator = default_data_collator else: # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). data_collator = DataCollatorForMultipleChoice( tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None) ) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Use the device given by the `accelerator` object. device = accelerator.device model.to(device) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("swag_no_trainer", experiment_config) # Metrics metric = evaluate.load("accuracy") # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"])) metric.add_batch( predictions=predictions, references=references, ) eval_metric = metric.compute() accelerator.print(f"epoch {epoch}: {eval_metric}") if args.with_tracking: accelerator.log( { "accuracy": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) all_results = {f"eval_{k}": v for k, v in eval_metric.items()} with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump(all_results, f) if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Finetuning any 🤗 Transformers model supported by AutoModelForSemanticSegmentation for semantic segmentation.""" import argparse import json import math import os import random from pathlib import Path import datasets import numpy as np import torch from datasets import load_dataset from PIL import Image from torch.utils.data import DataLoader from torchvision import transforms from torchvision.transforms import functional from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository, hf_hub_download from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForSemanticSegmentation, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt") def pad_if_smaller(img, size, fill=0): min_size = min(img.size) if min_size < size: original_width, original_height = img.size pad_height = size - original_height if original_height < size else 0 pad_width = size - original_width if original_width < size else 0 img = functional.pad(img, (0, 0, pad_width, pad_height), fill=fill) return img class Compose: def __init__(self, transforms): self.transforms = transforms def __call__(self, image, target): for t in self.transforms: image, target = t(image, target) return image, target class Identity: def __init__(self): pass def __call__(self, image, target): return image, target class Resize: def __init__(self, size): self.size = size def __call__(self, image, target): image = functional.resize(image, self.size) target = functional.resize(target, self.size, interpolation=transforms.InterpolationMode.NEAREST) return image, target class RandomResize: def __init__(self, min_size, max_size=None): self.min_size = min_size if max_size is None: max_size = min_size self.max_size = max_size def __call__(self, image, target): size = random.randint(self.min_size, self.max_size) image = functional.resize(image, size) target = functional.resize(target, size, interpolation=transforms.InterpolationMode.NEAREST) return image, target class RandomCrop: def __init__(self, size): self.size = size def __call__(self, image, target): image = pad_if_smaller(image, self.size) target = pad_if_smaller(target, self.size, fill=255) crop_params = transforms.RandomCrop.get_params(image, (self.size, self.size)) image = functional.crop(image, *crop_params) target = functional.crop(target, *crop_params) return image, target class RandomHorizontalFlip: def __init__(self, flip_prob): self.flip_prob = flip_prob def __call__(self, image, target): if random.random() < self.flip_prob: image = functional.hflip(image) target = functional.hflip(target) return image, target class PILToTensor: def __call__(self, image, target): image = functional.pil_to_tensor(image) target = torch.as_tensor(np.array(target), dtype=torch.int64) return image, target class ConvertImageDtype: def __init__(self, dtype): self.dtype = dtype def __call__(self, image, target): image = functional.convert_image_dtype(image, self.dtype) return image, target class Normalize: def __init__(self, mean, std): self.mean = mean self.std = std def __call__(self, image, target): image = functional.normalize(image, mean=self.mean, std=self.std) return image, target class ReduceLabels: def __call__(self, image, target): if not isinstance(target, np.ndarray): target = np.array(target).astype(np.uint8) # avoid using underflow conversion target[target == 0] = 255 target = target - 1 target[target == 254] = 255 target = Image.fromarray(target) return image, target def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task") parser.add_argument( "--model_name_or_path", type=str, help="Path to a pretrained model or model identifier from huggingface.co/models.", default="nvidia/mit-b0", ) parser.add_argument( "--dataset_name", type=str, help="Name of the dataset on the hub.", default="segments/sidewalk-semantic", ) parser.add_argument( "--reduce_labels", action="store_true", help="Whether or not to reduce all labels by 1 and replace background by 255.", ) parser.add_argument( "--train_val_split", type=float, default=0.15, help="Fraction of the dataset to be used for validation.", ) parser.add_argument( "--cache_dir", type=str, help="Path to a folder in which the model and dataset will be cached.", ) parser.add_argument( "--use_auth_token", action="store_true", help="Whether to use an authentication token to access the model repository.", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--adam_beta1", type=float, default=0.9, help="Beta1 for AdamW optimizer", ) parser.add_argument( "--adam_beta2", type=float, default=0.999, help="Beta2 for AdamW optimizer", ) parser.add_argument( "--adam_epsilon", type=float, default=1e-8, help="Epsilon for AdamW optimizer", ) parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="polynomial", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", required=False, action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if args.push_to_hub or args.with_tracking: if args.output_dir is None: raise ValueError( "Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified." ) if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_semantic_segmentation_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. # We set device_specific to True as we want different data augmentation per device. if args.seed is not None: set_seed(args.seed, device_specific=True) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Load dataset # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # TODO support datasets from local folders dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir) # Rename column names to standardized names (only "image" and "label" need to be present) if "pixel_values" in dataset["train"].column_names: dataset = dataset.rename_columns({"pixel_values": "image"}) if "annotation" in dataset["train"].column_names: dataset = dataset.rename_columns({"annotation": "label"}) # If we don't have a validation split, split off a percentage of train as validation. args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split if isinstance(args.train_val_split, float) and args.train_val_split > 0.0: split = dataset["train"].train_test_split(args.train_val_split) dataset["train"] = split["train"] dataset["validation"] = split["test"] # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. if args.dataset_name == "scene_parse_150": repo_id = "huggingface/label-files" filename = "ade20k-id2label.json" else: repo_id = args.dataset_name filename = "id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} label2id = {v: k for k, v in id2label.items()} # Load pretrained model and feature extractor config = AutoConfig.from_pretrained(args.model_name_or_path, id2label=id2label, label2id=label2id) feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_name_or_path) model = AutoModelForSemanticSegmentation.from_pretrained(args.model_name_or_path, config=config) # Preprocessing the datasets # Define torchvision transforms to be applied to each image + target. # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9 # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py if "shortest_edge" in feature_extractor.size: # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable. size = (feature_extractor.size["shortest_edge"], feature_extractor.size["shortest_edge"]) else: size = (feature_extractor.size["height"], feature_extractor.size["width"]) train_transforms = Compose( [ ReduceLabels() if args.reduce_labels else Identity(), RandomCrop(size=size), RandomHorizontalFlip(flip_prob=0.5), PILToTensor(), ConvertImageDtype(torch.float), Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std), ] ) # Define torchvision transform to be applied to each image. # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1) val_transforms = Compose( [ ReduceLabels() if args.reduce_labels else Identity(), Resize(size=size), PILToTensor(), ConvertImageDtype(torch.float), Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std), ] ) def preprocess_train(example_batch): pixel_values = [] labels = [] for image, target in zip(example_batch["image"], example_batch["label"]): image, target = train_transforms(image.convert("RGB"), target) pixel_values.append(image) labels.append(target) encoding = dict() encoding["pixel_values"] = torch.stack(pixel_values) encoding["labels"] = torch.stack(labels) return encoding def preprocess_val(example_batch): pixel_values = [] labels = [] for image, target in zip(example_batch["image"], example_batch["label"]): image, target = val_transforms(image.convert("RGB"), target) pixel_values.append(image) labels.append(target) encoding = dict() encoding["pixel_values"] = torch.stack(pixel_values) encoding["labels"] = torch.stack(labels) return encoding with accelerator.main_process_first(): train_dataset = dataset["train"].with_transform(preprocess_train) eval_dataset = dataset["validation"].with_transform(preprocess_val) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader( eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size ) # Optimizer optimizer = torch.optim.AdamW( list(model.parameters()), lr=args.learning_rate, betas=[args.adam_beta1, args.adam_beta2], eps=args.adam_epsilon, ) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Instantiate metric metric = evaluate.load("mean_iou") # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("semantic_segmentation_no_trainer", experiment_config) # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): if args.with_tracking: total_loss = 0 model.train() for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress {completed_steps} steps", blocking=False, auto_lfs_prune=True, ) if completed_steps >= args.max_train_steps: break logger.info("***** Running evaluation *****") model.eval() for step, batch in enumerate(tqdm(eval_dataloader, disable=not accelerator.is_local_main_process)): with torch.no_grad(): outputs = model(**batch) upsampled_logits = torch.nn.functional.interpolate( outputs.logits, size=batch["labels"].shape[-2:], mode="bilinear", align_corners=False ) predictions = upsampled_logits.argmax(dim=1) predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"])) metric.add_batch( predictions=predictions, references=references, ) eval_metrics = metric.compute( num_labels=len(id2label), ignore_index=255, reduce_labels=False, # we've already reduced the labels before ) logger.info(f"epoch {epoch}: {eval_metrics}") if args.with_tracking: accelerator.log( { "mean_iou": eval_metrics["mean_iou"], "mean_accuracy": eval_metrics["mean_accuracy"], "overall_accuracy": eval_metrics["overall_accuracy"], "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump({"eval_overall_accuracy": eval_metrics["overall_accuracy"]}, f) if __name__ == "__main__": main()

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Finetuning any 🤗 Transformers model supported by AutoModelForSemanticSegmentation for semantic segmentation.""" import argparse import json import math import os import random from pathlib import Path import datasets import numpy as np import torch from datasets import load_dataset from PIL import Image from torch.utils.data import DataLoader from torchvision import transforms from torchvision.transforms import functional from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository, hf_hub_download from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForSemanticSegmentation, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt") def pad_if_smaller(img, size, fill=0): min_size = min(img.size) if min_size < size: original_width, original_height = img.size pad_height = size - original_height if original_height < size else 0 pad_width = size - original_width if original_width < size else 0 img = functional.pad(img, (0, 0, pad_width, pad_height), fill=fill) return img class Compose: def __init__(self, transforms): self.transforms = transforms def __call__(self, image, target): for t in self.transforms: image, target = t(image, target) return image, target class Identity: def __init__(self): pass def __call__(self, image, target): return image, target class Resize: def __init__(self, size): self.size = size def __call__(self, image, target): image = functional.resize(image, self.size) target = functional.resize(target, self.size, interpolation=transforms.InterpolationMode.NEAREST) return image, target class RandomResize: def __init__(self, min_size, max_size=None): self.min_size = min_size if max_size is None: max_size = min_size self.max_size = max_size def __call__(self, image, target): size = random.randint(self.min_size, self.max_size) image = functional.resize(image, size) target = functional.resize(target, size, interpolation=transforms.InterpolationMode.NEAREST) return image, target class RandomCrop: def __init__(self, size): self.size = size def __call__(self, image, target): image = pad_if_smaller(image, self.size) target = pad_if_smaller(target, self.size, fill=255) crop_params = transforms.RandomCrop.get_params(image, (self.size, self.size)) image = functional.crop(image, *crop_params) target = functional.crop(target, *crop_params) return image, target class RandomHorizontalFlip: def __init__(self, flip_prob): self.flip_prob = flip_prob def __call__(self, image, target): if random.random() < self.flip_prob: image = functional.hflip(image) target = functional.hflip(target) return image, target class PILToTensor: def __call__(self, image, target): image = functional.pil_to_tensor(image) target = torch.as_tensor(np.array(target), dtype=torch.int64) return image, target class ConvertImageDtype: def __init__(self, dtype): self.dtype = dtype def __call__(self, image, target): image = functional.convert_image_dtype(image, self.dtype) return image, target class Normalize: def __init__(self, mean, std): self.mean = mean self.std = std def __call__(self, image, target): image = functional.normalize(image, mean=self.mean, std=self.std) return image, target class ReduceLabels: def __call__(self, image, target): if not isinstance(target, np.ndarray): target = np.array(target).astype(np.uint8) # avoid using underflow conversion target[target == 0] = 255 target = target - 1 target[target == 254] = 255 target = Image.fromarray(target) return image, target def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task") parser.add_argument( "--model_name_or_path", type=str, help="Path to a pretrained model or model identifier from huggingface.co/models.", default="nvidia/mit-b0", ) parser.add_argument( "--dataset_name", type=str, help="Name of the dataset on the hub.", default="segments/sidewalk-semantic", ) parser.add_argument( "--reduce_labels", action="store_true", help="Whether or not to reduce all labels by 1 and replace background by 255.", ) parser.add_argument( "--train_val_split", type=float, default=0.15, help="Fraction of the dataset to be used for validation.", ) parser.add_argument( "--cache_dir", type=str, help="Path to a folder in which the model and dataset will be cached.", ) parser.add_argument( "--use_auth_token", action="store_true", help="Whether to use an authentication token to access the model repository.", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--adam_beta1", type=float, default=0.9, help="Beta1 for AdamW optimizer", ) parser.add_argument( "--adam_beta2", type=float, default=0.999, help="Beta2 for AdamW optimizer", ) parser.add_argument( "--adam_epsilon", type=float, default=1e-8, help="Epsilon for AdamW optimizer", ) parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="polynomial", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", required=False, action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if args.push_to_hub or args.with_tracking: if args.output_dir is None: raise ValueError( "Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified." ) if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_semantic_segmentation_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. # We set device_specific to True as we want different data augmentation per device. if args.seed is not None: set_seed(args.seed, device_specific=True) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Load dataset # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # TODO support datasets from local folders dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir) # Rename column names to standardized names (only "image" and "label" need to be present) if "pixel_values" in dataset["train"].column_names: dataset = dataset.rename_columns({"pixel_values": "image"}) if "annotation" in dataset["train"].column_names: dataset = dataset.rename_columns({"annotation": "label"}) # If we don't have a validation split, split off a percentage of train as validation. args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split if isinstance(args.train_val_split, float) and args.train_val_split > 0.0: split = dataset["train"].train_test_split(args.train_val_split) dataset["train"] = split["train"] dataset["validation"] = split["test"] # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. if args.dataset_name == "scene_parse_150": repo_id = "huggingface/label-files" filename = "ade20k-id2label.json" else: repo_id = args.dataset_name filename = "id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} label2id = {v: k for k, v in id2label.items()} # Load pretrained model and feature extractor config = AutoConfig.from_pretrained(args.model_name_or_path, id2label=id2label, label2id=label2id) feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_name_or_path) model = AutoModelForSemanticSegmentation.from_pretrained(args.model_name_or_path, config=config) # Preprocessing the datasets # Define torchvision transforms to be applied to each image + target. # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9 # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py if "shortest_edge" in feature_extractor.size: # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable. size = (feature_extractor.size["shortest_edge"], feature_extractor.size["shortest_edge"]) else: size = (feature_extractor.size["height"], feature_extractor.size["width"]) train_transforms = Compose( [ ReduceLabels() if args.reduce_labels else Identity(), RandomCrop(size=size), RandomHorizontalFlip(flip_prob=0.5), PILToTensor(), ConvertImageDtype(torch.float), Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std), ] ) # Define torchvision transform to be applied to each image. # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1) val_transforms = Compose( [ ReduceLabels() if args.reduce_labels else Identity(), Resize(size=size), PILToTensor(), ConvertImageDtype(torch.float), Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std), ] ) def preprocess_train(example_batch): pixel_values = [] labels = [] for image, target in zip(example_batch["image"], example_batch["label"]): image, target = train_transforms(image.convert("RGB"), target) pixel_values.append(image) labels.append(target) encoding = dict() encoding["pixel_values"] = torch.stack(pixel_values) encoding["labels"] = torch.stack(labels) return encoding def preprocess_val(example_batch): pixel_values = [] labels = [] for image, target in zip(example_batch["image"], example_batch["label"]): image, target = val_transforms(image.convert("RGB"), target) pixel_values.append(image) labels.append(target) encoding = dict() encoding["pixel_values"] = torch.stack(pixel_values) encoding["labels"] = torch.stack(labels) return encoding with accelerator.main_process_first(): train_dataset = dataset["train"].with_transform(preprocess_train) eval_dataset = dataset["validation"].with_transform(preprocess_val) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader( eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size ) # Optimizer optimizer = torch.optim.AdamW( list(model.parameters()), lr=args.learning_rate, betas=[args.adam_beta1, args.adam_beta2], eps=args.adam_epsilon, ) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Instantiate metric metric = evaluate.load("mean_iou") # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("semantic_segmentation_no_trainer", experiment_config) # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): if args.with_tracking: total_loss = 0 model.train() for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress {completed_steps} steps", blocking=False, auto_lfs_prune=True, ) if completed_steps >= args.max_train_steps: break logger.info("***** Running evaluation *****") model.eval() for step, batch in enumerate(tqdm(eval_dataloader, disable=not accelerator.is_local_main_process)): with torch.no_grad(): outputs = model(**batch) upsampled_logits = torch.nn.functional.interpolate( outputs.logits, size=batch["labels"].shape[-2:], mode="bilinear", align_corners=False ) predictions = upsampled_logits.argmax(dim=1) predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"])) metric.add_batch( predictions=predictions, references=references, ) eval_metrics = metric.compute( num_labels=len(id2label), ignore_index=255, reduce_labels=False, # we've already reduced the labels before ) logger.info(f"epoch {epoch}: {eval_metrics}") if args.with_tracking: accelerator.log( { "mean_iou": eval_metrics["mean_iou"], "mean_accuracy": eval_metrics["mean_accuracy"], "overall_accuracy": eval_metrics["overall_accuracy"], "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: feature_extractor.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) all_results = {f"eval_{k}": v for k, v in eval_metrics.items()} with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump(all_results, f) if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/summarization/run_summarization_no_trainer.py

#!/usr/bin/env python # coding=utf-8 # Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning a 🤗 Transformers model on summarization. """ # You can also adapt this script on your own summarization task. Pointers for this are left as comments. import argparse import json import logging import math import os import random from pathlib import Path import datasets import nltk import numpy as np import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from filelock import FileLock from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, DataCollatorForSeq2Seq, SchedulerType, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, is_offline_mode, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt") # You should update this to your particular problem to have better documentation of `model_type` MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) try: nltk.data.find("tokenizers/punkt") except (LookupError, OSError): if is_offline_mode(): raise LookupError( "Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files" ) with FileLock(".lock") as lock: nltk.download("punkt", quiet=True) summarization_name_mapping = { "amazon_reviews_multi": ("review_body", "review_title"), "big_patent": ("description", "abstract"), "cnn_dailymail": ("article", "highlights"), "orange_sum": ("text", "summary"), "pn_summary": ("article", "summary"), "psc": ("extract_text", "summary_text"), "samsum": ("dialogue", "summary"), "thaisum": ("body", "summary"), "xglue": ("news_body", "news_title"), "xsum": ("document", "summary"), "wiki_summary": ("article", "highlights"), } def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a summarization task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--ignore_pad_token_for_loss", type=bool, default=True, help="Whether to ignore the tokens corresponding to padded labels in the loss computation or not.", ) parser.add_argument( "--max_source_length", type=int, default=1024, help=( "The maximum total input sequence length after " "tokenization.Sequences longer than this will be truncated, sequences shorter will be padded." ), ) parser.add_argument( "--source_prefix", type=str, default=None, help="A prefix to add before every source text (useful for T5 models).", ) parser.add_argument( "--preprocessing_num_workers", type=int, default=None, help="The number of processes to use for the preprocessing.", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument( "--max_target_length", type=int, default=128, help=( "The maximum total sequence length for target text after " "tokenization. Sequences longer than this will be truncated, sequences shorter will be padded." "during ``evaluate`` and ``predict``." ), ) parser.add_argument( "--val_max_target_length", type=int, default=None, help=( "The maximum total sequence length for validation " "target text after tokenization.Sequences longer than this will be truncated, sequences shorter will be " "padded. Will default to `max_target_length`.This argument is also used to override the ``max_length`` " "param of ``model.generate``, which is used during ``evaluate`` and ``predict``." ), ) parser.add_argument( "--max_length", type=int, default=128, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," " sequences shorter will be padded if `--pad_to_max_lengh` is passed." ), ) parser.add_argument( "--num_beams", type=int, default=None, help=( "Number of beams to use for evaluation. This argument will be " "passed to ``model.generate``, which is used during ``evaluate`` and ``predict``." ), ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--text_column", type=str, default=None, help="The name of the column in the datasets containing the full texts (for summarization).", ) parser.add_argument( "--summary_column", type=str, default=None, help="The name of the column in the datasets containing the summaries (for summarization).", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if args.dataset_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_summarization_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) if args.source_prefix is None and args.model_name_or_path in [ "t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b", ]: logger.warning( "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with " "`--source_prefix 'summarize: ' `" ) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = args.train_file.split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.config_name) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer) elif args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name_or_path: model = AutoModelForSeq2SeqLM.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ) else: logger.info("Training new model from scratch") model = AutoModelForSeq2SeqLM.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) if model.config.decoder_start_token_id is None: raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined") prefix = args.source_prefix if args.source_prefix is not None else "" # Preprocessing the datasets. # First we tokenize all the texts. column_names = raw_datasets["train"].column_names # Get the column names for input/target. dataset_columns = summarization_name_mapping.get(args.dataset_name, None) if args.text_column is None: text_column = dataset_columns[0] if dataset_columns is not None else column_names[0] else: text_column = args.text_column if text_column not in column_names: raise ValueError( f"--text_column' value '{args.text_column}' needs to be one of: {', '.join(column_names)}" ) if args.summary_column is None: summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1] else: summary_column = args.summary_column if summary_column not in column_names: raise ValueError( f"--summary_column' value '{args.summary_column}' needs to be one of: {', '.join(column_names)}" ) # Temporarily set max_target_length for training. max_target_length = args.max_target_length padding = "max_length" if args.pad_to_max_length else False def preprocess_function(examples): inputs = examples[text_column] targets = examples[summary_column] inputs = [prefix + inp for inp in inputs] model_inputs = tokenizer(inputs, max_length=args.max_source_length, padding=padding, truncation=True) # Tokenize targets with the `text_target` keyword argument labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True) # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore # padding in the loss. if padding == "max_length" and args.ignore_pad_token_for_loss: labels["input_ids"] = [ [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"] ] model_inputs["labels"] = labels["input_ids"] return model_inputs with accelerator.main_process_first(): processed_datasets = raw_datasets.map( preprocess_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on dataset", ) train_dataset = processed_datasets["train"] eval_dataset = processed_datasets["validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 1): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") label_pad_token_id = -100 if args.ignore_pad_token_for_loss else tokenizer.pad_token_id data_collator = DataCollatorForSeq2Seq( tokenizer, model=model, label_pad_token_id=label_pad_token_id, pad_to_multiple_of=8 if accelerator.use_fp16 else None, ) def postprocess_text(preds, labels): preds = [pred.strip() for pred in preds] labels = [label.strip() for label in labels] # rougeLSum expects newline after each sentence preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds] labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels] return preds, labels train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight", "layer_norm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("summarization_no_trainer", experiment_config) # Metric metric = evaluate.load("rouge") # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() if args.val_max_target_length is None: args.val_max_target_length = args.max_target_length gen_kwargs = { "max_length": args.val_max_target_length if args is not None else config.max_length, "num_beams": args.num_beams, } for step, batch in enumerate(eval_dataloader): with torch.no_grad(): generated_tokens = accelerator.unwrap_model(model).generate( batch["input_ids"], attention_mask=batch["attention_mask"], **gen_kwargs, ) generated_tokens = accelerator.pad_across_processes( generated_tokens, dim=1, pad_index=tokenizer.pad_token_id ) labels = batch["labels"] if not args.pad_to_max_length: # If we did not pad to max length, we need to pad the labels too labels = accelerator.pad_across_processes(batch["labels"], dim=1, pad_index=tokenizer.pad_token_id) generated_tokens, labels = accelerator.gather_for_metrics((generated_tokens, labels)) generated_tokens = generated_tokens.cpu().numpy() labels = labels.cpu().numpy() if args.ignore_pad_token_for_loss: # Replace -100 in the labels as we can't decode them. labels = np.where(labels != -100, labels, tokenizer.pad_token_id) if isinstance(generated_tokens, tuple): generated_tokens = generated_tokens[0] decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) decoded_preds, decoded_labels = accelerator.gather_for_metrics(decoded_preds, decoded_labels) metric.add_batch( predictions=decoded_preds, references=decoded_labels, ) result = metric.compute(use_stemmer=True) result = {k: round(v * 100, 4) for k, v in result.items()} logger.info(result) if args.with_tracking: result["train_loss"] = total_loss.item() / len(train_dataloader) result["epoch"] = epoch result["step"] = completed_steps accelerator.log(result, step=completed_steps) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump( { "eval_rouge1": result["rouge1"], "eval_rouge2": result["rouge2"], "eval_rougeL": result["rougeL"], "eval_rougeLsum": result["rougeLsum"], }, f, ) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning a 🤗 Transformers model on summarization. """ # You can also adapt this script on your own summarization task. Pointers for this are left as comments. import argparse import json import logging import math import os import random from pathlib import Path import datasets import nltk import numpy as np import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from filelock import FileLock from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, DataCollatorForSeq2Seq, SchedulerType, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, is_offline_mode, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt") # You should update this to your particular problem to have better documentation of `model_type` MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) try: nltk.data.find("tokenizers/punkt") except (LookupError, OSError): if is_offline_mode(): raise LookupError( "Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files" ) with FileLock(".lock") as lock: nltk.download("punkt", quiet=True) summarization_name_mapping = { "amazon_reviews_multi": ("review_body", "review_title"), "big_patent": ("description", "abstract"), "cnn_dailymail": ("article", "highlights"), "orange_sum": ("text", "summary"), "pn_summary": ("article", "summary"), "psc": ("extract_text", "summary_text"), "samsum": ("dialogue", "summary"), "thaisum": ("body", "summary"), "xglue": ("news_body", "news_title"), "xsum": ("document", "summary"), "wiki_summary": ("article", "highlights"), } def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a summarization task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--ignore_pad_token_for_loss", type=bool, default=True, help="Whether to ignore the tokens corresponding to padded labels in the loss computation or not.", ) parser.add_argument( "--max_source_length", type=int, default=1024, help=( "The maximum total input sequence length after " "tokenization.Sequences longer than this will be truncated, sequences shorter will be padded." ), ) parser.add_argument( "--source_prefix", type=str, default=None, help="A prefix to add before every source text (useful for T5 models).", ) parser.add_argument( "--preprocessing_num_workers", type=int, default=None, help="The number of processes to use for the preprocessing.", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument( "--max_target_length", type=int, default=128, help=( "The maximum total sequence length for target text after " "tokenization. Sequences longer than this will be truncated, sequences shorter will be padded." "during ``evaluate`` and ``predict``." ), ) parser.add_argument( "--val_max_target_length", type=int, default=None, help=( "The maximum total sequence length for validation " "target text after tokenization.Sequences longer than this will be truncated, sequences shorter will be " "padded. Will default to `max_target_length`.This argument is also used to override the ``max_length`` " "param of ``model.generate``, which is used during ``evaluate`` and ``predict``." ), ) parser.add_argument( "--max_length", type=int, default=128, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," " sequences shorter will be padded if `--pad_to_max_lengh` is passed." ), ) parser.add_argument( "--num_beams", type=int, default=None, help=( "Number of beams to use for evaluation. This argument will be " "passed to ``model.generate``, which is used during ``evaluate`` and ``predict``." ), ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--text_column", type=str, default=None, help="The name of the column in the datasets containing the full texts (for summarization).", ) parser.add_argument( "--summary_column", type=str, default=None, help="The name of the column in the datasets containing the summaries (for summarization).", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if args.dataset_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_summarization_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) if args.source_prefix is None and args.model_name_or_path in [ "t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b", ]: logger.warning( "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with " "`--source_prefix 'summarize: ' `" ) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = args.train_file.split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.config_name) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer) elif args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name_or_path: model = AutoModelForSeq2SeqLM.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ) else: logger.info("Training new model from scratch") model = AutoModelForSeq2SeqLM.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) if model.config.decoder_start_token_id is None: raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined") prefix = args.source_prefix if args.source_prefix is not None else "" # Preprocessing the datasets. # First we tokenize all the texts. column_names = raw_datasets["train"].column_names # Get the column names for input/target. dataset_columns = summarization_name_mapping.get(args.dataset_name, None) if args.text_column is None: text_column = dataset_columns[0] if dataset_columns is not None else column_names[0] else: text_column = args.text_column if text_column not in column_names: raise ValueError( f"--text_column' value '{args.text_column}' needs to be one of: {', '.join(column_names)}" ) if args.summary_column is None: summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1] else: summary_column = args.summary_column if summary_column not in column_names: raise ValueError( f"--summary_column' value '{args.summary_column}' needs to be one of: {', '.join(column_names)}" ) # Temporarily set max_target_length for training. max_target_length = args.max_target_length padding = "max_length" if args.pad_to_max_length else False def preprocess_function(examples): inputs = examples[text_column] targets = examples[summary_column] inputs = [prefix + inp for inp in inputs] model_inputs = tokenizer(inputs, max_length=args.max_source_length, padding=padding, truncation=True) # Tokenize targets with the `text_target` keyword argument labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True) # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore # padding in the loss. if padding == "max_length" and args.ignore_pad_token_for_loss: labels["input_ids"] = [ [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"] ] model_inputs["labels"] = labels["input_ids"] return model_inputs with accelerator.main_process_first(): processed_datasets = raw_datasets.map( preprocess_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on dataset", ) train_dataset = processed_datasets["train"] eval_dataset = processed_datasets["validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 1): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") label_pad_token_id = -100 if args.ignore_pad_token_for_loss else tokenizer.pad_token_id data_collator = DataCollatorForSeq2Seq( tokenizer, model=model, label_pad_token_id=label_pad_token_id, pad_to_multiple_of=8 if accelerator.use_fp16 else None, ) def postprocess_text(preds, labels): preds = [pred.strip() for pred in preds] labels = [label.strip() for label in labels] # rougeLSum expects newline after each sentence preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds] labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels] return preds, labels train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight", "layer_norm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("summarization_no_trainer", experiment_config) # Metric metric = evaluate.load("rouge") # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() if args.val_max_target_length is None: args.val_max_target_length = args.max_target_length gen_kwargs = { "max_length": args.val_max_target_length if args is not None else config.max_length, "num_beams": args.num_beams, } for step, batch in enumerate(eval_dataloader): with torch.no_grad(): generated_tokens = accelerator.unwrap_model(model).generate( batch["input_ids"], attention_mask=batch["attention_mask"], **gen_kwargs, ) generated_tokens = accelerator.pad_across_processes( generated_tokens, dim=1, pad_index=tokenizer.pad_token_id ) labels = batch["labels"] if not args.pad_to_max_length: # If we did not pad to max length, we need to pad the labels too labels = accelerator.pad_across_processes(batch["labels"], dim=1, pad_index=tokenizer.pad_token_id) generated_tokens, labels = accelerator.gather_for_metrics((generated_tokens, labels)) generated_tokens = generated_tokens.cpu().numpy() labels = labels.cpu().numpy() if args.ignore_pad_token_for_loss: # Replace -100 in the labels as we can't decode them. labels = np.where(labels != -100, labels, tokenizer.pad_token_id) if isinstance(generated_tokens, tuple): generated_tokens = generated_tokens[0] decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) decoded_preds, decoded_labels = accelerator.gather_for_metrics(decoded_preds, decoded_labels) metric.add_batch( predictions=decoded_preds, references=decoded_labels, ) result = metric.compute(use_stemmer=True) result = {k: round(v * 100, 4) for k, v in result.items()} logger.info(result) if args.with_tracking: result["train_loss"] = total_loss.item() / len(train_dataloader) result["epoch"] = epoch result["step"] = completed_steps accelerator.log(result, step=completed_steps) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) all_results = {f"eval_{k}": v for k, v in result.items()} with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump(all_results, f) if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/text-classification/run_glue_no_trainer.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Finetuning a 🤗 Transformers model for sequence classification on GLUE.""" import argparse import json import logging import math import os import random from pathlib import Path import datasets import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, PretrainedConfig, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task") parser.add_argument( "--task_name", type=str, default=None, help="The name of the glue task to train on.", choices=list(task_to_keys.keys()), ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--max_length", type=int, default=128, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," " sequences shorter will be padded if `--pad_to_max_lengh` is passed." ), ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=True, ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) parser.add_argument( "--ignore_mismatched_sizes", action="store_true", help="Whether or not to enable to load a pretrained model whose head dimensions are different.", ) args = parser.parse_args() # Sanity checks if args.task_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a task name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_glue_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator = ( Accelerator(log_with=args.report_to, logging_dir=args.output_dir) if args.with_tracking else Accelerator() ) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub). # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named # label if at least two columns are provided. # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this # single column. You can easily tweak this behavior (see below) # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.task_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset("glue", args.task_name) else: # Loading the dataset from local csv or json file. data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = (args.train_file if args.train_file is not None else args.validation_file).split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files) # See more about loading any type of standard or custom dataset at # https://huggingface.co/docs/datasets/loading_datasets.html. # Labels if args.task_name is not None: is_regression = args.task_name == "stsb" if not is_regression: label_list = raw_datasets["train"].features["label"].names num_labels = len(label_list) else: num_labels = 1 else: # Trying to have good defaults here, don't hesitate to tweak to your needs. is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"] if is_regression: num_labels = 1 else: # A useful fast method: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique label_list = raw_datasets["train"].unique("label") label_list.sort() # Let's sort it for determinism num_labels = len(label_list) # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name) tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer) model = AutoModelForSequenceClassification.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ignore_mismatched_sizes=args.ignore_mismatched_sizes, ) # Preprocessing the datasets if args.task_name is not None: sentence1_key, sentence2_key = task_to_keys[args.task_name] else: # Again, we try to have some nice defaults but don't hesitate to tweak to your use case. non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"] if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names: sentence1_key, sentence2_key = "sentence1", "sentence2" else: if len(non_label_column_names) >= 2: sentence1_key, sentence2_key = non_label_column_names[:2] else: sentence1_key, sentence2_key = non_label_column_names[0], None # Some models have set the order of the labels to use, so let's make sure we do use it. label_to_id = None if ( model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id and args.task_name is not None and not is_regression ): # Some have all caps in their config, some don't. label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()} if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)): logger.info( f"The configuration of the model provided the following label correspondence: {label_name_to_id}. " "Using it!" ) label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)} else: logger.warning( "Your model seems to have been trained with labels, but they don't match the dataset: ", f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}." "\nIgnoring the model labels as a result.", ) elif args.task_name is None and not is_regression: label_to_id = {v: i for i, v in enumerate(label_list)} if label_to_id is not None: model.config.label2id = label_to_id model.config.id2label = {id: label for label, id in config.label2id.items()} elif args.task_name is not None and not is_regression: model.config.label2id = {l: i for i, l in enumerate(label_list)} model.config.id2label = {id: label for label, id in config.label2id.items()} padding = "max_length" if args.pad_to_max_length else False def preprocess_function(examples): # Tokenize the texts texts = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer(*texts, padding=padding, max_length=args.max_length, truncation=True) if "label" in examples: if label_to_id is not None: # Map labels to IDs (not necessary for GLUE tasks) result["labels"] = [label_to_id[l] for l in examples["label"]] else: # In all cases, rename the column to labels because the model will expect that. result["labels"] = examples["label"] return result with accelerator.main_process_first(): processed_datasets = raw_datasets.map( preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names, desc="Running tokenizer on dataset", ) train_dataset = processed_datasets["train"] eval_dataset = processed_datasets["validation_matched" if args.task_name == "mnli" else "validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: if args.pad_to_max_length: # If padding was already done ot max length, we use the default data collator that will just convert everything # to tensors. data_collator = default_data_collator else: # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps, num_training_steps=args.max_train_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("glue_no_trainer", experiment_config) # Get the metric function if args.task_name is not None: metric = evaluate.load("glue", args.task_name) else: metric = evaluate.load("accuracy") # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() loss = loss / args.gradient_accumulation_steps accelerator.backward(loss) if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() samples_seen = 0 for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) if not is_regression else outputs.logits.squeeze() predictions, references = accelerator.gather((predictions, batch["labels"])) # If we are in a multiprocess environment, the last batch has duplicates if accelerator.num_processes > 1: if step == len(eval_dataloader) - 1: predictions = predictions[: len(eval_dataloader.dataset) - samples_seen] references = references[: len(eval_dataloader.dataset) - samples_seen] else: samples_seen += references.shape[0] metric.add_batch( predictions=predictions, references=references, ) eval_metric = metric.compute() logger.info(f"epoch {epoch}: {eval_metric}") if args.with_tracking: accelerator.log( { "accuracy" if args.task_name is not None else "glue": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) if args.task_name == "mnli": # Final evaluation on mismatched validation set eval_dataset = processed_datasets["validation_mismatched"] eval_dataloader = DataLoader( eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size ) eval_dataloader = accelerator.prepare(eval_dataloader) model.eval() for step, batch in enumerate(eval_dataloader): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) metric.add_batch( predictions=accelerator.gather(predictions), references=accelerator.gather(batch["labels"]), ) eval_metric = metric.compute() logger.info(f"mnli-mm: {eval_metric}") if args.output_dir is not None: with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump({"eval_accuracy": eval_metric["accuracy"]}, f) if __name__ == "__main__": main()

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Finetuning a 🤗 Transformers model for sequence classification on GLUE.""" import argparse import json import logging import math import os import random from pathlib import Path import datasets import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, PretrainedConfig, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task") parser.add_argument( "--task_name", type=str, default=None, help="The name of the glue task to train on.", choices=list(task_to_keys.keys()), ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--max_length", type=int, default=128, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," " sequences shorter will be padded if `--pad_to_max_lengh` is passed." ), ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=True, ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) parser.add_argument( "--ignore_mismatched_sizes", action="store_true", help="Whether or not to enable to load a pretrained model whose head dimensions are different.", ) args = parser.parse_args() # Sanity checks if args.task_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a task name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_glue_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator = ( Accelerator(log_with=args.report_to, logging_dir=args.output_dir) if args.with_tracking else Accelerator() ) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub). # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named # label if at least two columns are provided. # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this # single column. You can easily tweak this behavior (see below) # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.task_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset("glue", args.task_name) else: # Loading the dataset from local csv or json file. data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = (args.train_file if args.train_file is not None else args.validation_file).split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files) # See more about loading any type of standard or custom dataset at # https://huggingface.co/docs/datasets/loading_datasets.html. # Labels if args.task_name is not None: is_regression = args.task_name == "stsb" if not is_regression: label_list = raw_datasets["train"].features["label"].names num_labels = len(label_list) else: num_labels = 1 else: # Trying to have good defaults here, don't hesitate to tweak to your needs. is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"] if is_regression: num_labels = 1 else: # A useful fast method: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique label_list = raw_datasets["train"].unique("label") label_list.sort() # Let's sort it for determinism num_labels = len(label_list) # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name) tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer) model = AutoModelForSequenceClassification.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ignore_mismatched_sizes=args.ignore_mismatched_sizes, ) # Preprocessing the datasets if args.task_name is not None: sentence1_key, sentence2_key = task_to_keys[args.task_name] else: # Again, we try to have some nice defaults but don't hesitate to tweak to your use case. non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"] if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names: sentence1_key, sentence2_key = "sentence1", "sentence2" else: if len(non_label_column_names) >= 2: sentence1_key, sentence2_key = non_label_column_names[:2] else: sentence1_key, sentence2_key = non_label_column_names[0], None # Some models have set the order of the labels to use, so let's make sure we do use it. label_to_id = None if ( model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id and args.task_name is not None and not is_regression ): # Some have all caps in their config, some don't. label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()} if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)): logger.info( f"The configuration of the model provided the following label correspondence: {label_name_to_id}. " "Using it!" ) label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)} else: logger.warning( "Your model seems to have been trained with labels, but they don't match the dataset: ", f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}." "\nIgnoring the model labels as a result.", ) elif args.task_name is None and not is_regression: label_to_id = {v: i for i, v in enumerate(label_list)} if label_to_id is not None: model.config.label2id = label_to_id model.config.id2label = {id: label for label, id in config.label2id.items()} elif args.task_name is not None and not is_regression: model.config.label2id = {l: i for i, l in enumerate(label_list)} model.config.id2label = {id: label for label, id in config.label2id.items()} padding = "max_length" if args.pad_to_max_length else False def preprocess_function(examples): # Tokenize the texts texts = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer(*texts, padding=padding, max_length=args.max_length, truncation=True) if "label" in examples: if label_to_id is not None: # Map labels to IDs (not necessary for GLUE tasks) result["labels"] = [label_to_id[l] for l in examples["label"]] else: # In all cases, rename the column to labels because the model will expect that. result["labels"] = examples["label"] return result with accelerator.main_process_first(): processed_datasets = raw_datasets.map( preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names, desc="Running tokenizer on dataset", ) train_dataset = processed_datasets["train"] eval_dataset = processed_datasets["validation_matched" if args.task_name == "mnli" else "validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: if args.pad_to_max_length: # If padding was already done ot max length, we use the default data collator that will just convert everything # to tensors. data_collator = default_data_collator else: # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps, num_training_steps=args.max_train_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("glue_no_trainer", experiment_config) # Get the metric function if args.task_name is not None: metric = evaluate.load("glue", args.task_name) else: metric = evaluate.load("accuracy") # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() loss = loss / args.gradient_accumulation_steps accelerator.backward(loss) if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() samples_seen = 0 for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) if not is_regression else outputs.logits.squeeze() predictions, references = accelerator.gather((predictions, batch["labels"])) # If we are in a multiprocess environment, the last batch has duplicates if accelerator.num_processes > 1: if step == len(eval_dataloader) - 1: predictions = predictions[: len(eval_dataloader.dataset) - samples_seen] references = references[: len(eval_dataloader.dataset) - samples_seen] else: samples_seen += references.shape[0] metric.add_batch( predictions=predictions, references=references, ) eval_metric = metric.compute() logger.info(f"epoch {epoch}: {eval_metric}") if args.with_tracking: accelerator.log( { "accuracy" if args.task_name is not None else "glue": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) if args.task_name == "mnli": # Final evaluation on mismatched validation set eval_dataset = processed_datasets["validation_mismatched"] eval_dataloader = DataLoader( eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size ) eval_dataloader = accelerator.prepare(eval_dataloader) model.eval() for step, batch in enumerate(eval_dataloader): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) metric.add_batch( predictions=accelerator.gather(predictions), references=accelerator.gather(batch["labels"]), ) eval_metric = metric.compute() logger.info(f"mnli-mm: {eval_metric}") if args.output_dir is not None: all_results = {f"eval_{k}": v for k, v in eval_metric.items()} with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump(all_results, f) if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/token-classification/run_ner_no_trainer.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning a 🤗 Transformers model on token classification tasks (NER, POS, CHUNKS) relying on the accelerate library without using a Trainer. """ import argparse import json import logging import math import os import random from pathlib import Path import datasets import torch from datasets import ClassLabel, load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForTokenClassification, AutoTokenizer, DataCollatorForTokenClassification, PretrainedConfig, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt") # You should update this to your particular problem to have better documentation of `model_type` MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def parse_args(): parser = argparse.ArgumentParser( description="Finetune a transformers model on a text classification task (NER) with accelerate library" ) parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--text_column_name", type=str, default=None, help="The column name of text to input in the file (a csv or JSON file).", ) parser.add_argument( "--label_column_name", type=str, default=None, help="The column name of label to input in the file (a csv or JSON file).", ) parser.add_argument( "--max_length", type=int, default=128, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," " sequences shorter will be padded if `--pad_to_max_length` is passed." ), ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument( "--label_all_tokens", action="store_true", help="Setting labels of all special tokens to -100 and thus PyTorch will ignore them.", ) parser.add_argument( "--return_entity_level_metrics", action="store_true", help="Indication whether entity level metrics are to be returner.", ) parser.add_argument( "--task_name", type=str, default="ner", choices=["ner", "pos", "chunk"], help="The name of the task.", ) parser.add_argument( "--debug", action="store_true", help="Activate debug mode and run training only with a subset of data.", ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) parser.add_argument( "--ignore_mismatched_sizes", action="store_true", help="Whether or not to enable to load a pretrained model whose head dimensions are different.", ) args = parser.parse_args() # Sanity checks if args.task_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a task name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_ner_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator = ( Accelerator(log_with=args.report_to, logging_dir=args.output_dir) if args.with_tracking else Accelerator() ) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets for token classification task available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'tokens' or the first column if no column called # 'tokens' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = args.train_file.split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files) # Trim a number of training examples if args.debug: for split in raw_datasets.keys(): raw_datasets[split] = raw_datasets[split].select(range(100)) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. if raw_datasets["train"] is not None: column_names = raw_datasets["train"].column_names features = raw_datasets["train"].features else: column_names = raw_datasets["validation"].column_names features = raw_datasets["validation"].features if args.text_column_name is not None: text_column_name = args.text_column_name elif "tokens" in column_names: text_column_name = "tokens" else: text_column_name = column_names[0] if args.label_column_name is not None: label_column_name = args.label_column_name elif f"{args.task_name}_tags" in column_names: label_column_name = f"{args.task_name}_tags" else: label_column_name = column_names[1] # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the # unique labels. def get_label_list(labels): unique_labels = set() for label in labels: unique_labels = unique_labels | set(label) label_list = list(unique_labels) label_list.sort() return label_list # If the labels are of type ClassLabel, they are already integers and we have the map stored somewhere. # Otherwise, we have to get the list of labels manually. labels_are_int = isinstance(features[label_column_name].feature, ClassLabel) if labels_are_int: label_list = features[label_column_name].feature.names label_to_id = {i: i for i in range(len(label_list))} else: label_list = get_label_list(raw_datasets["train"][label_column_name]) label_to_id = {l: i for i, l in enumerate(label_list)} num_labels = len(label_list) # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.config_name, num_labels=num_labels) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") tokenizer_name_or_path = args.tokenizer_name if args.tokenizer_name else args.model_name_or_path if not tokenizer_name_or_path: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if config.model_type in {"bloom", "gpt2", "roberta"}: tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, use_fast=True, add_prefix_space=True) else: tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, use_fast=True) if args.model_name_or_path: model = AutoModelForTokenClassification.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ignore_mismatched_sizes=args.ignore_mismatched_sizes, ) else: logger.info("Training new model from scratch") model = AutoModelForTokenClassification.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # Model has labels -> use them. if model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id: if list(sorted(model.config.label2id.keys())) == list(sorted(label_list)): # Reorganize `label_list` to match the ordering of the model. if labels_are_int: label_to_id = {i: int(model.config.label2id[l]) for i, l in enumerate(label_list)} label_list = [model.config.id2label[i] for i in range(num_labels)] else: label_list = [model.config.id2label[i] for i in range(num_labels)] label_to_id = {l: i for i, l in enumerate(label_list)} else: logger.warning( "Your model seems to have been trained with labels, but they don't match the dataset: ", f"model labels: {list(sorted(model.config.label2id.keys()))}, dataset labels:" f" {list(sorted(label_list))}.\nIgnoring the model labels as a result.", ) # Set the correspondences label/ID inside the model config model.config.label2id = {l: i for i, l in enumerate(label_list)} model.config.id2label = {i: l for i, l in enumerate(label_list)} # Map that sends B-Xxx label to its I-Xxx counterpart b_to_i_label = [] for idx, label in enumerate(label_list): if label.startswith("B-") and label.replace("B-", "I-") in label_list: b_to_i_label.append(label_list.index(label.replace("B-", "I-"))) else: b_to_i_label.append(idx) # Preprocessing the datasets. # First we tokenize all the texts. padding = "max_length" if args.pad_to_max_length else False # Tokenize all texts and align the labels with them. def tokenize_and_align_labels(examples): tokenized_inputs = tokenizer( examples[text_column_name], max_length=args.max_length, padding=padding, truncation=True, # We use this argument because the texts in our dataset are lists of words (with a label for each word). is_split_into_words=True, ) labels = [] for i, label in enumerate(examples[label_column_name]): word_ids = tokenized_inputs.word_ids(batch_index=i) previous_word_idx = None label_ids = [] for word_idx in word_ids: # Special tokens have a word id that is None. We set the label to -100 so they are automatically # ignored in the loss function. if word_idx is None: label_ids.append(-100) # We set the label for the first token of each word. elif word_idx != previous_word_idx: label_ids.append(label_to_id[label[word_idx]]) # For the other tokens in a word, we set the label to either the current label or -100, depending on # the label_all_tokens flag. else: if args.label_all_tokens: label_ids.append(b_to_i_label[label_to_id[label[word_idx]]]) else: label_ids.append(-100) previous_word_idx = word_idx labels.append(label_ids) tokenized_inputs["labels"] = labels return tokenized_inputs with accelerator.main_process_first(): processed_raw_datasets = raw_datasets.map( tokenize_and_align_labels, batched=True, remove_columns=raw_datasets["train"].column_names, desc="Running tokenizer on dataset", ) train_dataset = processed_raw_datasets["train"] eval_dataset = processed_raw_datasets["validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: if args.pad_to_max_length: # If padding was already done ot max length, we use the default data collator that will just convert everything # to tensors. data_collator = default_data_collator else: # Otherwise, `DataCollatorForTokenClassification` will apply dynamic padding for us (by padding to the maximum length of # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). data_collator = DataCollatorForTokenClassification( tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None) ) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Use the device given by the `accelerator` object. device = accelerator.device model.to(device) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps, num_training_steps=args.max_train_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("ner_no_trainer", experiment_config) # Metrics metric = evaluate.load("seqeval") def get_labels(predictions, references): # Transform predictions and references tensos to numpy arrays if device.type == "cpu": y_pred = predictions.detach().clone().numpy() y_true = references.detach().clone().numpy() else: y_pred = predictions.detach().cpu().clone().numpy() y_true = references.detach().cpu().clone().numpy() # Remove ignored index (special tokens) true_predictions = [ [label_list[p] for (p, l) in zip(pred, gold_label) if l != -100] for pred, gold_label in zip(y_pred, y_true) ] true_labels = [ [label_list[l] for (p, l) in zip(pred, gold_label) if l != -100] for pred, gold_label in zip(y_pred, y_true) ] return true_predictions, true_labels def compute_metrics(): results = metric.compute() if args.return_entity_level_metrics: # Unpack nested dictionaries final_results = {} for key, value in results.items(): if isinstance(value, dict): for n, v in value.items(): final_results[f"{key}_{n}"] = v else: final_results[key] = value return final_results else: return { "precision": results["overall_precision"], "recall": results["overall_recall"], "f1": results["overall_f1"], "accuracy": results["overall_accuracy"], } # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() loss = loss / args.gradient_accumulation_steps accelerator.backward(loss) if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() samples_seen = 0 for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) labels = batch["labels"] if not args.pad_to_max_length: # necessary to pad predictions and labels for being gathered predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100) labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100) predictions_gathered, labels_gathered = accelerator.gather((predictions, labels)) # If we are in a multiprocess environment, the last batch has duplicates if accelerator.num_processes > 1: if step == len(eval_dataloader) - 1: predictions_gathered = predictions_gathered[: len(eval_dataloader.dataset) - samples_seen] labels_gathered = labels_gathered[: len(eval_dataloader.dataset) - samples_seen] else: samples_seen += labels_gathered.shape[0] preds, refs = get_labels(predictions_gathered, labels_gathered) metric.add_batch( predictions=preds, references=refs, ) # predictions and preferences are expected to be a nested list of labels, not label_ids eval_metric = compute_metrics() accelerator.print(f"epoch {epoch}:", eval_metric) if args.with_tracking: accelerator.log( { "seqeval": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump( {"eval_accuracy": eval_metric["accuracy"], "train_loss": total_loss.item() / len(train_dataloader)}, f ) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning a 🤗 Transformers model on token classification tasks (NER, POS, CHUNKS) relying on the accelerate library without using a Trainer. """ import argparse import json import logging import math import os import random from pathlib import Path import datasets import torch from datasets import ClassLabel, load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForTokenClassification, AutoTokenizer, DataCollatorForTokenClassification, PretrainedConfig, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") logger = get_logger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt") # You should update this to your particular problem to have better documentation of `model_type` MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def parse_args(): parser = argparse.ArgumentParser( description="Finetune a transformers model on a text classification task (NER) with accelerate library" ) parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--text_column_name", type=str, default=None, help="The column name of text to input in the file (a csv or JSON file).", ) parser.add_argument( "--label_column_name", type=str, default=None, help="The column name of label to input in the file (a csv or JSON file).", ) parser.add_argument( "--max_length", type=int, default=128, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," " sequences shorter will be padded if `--pad_to_max_length` is passed." ), ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument( "--label_all_tokens", action="store_true", help="Setting labels of all special tokens to -100 and thus PyTorch will ignore them.", ) parser.add_argument( "--return_entity_level_metrics", action="store_true", help="Indication whether entity level metrics are to be returner.", ) parser.add_argument( "--task_name", type=str, default="ner", choices=["ner", "pos", "chunk"], help="The name of the task.", ) parser.add_argument( "--debug", action="store_true", help="Activate debug mode and run training only with a subset of data.", ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) parser.add_argument( "--ignore_mismatched_sizes", action="store_true", help="Whether or not to enable to load a pretrained model whose head dimensions are different.", ) args = parser.parse_args() # Sanity checks if args.task_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a task name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_ner_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator = ( Accelerator(log_with=args.report_to, logging_dir=args.output_dir) if args.with_tracking else Accelerator() ) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets for token classification task available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'tokens' or the first column if no column called # 'tokens' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = args.train_file.split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files) # Trim a number of training examples if args.debug: for split in raw_datasets.keys(): raw_datasets[split] = raw_datasets[split].select(range(100)) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. if raw_datasets["train"] is not None: column_names = raw_datasets["train"].column_names features = raw_datasets["train"].features else: column_names = raw_datasets["validation"].column_names features = raw_datasets["validation"].features if args.text_column_name is not None: text_column_name = args.text_column_name elif "tokens" in column_names: text_column_name = "tokens" else: text_column_name = column_names[0] if args.label_column_name is not None: label_column_name = args.label_column_name elif f"{args.task_name}_tags" in column_names: label_column_name = f"{args.task_name}_tags" else: label_column_name = column_names[1] # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the # unique labels. def get_label_list(labels): unique_labels = set() for label in labels: unique_labels = unique_labels | set(label) label_list = list(unique_labels) label_list.sort() return label_list # If the labels are of type ClassLabel, they are already integers and we have the map stored somewhere. # Otherwise, we have to get the list of labels manually. labels_are_int = isinstance(features[label_column_name].feature, ClassLabel) if labels_are_int: label_list = features[label_column_name].feature.names label_to_id = {i: i for i in range(len(label_list))} else: label_list = get_label_list(raw_datasets["train"][label_column_name]) label_to_id = {l: i for i, l in enumerate(label_list)} num_labels = len(label_list) # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.config_name, num_labels=num_labels) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") tokenizer_name_or_path = args.tokenizer_name if args.tokenizer_name else args.model_name_or_path if not tokenizer_name_or_path: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if config.model_type in {"bloom", "gpt2", "roberta"}: tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, use_fast=True, add_prefix_space=True) else: tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, use_fast=True) if args.model_name_or_path: model = AutoModelForTokenClassification.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ignore_mismatched_sizes=args.ignore_mismatched_sizes, ) else: logger.info("Training new model from scratch") model = AutoModelForTokenClassification.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # Model has labels -> use them. if model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id: if list(sorted(model.config.label2id.keys())) == list(sorted(label_list)): # Reorganize `label_list` to match the ordering of the model. if labels_are_int: label_to_id = {i: int(model.config.label2id[l]) for i, l in enumerate(label_list)} label_list = [model.config.id2label[i] for i in range(num_labels)] else: label_list = [model.config.id2label[i] for i in range(num_labels)] label_to_id = {l: i for i, l in enumerate(label_list)} else: logger.warning( "Your model seems to have been trained with labels, but they don't match the dataset: ", f"model labels: {list(sorted(model.config.label2id.keys()))}, dataset labels:" f" {list(sorted(label_list))}.\nIgnoring the model labels as a result.", ) # Set the correspondences label/ID inside the model config model.config.label2id = {l: i for i, l in enumerate(label_list)} model.config.id2label = {i: l for i, l in enumerate(label_list)} # Map that sends B-Xxx label to its I-Xxx counterpart b_to_i_label = [] for idx, label in enumerate(label_list): if label.startswith("B-") and label.replace("B-", "I-") in label_list: b_to_i_label.append(label_list.index(label.replace("B-", "I-"))) else: b_to_i_label.append(idx) # Preprocessing the datasets. # First we tokenize all the texts. padding = "max_length" if args.pad_to_max_length else False # Tokenize all texts and align the labels with them. def tokenize_and_align_labels(examples): tokenized_inputs = tokenizer( examples[text_column_name], max_length=args.max_length, padding=padding, truncation=True, # We use this argument because the texts in our dataset are lists of words (with a label for each word). is_split_into_words=True, ) labels = [] for i, label in enumerate(examples[label_column_name]): word_ids = tokenized_inputs.word_ids(batch_index=i) previous_word_idx = None label_ids = [] for word_idx in word_ids: # Special tokens have a word id that is None. We set the label to -100 so they are automatically # ignored in the loss function. if word_idx is None: label_ids.append(-100) # We set the label for the first token of each word. elif word_idx != previous_word_idx: label_ids.append(label_to_id[label[word_idx]]) # For the other tokens in a word, we set the label to either the current label or -100, depending on # the label_all_tokens flag. else: if args.label_all_tokens: label_ids.append(b_to_i_label[label_to_id[label[word_idx]]]) else: label_ids.append(-100) previous_word_idx = word_idx labels.append(label_ids) tokenized_inputs["labels"] = labels return tokenized_inputs with accelerator.main_process_first(): processed_raw_datasets = raw_datasets.map( tokenize_and_align_labels, batched=True, remove_columns=raw_datasets["train"].column_names, desc="Running tokenizer on dataset", ) train_dataset = processed_raw_datasets["train"] eval_dataset = processed_raw_datasets["validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: if args.pad_to_max_length: # If padding was already done ot max length, we use the default data collator that will just convert everything # to tensors. data_collator = default_data_collator else: # Otherwise, `DataCollatorForTokenClassification` will apply dynamic padding for us (by padding to the maximum length of # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). data_collator = DataCollatorForTokenClassification( tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None) ) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Use the device given by the `accelerator` object. device = accelerator.device model.to(device) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps, num_training_steps=args.max_train_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("ner_no_trainer", experiment_config) # Metrics metric = evaluate.load("seqeval") def get_labels(predictions, references): # Transform predictions and references tensos to numpy arrays if device.type == "cpu": y_pred = predictions.detach().clone().numpy() y_true = references.detach().clone().numpy() else: y_pred = predictions.detach().cpu().clone().numpy() y_true = references.detach().cpu().clone().numpy() # Remove ignored index (special tokens) true_predictions = [ [label_list[p] for (p, l) in zip(pred, gold_label) if l != -100] for pred, gold_label in zip(y_pred, y_true) ] true_labels = [ [label_list[l] for (p, l) in zip(pred, gold_label) if l != -100] for pred, gold_label in zip(y_pred, y_true) ] return true_predictions, true_labels def compute_metrics(): results = metric.compute() if args.return_entity_level_metrics: # Unpack nested dictionaries final_results = {} for key, value in results.items(): if isinstance(value, dict): for n, v in value.items(): final_results[f"{key}_{n}"] = v else: final_results[key] = value return final_results else: return { "precision": results["overall_precision"], "recall": results["overall_recall"], "f1": results["overall_f1"], "accuracy": results["overall_accuracy"], } # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() loss = loss / args.gradient_accumulation_steps accelerator.backward(loss) if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() samples_seen = 0 for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) labels = batch["labels"] if not args.pad_to_max_length: # necessary to pad predictions and labels for being gathered predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100) labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100) predictions_gathered, labels_gathered = accelerator.gather((predictions, labels)) # If we are in a multiprocess environment, the last batch has duplicates if accelerator.num_processes > 1: if step == len(eval_dataloader) - 1: predictions_gathered = predictions_gathered[: len(eval_dataloader.dataset) - samples_seen] labels_gathered = labels_gathered[: len(eval_dataloader.dataset) - samples_seen] else: samples_seen += labels_gathered.shape[0] preds, refs = get_labels(predictions_gathered, labels_gathered) metric.add_batch( predictions=preds, references=refs, ) # predictions and preferences are expected to be a nested list of labels, not label_ids eval_metric = compute_metrics() accelerator.print(f"epoch {epoch}:", eval_metric) if args.with_tracking: accelerator.log( { "seqeval": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) all_results = {f"eval_{k}": v for k, v in eval_metric.items()} if args.with_tracking: all_results.update({"train_loss": total_loss.item() / len(train_dataloader)}) with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump(all_results, f) if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/flava/image_processing_flava.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for Flava.""" import math import random from functools import lru_cache from typing import Dict, Iterable, List, Optional, Tuple, Union import numpy as np from transformers.utils import is_vision_available from transformers.utils.generic import TensorType from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images from ...utils import logging if is_vision_available(): import PIL logger = logging.get_logger(__name__) # These values are taken from CLIP FLAVA_IMAGE_MEAN = [0.48145466, 0.4578275, 0.40821073] FLAVA_IMAGE_STD = [0.26862954, 0.26130258, 0.27577711] FLAVA_CODEBOOK_MEAN = [0.0, 0.0, 0.0] FLAVA_CODEBOOK_STD = [1.0, 1.0, 1.0] LOGIT_LAPLACE_EPS: float = 0.1 # Inspired from https://github.com/microsoft/unilm/blob/master/beit/masking_generator.py class FlavaMaskingGenerator: def __init__( self, input_size: Union[int, Tuple[int, int]] = 14, total_mask_patches: int = 75, mask_group_max_patches: Optional[int] = None, mask_group_min_patches: int = 16, mask_group_min_aspect_ratio: Optional[float] = 0.3, mask_group_max_aspect_ratio: float = None, ): if not isinstance(input_size, tuple): input_size = (input_size,) * 2 self.height, self.width = input_size self.num_patches = self.height * self.width self.total_mask_patches = total_mask_patches self.mask_group_min_patches = mask_group_min_patches self.mask_group_max_patches = total_mask_patches if mask_group_max_patches is None else mask_group_max_patches mask_group_max_aspect_ratio = mask_group_max_aspect_ratio or 1 / mask_group_min_aspect_ratio self.log_aspect_ratio = (math.log(mask_group_min_aspect_ratio), math.log(mask_group_max_aspect_ratio)) def __repr__(self): repr_str = "MaskingGenerator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % ( self.height, self.width, self.mask_group_min_patches, self.mask_group_max_patches, self.total_mask_patches, self.log_aspect_ratio[0], self.log_aspect_ratio[1], ) return repr_str def get_shape(self): return self.height, self.width def _mask(self, mask, max_mask_patches): delta = 0 for _attempt in range(10): target_area = random.uniform(self.mask_group_min_patches, max_mask_patches) aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio)) height = int(round(math.sqrt(target_area * aspect_ratio))) width = int(round(math.sqrt(target_area / aspect_ratio))) if width < self.width and height < self.height: top = random.randint(0, self.height - height) left = random.randint(0, self.width - width) num_masked = mask[top : top + height, left : left + width].sum() # Overlap if 0 < height * width - num_masked <= max_mask_patches: for i in range(top, top + height): for j in range(left, left + width): if mask[i, j] == 0: mask[i, j] = 1 delta += 1 if delta > 0: break return delta def __call__(self): mask = np.zeros(shape=self.get_shape(), dtype=int) mask_count = 0 while mask_count < self.total_mask_patches: max_mask_patches = self.total_mask_patches - mask_count max_mask_patches = min(max_mask_patches, self.mask_group_max_patches) delta = self._mask(mask, max_mask_patches) if delta == 0: break else: mask_count += delta return mask class FlavaImageProcessor(BaseImageProcessor): r""" Constructs a Flava image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the `do_resize` parameter in `preprocess`. size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by the `size` parameter in `preprocess`. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in `preprocess`. do_center_crop (`bool`, *optional*, defaults to `True`): Whether to center crop the images. Can be overridden by the `do_center_crop` parameter in `preprocess`. crop_size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of image after the center crop `(crop_size["height"], crop_size["width"])`. Can be overridden by the `crop_size` parameter in `preprocess`. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in `preprocess`. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in `preprocess`. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in `preprocess`. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. return_image_mask (`bool`, *optional*, defaults to `False`): Whether to return the image mask. Can be overridden by the `return_image_mask` parameter in `preprocess`. input_size_patches (`int`, *optional*, defaults to 14): Number of patches in the image in height and width direction. 14x14 = 196 total patches. Can be overridden by the `input_size_patches` parameter in `preprocess`. total_mask_patches (`int`, *optional*, defaults to 75): Total number of patches that should be masked. Can be overridden by the `total_mask_patches` parameter in `preprocess`. mask_group_min_patches (`int`, *optional*, defaults to 16): Minimum number of patches that should be masked. Can be overridden by the `mask_group_min_patches` parameter in `preprocess`. mask_group_max_patches (`int`, *optional*): Maximum number of patches that should be masked. Can be overridden by the `mask_group_max_patches` parameter in `preprocess`. mask_group_min_aspect_ratio (`float`, *optional*, defaults to 0.3): Minimum aspect ratio of the mask window. Can be overridden by the `mask_group_min_aspect_ratio` parameter in `preprocess`. mask_group_max_aspect_ratio (`float`, *optional*): Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter in `preprocess`. codebook_do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the input for codebook to a certain. Can be overridden by the `codebook_do_resize` parameter in `preprocess`. `codebook_size`. codebook_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): Resize the input for codebook to the given size. Can be overridden by the `codebook_size` parameter in `preprocess`. codebook_resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`): Resampling filter to use if resizing the codebook image. Can be overridden by the `codebook_resample` parameter in `preprocess`. codebook_do_center_crop (`bool`, *optional*, defaults to `True`): Whether to crop the input for codebook at the center. If the input size is smaller than `codebook_crop_size` along any edge, the image is padded with 0's and then center cropped. Can be overridden by the `codebook_do_center_crop` parameter in `preprocess`. codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): Desired output size for codebook input when applying center-cropping. Can be overridden by the `codebook_crop_size` parameter in `preprocess`. codebook_do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the input for codebook by the specified scale `codebook_rescale_factor`. Can be overridden by the `codebook_do_rescale` parameter in `preprocess`. codebook_rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Defines the scale factor to use if rescaling the codebook image. Can be overridden by the `codebook_rescale_factor` parameter in `preprocess`. codebook_do_map_pixels (`bool`, *optional*, defaults to `True`): Whether to map the pixel values of the codebook input to (1 - 2e)x + e. Can be overridden by the `codebook_do_map_pixels` parameter in `preprocess`. codebook_do_normalize (`bool`, *optional*, defaults to `True`): Whether or not to normalize the input for codebook with `codebook_image_mean` and `codebook_image_std`. Can be overridden by the `codebook_do_normalize` parameter in `preprocess`. codebook_image_mean (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0, 0, 0]`): The sequence of means for each channel, to be used when normalizing images for codebook. Can be overridden by the `codebook_image_mean` parameter in `preprocess`. codebook_image_std (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0.5, 0.5, 0.5]`): The sequence of standard deviations for each channel, to be used when normalizing images for codebook. Can be overridden by the `codebook_image_std` parameter in `preprocess`. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BICUBIC, do_center_crop: bool = True, crop_size: Dict[str, int] = None, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, Iterable[float]]] = None, image_std: Optional[Union[float, Iterable[float]]] = None, # Mask related params return_image_mask: bool = False, input_size_patches: int = 14, total_mask_patches: int = 75, mask_group_min_patches: int = 16, mask_group_max_patches: Optional[int] = None, mask_group_min_aspect_ratio: float = 0.3, mask_group_max_aspect_ratio: Optional[float] = None, # Codebook related params return_codebook_pixels: bool = False, codebook_do_resize: bool = True, codebook_size: bool = None, codebook_resample: int = PILImageResampling.LANCZOS, codebook_do_center_crop: bool = True, codebook_crop_size: int = None, codebook_do_rescale: bool = True, codebook_rescale_factor: Union[int, float] = 1 / 255, codebook_do_map_pixels: bool = True, codebook_do_normalize: bool = True, codebook_image_mean: Optional[Union[float, Iterable[float]]] = None, codebook_image_std: Optional[Union[float, Iterable[float]]] = None, **kwargs ) -> None: super().__init__(**kwargs) size = size if size is not None else {"height": 224, "width": 224} size = get_size_dict(size) crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} crop_size = get_size_dict(crop_size, param_name="crop_size") codebook_size = codebook_size if codebook_size is not None else {"height": 112, "width": 112} codebook_size = get_size_dict(codebook_size, param_name="codebook_size") codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112} codebook_crop_size = get_size_dict(codebook_crop_size, param_name="codebook_crop_size") self.do_resize = do_resize self.size = size self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else FLAVA_IMAGE_MEAN self.image_std = image_std if image_std is not None else FLAVA_IMAGE_STD self.return_image_mask = return_image_mask self.input_size_patches = input_size_patches self.total_mask_patches = total_mask_patches self.mask_group_min_patches = mask_group_min_patches self.mask_group_max_patches = mask_group_max_patches self.mask_group_min_aspect_ratio = mask_group_min_aspect_ratio self.mask_group_max_aspect_ratio = mask_group_max_aspect_ratio self.return_codebook_pixels = return_codebook_pixels self.codebook_do_resize = codebook_do_resize self.codebook_size = codebook_size self.codebook_resample = codebook_resample self.codebook_do_center_crop = codebook_do_center_crop self.codebook_crop_size = codebook_crop_size self.codebook_do_rescale = codebook_do_rescale self.codebook_rescale_factor = codebook_rescale_factor self.codebook_do_map_pixels = codebook_do_map_pixels self.codebook_do_normalize = codebook_do_normalize self.codebook_image_mean = codebook_image_mean self.codebook_image_mean = codebook_image_mean if codebook_image_mean is not None else FLAVA_CODEBOOK_MEAN self.codebook_image_std = codebook_image_std if codebook_image_std is not None else FLAVA_CODEBOOK_STD @lru_cache() def masking_generator( self, input_size_patches, total_mask_patches, mask_group_min_patches, mask_group_max_patches, mask_group_min_aspect_ratio, mask_group_max_aspect_ratio, ) -> FlavaMaskingGenerator: return FlavaMaskingGenerator( input_size=input_size_patches, total_mask_patches=total_mask_patches, mask_group_min_patches=mask_group_min_patches, mask_group_max_patches=mask_group_max_patches, mask_group_min_aspect_ratio=mask_group_min_aspect_ratio, mask_group_max_aspect_ratio=mask_group_max_aspect_ratio, ) def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PILImageResampling.BICUBIC, data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The size dictionary must contain 'height' and 'width' keys. Got {size.keys()}") return resize( image, size=(size["height"], size["width"]), resample=resample, data_format=data_format, **kwargs ) def center_crop( self, image: np.ndarray, size: Dict[str, int], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Center crop an image to `(size["height"], size["width"])`. If the input size is smaller than `crop_size` along any edge, the image is padded with 0's and then center cropped. Args: image (`np.ndarray`): Image to center crop. size (`Dict[str, int]`): Size of the output image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The size dictionary must contain 'height' and 'width' keys. Got {size.keys()}") return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs) def rescale( self, image: np.ndarray, scale: Union[int, float], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ): """ Rescale an image by a scale factor. image = image * scale. Args: image (`np.ndarray`): Image to rescale. scale (`int` or `float`): Scale to apply to the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return rescale(image, scale=scale, data_format=data_format, **kwargs) def normalize( self, image: np.ndarray, mean: Union[float, List[float]], std: Union[float, List[float]], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Normalize an image. image = (image - image_mean) / image_std. Args: image (`np.ndarray`): Image to normalize. image_mean (`float` or `List[float]`): Image mean. image_std (`float` or `List[float]`): Image standard deviation. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs) def map_pixels(self, image: np.ndarray) -> np.ndarray: return (1 - 2 * LOGIT_LAPLACE_EPS) * image + LOGIT_LAPLACE_EPS def _preprocess_image( self, image: ImageInput, do_resize: bool = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_center_crop: bool = None, crop_size: Dict[str, int] = None, do_rescale: bool = None, rescale_factor: float = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_map_pixels: bool = None, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, ) -> np.ndarray: """Preprocesses a single image.""" if do_resize and size is None or resample is None: raise ValueError("Size and resample must be specified if do_resize is True.") if do_rescale and rescale_factor is None: raise ValueError("Rescale factor must be specified if do_rescale is True.") if do_normalize and (image_mean is None or image_std is None): raise ValueError("Image mean and std must be specified if do_normalize is True.") # All transformations expect numpy arrays. image = to_numpy_array(image) if do_resize: image = self.resize(image=image, size=size, resample=resample) if do_center_crop: image = self.center_crop(image=image, size=crop_size) if do_rescale: image = self.rescale(image=image, scale=rescale_factor) if do_normalize: image = self.normalize(image=image, mean=image_mean, std=image_std) if do_map_pixels: image = self.map_pixels(image) if data_format is not None: image = to_channel_dimension_format(image, data_format) return image def preprocess( self, images: ImageInput, do_resize: Optional[bool] = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_center_crop: Optional[bool] = None, crop_size: Optional[Dict[str, int]] = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, # Mask related params return_image_mask: Optional[bool] = None, input_size_patches: Optional[int] = None, total_mask_patches: Optional[int] = None, mask_group_min_patches: Optional[int] = None, mask_group_max_patches: Optional[int] = None, mask_group_min_aspect_ratio: Optional[float] = None, mask_group_max_aspect_ratio: Optional[float] = None, # Codebook related params return_codebook_pixels: Optional[bool] = None, codebook_do_resize: Optional[bool] = None, codebook_size: Optional[Dict[str, int]] = None, codebook_resample: Optional[int] = None, codebook_do_center_crop: Optional[bool] = None, codebook_crop_size: Optional[Dict[str, int]] = None, codebook_do_rescale: Optional[bool] = None, codebook_rescale_factor: Optional[float] = None, codebook_do_map_pixels: Optional[bool] = None, codebook_do_normalize: Optional[bool] = None, codebook_image_mean: Optional[Iterable[float]] = None, codebook_image_std: Optional[Iterable[float]] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, **kwargs, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the image. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the center crop. Only has an effect if `do_center_crop` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. return_image_mask (`bool`, *optional*, defaults to `self.return_image_mask`): Whether to return the image mask. input_size_patches (`int`, *optional*, defaults to `self.input_size_patches`): Size of the patches to extract from the image. total_mask_patches (`int`, *optional*, defaults to `self.total_mask_patches`): Total number of patches to extract from the image. mask_group_min_patches (`int`, *optional*, defaults to `self.mask_group_min_patches`): Minimum number of patches to extract from the image. mask_group_max_patches (`int`, *optional*, defaults to `self.mask_group_max_patches`): Maximum number of patches to extract from the image. mask_group_min_aspect_ratio (`float`, *optional*, defaults to `self.mask_group_min_aspect_ratio`): Minimum aspect ratio of the patches to extract from the image. mask_group_max_aspect_ratio (`float`, *optional*, defaults to `self.mask_group_max_aspect_ratio`): Maximum aspect ratio of the patches to extract from the image. return_codebook_pixels (`bool`, *optional*, defaults to `self.return_codebook_pixels`): Whether to return the codebook pixels. codebook_do_resize (`bool`, *optional*, defaults to `self.codebook_do_resize`): Whether to resize the codebook pixels. codebook_size (`Dict[str, int]`, *optional*, defaults to `self.codebook_size`): Size of the codebook pixels. codebook_resample (`int`, *optional*, defaults to `self.codebook_resample`): Resampling filter to use if resizing the codebook pixels. This can be one of the enum `PILImageResampling`, Only has an effect if `codebook_do_resize` is set to `True`. codebook_do_center_crop (`bool`, *optional*, defaults to `self.codebook_do_center_crop`): Whether to center crop the codebook pixels. codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `self.codebook_crop_size`): Size of the center crop of the codebook pixels. Only has an effect if `codebook_do_center_crop` is set to `True`. codebook_do_rescale (`bool`, *optional*, defaults to `self.codebook_do_rescale`): Whether to rescale the codebook pixels values between [0 - 1]. codebook_rescale_factor (`float`, *optional*, defaults to `self.codebook_rescale_factor`): Rescale factor to rescale the codebook pixels by if `codebook_do_rescale` is set to `True`. codebook_do_map_pixels (`bool`, *optional*, defaults to `self.codebook_do_map_pixels`): Whether to map the codebook pixels values. codebook_do_normalize (`bool`, *optional*, defaults to `self.codebook_do_normalize`): Whether to normalize the codebook pixels. codebook_image_mean (`float` or `List[float]`, *optional*, defaults to `self.codebook_image_mean`): Codebook pixels mean to normalize the codebook pixels by if `codebook_do_normalize` is set to `True`. codebook_image_std (`float` or `List[float]`, *optional*, defaults to `self.codebook_image_std`): Codebook pixels standard deviation to normalize the codebook pixels by if `codebook_do_normalize` is set to `True`. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. """ do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size size = get_size_dict(size) resample = resample if resample is not None else self.resample do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop crop_size = crop_size if crop_size is not None else self.crop_size crop_size = get_size_dict(crop_size, param_name="crop_size") do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_normalize = do_normalize if do_normalize is not None else self.do_normalize image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std return_image_mask = return_image_mask if return_image_mask is not None else self.return_image_mask input_size_patches = input_size_patches if input_size_patches is not None else self.input_size_patches total_mask_patches = total_mask_patches if total_mask_patches is not None else self.total_mask_patches mask_group_min_patches = ( mask_group_min_patches if mask_group_min_patches is not None else self.mask_group_min_patches ) mask_group_max_patches = ( mask_group_max_patches if mask_group_max_patches is not None else self.mask_group_max_patches ) mask_group_min_aspect_ratio = ( mask_group_min_aspect_ratio if mask_group_min_aspect_ratio is not None else self.mask_group_min_aspect_ratio ) mask_group_max_aspect_ratio = ( mask_group_max_aspect_ratio if mask_group_max_aspect_ratio is not None else self.mask_group_max_aspect_ratio ) return_codebook_pixels = ( return_codebook_pixels if return_codebook_pixels is not None else self.return_codebook_pixels ) codebook_do_resize = codebook_do_resize if codebook_do_resize is not None else self.codebook_do_resize codebook_size = codebook_size if codebook_size is not None else self.codebook_size codebook_size = get_size_dict(codebook_size, param_name="codebook_size") codebook_resample = codebook_resample if codebook_resample is not None else self.codebook_resample codebook_do_rescale = codebook_do_rescale if codebook_do_rescale is not None else self.codebook_do_rescale codebook_rescale_factor = ( codebook_rescale_factor if codebook_rescale_factor is not None else self.codebook_rescale_factor ) codebook_do_center_crop = ( codebook_do_center_crop if codebook_do_center_crop is not None else self.codebook_do_center_crop ) codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else self.codebook_crop_size codebook_crop_size = get_size_dict(codebook_crop_size, param_name="codebook_crop_size") codebook_do_map_pixels = ( codebook_do_map_pixels if codebook_do_map_pixels is not None else self.codebook_do_map_pixels ) codebook_do_normalize = ( codebook_do_normalize if codebook_do_normalize is not None else self.codebook_do_normalize ) codebook_image_mean = codebook_image_mean if codebook_image_mean is not None else self.codebook_image_mean codebook_image_std = codebook_image_std if codebook_image_std is not None else self.codebook_image_std if not is_batched(images): images = [images] if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) processed_images = [ self._preprocess_image( image=img, do_resize=do_resize, size=size, resample=resample, do_center_crop=do_center_crop, crop_size=crop_size, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_map_pixels=False, data_format=data_format, ) for img in images ] data = {"pixel_values": processed_images} if return_codebook_pixels: codebook_images = [ self._preprocess_image( image=img, do_resize=codebook_do_resize, size=codebook_size, resample=codebook_resample, do_center_crop=codebook_do_center_crop, crop_size=codebook_crop_size, do_rescale=codebook_do_rescale, rescale_factor=codebook_rescale_factor, do_normalize=codebook_do_normalize, image_mean=codebook_image_mean, image_std=codebook_image_std, do_map_pixels=codebook_do_map_pixels, data_format=data_format, ) for img in images ] data["codebook_pixel_values"] = codebook_images if return_image_mask: mask_generator = self.masking_generator( input_size_patches=input_size_patches, total_mask_patches=total_mask_patches, mask_group_min_patches=mask_group_min_patches, mask_group_max_patches=mask_group_max_patches, mask_group_min_aspect_ratio=mask_group_min_aspect_ratio, mask_group_max_aspect_ratio=mask_group_max_aspect_ratio, ) masks = [mask_generator() for _ in images] data["bool_masked_pos"] = masks return BatchFeature(data=data, tensor_type=return_tensors)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for Flava.""" import math import random from functools import lru_cache from typing import Dict, Iterable, List, Optional, Tuple, Union import numpy as np from transformers.utils import is_vision_available from transformers.utils.generic import TensorType from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images from ...utils import logging if is_vision_available(): import PIL logger = logging.get_logger(__name__) # These values are taken from CLIP FLAVA_IMAGE_MEAN = [0.48145466, 0.4578275, 0.40821073] FLAVA_IMAGE_STD = [0.26862954, 0.26130258, 0.27577711] FLAVA_CODEBOOK_MEAN = [0.0, 0.0, 0.0] FLAVA_CODEBOOK_STD = [1.0, 1.0, 1.0] LOGIT_LAPLACE_EPS: float = 0.1 # Inspired from https://github.com/microsoft/unilm/blob/master/beit/masking_generator.py class FlavaMaskingGenerator: def __init__( self, input_size: Union[int, Tuple[int, int]] = 14, total_mask_patches: int = 75, mask_group_max_patches: Optional[int] = None, mask_group_min_patches: int = 16, mask_group_min_aspect_ratio: Optional[float] = 0.3, mask_group_max_aspect_ratio: float = None, ): if not isinstance(input_size, tuple): input_size = (input_size,) * 2 self.height, self.width = input_size self.num_patches = self.height * self.width self.total_mask_patches = total_mask_patches self.mask_group_min_patches = mask_group_min_patches self.mask_group_max_patches = total_mask_patches if mask_group_max_patches is None else mask_group_max_patches mask_group_max_aspect_ratio = mask_group_max_aspect_ratio or 1 / mask_group_min_aspect_ratio self.log_aspect_ratio = (math.log(mask_group_min_aspect_ratio), math.log(mask_group_max_aspect_ratio)) def __repr__(self): repr_str = "MaskingGenerator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % ( self.height, self.width, self.mask_group_min_patches, self.mask_group_max_patches, self.total_mask_patches, self.log_aspect_ratio[0], self.log_aspect_ratio[1], ) return repr_str def get_shape(self): return self.height, self.width def _mask(self, mask, max_mask_patches): delta = 0 for _attempt in range(10): target_area = random.uniform(self.mask_group_min_patches, max_mask_patches) aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio)) height = int(round(math.sqrt(target_area * aspect_ratio))) width = int(round(math.sqrt(target_area / aspect_ratio))) if width < self.width and height < self.height: top = random.randint(0, self.height - height) left = random.randint(0, self.width - width) num_masked = mask[top : top + height, left : left + width].sum() # Overlap if 0 < height * width - num_masked <= max_mask_patches: for i in range(top, top + height): for j in range(left, left + width): if mask[i, j] == 0: mask[i, j] = 1 delta += 1 if delta > 0: break return delta def __call__(self): mask = np.zeros(shape=self.get_shape(), dtype=int) mask_count = 0 while mask_count < self.total_mask_patches: max_mask_patches = self.total_mask_patches - mask_count max_mask_patches = min(max_mask_patches, self.mask_group_max_patches) delta = self._mask(mask, max_mask_patches) if delta == 0: break else: mask_count += delta return mask class FlavaImageProcessor(BaseImageProcessor): r""" Constructs a Flava image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the `do_resize` parameter in `preprocess`. size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by the `size` parameter in `preprocess`. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in `preprocess`. do_center_crop (`bool`, *optional*, defaults to `True`): Whether to center crop the images. Can be overridden by the `do_center_crop` parameter in `preprocess`. crop_size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of image after the center crop `(crop_size["height"], crop_size["width"])`. Can be overridden by the `crop_size` parameter in `preprocess`. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in `preprocess`. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in `preprocess`. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in `preprocess`. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. return_image_mask (`bool`, *optional*, defaults to `False`): Whether to return the image mask. Can be overridden by the `return_image_mask` parameter in `preprocess`. input_size_patches (`int`, *optional*, defaults to 14): Number of patches in the image in height and width direction. 14x14 = 196 total patches. Can be overridden by the `input_size_patches` parameter in `preprocess`. total_mask_patches (`int`, *optional*, defaults to 75): Total number of patches that should be masked. Can be overridden by the `total_mask_patches` parameter in `preprocess`. mask_group_min_patches (`int`, *optional*, defaults to 16): Minimum number of patches that should be masked. Can be overridden by the `mask_group_min_patches` parameter in `preprocess`. mask_group_max_patches (`int`, *optional*): Maximum number of patches that should be masked. Can be overridden by the `mask_group_max_patches` parameter in `preprocess`. mask_group_min_aspect_ratio (`float`, *optional*, defaults to 0.3): Minimum aspect ratio of the mask window. Can be overridden by the `mask_group_min_aspect_ratio` parameter in `preprocess`. mask_group_max_aspect_ratio (`float`, *optional*): Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter in `preprocess`. codebook_do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the input for codebook to a certain. Can be overridden by the `codebook_do_resize` parameter in `preprocess`. `codebook_size`. codebook_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): Resize the input for codebook to the given size. Can be overridden by the `codebook_size` parameter in `preprocess`. codebook_resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`): Resampling filter to use if resizing the codebook image. Can be overridden by the `codebook_resample` parameter in `preprocess`. codebook_do_center_crop (`bool`, *optional*, defaults to `True`): Whether to crop the input for codebook at the center. If the input size is smaller than `codebook_crop_size` along any edge, the image is padded with 0's and then center cropped. Can be overridden by the `codebook_do_center_crop` parameter in `preprocess`. codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): Desired output size for codebook input when applying center-cropping. Can be overridden by the `codebook_crop_size` parameter in `preprocess`. codebook_do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the input for codebook by the specified scale `codebook_rescale_factor`. Can be overridden by the `codebook_do_rescale` parameter in `preprocess`. codebook_rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Defines the scale factor to use if rescaling the codebook image. Can be overridden by the `codebook_rescale_factor` parameter in `preprocess`. codebook_do_map_pixels (`bool`, *optional*, defaults to `True`): Whether to map the pixel values of the codebook input to (1 - 2e)x + e. Can be overridden by the `codebook_do_map_pixels` parameter in `preprocess`. codebook_do_normalize (`bool`, *optional*, defaults to `True`): Whether or not to normalize the input for codebook with `codebook_image_mean` and `codebook_image_std`. Can be overridden by the `codebook_do_normalize` parameter in `preprocess`. codebook_image_mean (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0, 0, 0]`): The sequence of means for each channel, to be used when normalizing images for codebook. Can be overridden by the `codebook_image_mean` parameter in `preprocess`. codebook_image_std (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0.5, 0.5, 0.5]`): The sequence of standard deviations for each channel, to be used when normalizing images for codebook. Can be overridden by the `codebook_image_std` parameter in `preprocess`. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BICUBIC, do_center_crop: bool = True, crop_size: Dict[str, int] = None, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, Iterable[float]]] = None, image_std: Optional[Union[float, Iterable[float]]] = None, # Mask related params return_image_mask: bool = False, input_size_patches: int = 14, total_mask_patches: int = 75, mask_group_min_patches: int = 16, mask_group_max_patches: Optional[int] = None, mask_group_min_aspect_ratio: float = 0.3, mask_group_max_aspect_ratio: Optional[float] = None, # Codebook related params return_codebook_pixels: bool = False, codebook_do_resize: bool = True, codebook_size: bool = None, codebook_resample: int = PILImageResampling.LANCZOS, codebook_do_center_crop: bool = True, codebook_crop_size: int = None, codebook_do_rescale: bool = True, codebook_rescale_factor: Union[int, float] = 1 / 255, codebook_do_map_pixels: bool = True, codebook_do_normalize: bool = True, codebook_image_mean: Optional[Union[float, Iterable[float]]] = None, codebook_image_std: Optional[Union[float, Iterable[float]]] = None, **kwargs ) -> None: super().__init__(**kwargs) size = size if size is not None else {"height": 224, "width": 224} size = get_size_dict(size) crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} crop_size = get_size_dict(crop_size, param_name="crop_size") codebook_size = codebook_size if codebook_size is not None else {"height": 112, "width": 112} codebook_size = get_size_dict(codebook_size, param_name="codebook_size") codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112} codebook_crop_size = get_size_dict(codebook_crop_size, param_name="codebook_crop_size") self.do_resize = do_resize self.size = size self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else FLAVA_IMAGE_MEAN self.image_std = image_std if image_std is not None else FLAVA_IMAGE_STD self.return_image_mask = return_image_mask self.input_size_patches = input_size_patches self.total_mask_patches = total_mask_patches self.mask_group_min_patches = mask_group_min_patches self.mask_group_max_patches = mask_group_max_patches self.mask_group_min_aspect_ratio = mask_group_min_aspect_ratio self.mask_group_max_aspect_ratio = mask_group_max_aspect_ratio self.return_codebook_pixels = return_codebook_pixels self.codebook_do_resize = codebook_do_resize self.codebook_size = codebook_size self.codebook_resample = codebook_resample self.codebook_do_center_crop = codebook_do_center_crop self.codebook_crop_size = codebook_crop_size self.codebook_do_rescale = codebook_do_rescale self.codebook_rescale_factor = codebook_rescale_factor self.codebook_do_map_pixels = codebook_do_map_pixels self.codebook_do_normalize = codebook_do_normalize self.codebook_image_mean = codebook_image_mean self.codebook_image_mean = codebook_image_mean if codebook_image_mean is not None else FLAVA_CODEBOOK_MEAN self.codebook_image_std = codebook_image_std if codebook_image_std is not None else FLAVA_CODEBOOK_STD @lru_cache() def masking_generator( self, input_size_patches, total_mask_patches, mask_group_min_patches, mask_group_max_patches, mask_group_min_aspect_ratio, mask_group_max_aspect_ratio, ) -> FlavaMaskingGenerator: return FlavaMaskingGenerator( input_size=input_size_patches, total_mask_patches=total_mask_patches, mask_group_min_patches=mask_group_min_patches, mask_group_max_patches=mask_group_max_patches, mask_group_min_aspect_ratio=mask_group_min_aspect_ratio, mask_group_max_aspect_ratio=mask_group_max_aspect_ratio, ) def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PILImageResampling.BICUBIC, data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The size dictionary must contain 'height' and 'width' keys. Got {size.keys()}") return resize( image, size=(size["height"], size["width"]), resample=resample, data_format=data_format, **kwargs ) def center_crop( self, image: np.ndarray, size: Dict[str, int], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Center crop an image to `(size["height"], size["width"])`. If the input size is smaller than `crop_size` along any edge, the image is padded with 0's and then center cropped. Args: image (`np.ndarray`): Image to center crop. size (`Dict[str, int]`): Size of the output image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The size dictionary must contain 'height' and 'width' keys. Got {size.keys()}") return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs) def rescale( self, image: np.ndarray, scale: Union[int, float], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ): """ Rescale an image by a scale factor. image = image * scale. Args: image (`np.ndarray`): Image to rescale. scale (`int` or `float`): Scale to apply to the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return rescale(image, scale=scale, data_format=data_format, **kwargs) def normalize( self, image: np.ndarray, mean: Union[float, List[float]], std: Union[float, List[float]], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Normalize an image. image = (image - image_mean) / image_std. Args: image (`np.ndarray`): Image to normalize. image_mean (`float` or `List[float]`): Image mean. image_std (`float` or `List[float]`): Image standard deviation. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs) def map_pixels(self, image: np.ndarray) -> np.ndarray: return (1 - 2 * LOGIT_LAPLACE_EPS) * image + LOGIT_LAPLACE_EPS def _preprocess_image( self, image: ImageInput, do_resize: bool = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_center_crop: bool = None, crop_size: Dict[str, int] = None, do_rescale: bool = None, rescale_factor: float = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_map_pixels: bool = None, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, ) -> np.ndarray: """Preprocesses a single image.""" if do_resize and size is None or resample is None: raise ValueError("Size and resample must be specified if do_resize is True.") if do_rescale and rescale_factor is None: raise ValueError("Rescale factor must be specified if do_rescale is True.") if do_normalize and (image_mean is None or image_std is None): raise ValueError("Image mean and std must be specified if do_normalize is True.") # All transformations expect numpy arrays. image = to_numpy_array(image) if do_resize: image = self.resize(image=image, size=size, resample=resample) if do_center_crop: image = self.center_crop(image=image, size=crop_size) if do_rescale: image = self.rescale(image=image, scale=rescale_factor) if do_normalize: image = self.normalize(image=image, mean=image_mean, std=image_std) if do_map_pixels: image = self.map_pixels(image) if data_format is not None: image = to_channel_dimension_format(image, data_format) return image def preprocess( self, images: ImageInput, do_resize: Optional[bool] = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_center_crop: Optional[bool] = None, crop_size: Optional[Dict[str, int]] = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, # Mask related params return_image_mask: Optional[bool] = None, input_size_patches: Optional[int] = None, total_mask_patches: Optional[int] = None, mask_group_min_patches: Optional[int] = None, mask_group_max_patches: Optional[int] = None, mask_group_min_aspect_ratio: Optional[float] = None, mask_group_max_aspect_ratio: Optional[float] = None, # Codebook related params return_codebook_pixels: Optional[bool] = None, codebook_do_resize: Optional[bool] = None, codebook_size: Optional[Dict[str, int]] = None, codebook_resample: Optional[int] = None, codebook_do_center_crop: Optional[bool] = None, codebook_crop_size: Optional[Dict[str, int]] = None, codebook_do_rescale: Optional[bool] = None, codebook_rescale_factor: Optional[float] = None, codebook_do_map_pixels: Optional[bool] = None, codebook_do_normalize: Optional[bool] = None, codebook_image_mean: Optional[Iterable[float]] = None, codebook_image_std: Optional[Iterable[float]] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, **kwargs, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the image. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the center crop. Only has an effect if `do_center_crop` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. return_image_mask (`bool`, *optional*, defaults to `self.return_image_mask`): Whether to return the image mask. input_size_patches (`int`, *optional*, defaults to `self.input_size_patches`): Size of the patches to extract from the image. total_mask_patches (`int`, *optional*, defaults to `self.total_mask_patches`): Total number of patches to extract from the image. mask_group_min_patches (`int`, *optional*, defaults to `self.mask_group_min_patches`): Minimum number of patches to extract from the image. mask_group_max_patches (`int`, *optional*, defaults to `self.mask_group_max_patches`): Maximum number of patches to extract from the image. mask_group_min_aspect_ratio (`float`, *optional*, defaults to `self.mask_group_min_aspect_ratio`): Minimum aspect ratio of the patches to extract from the image. mask_group_max_aspect_ratio (`float`, *optional*, defaults to `self.mask_group_max_aspect_ratio`): Maximum aspect ratio of the patches to extract from the image. return_codebook_pixels (`bool`, *optional*, defaults to `self.return_codebook_pixels`): Whether to return the codebook pixels. codebook_do_resize (`bool`, *optional*, defaults to `self.codebook_do_resize`): Whether to resize the codebook pixels. codebook_size (`Dict[str, int]`, *optional*, defaults to `self.codebook_size`): Size of the codebook pixels. codebook_resample (`int`, *optional*, defaults to `self.codebook_resample`): Resampling filter to use if resizing the codebook pixels. This can be one of the enum `PILImageResampling`, Only has an effect if `codebook_do_resize` is set to `True`. codebook_do_center_crop (`bool`, *optional*, defaults to `self.codebook_do_center_crop`): Whether to center crop the codebook pixels. codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `self.codebook_crop_size`): Size of the center crop of the codebook pixels. Only has an effect if `codebook_do_center_crop` is set to `True`. codebook_do_rescale (`bool`, *optional*, defaults to `self.codebook_do_rescale`): Whether to rescale the codebook pixels values between [0 - 1]. codebook_rescale_factor (`float`, *optional*, defaults to `self.codebook_rescale_factor`): Rescale factor to rescale the codebook pixels by if `codebook_do_rescale` is set to `True`. codebook_do_map_pixels (`bool`, *optional*, defaults to `self.codebook_do_map_pixels`): Whether to map the codebook pixels values. codebook_do_normalize (`bool`, *optional*, defaults to `self.codebook_do_normalize`): Whether to normalize the codebook pixels. codebook_image_mean (`float` or `List[float]`, *optional*, defaults to `self.codebook_image_mean`): Codebook pixels mean to normalize the codebook pixels by if `codebook_do_normalize` is set to `True`. codebook_image_std (`float` or `List[float]`, *optional*, defaults to `self.codebook_image_std`): Codebook pixels standard deviation to normalize the codebook pixels by if `codebook_do_normalize` is set to `True`. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. """ do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size size = get_size_dict(size) resample = resample if resample is not None else self.resample do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop crop_size = crop_size if crop_size is not None else self.crop_size crop_size = get_size_dict(crop_size, param_name="crop_size") do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_normalize = do_normalize if do_normalize is not None else self.do_normalize image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std return_image_mask = return_image_mask if return_image_mask is not None else self.return_image_mask input_size_patches = input_size_patches if input_size_patches is not None else self.input_size_patches total_mask_patches = total_mask_patches if total_mask_patches is not None else self.total_mask_patches mask_group_min_patches = ( mask_group_min_patches if mask_group_min_patches is not None else self.mask_group_min_patches ) mask_group_max_patches = ( mask_group_max_patches if mask_group_max_patches is not None else self.mask_group_max_patches ) mask_group_min_aspect_ratio = ( mask_group_min_aspect_ratio if mask_group_min_aspect_ratio is not None else self.mask_group_min_aspect_ratio ) mask_group_max_aspect_ratio = ( mask_group_max_aspect_ratio if mask_group_max_aspect_ratio is not None else self.mask_group_max_aspect_ratio ) return_codebook_pixels = ( return_codebook_pixels if return_codebook_pixels is not None else self.return_codebook_pixels ) codebook_do_resize = codebook_do_resize if codebook_do_resize is not None else self.codebook_do_resize codebook_size = codebook_size if codebook_size is not None else self.codebook_size codebook_size = get_size_dict(codebook_size, param_name="codebook_size") codebook_resample = codebook_resample if codebook_resample is not None else self.codebook_resample codebook_do_rescale = codebook_do_rescale if codebook_do_rescale is not None else self.codebook_do_rescale codebook_rescale_factor = ( codebook_rescale_factor if codebook_rescale_factor is not None else self.codebook_rescale_factor ) codebook_do_center_crop = ( codebook_do_center_crop if codebook_do_center_crop is not None else self.codebook_do_center_crop ) codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else self.codebook_crop_size codebook_crop_size = get_size_dict(codebook_crop_size, param_name="codebook_crop_size") codebook_do_map_pixels = ( codebook_do_map_pixels if codebook_do_map_pixels is not None else self.codebook_do_map_pixels ) codebook_do_normalize = ( codebook_do_normalize if codebook_do_normalize is not None else self.codebook_do_normalize ) codebook_image_mean = codebook_image_mean if codebook_image_mean is not None else self.codebook_image_mean codebook_image_std = codebook_image_std if codebook_image_std is not None else self.codebook_image_std if not is_batched(images): images = [images] if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) processed_images = [ self._preprocess_image( image=img, do_resize=do_resize, size=size, resample=resample, do_center_crop=do_center_crop, crop_size=crop_size, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_map_pixels=False, data_format=data_format, ) for img in images ] data = {"pixel_values": processed_images} if return_codebook_pixels: codebook_images = [ self._preprocess_image( image=img, do_resize=codebook_do_resize, size=codebook_size, resample=codebook_resample, do_center_crop=codebook_do_center_crop, crop_size=codebook_crop_size, do_rescale=codebook_do_rescale, rescale_factor=codebook_rescale_factor, do_normalize=codebook_do_normalize, image_mean=codebook_image_mean, image_std=codebook_image_std, do_map_pixels=codebook_do_map_pixels, data_format=data_format, ) for img in images ] data["codebook_pixel_values"] = codebook_images if return_image_mask: mask_generator = self.masking_generator( input_size_patches=input_size_patches, total_mask_patches=total_mask_patches, mask_group_min_patches=mask_group_min_patches, mask_group_max_patches=mask_group_max_patches, mask_group_min_aspect_ratio=mask_group_min_aspect_ratio, mask_group_max_aspect_ratio=mask_group_max_aspect_ratio, ) masks = [mask_generator() for _ in images] data["bool_masked_pos"] = masks return BatchFeature(data=data, tensor_type=return_tensors)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/megatron_gpt2/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/blenderbot/modeling_tf_blenderbot.py

# coding=utf-8 # Copyright 2021 The Facebook, Inc and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 Blenderbot model.""" import os import random import warnings from typing import List, Optional, Tuple, Union import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutput, TFBaseModelOutputWithPastAndCrossAttentions, TFSeq2SeqLMOutput, TFSeq2SeqModelOutput, ) # Public API from ...modeling_tf_utils import ( DUMMY_INPUTS, TFCausalLanguageModelingLoss, TFPreTrainedModel, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import ( ContextManagers, add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_blenderbot import BlenderbotConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/blenderbot-400M-distill" _CONFIG_FOR_DOC = "BlenderbotConfig" _TOKENIZER_FOR_DOC = "BlenderbotTokenizer" LARGE_NEGATIVE = -1e8 # Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int): pad_token_id = tf.cast(pad_token_id, input_ids.dtype) decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype) start_tokens = tf.fill( (shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype) ) shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = tf.where( shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)), shifted_input_ids, ) # "Verify that `labels` has only positive values and -100" assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype)) # Make sure the assertion op is called by wrapping the result in an identity no-op with tf.control_dependencies([assert_gte0]): shifted_input_ids = tf.identity(shifted_input_ids) return shifted_input_ids # Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0): """ Make causal mask used for bi-directional self-attention. """ bsz = input_ids_shape[0] tgt_len = input_ids_shape[1] mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE mask_cond = tf.range(shape_list(mask)[-1]) mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask) if past_key_values_length > 0: mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1) return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1)) # Copied from transformers.models.bart.modeling_tf_bart._expand_mask def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ src_len = shape_list(mask)[1] tgt_len = tgt_len if tgt_len is not None else src_len one_cst = tf.constant(1.0) mask = tf.cast(mask, dtype=one_cst.dtype) expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1)) return (one_cst - expanded_mask) * LARGE_NEGATIVE class TFBlenderbotLearnedPositionalEmbedding(tf.keras.layers.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs): super().__init__(num_embeddings, embedding_dim, **kwargs) def call( self, input_shape: tf.TensorShape, past_key_values_length: int = 0, position_ids: Optional[tf.Tensor] = None ): """Input is expected to be of size [bsz x seqlen].""" if position_ids is None: seq_len = input_shape[1] position_ids = tf.range(seq_len, delta=1, name="range") position_ids += past_key_values_length return super().call(tf.cast(position_ids, dtype=tf.int32)) # Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with Bart->Blenderbot class TFBlenderbotAttention(tf.keras.layers.Layer): """Multi-headed attention from "Attention Is All You Need""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, **kwargs, ): super().__init__(**kwargs) self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = tf.keras.layers.Dropout(dropout) self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj") self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj") self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj") self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj") def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int): return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3)) def call( self, hidden_states: tf.Tensor, key_value_states: Optional[tf.Tensor] = None, past_key_value: Optional[Tuple[Tuple[tf.Tensor]]] = None, attention_mask: Optional[tf.Tensor] = None, layer_head_mask: Optional[tf.Tensor] = None, training: Optional[bool] = False, ) -> Tuple[tf.Tensor, Optional[tf.Tensor]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, embed_dim = shape_list(hidden_states) # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = tf.concat([past_key_value[0], key_states], axis=2) value_states = tf.concat([past_key_value[1], value_states], axis=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape) key_states = tf.reshape(key_states, proj_shape) value_states = tf.reshape(value_states, proj_shape) src_len = shape_list(key_states)[1] attn_weights = tf.matmul(query_states, key_states, transpose_b=True) tf.debugging.assert_equal( shape_list(attn_weights), [bsz * self.num_heads, tgt_len, src_len], message=( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {shape_list(attn_weights)}" ), ) if attention_mask is not None: tf.debugging.assert_equal( shape_list(attention_mask), [bsz, 1, tgt_len, src_len], message=( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" f" {shape_list(attention_mask)}" ), ) attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype) attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) attn_weights = stable_softmax(attn_weights, axis=-1) if layer_head_mask is not None: tf.debugging.assert_equal( shape_list(layer_head_mask), [self.num_heads], message=( f"Head mask for a single layer should be of size {(self.num_heads)}, but is" f" {shape_list(layer_head_mask)}" ), ) attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape( attn_weights, (bsz, self.num_heads, tgt_len, src_len) ) attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) attn_probs = self.dropout(attn_weights, training=training) attn_output = tf.matmul(attn_probs, value_states) tf.debugging.assert_equal( shape_list(attn_output), [bsz * self.num_heads, tgt_len, self.head_dim], message=( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {shape_list(attn_output)}" ), ) attn_output = tf.transpose( tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3) ) attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim)) attn_output = self.out_proj(attn_output) attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) return attn_output, attn_weights, past_key_value # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartEncoderLayer with MBart->Blenderbot class TFBlenderbotEncoderLayer(tf.keras.layers.Layer): def __init__(self, config: BlenderbotConfig, **kwargs): super().__init__(**kwargs) self.embed_dim = config.d_model self.self_attn = TFBlenderbotAttention( self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn" ) self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm") self.dropout = tf.keras.layers.Dropout(config.dropout) self.activation_fn = get_tf_activation(config.activation_function) self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout) self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1") self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2") self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training: Optional[bool] = False, ): """ Args: hidden_states (`tf.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)* attention_mask (`tf.Tensor`): attention mask of size *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size *(encoder_attention_heads,)* """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) hidden_states, self_attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask ) tf.debugging.assert_equal( shape_list(hidden_states), shape_list(residual), message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}", ) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout(hidden_states, training=training) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states return hidden_states, self_attn_weights # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartDecoderLayer with MBart->Blenderbot class TFBlenderbotDecoderLayer(tf.keras.layers.Layer): def __init__(self, config: BlenderbotConfig, **kwargs): super().__init__(**kwargs) self.embed_dim = config.d_model self.self_attn = TFBlenderbotAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, name="self_attn", is_decoder=True, ) self.dropout = tf.keras.layers.Dropout(config.dropout) self.activation_fn = get_tf_activation(config.activation_function) self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout) self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm") self.encoder_attn = TFBlenderbotAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, name="encoder_attn", is_decoder=True, ) self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm") self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1") self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2") self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm") def call( self, hidden_states: tf.Tensor, attention_mask: Optional[tf.Tensor] = None, encoder_hidden_states: Optional[tf.Tensor] = None, encoder_attention_mask: Optional[tf.Tensor] = None, layer_head_mask: Optional[tf.Tensor] = None, cross_attn_layer_head_mask: Optional[tf.Tensor] = None, past_key_value: Optional[Tuple[tf.Tensor]] = None, training: Optional[bool] = False, ) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]: """ Args: hidden_states (`tf.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)* attention_mask (`tf.Tensor`): attention mask of size *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. encoder_hidden_states (`tf.Tensor`): cross attention input to the layer of shape *(seq_len, batch, embed_dim)* encoder_attention_mask (`tf.Tensor`): encoder attention mask of size *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size *(decoder_attention_heads,)* cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module. *(decoder_attention_heads,)* past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, ) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, ) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout(hidden_states, training=training) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states return ( hidden_states, self_attn_weights, cross_attn_weights, present_key_value, ) class TFBlenderbotPreTrainedModel(TFPreTrainedModel): config_class = BlenderbotConfig base_model_prefix = "model" @property def dummy_inputs(self): pad_token = 1 input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32) decoder_input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32) dummy_inputs = { "decoder_input_ids": decoder_input_ids, "attention_mask": tf.math.not_equal(input_ids, pad_token), "input_ids": input_ids, } return dummy_inputs @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"), "decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"), "decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"), } ] ) # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.serving def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) BLENDERBOT_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Args: config ([`BlenderbotConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ BLENDERBOT_GENERATION_EXAMPLE = r""" Conversation example:: >>> from transformers import BlenderbotTokenizer, TFBlenderbotForConditionalGeneration >>> mname = 'facebook/blenderbot-400M-distill' >>> model = TFBlenderbotForConditionalGeneration.from_pretrained(mname) >>> tokenizer = BlenderbotTokenizer.from_pretrained(mname) >>> UTTERANCE = "My friends are cool but they eat too many carbs." >>> print("Human: ", UTTERANCE) >>> inputs = tokenizer([UTTERANCE], return_tensors='tf') >>> reply_ids = model.generate(**inputs) >>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]) >>> REPLY = "I'm not sure" >>> print("Human: ", REPLY) >>> NEXT_UTTERANCE = ( ... "My friends are cool but they eat too many carbs.</s> <s>That's unfortunate. " ... "Are they trying to lose weight or are they just trying to be healthier?</s> " ... "<s> I'm not sure." ... ) >>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors='tf') >>> next_reply_ids = model.generate(**inputs) >>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0]) """ BLENDERBOT_INPUTS_DOCSTRING = r""" Args: input_ids (`tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BlenderbotTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`BlenderbotTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) Blenderbot uses the `bos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): will be made by default and ignore pad tokens. It is not recommended to set this for most use cases. decoder_position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tf.FloatTensor`, *optional*): hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape `(batch_size, sequence_length, hidden_size)` is a sequence of past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @keras_serializable class TFBlenderbotEncoder(tf.keras.layers.Layer): config_class = BlenderbotConfig """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`TFBlenderbotEncoderLayer`]. Args: config: BlenderbotConfig """ def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs): super().__init__(**kwargs) self.config = config self.dropout = tf.keras.layers.Dropout(config.dropout) self.layerdrop = config.encoder_layerdrop self.padding_idx = config.pad_token_id self.max_source_positions = config.max_position_embeddings self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0 self.embed_tokens = embed_tokens self.embed_positions = TFBlenderbotLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, name="embed_positions", ) self.layers = [TFBlenderbotEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)] self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm") def get_embed_tokens(self): return self.embed_tokens def set_embed_tokens(self, embed_tokens): self.embed_tokens = embed_tokens @unpack_inputs def call( self, input_ids=None, inputs_embeds=None, attention_mask=None, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ): """ Args: input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`BlenderbotTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs_embeds is None: # if `self.embed_tokens.load_weight_prefix` is set, runs the embedding operation with the correct name # scope, so that its weights are registered with the desired name for loading/storing. When `tf.name_scope` # is used with a name ending in `/`, that name replaces the current name scope. # (embeddings with tf.name_scope: self.embed_tokens.load_weight_prefix/self.embed_tokens.name/embeddings:0) context = [] if hasattr(self.embed_tokens, "load_weight_prefix"): context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/")) with ContextManagers(context): # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound # indices on GPU, returning zeros instead. This is a dangerous silent behavior. tf.debugging.assert_less( input_ids, tf.cast(self.embed_tokens.input_dim, dtype=input_ids.dtype), message=( "input_ids must be smaller than the embedding layer's input dimension (got" f" {tf.math.reduce_max(input_ids)} >= {self.embed_tokens.input_dim})" ), ) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(input_shape) hidden_states = inputs_embeds + embed_pos hidden_states = self.dropout(hidden_states, training=training) # check attention mask and invert if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask) else: attention_mask = None encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: tf.debugging.assert_equal( shape_list(head_mask)[0], len(self.layers), message=( f"The head_mask should be specified for {len(self.layers)} layers, but it is for" f" {shape_list(head_mask)[0]}." ), ) # encoder layers for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if training and (dropout_probability < self.layerdrop): # skip the layer continue hidden_states, attn = encoder_layer( hidden_states, attention_mask, head_mask[idx] if head_mask is not None else None, ) if output_attentions: all_attentions += (attn,) hidden_states = self.layer_norm(hidden_states) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) @keras_serializable class TFBlenderbotDecoder(tf.keras.layers.Layer): config_class = BlenderbotConfig """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFBlenderbotDecoderLayer`] Args: config: BlenderbotConfig embed_tokens: output embedding """ def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs): super().__init__(**kwargs) self.config = config self.padding_idx = config.pad_token_id self.embed_tokens = embed_tokens self.layerdrop = config.decoder_layerdrop self.embed_positions = TFBlenderbotLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, name="embed_positions", ) self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0 self.layers = [TFBlenderbotDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)] self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm") self.dropout = tf.keras.layers.Dropout(config.dropout) def get_embed_tokens(self): return self.embed_tokens def set_embed_tokens(self, embed_tokens): self.embed_tokens = embed_tokens @unpack_inputs def call( self, input_ids=None, inputs_embeds=None, attention_mask=None, position_ids=None, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ): r""" Args: input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`BlenderbotTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0 # embed positions if position_ids is None: positions = self.embed_positions(input_shape, past_key_values_length) else: positions = self.embed_positions(input_shape, position_ids=position_ids) if inputs_embeds is None: context = [] if hasattr(self.embed_tokens, "load_weight_prefix"): context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/")) with ContextManagers(context): # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound # indices on GPU, returning zeros instead. This is a dangerous silent behavior. tf.debugging.assert_less( input_ids, tf.cast(self.embed_tokens.input_dim, dtype=input_ids.dtype), message=( "input_ids must be smaller than the embedding layer's input dimension (got" f" {tf.math.reduce_max(input_ids)} >= {self.embed_tokens.input_dim})" ), ) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale hidden_states = inputs_embeds # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length) else: combined_attention_mask = _expand_mask( tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1] ) if attention_mask is not None: combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1]) if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1]) hidden_states = hidden_states + positions hidden_states = self.dropout(hidden_states, training=training) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None present_key_values = () if use_cache else None # check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]: if attn_mask is not None: tf.debugging.assert_equal( shape_list(attn_mask)[0], len(self.layers), message=( f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for" f" {shape_list(attn_mask)[0]}." ), ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if training and (dropout_probability < self.layerdrop): continue past_key_value = past_key_values[idx] if past_key_values is not None else None hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer( hidden_states, attention_mask=combined_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=head_mask[idx] if head_mask is not None else None, cross_attn_layer_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, past_key_value=past_key_value, ) if use_cache: present_key_values += (present_key_value,) if output_attentions: all_self_attns += (layer_self_attn,) if encoder_hidden_states is not None: all_cross_attns += (layer_cross_attn,) hidden_states = self.layer_norm(hidden_states) if output_hidden_states: all_hidden_states += (hidden_states,) if not return_dict: return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns else: return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attns, ) @keras_serializable class TFBlenderbotMainLayer(tf.keras.layers.Layer): config_class = BlenderbotConfig def __init__(self, config: BlenderbotConfig, **kwargs): super().__init__(**kwargs) self.config = config self.shared = tf.keras.layers.Embedding( input_dim=config.vocab_size, output_dim=config.d_model, embeddings_initializer=tf.keras.initializers.TruncatedNormal(stddev=self.config.init_std), name="model.shared", ) # Additional attribute to specify the expected name scope of the layer (for loading/storing weights) self.shared.load_weight_prefix = "model.shared" self.encoder = TFBlenderbotEncoder(config, self.shared, name="encoder") self.decoder = TFBlenderbotDecoder(config, self.shared, name="decoder") def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.embed_tokens = self.shared self.decoder.embed_tokens = self.shared @unpack_inputs def call( self, input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, decoder_position_ids=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs ): output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) # If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput): encoder_outputs = TFBaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False elif not return_dict and not isinstance(encoder_outputs, tuple): encoder_outputs = encoder_outputs.to_tuple() decoder_outputs = self.decoder( decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) if not return_dict: return decoder_outputs + encoder_outputs return TFSeq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare BLENDERBOT Model outputting raw hidden-states without any specific head on top.", BLENDERBOT_START_DOCSTRING, ) class TFBlenderbotModel(TFBlenderbotPreTrainedModel): def __init__(self, config: BlenderbotConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.model = TFBlenderbotMainLayer(config, name="model") def get_encoder(self): return self.model.encoder def get_decoder(self): return self.model.decoder @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs): if pretrained_model_name_or_path == "facebook/blenderbot-90M": from ..blenderbot_small import TFBlenderbotSmallModel warnings.warn( "The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical" " checkpoint `facebook/small_blenderbot-90M` with" " `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')`" " instead.", FutureWarning, ) return TFBlenderbotSmallModel.from_pretrained(pretrained_model_name_or_path) return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) @unpack_inputs @add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[tf.Tensor] = None, attention_mask: Optional[tf.Tensor] = None, decoder_input_ids: Optional[tf.Tensor] = None, decoder_attention_mask: Optional[tf.Tensor] = None, decoder_position_ids: Optional[tf.Tensor] = None, head_mask: Optional[tf.Tensor] = None, decoder_head_mask: Optional[tf.Tensor] = None, cross_attn_head_mask: Optional[tf.Tensor] = None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, past_key_values: Optional[List[tf.Tensor]] = None, inputs_embeds: Optional[tf.Tensor] = None, decoder_inputs_embeds: Optional[tf.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, **kwargs ) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]: outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output def serving_output(self, output): pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None return TFSeq2SeqModelOutput( last_hidden_state=output.last_hidden_state, past_key_values=pkv, decoder_hidden_states=dec_hs, decoder_attentions=dec_attns, cross_attentions=cross_attns, encoder_last_hidden_state=output.encoder_last_hidden_state, encoder_hidden_states=enc_hs, encoder_attentions=enc_attns, ) # Copied from transformers.models.bart.modeling_tf_bart.BiasLayer class BiasLayer(tf.keras.layers.Layer): """ Bias as a layer. It is used for serialization purposes: `tf.keras.Model.save_weights` stores on a per-layer basis, so all weights have to be registered in a layer. """ def __init__(self, shape, initializer, trainable, name, **kwargs): super().__init__(name=name, **kwargs) # Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of # "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see: # https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214 self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable) def call(self, x): return x + self.bias @add_start_docstrings( "The BLENDERBOT Model with a language modeling head. Can be used for summarization.", BLENDERBOT_START_DOCSTRING, ) class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausalLanguageModelingLoss): _keys_to_ignore_on_load_unexpected = [ r"model.encoder.embed_tokens.weight", r"model.decoder.embed_tokens.weight", ] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.model = TFBlenderbotMainLayer(config, name="model") self.use_cache = config.use_cache # final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency. self.bias_layer = BiasLayer( name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False ) def get_decoder(self): return self.model.decoder def get_encoder(self): return self.model.encoder def get_output_embeddings(self): return self.get_input_embeddings() def set_output_embeddings(self, value): self.set_input_embeddings(value) def get_bias(self): return {"final_logits_bias": self.bias_layer.bias} def set_bias(self, value): # Replaces the existing layers containing bias for correct (de)serialization. vocab_size = value["final_logits_bias"].shape[-1] self.bias_layer = BiasLayer( name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False ) self.bias_layer.bias.assign(value["final_logits_bias"]) @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs): if pretrained_model_name_or_path == "facebook/blenderbot-90M": from ..blenderbot_small import TFBlenderbotSmallForConditionalGeneration warnings.warn( "The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical" " checkpoint `facebook/small_blenderbot-90M` with" " `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')`" " instead.", FutureWarning, ) return TFBlenderbotSmallForConditionalGeneration.from_pretrained(pretrained_model_name_or_path) return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) @unpack_inputs @add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE) def call( self, input_ids: Optional[tf.Tensor] = None, attention_mask: Optional[tf.Tensor] = None, decoder_input_ids: Optional[tf.Tensor] = None, decoder_attention_mask: Optional[tf.Tensor] = None, decoder_position_ids: Optional[tf.Tensor] = None, head_mask: Optional[tf.Tensor] = None, decoder_head_mask: Optional[tf.Tensor] = None, cross_attn_head_mask: Optional[tf.Tensor] = None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, past_key_values: Optional[List[tf.Tensor]] = None, inputs_embeds: Optional[tf.Tensor] = None, decoder_inputs_embeds: Optional[tf.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[tf.Tensor] = None, training: Optional[bool] = False, ) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]: r""" labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: """ if labels is not None: labels = tf.where( labels == self.config.pad_token_id, tf.cast(tf.fill(shape_list(labels), -100), labels.dtype), labels, ) use_cache = False if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True) lm_logits = self.bias_layer(lm_logits) masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits) if not return_dict: output = (lm_logits,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return TFSeq2SeqLMOutput( loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, # index 1 of d outputs decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs cross_attentions=outputs.cross_attentions, # index 4 of d outputs encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out encoder_attentions=outputs.encoder_attentions, # 2 of e out ) # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output def serving_output(self, output): pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None return TFSeq2SeqLMOutput( logits=output.logits, past_key_values=pkv, decoder_hidden_states=dec_hs, decoder_attentions=dec_attns, cross_attentions=cross_attns, encoder_last_hidden_state=output.encoder_last_hidden_state, encoder_hidden_states=enc_hs, encoder_attentions=enc_attns, ) # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.prepare_inputs_for_generation def prepare_inputs_for_generation( self, decoder_input_ids, past=None, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs ): # cut decoder_input_ids if past is used if past is not None: decoder_input_ids = decoder_input_ids[:, -1:] if decoder_attention_mask is not None: # xla decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:] elif past is not None: # no xla + past decoder_position_ids = past[0][0].shape[2] else: # no xla + no past decoder_position_ids = tf.range(decoder_input_ids.shape[1]) return { "input_ids": None, # encoder_outputs is defined. input_ids not needed "encoder_outputs": encoder_outputs, "past_key_values": past, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "decoder_position_ids": decoder_position_ids, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, # change this to avoid caching (presumably for debugging) } @staticmethod # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration._reorder_cache def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: # cached cross_attention states don't have to be reordered -> they are always the same reordered_past += ( tuple(tf.gather(past_state, beam_idx, axis=0) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past

# coding=utf-8 # Copyright 2021 The Facebook, Inc and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 Blenderbot model.""" import os import random import warnings from typing import List, Optional, Tuple, Union import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutput, TFBaseModelOutputWithPastAndCrossAttentions, TFSeq2SeqLMOutput, TFSeq2SeqModelOutput, ) # Public API from ...modeling_tf_utils import ( DUMMY_INPUTS, TFCausalLanguageModelingLoss, TFPreTrainedModel, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import ( ContextManagers, add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_blenderbot import BlenderbotConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/blenderbot-400M-distill" _CONFIG_FOR_DOC = "BlenderbotConfig" _TOKENIZER_FOR_DOC = "BlenderbotTokenizer" LARGE_NEGATIVE = -1e8 # Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int): pad_token_id = tf.cast(pad_token_id, input_ids.dtype) decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype) start_tokens = tf.fill( (shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype) ) shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = tf.where( shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)), shifted_input_ids, ) # "Verify that `labels` has only positive values and -100" assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype)) # Make sure the assertion op is called by wrapping the result in an identity no-op with tf.control_dependencies([assert_gte0]): shifted_input_ids = tf.identity(shifted_input_ids) return shifted_input_ids # Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0): """ Make causal mask used for bi-directional self-attention. """ bsz = input_ids_shape[0] tgt_len = input_ids_shape[1] mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE mask_cond = tf.range(shape_list(mask)[-1]) mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask) if past_key_values_length > 0: mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1) return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1)) # Copied from transformers.models.bart.modeling_tf_bart._expand_mask def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ src_len = shape_list(mask)[1] tgt_len = tgt_len if tgt_len is not None else src_len one_cst = tf.constant(1.0) mask = tf.cast(mask, dtype=one_cst.dtype) expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1)) return (one_cst - expanded_mask) * LARGE_NEGATIVE class TFBlenderbotLearnedPositionalEmbedding(tf.keras.layers.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs): super().__init__(num_embeddings, embedding_dim, **kwargs) def call( self, input_shape: tf.TensorShape, past_key_values_length: int = 0, position_ids: Optional[tf.Tensor] = None ): """Input is expected to be of size [bsz x seqlen].""" if position_ids is None: seq_len = input_shape[1] position_ids = tf.range(seq_len, delta=1, name="range") position_ids += past_key_values_length return super().call(tf.cast(position_ids, dtype=tf.int32)) # Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with Bart->Blenderbot class TFBlenderbotAttention(tf.keras.layers.Layer): """Multi-headed attention from "Attention Is All You Need""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, **kwargs, ): super().__init__(**kwargs) self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = tf.keras.layers.Dropout(dropout) self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj") self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj") self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj") self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj") def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int): return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3)) def call( self, hidden_states: tf.Tensor, key_value_states: Optional[tf.Tensor] = None, past_key_value: Optional[Tuple[Tuple[tf.Tensor]]] = None, attention_mask: Optional[tf.Tensor] = None, layer_head_mask: Optional[tf.Tensor] = None, training: Optional[bool] = False, ) -> Tuple[tf.Tensor, Optional[tf.Tensor]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, embed_dim = shape_list(hidden_states) # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = tf.concat([past_key_value[0], key_states], axis=2) value_states = tf.concat([past_key_value[1], value_states], axis=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape) key_states = tf.reshape(key_states, proj_shape) value_states = tf.reshape(value_states, proj_shape) src_len = shape_list(key_states)[1] attn_weights = tf.matmul(query_states, key_states, transpose_b=True) tf.debugging.assert_equal( shape_list(attn_weights), [bsz * self.num_heads, tgt_len, src_len], message=( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {shape_list(attn_weights)}" ), ) if attention_mask is not None: tf.debugging.assert_equal( shape_list(attention_mask), [bsz, 1, tgt_len, src_len], message=( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" f" {shape_list(attention_mask)}" ), ) attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype) attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) attn_weights = stable_softmax(attn_weights, axis=-1) if layer_head_mask is not None: tf.debugging.assert_equal( shape_list(layer_head_mask), [self.num_heads], message=( f"Head mask for a single layer should be of size {(self.num_heads)}, but is" f" {shape_list(layer_head_mask)}" ), ) attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape( attn_weights, (bsz, self.num_heads, tgt_len, src_len) ) attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) attn_probs = self.dropout(attn_weights, training=training) attn_output = tf.matmul(attn_probs, value_states) tf.debugging.assert_equal( shape_list(attn_output), [bsz * self.num_heads, tgt_len, self.head_dim], message=( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {shape_list(attn_output)}" ), ) attn_output = tf.transpose( tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3) ) attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim)) attn_output = self.out_proj(attn_output) attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) return attn_output, attn_weights, past_key_value # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartEncoderLayer with MBart->Blenderbot class TFBlenderbotEncoderLayer(tf.keras.layers.Layer): def __init__(self, config: BlenderbotConfig, **kwargs): super().__init__(**kwargs) self.embed_dim = config.d_model self.self_attn = TFBlenderbotAttention( self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn" ) self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm") self.dropout = tf.keras.layers.Dropout(config.dropout) self.activation_fn = get_tf_activation(config.activation_function) self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout) self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1") self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2") self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training: Optional[bool] = False, ): """ Args: hidden_states (`tf.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)* attention_mask (`tf.Tensor`): attention mask of size *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size *(encoder_attention_heads,)* """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) hidden_states, self_attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask ) tf.debugging.assert_equal( shape_list(hidden_states), shape_list(residual), message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}", ) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout(hidden_states, training=training) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states return hidden_states, self_attn_weights # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartDecoderLayer with MBart->Blenderbot class TFBlenderbotDecoderLayer(tf.keras.layers.Layer): def __init__(self, config: BlenderbotConfig, **kwargs): super().__init__(**kwargs) self.embed_dim = config.d_model self.self_attn = TFBlenderbotAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, name="self_attn", is_decoder=True, ) self.dropout = tf.keras.layers.Dropout(config.dropout) self.activation_fn = get_tf_activation(config.activation_function) self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout) self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm") self.encoder_attn = TFBlenderbotAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, name="encoder_attn", is_decoder=True, ) self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm") self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1") self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2") self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm") def call( self, hidden_states: tf.Tensor, attention_mask: Optional[tf.Tensor] = None, encoder_hidden_states: Optional[tf.Tensor] = None, encoder_attention_mask: Optional[tf.Tensor] = None, layer_head_mask: Optional[tf.Tensor] = None, cross_attn_layer_head_mask: Optional[tf.Tensor] = None, past_key_value: Optional[Tuple[tf.Tensor]] = None, training: Optional[bool] = False, ) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]: """ Args: hidden_states (`tf.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)* attention_mask (`tf.Tensor`): attention mask of size *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. encoder_hidden_states (`tf.Tensor`): cross attention input to the layer of shape *(seq_len, batch, embed_dim)* encoder_attention_mask (`tf.Tensor`): encoder attention mask of size *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size *(decoder_attention_heads,)* cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module. *(decoder_attention_heads,)* past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, ) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, ) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout(hidden_states, training=training) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states return ( hidden_states, self_attn_weights, cross_attn_weights, present_key_value, ) class TFBlenderbotPreTrainedModel(TFPreTrainedModel): config_class = BlenderbotConfig base_model_prefix = "model" @property def dummy_inputs(self): pad_token = 1 input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32) decoder_input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32) dummy_inputs = { "decoder_input_ids": decoder_input_ids, "attention_mask": tf.math.not_equal(input_ids, pad_token), "input_ids": input_ids, } return dummy_inputs @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"), "decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"), "decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"), } ] ) # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.serving def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) BLENDERBOT_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Args: config ([`BlenderbotConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ BLENDERBOT_GENERATION_EXAMPLE = r""" Conversation example:: >>> from transformers import BlenderbotTokenizer, TFBlenderbotForConditionalGeneration >>> mname = 'facebook/blenderbot-400M-distill' >>> model = TFBlenderbotForConditionalGeneration.from_pretrained(mname) >>> tokenizer = BlenderbotTokenizer.from_pretrained(mname) >>> UTTERANCE = "My friends are cool but they eat too many carbs." >>> print("Human: ", UTTERANCE) >>> inputs = tokenizer([UTTERANCE], return_tensors='tf') >>> reply_ids = model.generate(**inputs) >>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]) >>> REPLY = "I'm not sure" >>> print("Human: ", REPLY) >>> NEXT_UTTERANCE = ( ... "My friends are cool but they eat too many carbs.</s> <s>That's unfortunate. " ... "Are they trying to lose weight or are they just trying to be healthier?</s> " ... "<s> I'm not sure." ... ) >>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors='tf') >>> next_reply_ids = model.generate(**inputs) >>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0]) """ BLENDERBOT_INPUTS_DOCSTRING = r""" Args: input_ids (`tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BlenderbotTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`BlenderbotTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) Blenderbot uses the `bos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): will be made by default and ignore pad tokens. It is not recommended to set this for most use cases. decoder_position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tf.FloatTensor`, *optional*): hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape `(batch_size, sequence_length, hidden_size)` is a sequence of past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @keras_serializable class TFBlenderbotEncoder(tf.keras.layers.Layer): config_class = BlenderbotConfig """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`TFBlenderbotEncoderLayer`]. Args: config: BlenderbotConfig """ def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs): super().__init__(**kwargs) self.config = config self.dropout = tf.keras.layers.Dropout(config.dropout) self.layerdrop = config.encoder_layerdrop self.padding_idx = config.pad_token_id self.max_source_positions = config.max_position_embeddings self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0 self.embed_tokens = embed_tokens self.embed_positions = TFBlenderbotLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, name="embed_positions", ) self.layers = [TFBlenderbotEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)] self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm") def get_embed_tokens(self): return self.embed_tokens def set_embed_tokens(self, embed_tokens): self.embed_tokens = embed_tokens @unpack_inputs def call( self, input_ids=None, inputs_embeds=None, attention_mask=None, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ): """ Args: input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`BlenderbotTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs_embeds is None: # if `self.embed_tokens.load_weight_prefix` is set, runs the embedding operation with the correct name # scope, so that its weights are registered with the desired name for loading/storing. When `tf.name_scope` # is used with a name ending in `/`, that name replaces the current name scope. # (embeddings with tf.name_scope: self.embed_tokens.load_weight_prefix/self.embed_tokens.name/embeddings:0) context = [] if hasattr(self.embed_tokens, "load_weight_prefix"): context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/")) with ContextManagers(context): # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound # indices on GPU, returning zeros instead. This is a dangerous silent behavior. tf.debugging.assert_less( input_ids, tf.cast(self.embed_tokens.input_dim, dtype=input_ids.dtype), message=( "input_ids must be smaller than the embedding layer's input dimension (got" f" {tf.math.reduce_max(input_ids)} >= {self.embed_tokens.input_dim})" ), ) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(input_shape) hidden_states = inputs_embeds + embed_pos hidden_states = self.dropout(hidden_states, training=training) # check attention mask and invert if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask) else: attention_mask = None encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: tf.debugging.assert_equal( shape_list(head_mask)[0], len(self.layers), message=( f"The head_mask should be specified for {len(self.layers)} layers, but it is for" f" {shape_list(head_mask)[0]}." ), ) # encoder layers for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if training and (dropout_probability < self.layerdrop): # skip the layer continue hidden_states, attn = encoder_layer( hidden_states, attention_mask, head_mask[idx] if head_mask is not None else None, ) if output_attentions: all_attentions += (attn,) hidden_states = self.layer_norm(hidden_states) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) @keras_serializable class TFBlenderbotDecoder(tf.keras.layers.Layer): config_class = BlenderbotConfig """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFBlenderbotDecoderLayer`] Args: config: BlenderbotConfig embed_tokens: output embedding """ def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs): super().__init__(**kwargs) self.config = config self.padding_idx = config.pad_token_id self.embed_tokens = embed_tokens self.layerdrop = config.decoder_layerdrop self.embed_positions = TFBlenderbotLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, name="embed_positions", ) self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0 self.layers = [TFBlenderbotDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)] self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm") self.dropout = tf.keras.layers.Dropout(config.dropout) def get_embed_tokens(self): return self.embed_tokens def set_embed_tokens(self, embed_tokens): self.embed_tokens = embed_tokens @unpack_inputs def call( self, input_ids=None, inputs_embeds=None, attention_mask=None, position_ids=None, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ): r""" Args: input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`BlenderbotTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0 # embed positions if position_ids is None: positions = self.embed_positions(input_shape, past_key_values_length) else: positions = self.embed_positions(input_shape, position_ids=position_ids) if inputs_embeds is None: context = [] if hasattr(self.embed_tokens, "load_weight_prefix"): context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/")) with ContextManagers(context): # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound # indices on GPU, returning zeros instead. This is a dangerous silent behavior. tf.debugging.assert_less( input_ids, tf.cast(self.embed_tokens.input_dim, dtype=input_ids.dtype), message=( "input_ids must be smaller than the embedding layer's input dimension (got" f" {tf.math.reduce_max(input_ids)} >= {self.embed_tokens.input_dim})" ), ) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale hidden_states = inputs_embeds # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length) else: combined_attention_mask = _expand_mask( tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1] ) if attention_mask is not None: combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1]) if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1]) hidden_states = hidden_states + positions hidden_states = self.dropout(hidden_states, training=training) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None present_key_values = () if use_cache else None # check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]: if attn_mask is not None: tf.debugging.assert_equal( shape_list(attn_mask)[0], len(self.layers), message=( f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for" f" {shape_list(attn_mask)[0]}." ), ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if training and (dropout_probability < self.layerdrop): continue past_key_value = past_key_values[idx] if past_key_values is not None else None hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer( hidden_states, attention_mask=combined_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=head_mask[idx] if head_mask is not None else None, cross_attn_layer_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, past_key_value=past_key_value, ) if use_cache: present_key_values += (present_key_value,) if output_attentions: all_self_attns += (layer_self_attn,) if encoder_hidden_states is not None: all_cross_attns += (layer_cross_attn,) hidden_states = self.layer_norm(hidden_states) if output_hidden_states: all_hidden_states += (hidden_states,) if not return_dict: return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns else: return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attns, ) @keras_serializable class TFBlenderbotMainLayer(tf.keras.layers.Layer): config_class = BlenderbotConfig def __init__(self, config: BlenderbotConfig, **kwargs): super().__init__(**kwargs) self.config = config self.shared = tf.keras.layers.Embedding( input_dim=config.vocab_size, output_dim=config.d_model, embeddings_initializer=tf.keras.initializers.TruncatedNormal(stddev=self.config.init_std), name="model.shared", ) # Additional attribute to specify the expected name scope of the layer (for loading/storing weights) self.shared.load_weight_prefix = "model.shared" self.encoder = TFBlenderbotEncoder(config, self.shared, name="encoder") self.decoder = TFBlenderbotDecoder(config, self.shared, name="decoder") def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.embed_tokens = self.shared self.decoder.embed_tokens = self.shared @unpack_inputs def call( self, input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, decoder_position_ids=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs ): output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) # If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput): encoder_outputs = TFBaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False elif not return_dict and not isinstance(encoder_outputs, tuple): encoder_outputs = encoder_outputs.to_tuple() decoder_outputs = self.decoder( decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) if not return_dict: return decoder_outputs + encoder_outputs return TFSeq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare BLENDERBOT Model outputting raw hidden-states without any specific head on top.", BLENDERBOT_START_DOCSTRING, ) class TFBlenderbotModel(TFBlenderbotPreTrainedModel): def __init__(self, config: BlenderbotConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.model = TFBlenderbotMainLayer(config, name="model") def get_encoder(self): return self.model.encoder def get_decoder(self): return self.model.decoder @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs): if pretrained_model_name_or_path == "facebook/blenderbot-90M": from ..blenderbot_small import TFBlenderbotSmallModel warnings.warn( "The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical" " checkpoint `facebook/small_blenderbot-90M` with" " `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')`" " instead.", FutureWarning, ) return TFBlenderbotSmallModel.from_pretrained(pretrained_model_name_or_path) return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) @unpack_inputs @add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[tf.Tensor] = None, attention_mask: Optional[tf.Tensor] = None, decoder_input_ids: Optional[tf.Tensor] = None, decoder_attention_mask: Optional[tf.Tensor] = None, decoder_position_ids: Optional[tf.Tensor] = None, head_mask: Optional[tf.Tensor] = None, decoder_head_mask: Optional[tf.Tensor] = None, cross_attn_head_mask: Optional[tf.Tensor] = None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, past_key_values: Optional[List[tf.Tensor]] = None, inputs_embeds: Optional[tf.Tensor] = None, decoder_inputs_embeds: Optional[tf.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, **kwargs ) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]: outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output def serving_output(self, output): pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None return TFSeq2SeqModelOutput( last_hidden_state=output.last_hidden_state, past_key_values=pkv, decoder_hidden_states=dec_hs, decoder_attentions=dec_attns, cross_attentions=cross_attns, encoder_last_hidden_state=output.encoder_last_hidden_state, encoder_hidden_states=enc_hs, encoder_attentions=enc_attns, ) # Copied from transformers.models.bart.modeling_tf_bart.BiasLayer class BiasLayer(tf.keras.layers.Layer): """ Bias as a layer. It is used for serialization purposes: `tf.keras.Model.save_weights` stores on a per-layer basis, so all weights have to be registered in a layer. """ def __init__(self, shape, initializer, trainable, name, **kwargs): super().__init__(name=name, **kwargs) # Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of # "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see: # https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214 self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable) def call(self, x): return x + self.bias @add_start_docstrings( "The BLENDERBOT Model with a language modeling head. Can be used for summarization.", BLENDERBOT_START_DOCSTRING, ) class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausalLanguageModelingLoss): _keys_to_ignore_on_load_unexpected = [ r"model.encoder.embed_tokens.weight", r"model.decoder.embed_tokens.weight", ] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.model = TFBlenderbotMainLayer(config, name="model") self.use_cache = config.use_cache # final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency. self.bias_layer = BiasLayer( name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False ) def get_decoder(self): return self.model.decoder def get_encoder(self): return self.model.encoder def get_output_embeddings(self): return self.get_input_embeddings() def set_output_embeddings(self, value): self.set_input_embeddings(value) def get_bias(self): return {"final_logits_bias": self.bias_layer.bias} def set_bias(self, value): # Replaces the existing layers containing bias for correct (de)serialization. vocab_size = value["final_logits_bias"].shape[-1] self.bias_layer = BiasLayer( name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False ) self.bias_layer.bias.assign(value["final_logits_bias"]) @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs): if pretrained_model_name_or_path == "facebook/blenderbot-90M": from ..blenderbot_small import TFBlenderbotSmallForConditionalGeneration warnings.warn( "The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical" " checkpoint `facebook/small_blenderbot-90M` with" " `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')`" " instead.", FutureWarning, ) return TFBlenderbotSmallForConditionalGeneration.from_pretrained(pretrained_model_name_or_path) return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) @unpack_inputs @add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE) def call( self, input_ids: Optional[tf.Tensor] = None, attention_mask: Optional[tf.Tensor] = None, decoder_input_ids: Optional[tf.Tensor] = None, decoder_attention_mask: Optional[tf.Tensor] = None, decoder_position_ids: Optional[tf.Tensor] = None, head_mask: Optional[tf.Tensor] = None, decoder_head_mask: Optional[tf.Tensor] = None, cross_attn_head_mask: Optional[tf.Tensor] = None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, past_key_values: Optional[List[tf.Tensor]] = None, inputs_embeds: Optional[tf.Tensor] = None, decoder_inputs_embeds: Optional[tf.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[tf.Tensor] = None, training: Optional[bool] = False, ) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]: r""" labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: """ if labels is not None: labels = tf.where( labels == self.config.pad_token_id, tf.cast(tf.fill(shape_list(labels), -100), labels.dtype), labels, ) use_cache = False if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True) lm_logits = self.bias_layer(lm_logits) masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits) if not return_dict: output = (lm_logits,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return TFSeq2SeqLMOutput( loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, # index 1 of d outputs decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs cross_attentions=outputs.cross_attentions, # index 4 of d outputs encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out encoder_attentions=outputs.encoder_attentions, # 2 of e out ) # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output def serving_output(self, output): pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None return TFSeq2SeqLMOutput( logits=output.logits, past_key_values=pkv, decoder_hidden_states=dec_hs, decoder_attentions=dec_attns, cross_attentions=cross_attns, encoder_last_hidden_state=output.encoder_last_hidden_state, encoder_hidden_states=enc_hs, encoder_attentions=enc_attns, ) # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.prepare_inputs_for_generation def prepare_inputs_for_generation( self, decoder_input_ids, past=None, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs ): # cut decoder_input_ids if past is used if past is not None: decoder_input_ids = decoder_input_ids[:, -1:] if decoder_attention_mask is not None: # xla decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:] elif past is not None: # no xla + past decoder_position_ids = past[0][0].shape[2] else: # no xla + no past decoder_position_ids = tf.range(decoder_input_ids.shape[1]) return { "input_ids": None, # encoder_outputs is defined. input_ids not needed "encoder_outputs": encoder_outputs, "past_key_values": past, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "decoder_position_ids": decoder_position_ids, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, # change this to avoid caching (presumably for debugging) } @staticmethod # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration._reorder_cache def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: # cached cross_attention states don't have to be reordered -> they are always the same reordered_past += ( tuple(tf.gather(past_state, beam_idx, axis=0) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/speech_to_text/test_modeling_tf_speech_to_text.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the TensorFlow Speech2Text model. """ import inspect import unittest from transformers import Speech2TextConfig from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from transformers.utils import cached_property, is_tf_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration, TFSpeech2TextModel def prepare_speech_to_text_inputs_dict( config, input_features, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = tf.math.not_equal(input_features, 0) if decoder_attention_mask is None: decoder_attention_mask = tf.math.not_equal(decoder_input_ids, config.pad_token_id) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_features": input_features, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class TFSpeech2TextModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, num_conv_layers=2, conv_kernel_sizes=(5, 5), conv_channels=32, input_feat_per_channel=24, input_channels=1, hidden_act="relu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, max_source_positions=20, max_target_positions=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, scale_embedding=False, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.num_conv_layers = num_conv_layers self.conv_kernel_sizes = conv_kernel_sizes self.conv_channels = conv_channels self.input_feat_per_channel = input_feat_per_channel self.input_channels = input_channels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.scale_embedding = scale_embedding def prepare_config_and_inputs(self): input_features = floats_tensor( [self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size ) attention_mask = tf.ones([self.batch_size, self.seq_length], dtype=tf.int64) decoder_input_ids = tf.math.maximum(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 2) config = self.get_config() inputs_dict = prepare_speech_to_text_inputs_dict( config, input_features=input_features, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, ) return config, inputs_dict def get_config(self): return Speech2TextConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, num_conv_layers=self.num_conv_layers, conv_kernel_sizes=self.conv_kernel_sizes, conv_channels=self.conv_channels, input_feat_per_channel=self.input_feat_per_channel, input_channels=self.input_channels, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, max_source_positions=self.max_source_positions, max_target_positions=self.max_target_positions, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, scale_embedding=self.scale_embedding, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def get_subsampled_output_lengths(self, input_lengths): """ Computes the output length of the convolutional layers """ for _ in range(self.num_conv_layers): input_lengths = (input_lengths - 1) // 2 + 1 return input_lengths def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFSpeech2TextModel(config=config).get_decoder() input_ids = inputs_dict["decoder_input_ids"] attention_mask = inputs_dict["decoder_attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) _, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = tf.math.maximum(ids_tensor((self.batch_size, 3), config.vocab_size), 2) next_attn_mask = ids_tensor((self.batch_size, 3), 2, dtype=tf.int64) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, atol=1e-2) @require_tf class TFSpeech2TextModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFSpeech2TextModel, TFSpeech2TextForConditionalGeneration) if is_tf_available() else () all_generative_model_classes = (TFSpeech2TextForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_missing_keys = False test_onnx = False input_name = "input_ids" def setUp(self): self.model_tester = TFSpeech2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Speech2TextConfig) self.maxDiff = 3000 def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) # not implemented currently def test_inputs_embeds(self): pass # training is not supported yet def test_training(self): pass def test_training_gradient_checkpointing(self): pass def test_generate_fp16(self): pass def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length else: seq_length = self.model_tester.seq_length subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length) self.assertListEqual( list(hidden_states[0].shape[-2:]), [subsampled_seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length) subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) out_len = len(outputs) correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, subsampled_encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) def test_resize_token_embeddings(self): # Overwritten method from parent; see `test_resize_embeddings_untied` pass def test_resize_tokens_embeddings(self): # see `test_resize_embeddings_untied` pass def test_resize_embeddings_untied(self): # TODO: copy test from PT. Not working at the moment because the test relies on `model.resize_token_embeddings`, # whose TF implementation assumes the use of `TFWrappedEmbeddings`. But with a `TFWrappedEmbeddings` we can't # load the weights from PT (also, it induces TF1 behavior, so we might want to rework how # `model.resize_token_embeddings` operates). pass def test_generate_without_input_ids(self): pass @staticmethod def _get_encoder_outputs( model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1 ): encoder = model.get_encoder() encoder_outputs = encoder( input_ids, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) encoder_outputs["last_hidden_state"] = tf.repeat(encoder_outputs.last_hidden_state, num_interleave, axis=0) input_ids = input_ids[:, :, 0] input_ids = tf.zeros_like(input_ids[:, :1], dtype=tf.int64) + model._get_decoder_start_token_id() attention_mask = None return encoder_outputs, input_ids, attention_mask def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1): batch_size, seq_length = input_ids.shape[:2] subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length) num_sequences_in_output = batch_size * num_return_sequences gen_len = ( output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length ) # scores self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config) # Attentions # encoder self._check_encoder_attention_for_generate( output.encoder_attentions, batch_size, config, subsampled_seq_length ) # decoder self._check_attentions_for_generate( num_sequences_in_output, output.decoder_attentions, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # Hidden States # encoder self._check_encoder_hidden_states_for_generate( output.encoder_hidden_states, batch_size, config, subsampled_seq_length ) # decoder self._check_hidden_states_for_generate( num_sequences_in_output, output.decoder_hidden_states, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_no_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_features with self.assertRaises(AssertionError): model.generate(do_sample=True, max_length=5) # num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True)) with self.assertRaises(ValueError): # generating multiple sequences when no beam search generation # is not allowed as it would always generate the same sequences model.generate(input_features, do_sample=False, num_return_sequences=2) # num_return_sequences > 1, sample self._check_generated_ids(model.generate(input_features, do_sample=True, num_return_sequences=2)) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_ids, num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True, num_beams=2)) with self.assertRaises(ValueError): # generating more sequences than having beams leads is not possible model.generate(input_features, do_sample=False, num_return_sequences=3, num_beams=2) # num_return_sequences > 1, sample self._check_generated_ids( model.generate( input_features, do_sample=True, num_beams=2, num_return_sequences=2, ) ) # num_return_sequences > 1, greedy self._check_generated_ids( model.generate(input_features, do_sample=False, num_beams=2, num_return_sequences=2) ) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent -- the input is `input_features`, not `input_ids` def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_features", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @require_tf @require_sentencepiece @require_tokenizers @slow class TFSpeech2TextModelIntegrationTests(unittest.TestCase): @cached_property def default_processor(self): return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr") def _load_datasamples(self, num_samples): from datasets import load_dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_generation_librispeech(self): model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = self.default_processor input_speech = self._load_datasamples(1) input_features = processor(input_speech, return_tensors="tf").input_features generated_ids = model.generate(input_features) generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel" ] self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS) def test_generation_librispeech_batched(self): model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = self.default_processor input_speech = self._load_datasamples(4) inputs = processor(input_speech, return_tensors="tf", padding=True) generated_ids = model.generate(inputs.input_features, attention_mask=inputs.attention_mask) generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel", "nor is mister cultar's manner less interesting than his matter", "he tells us that at this festive season of the year with christmas and roast beef looming before us" " similes drawn from eating and its results occur most readily to the mind", "he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it" " but little of rocky ithaca", ] self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the TensorFlow Speech2Text model. """ import inspect import unittest from transformers import Speech2TextConfig from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from transformers.utils import cached_property, is_tf_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration, TFSpeech2TextModel def prepare_speech_to_text_inputs_dict( config, input_features, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = tf.math.not_equal(input_features, 0) if decoder_attention_mask is None: decoder_attention_mask = tf.math.not_equal(decoder_input_ids, config.pad_token_id) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_features": input_features, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class TFSpeech2TextModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, num_conv_layers=2, conv_kernel_sizes=(5, 5), conv_channels=32, input_feat_per_channel=24, input_channels=1, hidden_act="relu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, max_source_positions=20, max_target_positions=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, scale_embedding=False, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.num_conv_layers = num_conv_layers self.conv_kernel_sizes = conv_kernel_sizes self.conv_channels = conv_channels self.input_feat_per_channel = input_feat_per_channel self.input_channels = input_channels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.scale_embedding = scale_embedding def prepare_config_and_inputs(self): input_features = floats_tensor( [self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size ) attention_mask = tf.ones([self.batch_size, self.seq_length], dtype=tf.int64) decoder_input_ids = tf.math.maximum(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 2) config = self.get_config() inputs_dict = prepare_speech_to_text_inputs_dict( config, input_features=input_features, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, ) return config, inputs_dict def get_config(self): return Speech2TextConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, num_conv_layers=self.num_conv_layers, conv_kernel_sizes=self.conv_kernel_sizes, conv_channels=self.conv_channels, input_feat_per_channel=self.input_feat_per_channel, input_channels=self.input_channels, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, max_source_positions=self.max_source_positions, max_target_positions=self.max_target_positions, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, scale_embedding=self.scale_embedding, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def get_subsampled_output_lengths(self, input_lengths): """ Computes the output length of the convolutional layers """ for _ in range(self.num_conv_layers): input_lengths = (input_lengths - 1) // 2 + 1 return input_lengths def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFSpeech2TextModel(config=config).get_decoder() input_ids = inputs_dict["decoder_input_ids"] attention_mask = inputs_dict["decoder_attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) _, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = tf.math.maximum(ids_tensor((self.batch_size, 3), config.vocab_size), 2) next_attn_mask = ids_tensor((self.batch_size, 3), 2, dtype=tf.int64) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, atol=1e-2) @require_tf class TFSpeech2TextModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFSpeech2TextModel, TFSpeech2TextForConditionalGeneration) if is_tf_available() else () all_generative_model_classes = (TFSpeech2TextForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_missing_keys = False test_onnx = False input_name = "input_ids" def setUp(self): self.model_tester = TFSpeech2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Speech2TextConfig) self.maxDiff = 3000 def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) # not implemented currently def test_inputs_embeds(self): pass # training is not supported yet def test_training(self): pass def test_training_gradient_checkpointing(self): pass def test_generate_fp16(self): pass def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length else: seq_length = self.model_tester.seq_length subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length) self.assertListEqual( list(hidden_states[0].shape[-2:]), [subsampled_seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length) subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) out_len = len(outputs) correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, subsampled_encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) def test_resize_token_embeddings(self): # Overwritten method from parent; see `test_resize_embeddings_untied` pass def test_resize_tokens_embeddings(self): # see `test_resize_embeddings_untied` pass def test_resize_embeddings_untied(self): # TODO: copy test from PT. Not working at the moment because the test relies on `model.resize_token_embeddings`, # whose TF implementation assumes the use of `TFWrappedEmbeddings`. But with a `TFWrappedEmbeddings` we can't # load the weights from PT (also, it induces TF1 behavior, so we might want to rework how # `model.resize_token_embeddings` operates). pass def test_generate_without_input_ids(self): pass @staticmethod def _get_encoder_outputs( model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1 ): encoder = model.get_encoder() encoder_outputs = encoder( input_ids, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) encoder_outputs["last_hidden_state"] = tf.repeat(encoder_outputs.last_hidden_state, num_interleave, axis=0) input_ids = input_ids[:, :, 0] input_ids = tf.zeros_like(input_ids[:, :1], dtype=tf.int64) + model._get_decoder_start_token_id() attention_mask = None return encoder_outputs, input_ids, attention_mask def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1): batch_size, seq_length = input_ids.shape[:2] subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length) num_sequences_in_output = batch_size * num_return_sequences gen_len = ( output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length ) # scores self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config) # Attentions # encoder self._check_encoder_attention_for_generate( output.encoder_attentions, batch_size, config, subsampled_seq_length ) # decoder self._check_attentions_for_generate( num_sequences_in_output, output.decoder_attentions, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # Hidden States # encoder self._check_encoder_hidden_states_for_generate( output.encoder_hidden_states, batch_size, config, subsampled_seq_length ) # decoder self._check_hidden_states_for_generate( num_sequences_in_output, output.decoder_hidden_states, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_no_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_features with self.assertRaises(AssertionError): model.generate(do_sample=True, max_length=5) # num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True)) with self.assertRaises(ValueError): # generating multiple sequences when no beam search generation # is not allowed as it would always generate the same sequences model.generate(input_features, do_sample=False, num_return_sequences=2) # num_return_sequences > 1, sample self._check_generated_ids(model.generate(input_features, do_sample=True, num_return_sequences=2)) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_ids, num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True, num_beams=2)) with self.assertRaises(ValueError): # generating more sequences than having beams leads is not possible model.generate(input_features, do_sample=False, num_return_sequences=3, num_beams=2) # num_return_sequences > 1, sample self._check_generated_ids( model.generate( input_features, do_sample=True, num_beams=2, num_return_sequences=2, ) ) # num_return_sequences > 1, greedy self._check_generated_ids( model.generate(input_features, do_sample=False, num_beams=2, num_return_sequences=2) ) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent -- the input is `input_features`, not `input_ids` def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_features", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @require_tf @require_sentencepiece @require_tokenizers @slow class TFSpeech2TextModelIntegrationTests(unittest.TestCase): @cached_property def default_processor(self): return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr") def _load_datasamples(self, num_samples): from datasets import load_dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_generation_librispeech(self): model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = self.default_processor input_speech = self._load_datasamples(1) input_features = processor(input_speech, return_tensors="tf").input_features generated_ids = model.generate(input_features) generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel" ] self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS) def test_generation_librispeech_batched(self): model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = self.default_processor input_speech = self._load_datasamples(4) inputs = processor(input_speech, return_tensors="tf", padding=True) generated_ids = model.generate(inputs.input_features, attention_mask=inputs.attention_mask) generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel", "nor is mister cultar's manner less interesting than his matter", "he tells us that at this festive season of the year with christmas and roast beef looming before us" " similes drawn from eating and its results occur most readily to the mind", "he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it" " but little of rocky ithaca", ] self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Classes to support Vision-Encoder-Text-Decoder architectures""" import os from typing import Optional, Tuple, Union import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput from ...modeling_flax_utils import FlaxPreTrainedModel from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from ..auto.configuration_auto import AutoConfig from ..auto.modeling_flax_auto import FlaxAutoModel, FlaxAutoModelForCausalLM from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VisionEncoderDecoderConfig" VISION_ENCODER_DECODER_START_DOCSTRING = r""" This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via [`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`] function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, like image captioning. The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical character recognition (OCR) yields a significant performance improvement. After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Parameters: config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r""" Args: pixel_values (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using the vision model's feature extractor. For example, using [`ViTFeatureExtractor`]. See [`ViTFeatureExtractor.__call__`] for details. decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. decoder_position_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.decoder.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): If set to `True`, the model will return a [`~utils.FlaxSeq2SeqLMOutput`] instead of a plain tuple. """ VISION_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING = r""" Args: pixel_values (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using the vision model's feature extractor. For example, using [`ViTFeatureExtractor`]. See [`ViTFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): If set to `True`, the model will return a [`~utils.FlaxBaseModelOutput`] instead of a plain tuple. """ VISION_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING = r""" Args: decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). For sequence to sequence training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training. encoder_outputs (`tuple(tuple(jnp.ndarray)`): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. decoder_position_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.decoder.max_position_embeddings - 1]`. past_key_values (`Dict[str, jnp.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): If set to `True`, the model will return a [`~utils.FlaxCausalLMOutputWithCrossAttentions`] instead of a plain tuple. """ class FlaxVisionEncoderDecoderModule(nn.Module): config: VisionEncoderDecoderConfig dtype: jnp.dtype = jnp.float32 def setup(self): encoder_config = self.config.encoder decoder_config = self.config.decoder # Copied from `modeling_hybrid_clip.py` with modifications. from ...models.auto.modeling_flax_auto import FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_MAPPING encoder_module = FLAX_MODEL_MAPPING[encoder_config.__class__].module_class decoder_module = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING[decoder_config.__class__].module_class self.encoder = encoder_module(encoder_config, dtype=self.dtype) self.decoder = decoder_module(decoder_config, dtype=self.dtype) # encoder outputs might need to be projected to different dimension for decoder if ( self.encoder.config.hidden_size != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None ): self.enc_to_dec_proj = nn.Dense( self.decoder.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.decoder.config.initializer_range), dtype=self.dtype, ) else: self.enc_to_dec_proj = None def _get_encoder_module(self): return self.encoder def _get_projection_module(self): return self.enc_to_dec_proj def _get_decoder_module(self): return self.decoder def __call__( self, pixel_values, decoder_input_ids, decoder_attention_mask, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): encoder_outputs = self.encoder( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) encoder_hidden_states = encoder_outputs[0] # optionally project encoder_hidden_states if self.enc_to_dec_proj is not None: encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) # The advantage of explicitly setting this is TPU XLA compiler knows as soon as possible what shape this # variable has and can better optimize. Also passing `None` can lead to some problems when jitting the model. # In Flax/JAX, we only want to pass `None` for non-tensor function inputs. For all tensor function inputs, we # should always pass a tensor and not `None`. batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) if not return_dict: return decoder_outputs + encoder_outputs return FlaxSeq2SeqLMOutput( logits=decoder_outputs.logits, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING) class FlaxVisionEncoderDecoderModel(FlaxPreTrainedModel): r""" [`FlaxVisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with the module (flax.nn.Module) of one of the base vision model classes of the library as encoder module and another one as decoder module when created with the :meth*~transformers.FlaxAutoModel.from_pretrained* class method for the encoder and :meth*~transformers.FlaxAutoModelForCausalLM.from_pretrained* class method for the decoder. """ config_class = VisionEncoderDecoderConfig base_model_prefix = "vision_encoder_decoder" main_input_name = "pixel_values" module_class = FlaxVisionEncoderDecoderModule def __init__( self, config: VisionEncoderDecoderConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): if not _do_init: raise ValueError( "`FlaxVisionEncoderDecoderModel` cannot be created without initializing, `_do_init` must be `True`." ) if input_shape is None: num_channels = getattr(config.encoder, "num_channels", 3) input_shape = ( (1, config.encoder.image_size, config.encoder.image_size, num_channels), (1, 1), ) if config.decoder.cross_attention_hidden_size is not None: if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size: raise ValueError( "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal" f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for" f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for" " `config.encoder.hidden_size`." ) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: encoder_input_shape, decoder_input_shape = input_shape # init input tensors pixel_values = jnp.zeros(encoder_input_shape, dtype=self.dtype) decoder_input_ids = jnp.zeros(decoder_input_shape, dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) batch_size, _, _, _ = pixel_values.shape decoder_batch_size, decoder_sequence_length = decoder_input_ids.shape if not decoder_batch_size == batch_size: raise ValueError( f"The inputs of encoder and decoder should have the same batch size, but got {batch_size} for encoder " f"and {decoder_batch_size} for decoder." ) decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_sequence_length)[None, :], (decoder_batch_size, decoder_sequence_length) ) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init( rngs, pixel_values, decoder_input_ids, decoder_attention_mask, decoder_position_ids, )["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length, encoder_outputs): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. """ # init input variables to retrieve cache decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) decoder_position_ids = jnp.broadcast_to( jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape ) def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, **kwargs, ) init_variables = self.module.init( jax.random.PRNGKey(0), decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], init_cache=True, method=_decoder_forward, # we only need to call the decoder to init the cache ) return unfreeze(init_variables["cache"]) @add_start_docstrings(VISION_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=_CONFIG_FOR_DOC) def encode( self, pixel_values: jnp.ndarray, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import ViTFeatureExtractor, FlaxVisionEncoderDecoderModel >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") >>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "gpt2" ... ) >>> pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values >>> encoder_outputs = model.encode(pixel_values) ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # `FlaxViTModel` expects channel first format, but `FlaxViTModule` expects channel last format. # Currently, we assume this holds for all Flax vision models, and perform a transpose here. pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _encoder_forward(module, pixel_values, **kwargs): encode_module = module._get_encoder_module() return encode_module(pixel_values, **kwargs) outputs = self.module.apply( {"params": params or self.params}, pixel_values=jnp.array(pixel_values, dtype=self.dtype), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, method=_encoder_forward, ) if return_dict: outputs = FlaxBaseModelOutput( last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) return outputs @add_start_docstrings(VISION_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def decode( self, decoder_input_ids, encoder_outputs, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import ViTFeatureExtractor, FlaxVisionEncoderDecoderModel >>> import jax.numpy as jnp >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") >>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "gpt2" ... ) >>> pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values >>> encoder_outputs = model.encode(pixel_values) >>> decoder_start_token_id = model.config.decoder.bos_token_id >>> decoder_input_ids = jnp.ones((pixel_values.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> logits = outputs.logits ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict encoder_hidden_states = encoder_outputs[0] batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by FlaxBartAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward( module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, encoder_hidden_states, **kwargs ): projection_module = module._get_projection_module() decoder_module = module._get_decoder_module() # optionally project encoder_hidden_states if projection_module is not None: encoder_hidden_states = projection_module(encoder_hidden_states) return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, encoder_hidden_states, **kwargs, ) outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, method=_decoder_forward, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past = outputs outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past = outputs outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs @add_start_docstrings_to_model_forward(VISION_ENCODER_DECODER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def __call__( self, pixel_values: jnp.ndarray, decoder_input_ids: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Examples: ```python >>> from transformers import FlaxVisionEncoderDecoderModel, ViTFeatureExtractor, GPT2Tokenizer >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") >>> # load output tokenizer >>> tokenizer_output = GPT2Tokenizer.from_pretrained("gpt2") >>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "gpt2" ... ) >>> pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values >>> # use GPT2's eos_token as the pad as well as eos token >>> model.config.eos_token_id = model.config.decoder.eos_token_id >>> model.config.pad_token_id = model.config.eos_token_id >>> # generation >>> sequences = model.generate(pixel_values, num_beams=4, max_length=12).sequences >>> captions = tokenizer_output.batch_decode(sequences, skip_special_tokens=True) ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # prepare encoder inputs # `FlaxViTModel` expects channel first format, but `FlaxViTModule` expects channel last format. # Currently, we assume this holds for all Flax vision models, and perform a transpose here. pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # prepare decoder inputs if decoder_input_ids is None: raise ValueError("`decoder_input_ids` can't be `None`.") if decoder_attention_mask is None: decoder_attention_mask = jnp.ones_like(decoder_input_ids) if decoder_position_ids is None: batch_size, sequence_length = decoder_input_ids.shape decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} return self.module.apply( {"params": params or self.params}, pixel_values=jnp.array(pixel_values, dtype=self.dtype), decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, ) def prepare_inputs_for_generation( self, decoder_input_ids, max_length, decoder_attention_mask: Optional[jnp.DeviceArray] = None, encoder_outputs=None, **kwargs ): # initializing the cache batch_size, seq_length = decoder_input_ids.shape past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if decoder_attention_mask is not None: decoder_position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0)) else: decoder_position_ids = jnp.broadcast_to( jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length) ) return { "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "decoder_attention_mask": extended_attention_mask, "decoder_position_ids": decoder_position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 return model_kwargs @classmethod def from_encoder_decoder_pretrained( cls, encoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, decoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, *model_args, **kwargs ) -> FlaxPreTrainedModel: r""" Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model checkpoints. Params: encoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*): Information necessary to initiate the encoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An example is `google/vit-base-patch16-224-in21k`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. decoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*, defaults to `None`): Information necessary to initiate the decoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter. - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import FlaxVisionEncoderDecoderModel >>> # initialize a vit-gpt2 from a pretrained ViT and a pretrained GPT2 model. Note that the cross-attention layers will be randomly initialized >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "gpt2" ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./vit-gpt2") >>> # load fine-tuned model >>> model = FlaxVisionEncoderDecoderModel.from_pretrained("./vit-gpt2") ```""" kwargs_encoder = { argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_") } kwargs_decoder = { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } # remove encoder, decoder kwargs from kwargs for key in kwargs_encoder.keys(): del kwargs["encoder_" + key] for key in kwargs_decoder.keys(): del kwargs["decoder_" + key] # Load and initialize the encoder and decoder # The distinction between encoder and decoder at the model level is made # by the value of the flag `is_decoder` that we need to set correctly. encoder = kwargs_encoder.pop("model", None) if encoder is None: if encoder_pretrained_model_name_or_path is None: raise ValueError( "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_encoder: encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path) if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: logger.info( f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model " "from a decoder model. Cross-attention and casual mask are disabled." ) encoder_config.is_decoder = False encoder_config.add_cross_attention = False kwargs_encoder["config"] = encoder_config encoder = FlaxAutoModel.from_pretrained( encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder ) decoder = kwargs_decoder.pop("model", None) if decoder is None: if decoder_pretrained_model_name_or_path is None: raise ValueError( "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_decoder: decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path) if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False: logger.info( f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention" f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if" f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers." ) decoder_config.is_decoder = True decoder_config.add_cross_attention = True kwargs_decoder["config"] = decoder_config if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: logger.warning( f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. " f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, " "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` " "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a " "`decoder_config` to `.from_encoder_decoder_pretrained(...)`" ) decoder = FlaxAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder) # instantiate config with corresponding kwargs dtype = kwargs.pop("dtype", jnp.float32) config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs) # init model model = cls(config, dtype=dtype) model.params["encoder"] = encoder.params model.params["decoder"] = decoder.params return model

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Classes to support Vision-Encoder-Text-Decoder architectures""" import os from typing import Optional, Tuple, Union import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput from ...modeling_flax_utils import FlaxPreTrainedModel from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from ..auto.configuration_auto import AutoConfig from ..auto.modeling_flax_auto import FlaxAutoModel, FlaxAutoModelForCausalLM from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VisionEncoderDecoderConfig" VISION_ENCODER_DECODER_START_DOCSTRING = r""" This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via [`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`] function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, like image captioning. The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical character recognition (OCR) yields a significant performance improvement. After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Parameters: config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r""" Args: pixel_values (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using the vision model's feature extractor. For example, using [`ViTFeatureExtractor`]. See [`ViTFeatureExtractor.__call__`] for details. decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. decoder_position_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.decoder.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): If set to `True`, the model will return a [`~utils.FlaxSeq2SeqLMOutput`] instead of a plain tuple. """ VISION_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING = r""" Args: pixel_values (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using the vision model's feature extractor. For example, using [`ViTFeatureExtractor`]. See [`ViTFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): If set to `True`, the model will return a [`~utils.FlaxBaseModelOutput`] instead of a plain tuple. """ VISION_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING = r""" Args: decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). For sequence to sequence training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training. encoder_outputs (`tuple(tuple(jnp.ndarray)`): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. decoder_position_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.decoder.max_position_embeddings - 1]`. past_key_values (`Dict[str, jnp.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): If set to `True`, the model will return a [`~utils.FlaxCausalLMOutputWithCrossAttentions`] instead of a plain tuple. """ class FlaxVisionEncoderDecoderModule(nn.Module): config: VisionEncoderDecoderConfig dtype: jnp.dtype = jnp.float32 def setup(self): encoder_config = self.config.encoder decoder_config = self.config.decoder # Copied from `modeling_hybrid_clip.py` with modifications. from ...models.auto.modeling_flax_auto import FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_MAPPING encoder_module = FLAX_MODEL_MAPPING[encoder_config.__class__].module_class decoder_module = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING[decoder_config.__class__].module_class self.encoder = encoder_module(encoder_config, dtype=self.dtype) self.decoder = decoder_module(decoder_config, dtype=self.dtype) # encoder outputs might need to be projected to different dimension for decoder if ( self.encoder.config.hidden_size != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None ): self.enc_to_dec_proj = nn.Dense( self.decoder.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.decoder.config.initializer_range), dtype=self.dtype, ) else: self.enc_to_dec_proj = None def _get_encoder_module(self): return self.encoder def _get_projection_module(self): return self.enc_to_dec_proj def _get_decoder_module(self): return self.decoder def __call__( self, pixel_values, decoder_input_ids, decoder_attention_mask, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): encoder_outputs = self.encoder( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) encoder_hidden_states = encoder_outputs[0] # optionally project encoder_hidden_states if self.enc_to_dec_proj is not None: encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) # The advantage of explicitly setting this is TPU XLA compiler knows as soon as possible what shape this # variable has and can better optimize. Also passing `None` can lead to some problems when jitting the model. # In Flax/JAX, we only want to pass `None` for non-tensor function inputs. For all tensor function inputs, we # should always pass a tensor and not `None`. batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) if not return_dict: return decoder_outputs + encoder_outputs return FlaxSeq2SeqLMOutput( logits=decoder_outputs.logits, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING) class FlaxVisionEncoderDecoderModel(FlaxPreTrainedModel): r""" [`FlaxVisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with the module (flax.nn.Module) of one of the base vision model classes of the library as encoder module and another one as decoder module when created with the :meth*~transformers.FlaxAutoModel.from_pretrained* class method for the encoder and :meth*~transformers.FlaxAutoModelForCausalLM.from_pretrained* class method for the decoder. """ config_class = VisionEncoderDecoderConfig base_model_prefix = "vision_encoder_decoder" main_input_name = "pixel_values" module_class = FlaxVisionEncoderDecoderModule def __init__( self, config: VisionEncoderDecoderConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): if not _do_init: raise ValueError( "`FlaxVisionEncoderDecoderModel` cannot be created without initializing, `_do_init` must be `True`." ) if input_shape is None: num_channels = getattr(config.encoder, "num_channels", 3) input_shape = ( (1, config.encoder.image_size, config.encoder.image_size, num_channels), (1, 1), ) if config.decoder.cross_attention_hidden_size is not None: if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size: raise ValueError( "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal" f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for" f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for" " `config.encoder.hidden_size`." ) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: encoder_input_shape, decoder_input_shape = input_shape # init input tensors pixel_values = jnp.zeros(encoder_input_shape, dtype=self.dtype) decoder_input_ids = jnp.zeros(decoder_input_shape, dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) batch_size, _, _, _ = pixel_values.shape decoder_batch_size, decoder_sequence_length = decoder_input_ids.shape if not decoder_batch_size == batch_size: raise ValueError( f"The inputs of encoder and decoder should have the same batch size, but got {batch_size} for encoder " f"and {decoder_batch_size} for decoder." ) decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_sequence_length)[None, :], (decoder_batch_size, decoder_sequence_length) ) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init( rngs, pixel_values, decoder_input_ids, decoder_attention_mask, decoder_position_ids, )["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length, encoder_outputs): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. """ # init input variables to retrieve cache decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) decoder_position_ids = jnp.broadcast_to( jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape ) def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, **kwargs, ) init_variables = self.module.init( jax.random.PRNGKey(0), decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], init_cache=True, method=_decoder_forward, # we only need to call the decoder to init the cache ) return unfreeze(init_variables["cache"]) @add_start_docstrings(VISION_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=_CONFIG_FOR_DOC) def encode( self, pixel_values: jnp.ndarray, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import ViTFeatureExtractor, FlaxVisionEncoderDecoderModel >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") >>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "gpt2" ... ) >>> pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values >>> encoder_outputs = model.encode(pixel_values) ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # `FlaxViTModel` expects channel first format, but `FlaxViTModule` expects channel last format. # Currently, we assume this holds for all Flax vision models, and perform a transpose here. pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _encoder_forward(module, pixel_values, **kwargs): encode_module = module._get_encoder_module() return encode_module(pixel_values, **kwargs) outputs = self.module.apply( {"params": params or self.params}, pixel_values=jnp.array(pixel_values, dtype=self.dtype), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, method=_encoder_forward, ) if return_dict: outputs = FlaxBaseModelOutput( last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) return outputs @add_start_docstrings(VISION_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def decode( self, decoder_input_ids, encoder_outputs, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import ViTFeatureExtractor, FlaxVisionEncoderDecoderModel >>> import jax.numpy as jnp >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") >>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "gpt2" ... ) >>> pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values >>> encoder_outputs = model.encode(pixel_values) >>> decoder_start_token_id = model.config.decoder.bos_token_id >>> decoder_input_ids = jnp.ones((pixel_values.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> logits = outputs.logits ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict encoder_hidden_states = encoder_outputs[0] batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by FlaxBartAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward( module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, encoder_hidden_states, **kwargs ): projection_module = module._get_projection_module() decoder_module = module._get_decoder_module() # optionally project encoder_hidden_states if projection_module is not None: encoder_hidden_states = projection_module(encoder_hidden_states) return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, encoder_hidden_states, **kwargs, ) outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, method=_decoder_forward, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past = outputs outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past = outputs outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs @add_start_docstrings_to_model_forward(VISION_ENCODER_DECODER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def __call__( self, pixel_values: jnp.ndarray, decoder_input_ids: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Examples: ```python >>> from transformers import FlaxVisionEncoderDecoderModel, ViTFeatureExtractor, GPT2Tokenizer >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") >>> # load output tokenizer >>> tokenizer_output = GPT2Tokenizer.from_pretrained("gpt2") >>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "gpt2" ... ) >>> pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values >>> # use GPT2's eos_token as the pad as well as eos token >>> model.config.eos_token_id = model.config.decoder.eos_token_id >>> model.config.pad_token_id = model.config.eos_token_id >>> # generation >>> sequences = model.generate(pixel_values, num_beams=4, max_length=12).sequences >>> captions = tokenizer_output.batch_decode(sequences, skip_special_tokens=True) ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # prepare encoder inputs # `FlaxViTModel` expects channel first format, but `FlaxViTModule` expects channel last format. # Currently, we assume this holds for all Flax vision models, and perform a transpose here. pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # prepare decoder inputs if decoder_input_ids is None: raise ValueError("`decoder_input_ids` can't be `None`.") if decoder_attention_mask is None: decoder_attention_mask = jnp.ones_like(decoder_input_ids) if decoder_position_ids is None: batch_size, sequence_length = decoder_input_ids.shape decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} return self.module.apply( {"params": params or self.params}, pixel_values=jnp.array(pixel_values, dtype=self.dtype), decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, ) def prepare_inputs_for_generation( self, decoder_input_ids, max_length, decoder_attention_mask: Optional[jnp.DeviceArray] = None, encoder_outputs=None, **kwargs ): # initializing the cache batch_size, seq_length = decoder_input_ids.shape past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if decoder_attention_mask is not None: decoder_position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0)) else: decoder_position_ids = jnp.broadcast_to( jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length) ) return { "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "decoder_attention_mask": extended_attention_mask, "decoder_position_ids": decoder_position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 return model_kwargs @classmethod def from_encoder_decoder_pretrained( cls, encoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, decoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, *model_args, **kwargs ) -> FlaxPreTrainedModel: r""" Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model checkpoints. Params: encoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*): Information necessary to initiate the encoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An example is `google/vit-base-patch16-224-in21k`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. decoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*, defaults to `None`): Information necessary to initiate the decoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter. - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import FlaxVisionEncoderDecoderModel >>> # initialize a vit-gpt2 from a pretrained ViT and a pretrained GPT2 model. Note that the cross-attention layers will be randomly initialized >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "gpt2" ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./vit-gpt2") >>> # load fine-tuned model >>> model = FlaxVisionEncoderDecoderModel.from_pretrained("./vit-gpt2") ```""" kwargs_encoder = { argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_") } kwargs_decoder = { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } # remove encoder, decoder kwargs from kwargs for key in kwargs_encoder.keys(): del kwargs["encoder_" + key] for key in kwargs_decoder.keys(): del kwargs["decoder_" + key] # Load and initialize the encoder and decoder # The distinction between encoder and decoder at the model level is made # by the value of the flag `is_decoder` that we need to set correctly. encoder = kwargs_encoder.pop("model", None) if encoder is None: if encoder_pretrained_model_name_or_path is None: raise ValueError( "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_encoder: encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path) if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: logger.info( f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model " "from a decoder model. Cross-attention and casual mask are disabled." ) encoder_config.is_decoder = False encoder_config.add_cross_attention = False kwargs_encoder["config"] = encoder_config encoder = FlaxAutoModel.from_pretrained( encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder ) decoder = kwargs_decoder.pop("model", None) if decoder is None: if decoder_pretrained_model_name_or_path is None: raise ValueError( "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_decoder: decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path) if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False: logger.info( f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention" f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if" f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers." ) decoder_config.is_decoder = True decoder_config.add_cross_attention = True kwargs_decoder["config"] = decoder_config if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: logger.warning( f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. " f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, " "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` " "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a " "`decoder_config` to `.from_encoder_decoder_pretrained(...)`" ) decoder = FlaxAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder) # instantiate config with corresponding kwargs dtype = kwargs.pop("dtype", jnp.float32) config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs) # init model model = cls(config, dtype=dtype) model.params["encoder"] = encoder.params model.params["decoder"] = decoder.params return model

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/xlm/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _import_structure = { "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"], "tokenization_xlm": ["XLMTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_xlm"] = [ "XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMForMultipleChoice", "XLMForQuestionAnswering", "XLMForQuestionAnsweringSimple", "XLMForSequenceClassification", "XLMForTokenClassification", "XLMModel", "XLMPreTrainedModel", "XLMWithLMHeadModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_xlm"] = [ "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMForMultipleChoice", "TFXLMForQuestionAnsweringSimple", "TFXLMForSequenceClassification", "TFXLMForTokenClassification", "TFXLMMainLayer", "TFXLMModel", "TFXLMPreTrainedModel", "TFXLMWithLMHeadModel", ] if TYPE_CHECKING: from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig from .tokenization_xlm import XLMTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_xlm import ( XLM_PRETRAINED_MODEL_ARCHIVE_LIST, XLMForMultipleChoice, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, XLMForSequenceClassification, XLMForTokenClassification, XLMModel, XLMPreTrainedModel, XLMWithLMHeadModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_xlm import ( TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMForMultipleChoice, TFXLMForQuestionAnsweringSimple, TFXLMForSequenceClassification, TFXLMForTokenClassification, TFXLMMainLayer, TFXLMModel, TFXLMPreTrainedModel, TFXLMWithLMHeadModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _import_structure = { "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"], "tokenization_xlm": ["XLMTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_xlm"] = [ "XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMForMultipleChoice", "XLMForQuestionAnswering", "XLMForQuestionAnsweringSimple", "XLMForSequenceClassification", "XLMForTokenClassification", "XLMModel", "XLMPreTrainedModel", "XLMWithLMHeadModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_xlm"] = [ "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMForMultipleChoice", "TFXLMForQuestionAnsweringSimple", "TFXLMForSequenceClassification", "TFXLMForTokenClassification", "TFXLMMainLayer", "TFXLMModel", "TFXLMPreTrainedModel", "TFXLMWithLMHeadModel", ] if TYPE_CHECKING: from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig from .tokenization_xlm import XLMTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_xlm import ( XLM_PRETRAINED_MODEL_ARCHIVE_LIST, XLMForMultipleChoice, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, XLMForSequenceClassification, XLMForTokenClassification, XLMModel, XLMPreTrainedModel, XLMWithLMHeadModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_xlm import ( TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMForMultipleChoice, TFXLMForQuestionAnsweringSimple, TFXLMForSequenceClassification, TFXLMForTokenClassification, TFXLMMainLayer, TFXLMModel, TFXLMPreTrainedModel, TFXLMWithLMHeadModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/mlm_wwm/run_chinese_ref.py

import argparse import json from typing import List from ltp import LTP from transformers.models.bert.tokenization_bert import BertTokenizer def _is_chinese_char(cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x20000 and cp <= 0x2A6DF) # or (cp >= 0x2A700 and cp <= 0x2B73F) # or (cp >= 0x2B740 and cp <= 0x2B81F) # or (cp >= 0x2B820 and cp <= 0x2CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2F800 and cp <= 0x2FA1F) # ): # return True return False def is_chinese(word: str): # word like '180' or '身高' or '神' for char in word: char = ord(char) if not _is_chinese_char(char): return 0 return 1 def get_chinese_word(tokens: List[str]): word_set = set() for token in tokens: chinese_word = len(token) > 1 and is_chinese(token) if chinese_word: word_set.add(token) word_list = list(word_set) return word_list def add_sub_symbol(bert_tokens: List[str], chinese_word_set: set()): if not chinese_word_set: return bert_tokens max_word_len = max([len(w) for w in chinese_word_set]) bert_word = bert_tokens start, end = 0, len(bert_word) while start < end: single_word = True if is_chinese(bert_word[start]): l = min(end - start, max_word_len) for i in range(l, 1, -1): whole_word = "".join(bert_word[start : start + i]) if whole_word in chinese_word_set: for j in range(start + 1, start + i): bert_word[j] = "##" + bert_word[j] start = start + i single_word = False break if single_word: start += 1 return bert_word def prepare_ref(lines: List[str], ltp_tokenizer: LTP, bert_tokenizer: BertTokenizer): ltp_res = [] for i in range(0, len(lines), 100): res = ltp_tokenizer.pipeline(lines[i : i + 100], tasks=["cws"]).cws res = [get_chinese_word(r) for r in res] ltp_res.extend(res) assert len(ltp_res) == len(lines) bert_res = [] for i in range(0, len(lines), 100): res = bert_tokenizer(lines[i : i + 100], add_special_tokens=True, truncation=True, max_length=512) bert_res.extend(res["input_ids"]) assert len(bert_res) == len(lines) ref_ids = [] for input_ids, chinese_word in zip(bert_res, ltp_res): input_tokens = [] for id in input_ids: token = bert_tokenizer._convert_id_to_token(id) input_tokens.append(token) input_tokens = add_sub_symbol(input_tokens, chinese_word) ref_id = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(input_tokens): if token[:2] == "##": clean_token = token[2:] # save chinese tokens' pos if len(clean_token) == 1 and _is_chinese_char(ord(clean_token)): ref_id.append(i) ref_ids.append(ref_id) assert len(ref_ids) == len(bert_res) return ref_ids def main(args): # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm) # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp) with open(args.file_name, "r", encoding="utf-8") as f: data = f.readlines() data = [line.strip() for line in data if len(line) > 0 and not line.isspace()] # avoid delimiter like '\u2029' ltp_tokenizer = LTP(args.ltp) # faster in GPU device bert_tokenizer = BertTokenizer.from_pretrained(args.bert) ref_ids = prepare_ref(data, ltp_tokenizer, bert_tokenizer) with open(args.save_path, "w", encoding="utf-8") as f: data = [json.dumps(ref) + "\n" for ref in ref_ids] f.writelines(data) if __name__ == "__main__": parser = argparse.ArgumentParser(description="prepare_chinese_ref") parser.add_argument( "--file_name", required=False, type=str, default="./resources/chinese-demo.txt", help="file need process, same as training data in lm", ) parser.add_argument( "--ltp", required=False, type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path", ) parser.add_argument( "--bert", required=False, type=str, default="./resources/robert", help="resources for Bert tokenizer", ) parser.add_argument( "--save_path", required=False, type=str, default="./resources/ref.txt", help="path to save res", ) args = parser.parse_args() main(args)

import argparse import json from typing import List from ltp import LTP from transformers.models.bert.tokenization_bert import BertTokenizer def _is_chinese_char(cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x20000 and cp <= 0x2A6DF) # or (cp >= 0x2A700 and cp <= 0x2B73F) # or (cp >= 0x2B740 and cp <= 0x2B81F) # or (cp >= 0x2B820 and cp <= 0x2CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2F800 and cp <= 0x2FA1F) # ): # return True return False def is_chinese(word: str): # word like '180' or '身高' or '神' for char in word: char = ord(char) if not _is_chinese_char(char): return 0 return 1 def get_chinese_word(tokens: List[str]): word_set = set() for token in tokens: chinese_word = len(token) > 1 and is_chinese(token) if chinese_word: word_set.add(token) word_list = list(word_set) return word_list def add_sub_symbol(bert_tokens: List[str], chinese_word_set: set()): if not chinese_word_set: return bert_tokens max_word_len = max([len(w) for w in chinese_word_set]) bert_word = bert_tokens start, end = 0, len(bert_word) while start < end: single_word = True if is_chinese(bert_word[start]): l = min(end - start, max_word_len) for i in range(l, 1, -1): whole_word = "".join(bert_word[start : start + i]) if whole_word in chinese_word_set: for j in range(start + 1, start + i): bert_word[j] = "##" + bert_word[j] start = start + i single_word = False break if single_word: start += 1 return bert_word def prepare_ref(lines: List[str], ltp_tokenizer: LTP, bert_tokenizer: BertTokenizer): ltp_res = [] for i in range(0, len(lines), 100): res = ltp_tokenizer.pipeline(lines[i : i + 100], tasks=["cws"]).cws res = [get_chinese_word(r) for r in res] ltp_res.extend(res) assert len(ltp_res) == len(lines) bert_res = [] for i in range(0, len(lines), 100): res = bert_tokenizer(lines[i : i + 100], add_special_tokens=True, truncation=True, max_length=512) bert_res.extend(res["input_ids"]) assert len(bert_res) == len(lines) ref_ids = [] for input_ids, chinese_word in zip(bert_res, ltp_res): input_tokens = [] for id in input_ids: token = bert_tokenizer._convert_id_to_token(id) input_tokens.append(token) input_tokens = add_sub_symbol(input_tokens, chinese_word) ref_id = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(input_tokens): if token[:2] == "##": clean_token = token[2:] # save chinese tokens' pos if len(clean_token) == 1 and _is_chinese_char(ord(clean_token)): ref_id.append(i) ref_ids.append(ref_id) assert len(ref_ids) == len(bert_res) return ref_ids def main(args): # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm) # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp) with open(args.file_name, "r", encoding="utf-8") as f: data = f.readlines() data = [line.strip() for line in data if len(line) > 0 and not line.isspace()] # avoid delimiter like '\u2029' ltp_tokenizer = LTP(args.ltp) # faster in GPU device bert_tokenizer = BertTokenizer.from_pretrained(args.bert) ref_ids = prepare_ref(data, ltp_tokenizer, bert_tokenizer) with open(args.save_path, "w", encoding="utf-8") as f: data = [json.dumps(ref) + "\n" for ref in ref_ids] f.writelines(data) if __name__ == "__main__": parser = argparse.ArgumentParser(description="prepare_chinese_ref") parser.add_argument( "--file_name", required=False, type=str, default="./resources/chinese-demo.txt", help="file need process, same as training data in lm", ) parser.add_argument( "--ltp", required=False, type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path", ) parser.add_argument( "--bert", required=False, type=str, default="./resources/robert", help="resources for Bert tokenizer", ) parser.add_argument( "--save_path", required=False, type=str, default="./resources/ref.txt", help="path to save res", ) args = parser.parse_args() main(args)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/vilt/test_feature_extraction_vilt.py

# coding=utf-8 # Copyright 2021 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import ViltFeatureExtractor class ViltFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, size_divisor=2, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): size = size if size is not None else {"shortest_edge": 30} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.size_divisor = size_divisor self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std def prepare_feat_extract_dict(self): return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, "size_divisor": self.size_divisor, } def get_expected_values(self, image_inputs, batched=False): """ This function computes the expected height and width when providing images to ViltFeatureExtractor, assuming do_resize is set to True with a scalar size and size_divisor. """ if not batched: size = self.size["shortest_edge"] image = image_inputs[0] if isinstance(image, Image.Image): w, h = image.size else: h, w = image.shape[1], image.shape[2] scale = size / min(w, h) if h < w: newh, neww = size, scale * w else: newh, neww = scale * h, size max_size = int((1333 / 800) * size) if max(newh, neww) > max_size: scale = max_size / max(newh, neww) newh = newh * scale neww = neww * scale newh, neww = int(newh + 0.5), int(neww + 0.5) expected_height, expected_width = ( newh // self.size_divisor * self.size_divisor, neww // self.size_divisor * self.size_divisor, ) else: expected_values = [] for image in image_inputs: expected_height, expected_width = self.get_expected_values([image]) expected_values.append((expected_height, expected_width)) expected_height = max(expected_values, key=lambda item: item[0])[0] expected_width = max(expected_values, key=lambda item: item[1])[1] return expected_height, expected_width @require_torch @require_vision class ViltFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = ViltFeatureExtractor if is_vision_available() else None def setUp(self): self.feature_extract_tester = ViltFeatureExtractionTester(self) @property def feat_extract_dict(self): return self.feature_extract_tester.prepare_feat_extract_dict() def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "image_mean")) self.assertTrue(hasattr(feature_extractor, "image_std")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "size")) self.assertTrue(hasattr(feature_extractor, "size_divisor")) def test_batch_feature(self): pass def test_call_pil(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PIL images image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs) self.assertEqual( encoded_images.shape, (1, self.feature_extract_tester.num_channels, expected_height, expected_width), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True) self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, expected_height, expected_width, ), ) def test_call_numpy(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random numpy tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True) for image in image_inputs: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs) self.assertEqual( encoded_images.shape, (1, self.feature_extract_tester.num_channels, expected_height, expected_width), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True) self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, expected_height, expected_width, ), ) def test_call_pytorch(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PyTorch tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs) self.assertEqual( encoded_images.shape, (1, self.feature_extract_tester.num_channels, expected_height, expected_width), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True) self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, expected_height, expected_width, ), ) def test_equivalence_pad_and_create_pixel_mask(self): # Initialize feature_extractors feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict) feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False, do_rescale=False) # create random PyTorch tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test whether the method "pad_and_return_pixel_mask" and calling the feature extractor return the same tensors encoded_images_with_method = feature_extractor_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt") encoded_images = feature_extractor_2(image_inputs, return_tensors="pt") self.assertTrue( torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4) ) self.assertTrue( torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4) )

# coding=utf-8 # Copyright 2021 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import ViltFeatureExtractor class ViltFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, size_divisor=2, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): size = size if size is not None else {"shortest_edge": 30} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.size_divisor = size_divisor self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std def prepare_feat_extract_dict(self): return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, "size_divisor": self.size_divisor, } def get_expected_values(self, image_inputs, batched=False): """ This function computes the expected height and width when providing images to ViltFeatureExtractor, assuming do_resize is set to True with a scalar size and size_divisor. """ if not batched: size = self.size["shortest_edge"] image = image_inputs[0] if isinstance(image, Image.Image): w, h = image.size else: h, w = image.shape[1], image.shape[2] scale = size / min(w, h) if h < w: newh, neww = size, scale * w else: newh, neww = scale * h, size max_size = int((1333 / 800) * size) if max(newh, neww) > max_size: scale = max_size / max(newh, neww) newh = newh * scale neww = neww * scale newh, neww = int(newh + 0.5), int(neww + 0.5) expected_height, expected_width = ( newh // self.size_divisor * self.size_divisor, neww // self.size_divisor * self.size_divisor, ) else: expected_values = [] for image in image_inputs: expected_height, expected_width = self.get_expected_values([image]) expected_values.append((expected_height, expected_width)) expected_height = max(expected_values, key=lambda item: item[0])[0] expected_width = max(expected_values, key=lambda item: item[1])[1] return expected_height, expected_width @require_torch @require_vision class ViltFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = ViltFeatureExtractor if is_vision_available() else None def setUp(self): self.feature_extract_tester = ViltFeatureExtractionTester(self) @property def feat_extract_dict(self): return self.feature_extract_tester.prepare_feat_extract_dict() def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "image_mean")) self.assertTrue(hasattr(feature_extractor, "image_std")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "size")) self.assertTrue(hasattr(feature_extractor, "size_divisor")) def test_batch_feature(self): pass def test_call_pil(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PIL images image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs) self.assertEqual( encoded_images.shape, (1, self.feature_extract_tester.num_channels, expected_height, expected_width), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True) self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, expected_height, expected_width, ), ) def test_call_numpy(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random numpy tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True) for image in image_inputs: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs) self.assertEqual( encoded_images.shape, (1, self.feature_extract_tester.num_channels, expected_height, expected_width), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True) self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, expected_height, expected_width, ), ) def test_call_pytorch(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PyTorch tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs) self.assertEqual( encoded_images.shape, (1, self.feature_extract_tester.num_channels, expected_height, expected_width), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True) self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, expected_height, expected_width, ), ) def test_equivalence_pad_and_create_pixel_mask(self): # Initialize feature_extractors feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict) feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False, do_rescale=False) # create random PyTorch tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test whether the method "pad_and_return_pixel_mask" and calling the feature extractor return the same tensors encoded_images_with_method = feature_extractor_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt") encoded_images = feature_extractor_2(image_inputs, return_tensors="pt") self.assertTrue( torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4) ) self.assertTrue( torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4) )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/jukebox/test_modeling_jukebox.py

# coding=utf-8 # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, slow from transformers.trainer_utils import set_seed if is_torch_available(): import torch from transformers import JukeboxModel, JukeboxTokenizer @require_torch class Jukebox1bModelTester(unittest.TestCase): all_model_classes = (JukeboxModel,) if is_torch_available() else () model_id = "openai/jukebox-1b-lyrics" metas = dict( artist="Zac Brown Band", genres="Country", lyrics="""I met a traveller from an antique land, Who said "Two vast and trunkless legs of stone Stand in the desert. . . . Near them, on the sand, Half sunk a shattered visage lies, whose frown, And wrinkled lip, and sneer of cold command, Tell that its sculptor well those passions read Which yet survive, stamped on these lifeless things, The hand that mocked them, and the heart that fed; And on the pedestal, these words appear: My name is Ozymandias, King of Kings; Look on my Works, ye Mighty, and despair! Nothing beside remains. Round the decay Of that colossal Wreck, boundless and bare The lone and level sands stretch far away """, ) # fmt: off EXPECTED_OUTPUT_2 = [ 1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534, 1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445, 333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883, 1405, 1276, 1455, 1228 ] EXPECTED_OUTPUT_1 = [ 1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416, 844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125, 191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872, 1262, 869, 1728, 747 ] EXPECTED_OUTPUT_0 = [ 1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442, 616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902, 293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008, 1186, 1015, 1777, 268 ] EXPECTED_Y_COND = [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76] EXPECTED_PRIMED_0 = [ 390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002, 1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002, 1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907, 1907, 1788, 596, 1626 ] EXPECTED_PRIMED_1 = [ 1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824, 1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599, 1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895, 1063, 1433, 1433, 1599 ] EXPECTED_PRIMED_2 = [ 1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927, 96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331, 1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619, 197, 391, 302, 1930 ] EXPECTED_VQVAE_ENCODE = [ 390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002, 1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002, 1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907, 1907, 1788, 596, 1626 ] EXPECTED_VQVAE_DECODE = [ -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664, -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025, 0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614, 0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336, 0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531 ] EXPECTED_AUDIO_COND = [ 0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440, 0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617, -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970, -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082 ] EXPECTED_META_COND = [ 0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068, 0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743, -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536, -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456 ] EXPECTED_LYRIC_COND = [ 76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76 ] # fmt: on def prepare_inputs(self): tokenizer = JukeboxTokenizer.from_pretrained(self.model_id) tokens = tokenizer(**self.metas)["input_ids"] return tokens @slow def test_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() labels = self.prepare_inputs() set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=40 * model.priors[0].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_OUTPUT_2)) set_seed(0) zs = model._sample(zs, labels, [1], sample_length=40 * model.priors[1].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[1][0], torch.tensor(self.EXPECTED_OUTPUT_1)) set_seed(0) zs = model._sample(zs, labels, [2], sample_length=40 * model.priors[2].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[2][0], torch.tensor(self.EXPECTED_OUTPUT_0)) @slow def test_conditioning(self): torch.backends.cuda.matmul.allow_tf32 = False model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() labels = self.prepare_inputs() set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long) for _ in range(3)] top_prior = model.priors[0] start = 0 music_token_conds = top_prior.get_music_tokens_conds(zs, start=start, end=start + top_prior.n_ctx) metadata = top_prior.get_metadata(labels[0].clone(), start, 1058304, 0) self.assertIsNone(music_token_conds) self.assertListEqual(metadata.numpy()[0][:10].tolist(), self.EXPECTED_Y_COND) audio_conditioning, metadata_conditioning, lyric_tokens = top_prior.get_cond(music_token_conds, metadata) torch.testing.assert_allclose( audio_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_AUDIO_COND), atol=1e-4, rtol=1e-4 ) torch.testing.assert_allclose( metadata_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_META_COND), atol=1e-4, rtol=1e-4 ) torch.testing.assert_allclose( lyric_tokens[0, :30].detach(), torch.tensor(self.EXPECTED_LYRIC_COND), atol=1e-4, rtol=1e-4 ) @slow def test_primed_sampling(self): torch.backends.cuda.matmul.allow_tf32 = False model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() set_seed(0) waveform = torch.rand((1, 5120, 1)) tokens = [i for i in self.prepare_inputs()] zs = [model.vqvae.encode(waveform, start_level=2, bs_chunks=waveform.shape[0])[0], None, None] zs = model._sample( zs, tokens, sample_levels=[0], save_results=False, sample_length=40 * model.priors[0].raw_to_tokens ) torch.testing.assert_allclose(zs[0][0][:40], torch.tensor(self.EXPECTED_PRIMED_0)) upper_2 = torch.cat((zs[0], torch.zeros(1, 2048 - zs[0].shape[-1])), dim=-1).long() zs = [upper_2, model.vqvae.encode(waveform, start_level=1, bs_chunks=waveform.shape[0])[0], None] zs = model._sample( zs, tokens, sample_levels=[1], save_results=False, sample_length=40 * model.priors[1].raw_to_tokens ) torch.testing.assert_allclose(zs[1][0][:40], torch.tensor(self.EXPECTED_PRIMED_1)) upper_1 = torch.cat((zs[1], torch.zeros(1, 2048 - zs[1].shape[-1])), dim=-1).long() zs = [upper_2, upper_1, model.vqvae.encode(waveform, start_level=0, bs_chunks=waveform.shape[0])[0]] zs = model._sample( zs, tokens, sample_levels=[2], save_results=False, sample_length=40 * model.priors[2].raw_to_tokens ) torch.testing.assert_allclose(zs[2][0][:40].cpu(), torch.tensor(self.EXPECTED_PRIMED_2)) @slow def test_vqvae(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() set_seed(0) x = torch.rand((1, 5120, 1)) with torch.no_grad(): zs = model.vqvae.encode(x, start_level=2, bs_chunks=x.shape[0]) torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_VQVAE_ENCODE)) with torch.no_grad(): x = model.vqvae.decode(zs, start_level=2, bs_chunks=x.shape[0]) torch.testing.assert_allclose(x[0, :40, 0], torch.tensor(self.EXPECTED_VQVAE_DECODE), atol=1e-4, rtol=1e-4) @require_torch class Jukebox5bModelTester(unittest.TestCase): all_model_classes = (JukeboxModel,) if is_torch_available() else () model_id = "openai/jukebox-5b-lyrics" metas = dict( artist="Zac Brown Band", genres="Country", lyrics="""I met a traveller from an antique land, Who said "Two vast and trunkless legs of stone Stand in the desert. . . . Near them, on the sand, Half sunk a shattered visage lies, whose frown, And wrinkled lip, and sneer of cold command, Tell that its sculptor well those passions read Which yet survive, stamped on these lifeless things, The hand that mocked them, and the heart that fed; And on the pedestal, these words appear: My name is Ozymandias, King of Kings; Look on my Works, ye Mighty, and despair! Nothing beside remains. Round the decay Of that colossal Wreck, boundless and bare The lone and level sands stretch far away """, ) # fmt: off EXPECTED_OUTPUT_2 = [ 1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272 ] EXPECTED_OUTPUT_1 = [ 1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416 ] EXPECTED_OUTPUT_0 = [ 1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616, 616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290, 290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755, 234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290, 1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616 ] EXPECTED_GPU_OUTPUTS_2 = [ 1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653 ] EXPECTED_GPU_OUTPUTS_1 = [ 1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416 ] EXPECTED_GPU_OUTPUTS_0 = [ 491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613, 844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613, 185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979, 307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842, 307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89 ] # fmt: on def prepare_inputs(self, model_id): tokenizer = JukeboxTokenizer.from_pretrained(model_id) tokens = tokenizer(**self.metas)["input_ids"] return tokens @slow def test_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() labels = self.prepare_inputs(self.model_id) set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_OUTPUT_2)) set_seed(0) zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[1][0], torch.tensor(self.EXPECTED_OUTPUT_1)) set_seed(0) zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[2][0], torch.tensor(self.EXPECTED_OUTPUT_0)) @slow def test_slow_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval().to("cuda") labels = [i.cuda() for i in self.prepare_inputs(self.model_id)] set_seed(0) model.priors[0].cuda() zs = [torch.zeros(1, 0, dtype=torch.long).cuda() for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[0][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2)) model.priors[0].cpu() set_seed(0) model.priors[1].cuda() zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[1][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_1)) model.priors[1].cpu() set_seed(0) model.priors[2].cuda() zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[2][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_0)) @slow def test_fp16_slow_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval().half().to("cuda") labels = [i.cuda() for i in self.prepare_inputs(self.model_id)] set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long).cuda() for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[0][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2))

# coding=utf-8 # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, slow from transformers.trainer_utils import set_seed if is_torch_available(): import torch from transformers import JukeboxModel, JukeboxTokenizer @require_torch class Jukebox1bModelTester(unittest.TestCase): all_model_classes = (JukeboxModel,) if is_torch_available() else () model_id = "openai/jukebox-1b-lyrics" metas = dict( artist="Zac Brown Band", genres="Country", lyrics="""I met a traveller from an antique land, Who said "Two vast and trunkless legs of stone Stand in the desert. . . . Near them, on the sand, Half sunk a shattered visage lies, whose frown, And wrinkled lip, and sneer of cold command, Tell that its sculptor well those passions read Which yet survive, stamped on these lifeless things, The hand that mocked them, and the heart that fed; And on the pedestal, these words appear: My name is Ozymandias, King of Kings; Look on my Works, ye Mighty, and despair! Nothing beside remains. Round the decay Of that colossal Wreck, boundless and bare The lone and level sands stretch far away """, ) # fmt: off EXPECTED_OUTPUT_2 = [ 1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534, 1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445, 333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883, 1405, 1276, 1455, 1228 ] EXPECTED_OUTPUT_1 = [ 1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416, 844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125, 191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872, 1262, 869, 1728, 747 ] EXPECTED_OUTPUT_0 = [ 1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442, 616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902, 293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008, 1186, 1015, 1777, 268 ] EXPECTED_Y_COND = [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76] EXPECTED_PRIMED_0 = [ 390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002, 1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002, 1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907, 1907, 1788, 596, 1626 ] EXPECTED_PRIMED_1 = [ 1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824, 1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599, 1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895, 1063, 1433, 1433, 1599 ] EXPECTED_PRIMED_2 = [ 1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927, 96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331, 1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619, 197, 391, 302, 1930 ] EXPECTED_VQVAE_ENCODE = [ 390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002, 1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002, 1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907, 1907, 1788, 596, 1626 ] EXPECTED_VQVAE_DECODE = [ -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664, -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025, 0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614, 0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336, 0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531 ] EXPECTED_AUDIO_COND = [ 0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440, 0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617, -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970, -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082 ] EXPECTED_META_COND = [ 0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068, 0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743, -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536, -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456 ] EXPECTED_LYRIC_COND = [ 76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76 ] # fmt: on def prepare_inputs(self): tokenizer = JukeboxTokenizer.from_pretrained(self.model_id) tokens = tokenizer(**self.metas)["input_ids"] return tokens @slow def test_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() labels = self.prepare_inputs() set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=40 * model.priors[0].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_OUTPUT_2)) set_seed(0) zs = model._sample(zs, labels, [1], sample_length=40 * model.priors[1].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[1][0], torch.tensor(self.EXPECTED_OUTPUT_1)) set_seed(0) zs = model._sample(zs, labels, [2], sample_length=40 * model.priors[2].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[2][0], torch.tensor(self.EXPECTED_OUTPUT_0)) @slow def test_conditioning(self): torch.backends.cuda.matmul.allow_tf32 = False model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() labels = self.prepare_inputs() set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long) for _ in range(3)] top_prior = model.priors[0] start = 0 music_token_conds = top_prior.get_music_tokens_conds(zs, start=start, end=start + top_prior.n_ctx) metadata = top_prior.get_metadata(labels[0].clone(), start, 1058304, 0) self.assertIsNone(music_token_conds) self.assertListEqual(metadata.numpy()[0][:10].tolist(), self.EXPECTED_Y_COND) audio_conditioning, metadata_conditioning, lyric_tokens = top_prior.get_cond(music_token_conds, metadata) torch.testing.assert_allclose( audio_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_AUDIO_COND), atol=1e-4, rtol=1e-4 ) torch.testing.assert_allclose( metadata_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_META_COND), atol=1e-4, rtol=1e-4 ) torch.testing.assert_allclose( lyric_tokens[0, :30].detach(), torch.tensor(self.EXPECTED_LYRIC_COND), atol=1e-4, rtol=1e-4 ) @slow def test_primed_sampling(self): torch.backends.cuda.matmul.allow_tf32 = False model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() set_seed(0) waveform = torch.rand((1, 5120, 1)) tokens = [i for i in self.prepare_inputs()] zs = [model.vqvae.encode(waveform, start_level=2, bs_chunks=waveform.shape[0])[0], None, None] zs = model._sample( zs, tokens, sample_levels=[0], save_results=False, sample_length=40 * model.priors[0].raw_to_tokens ) torch.testing.assert_allclose(zs[0][0][:40], torch.tensor(self.EXPECTED_PRIMED_0)) upper_2 = torch.cat((zs[0], torch.zeros(1, 2048 - zs[0].shape[-1])), dim=-1).long() zs = [upper_2, model.vqvae.encode(waveform, start_level=1, bs_chunks=waveform.shape[0])[0], None] zs = model._sample( zs, tokens, sample_levels=[1], save_results=False, sample_length=40 * model.priors[1].raw_to_tokens ) torch.testing.assert_allclose(zs[1][0][:40], torch.tensor(self.EXPECTED_PRIMED_1)) upper_1 = torch.cat((zs[1], torch.zeros(1, 2048 - zs[1].shape[-1])), dim=-1).long() zs = [upper_2, upper_1, model.vqvae.encode(waveform, start_level=0, bs_chunks=waveform.shape[0])[0]] zs = model._sample( zs, tokens, sample_levels=[2], save_results=False, sample_length=40 * model.priors[2].raw_to_tokens ) torch.testing.assert_allclose(zs[2][0][:40].cpu(), torch.tensor(self.EXPECTED_PRIMED_2)) @slow def test_vqvae(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() set_seed(0) x = torch.rand((1, 5120, 1)) with torch.no_grad(): zs = model.vqvae.encode(x, start_level=2, bs_chunks=x.shape[0]) torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_VQVAE_ENCODE)) with torch.no_grad(): x = model.vqvae.decode(zs, start_level=2, bs_chunks=x.shape[0]) torch.testing.assert_allclose(x[0, :40, 0], torch.tensor(self.EXPECTED_VQVAE_DECODE), atol=1e-4, rtol=1e-4) @require_torch class Jukebox5bModelTester(unittest.TestCase): all_model_classes = (JukeboxModel,) if is_torch_available() else () model_id = "openai/jukebox-5b-lyrics" metas = dict( artist="Zac Brown Band", genres="Country", lyrics="""I met a traveller from an antique land, Who said "Two vast and trunkless legs of stone Stand in the desert. . . . Near them, on the sand, Half sunk a shattered visage lies, whose frown, And wrinkled lip, and sneer of cold command, Tell that its sculptor well those passions read Which yet survive, stamped on these lifeless things, The hand that mocked them, and the heart that fed; And on the pedestal, these words appear: My name is Ozymandias, King of Kings; Look on my Works, ye Mighty, and despair! Nothing beside remains. Round the decay Of that colossal Wreck, boundless and bare The lone and level sands stretch far away """, ) # fmt: off EXPECTED_OUTPUT_2 = [ 1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272 ] EXPECTED_OUTPUT_1 = [ 1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416 ] EXPECTED_OUTPUT_0 = [ 1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616, 616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290, 290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755, 234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290, 1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616 ] EXPECTED_GPU_OUTPUTS_2 = [ 1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653 ] EXPECTED_GPU_OUTPUTS_1 = [ 1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416 ] EXPECTED_GPU_OUTPUTS_0 = [ 491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613, 844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613, 185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979, 307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842, 307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89 ] # fmt: on def prepare_inputs(self, model_id): tokenizer = JukeboxTokenizer.from_pretrained(model_id) tokens = tokenizer(**self.metas)["input_ids"] return tokens @slow def test_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() labels = self.prepare_inputs(self.model_id) set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_OUTPUT_2)) set_seed(0) zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[1][0], torch.tensor(self.EXPECTED_OUTPUT_1)) set_seed(0) zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[2][0], torch.tensor(self.EXPECTED_OUTPUT_0)) @slow def test_slow_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval().to("cuda") labels = [i.cuda() for i in self.prepare_inputs(self.model_id)] set_seed(0) model.priors[0].cuda() zs = [torch.zeros(1, 0, dtype=torch.long).cuda() for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[0][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2)) model.priors[0].cpu() set_seed(0) model.priors[1].cuda() zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[1][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_1)) model.priors[1].cpu() set_seed(0) model.priors[2].cuda() zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[2][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_0)) @slow def test_fp16_slow_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval().half().to("cuda") labels = [i.cuda() for i in self.prepare_inputs(self.model_id)] set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long).cuda() for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False) torch.testing.assert_allclose(zs[0][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2))

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/perceiver/image_processing_perceiver.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for Perceiver.""" from typing import Dict, List, Optional, Union import numpy as np from transformers.utils import is_vision_available from transformers.utils.generic import TensorType from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format from ...image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, is_batched, to_numpy_array, valid_images, ) from ...utils import logging if is_vision_available(): import PIL logger = logging.get_logger(__name__) class PerceiverImageProcessor(BaseImageProcessor): r""" Constructs a Perceiver image processor. Args: do_center_crop (`bool`, `optional`, defaults to `True`): Whether or not to center crop the image. If the input size if smaller than `crop_size` along any edge, the image will be padded with zeros and then center cropped. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 256, "width": 256}`): Desired output size when applying center-cropping. Can be overridden by the `crop_size` parameter in the `preprocess` method. do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image to `(size["height"], size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Defines the resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize: Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_center_crop: bool = True, crop_size: Dict[str, int] = None, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BICUBIC, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, **kwargs ) -> None: super().__init__(**kwargs) crop_size = crop_size if crop_size is not None else {"height": 256, "width": 256} crop_size = get_size_dict(crop_size, param_name="crop_size") size = size if size is not None else {"height": 224, "width": 224} size = get_size_dict(size) self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_resize = do_resize self.size = size self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD def center_crop( self, image: np.ndarray, crop_size: Dict[str, int], size: Optional[int] = None, data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Center crop an image to `(size["height"] / crop_size["height"] * min_dim, size["width"] / crop_size["width"] * min_dim)`. Where `min_dim = min(size["height"], size["width"])`. If the input size is smaller than `crop_size` along any edge, the image will be padded with zeros and then center cropped. Args: image (`np.ndarray`): Image to center crop. crop_size (`Dict[str, int]`): Desired output size after applying the center crop. size (`Dict[str, int]`, *optional*): Size of the image after resizing. If not provided, the self.size attribute will be used. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size = self.size if size is None else size size = get_size_dict(size) crop_size = get_size_dict(crop_size, param_name="crop_size") height, width = get_image_size(image) min_dim = min(height, width) cropped_height = (size["height"] / crop_size["height"]) * min_dim cropped_width = (size["width"] / crop_size["width"]) * min_dim return center_crop(image, size=(cropped_height, cropped_width), data_format=data_format, **kwargs) def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PIL.Image.BICUBIC, data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PIL.Image.BICUBIC`): Resampling filter to use when resizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}") return resize( image, size=(size["height"], size["width"]), resample=resample, data_format=data_format, **kwargs ) def rescale( self, image: np.ndarray, scale: Union[int, float], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ): """ Rescale an image by a scale factor. image = image * scale. Args: image (`np.ndarray`): Image to rescale. scale (`int` or `float`): Scale to apply to the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return rescale(image, scale=scale, data_format=data_format, **kwargs) def normalize( self, image: np.ndarray, mean: Union[float, List[float]], std: Union[float, List[float]], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Normalize an image. image = (image - image_mean) / image_std. Args: image (`np.ndarray`): Image to normalize. mean (`float` or `List[float]`): Image mean. std (`float` or `List[float]`): Image standard deviation. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs) def preprocess( self, images: ImageInput, do_center_crop: Optional[bool] = None, crop_size: Optional[Dict[str, int]] = None, do_resize: Optional[bool] = None, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, **kwargs, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image to `crop_size`. crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): Desired output size after applying the center crop. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after resizing. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. """ do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop crop_size = crop_size if crop_size is not None else self.crop_size crop_size = get_size_dict(crop_size, param_name="crop_size") do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size size = get_size_dict(size) resample = resample if resample is not None else self.resample do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_normalize = do_normalize if do_normalize is not None else self.do_normalize image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std if not is_batched(images): images = [images] if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) if do_center_crop and crop_size is None: raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided.") if do_resize and size is None: raise ValueError("Size must be specified if do_resize is True.") if do_rescale and rescale_factor is None: raise ValueError("Rescale factor must be specified if do_rescale is True.") if do_normalize and (image_mean is None or image_std is None): raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True.") # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if do_center_crop: images = [self.center_crop(image, crop_size, size=size) for image in images] if do_resize: images = [self.resize(image=image, size=size, resample=resample) for image in images] if do_rescale: images = [self.rescale(image=image, scale=rescale_factor) for image in images] if do_normalize: images = [self.normalize(image=image, mean=image_mean, std=image_std) for image in images] images = [to_channel_dimension_format(image, data_format) for image in images] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for Perceiver.""" from typing import Dict, List, Optional, Union import numpy as np from transformers.utils import is_vision_available from transformers.utils.generic import TensorType from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format from ...image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, is_batched, to_numpy_array, valid_images, ) from ...utils import logging if is_vision_available(): import PIL logger = logging.get_logger(__name__) class PerceiverImageProcessor(BaseImageProcessor): r""" Constructs a Perceiver image processor. Args: do_center_crop (`bool`, `optional`, defaults to `True`): Whether or not to center crop the image. If the input size if smaller than `crop_size` along any edge, the image will be padded with zeros and then center cropped. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 256, "width": 256}`): Desired output size when applying center-cropping. Can be overridden by the `crop_size` parameter in the `preprocess` method. do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image to `(size["height"], size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Defines the resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize: Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_center_crop: bool = True, crop_size: Dict[str, int] = None, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BICUBIC, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, **kwargs ) -> None: super().__init__(**kwargs) crop_size = crop_size if crop_size is not None else {"height": 256, "width": 256} crop_size = get_size_dict(crop_size, param_name="crop_size") size = size if size is not None else {"height": 224, "width": 224} size = get_size_dict(size) self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_resize = do_resize self.size = size self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD def center_crop( self, image: np.ndarray, crop_size: Dict[str, int], size: Optional[int] = None, data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Center crop an image to `(size["height"] / crop_size["height"] * min_dim, size["width"] / crop_size["width"] * min_dim)`. Where `min_dim = min(size["height"], size["width"])`. If the input size is smaller than `crop_size` along any edge, the image will be padded with zeros and then center cropped. Args: image (`np.ndarray`): Image to center crop. crop_size (`Dict[str, int]`): Desired output size after applying the center crop. size (`Dict[str, int]`, *optional*): Size of the image after resizing. If not provided, the self.size attribute will be used. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size = self.size if size is None else size size = get_size_dict(size) crop_size = get_size_dict(crop_size, param_name="crop_size") height, width = get_image_size(image) min_dim = min(height, width) cropped_height = (size["height"] / crop_size["height"]) * min_dim cropped_width = (size["width"] / crop_size["width"]) * min_dim return center_crop(image, size=(cropped_height, cropped_width), data_format=data_format, **kwargs) def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PIL.Image.BICUBIC, data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PIL.Image.BICUBIC`): Resampling filter to use when resizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}") return resize( image, size=(size["height"], size["width"]), resample=resample, data_format=data_format, **kwargs ) def rescale( self, image: np.ndarray, scale: Union[int, float], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ): """ Rescale an image by a scale factor. image = image * scale. Args: image (`np.ndarray`): Image to rescale. scale (`int` or `float`): Scale to apply to the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return rescale(image, scale=scale, data_format=data_format, **kwargs) def normalize( self, image: np.ndarray, mean: Union[float, List[float]], std: Union[float, List[float]], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Normalize an image. image = (image - image_mean) / image_std. Args: image (`np.ndarray`): Image to normalize. mean (`float` or `List[float]`): Image mean. std (`float` or `List[float]`): Image standard deviation. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs) def preprocess( self, images: ImageInput, do_center_crop: Optional[bool] = None, crop_size: Optional[Dict[str, int]] = None, do_resize: Optional[bool] = None, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, **kwargs, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image to `crop_size`. crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): Desired output size after applying the center crop. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after resizing. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. """ do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop crop_size = crop_size if crop_size is not None else self.crop_size crop_size = get_size_dict(crop_size, param_name="crop_size") do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size size = get_size_dict(size) resample = resample if resample is not None else self.resample do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_normalize = do_normalize if do_normalize is not None else self.do_normalize image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std if not is_batched(images): images = [images] if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) if do_center_crop and crop_size is None: raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided.") if do_resize and size is None: raise ValueError("Size must be specified if do_resize is True.") if do_rescale and rescale_factor is None: raise ValueError("Rescale factor must be specified if do_rescale is True.") if do_normalize and (image_mean is None or image_std is None): raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True.") # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if do_center_crop: images = [self.center_crop(image, crop_size, size=size) for image in images] if do_resize: images = [self.resize(image=image, size=size, resample=resample) for image in images] if do_rescale: images = [self.rescale(image=image, scale=rescale_factor) for image in images] if do_normalize: images = [self.normalize(image=image, mean=image_mean, std=image_std) for image in images] images = [to_channel_dimension_format(image, data_format) for image in images] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/mbart50/__init__.py

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/t5/modeling_t5.py

# coding=utf-8 # Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch T5 model.""" import copy import math import os import warnings from typing import Optional, Tuple, Union import torch from torch import nn from torch.nn import CrossEntropyLoss from torch.utils.checkpoint import checkpoint from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import ALL_LAYERNORM_LAYERS, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging, replace_return_docstrings, ) from ...utils.model_parallel_utils import assert_device_map, get_device_map from .configuration_t5 import T5Config logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "T5Config" _TOKENIZER_FOR_DOC = "T5Tokenizer" _CHECKPOINT_FOR_DOC = "t5-small" #################################################### # This dict contains ids and associated url # for the pretrained weights provided with the models #################################################### T5_PRETRAINED_MODEL_ARCHIVE_LIST = [ "t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b", # See all T5 models at https://huggingface.co/models?filter=t5 ] #################################################### # This is a conversion method from TF 1.0 to PyTorch # More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28 #################################################### def load_tf_weights_in_t5(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] tf_weights = {} for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) tf_weights[name] = array for txt_name in names: name = txt_name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any( n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name ): logger.info(f"Skipping {'/'.join(name)}") tf_weights.pop(txt_name, None) continue if "_slot_" in name[-1]: logger.info(f"Skipping {'/'.join(name)}") tf_weights.pop(txt_name, None) continue pointer = model array = tf_weights[txt_name] for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] in ["kernel", "scale", "embedding"]: pointer = getattr(pointer, "weight") elif scope_names[0] == "self_attention": pointer = getattr(pointer, "layer") pointer = pointer[0] elif scope_names[0] == "enc_dec_attention": pointer = getattr(pointer, "layer") pointer = pointer[1] elif scope_names[0] == "dense_relu_dense": pointer = getattr(pointer, "layer") pointer = pointer[2] elif scope_names[0] == "rms_norm": if hasattr(pointer, "layer_norm"): pointer = getattr(pointer, "layer_norm") elif hasattr(pointer, "final_layer_norm"): pointer = getattr(pointer, "final_layer_norm") elif scope_names[0] == "scale": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier") elif scope_names[0] == "decoder" and name[1] == "logits": continue elif scope_names[0] == "logits": pointer = getattr(pointer, "lm_head") elif scope_names[0] == "wi" and len(scope_names) > 1 and scope_names[1].isdigit(): pointer = getattr(pointer, f"wi_{scope_names[1]}") continue else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if scope_names[0] not in ["kernel", "scale", "embedding"]: pointer = getattr(pointer, "weight") if scope_names[0] != "embedding": logger.info(f"Transposing numpy weight of shape {array.shape} for {name}") array = np.transpose(array) try: assert ( pointer.shape == array.shape ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array.astype(np.float32)) tf_weights.pop(txt_name, None) logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}.") return model #################################################### # PyTorch Models are constructed by sub-classing # - torch.nn.Module for the layers and # - PreTrainedModel for the models (it-self a sub-class of nn.Module) #################################################### PARALLELIZE_DOCSTRING = r""" This is an experimental feature and is a subject to change at a moment's notice. Uses a device map to distribute attention modules of the model across several devices. If no device map is given, it will evenly distribute blocks across all devices. Args: device_map (`Dict[int, list]`, optional, defaults to None): A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always automatically mapped to the first device (for esoteric reasons). That means that the first device should have fewer attention modules mapped to it than other devices. For reference, the t5 models have the following number of attention modules: - t5-small: 6 - t5-base: 12 - t5-large: 24 - t5-3b: 24 - t5-11b: 24 Example: ```python # Here is an example of a device map on a machine with 4 GPUs using t5-3b, which has a total of 24 attention modules: model = T5ForConditionalGeneration.from_pretrained("t5-3b") device_map = { 0: [0, 1, 2], 1: [3, 4, 5, 6, 7, 8, 9], 2: [10, 11, 12, 13, 14, 15, 16], 3: [17, 18, 19, 20, 21, 22, 23], } model.parallelize(device_map) ``` """ DEPARALLELIZE_DOCSTRING = r""" Moves the model to cpu from a model parallel state. Example: ```python # On a 4 GPU machine with t5-3b: model = T5ForConditionalGeneration.from_pretrained("t5-3b") device_map = { 0: [0, 1, 2], 1: [3, 4, 5, 6, 7, 8, 9], 2: [10, 11, 12, 13, 14, 15, 16], 3: [17, 18, 19, 20, 21, 22, 23], } model.parallelize(device_map) # Splits the model across several devices model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache() ``` """ class T5LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Construct a layernorm module in the T5 style. No bias and no subtraction of mean. """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for # half-precision inputs is done in fp32 variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) # convert into half-precision if necessary if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states try: from apex.normalization import FusedRMSNorm T5LayerNorm = FusedRMSNorm # noqa logger.info("Discovered apex.normalization.FusedRMSNorm - will use it instead of T5LayerNorm") except ImportError: # using the normal T5LayerNorm pass except Exception: logger.warning("discovered apex but it failed to load, falling back to T5LayerNorm") pass ALL_LAYERNORM_LAYERS.append(T5LayerNorm) class T5DenseActDense(nn.Module): def __init__(self, config: T5Config): super().__init__() self.wi = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.act = ACT2FN[config.dense_act_fn] def forward(self, hidden_states): hidden_states = self.wi(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.wo(hidden_states) return hidden_states class T5DenseGatedActDense(nn.Module): def __init__(self, config: T5Config): super().__init__() self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.act = ACT2FN[config.dense_act_fn] def forward(self, hidden_states): hidden_gelu = self.act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) hidden_states = self.wo(hidden_states) return hidden_states class T5LayerFF(nn.Module): def __init__(self, config: T5Config): super().__init__() if config.is_gated_act: self.DenseReluDense = T5DenseGatedActDense(config) else: self.DenseReluDense = T5DenseActDense(config) self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward(self, hidden_states): forwarded_states = self.layer_norm(hidden_states) forwarded_states = self.DenseReluDense(forwarded_states) hidden_states = hidden_states + self.dropout(forwarded_states) return hidden_states class T5Attention(nn.Module): def __init__(self, config: T5Config, has_relative_attention_bias=False): super().__init__() self.is_decoder = config.is_decoder self.has_relative_attention_bias = has_relative_attention_bias self.relative_attention_num_buckets = config.relative_attention_num_buckets self.relative_attention_max_distance = config.relative_attention_max_distance self.d_model = config.d_model self.key_value_proj_dim = config.d_kv self.n_heads = config.num_heads self.dropout = config.dropout_rate self.inner_dim = self.n_heads * self.key_value_proj_dim # Mesh TensorFlow initialization to avoid scaling before softmax self.q = nn.Linear(self.d_model, self.inner_dim, bias=False) self.k = nn.Linear(self.d_model, self.inner_dim, bias=False) self.v = nn.Linear(self.d_model, self.inner_dim, bias=False) self.o = nn.Linear(self.inner_dim, self.d_model, bias=False) if self.has_relative_attention_bias: self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads) self.pruned_heads = set() self.gradient_checkpointing = False def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads ) # Prune linear layers self.q = prune_linear_layer(self.q, index) self.k = prune_linear_layer(self.k, index) self.v = prune_linear_layer(self.v, index) self.o = prune_linear_layer(self.o, index, dim=1) # Update hyper params self.n_heads = self.n_heads - len(heads) self.inner_dim = self.key_value_proj_dim * self.n_heads self.pruned_heads = self.pruned_heads.union(heads) @staticmethod def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ relative_buckets = 0 if bidirectional: num_buckets //= 2 relative_buckets += (relative_position > 0).to(torch.long) * num_buckets relative_position = torch.abs(relative_position) else: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) # now relative_position is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = relative_position < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance relative_position_if_large = max_exact + ( torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) relative_position_if_large = torch.min( relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) ) relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) return relative_buckets def compute_bias(self, query_length, key_length, device=None): """Compute binned relative position bias""" if device is None: device = self.relative_attention_bias.weight.device context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] relative_position = memory_position - context_position # shape (query_length, key_length) relative_position_bucket = self._relative_position_bucket( relative_position, # shape (query_length, key_length) bidirectional=(not self.is_decoder), num_buckets=self.relative_attention_num_buckets, max_distance=self.relative_attention_max_distance, ) values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads) values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) return values def forward( self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, output_attentions=False, ): """ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). """ # Input is (batch_size, seq_length, dim) # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length) # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head) batch_size, seq_length = hidden_states.shape[:2] real_seq_length = seq_length if past_key_value is not None: assert ( len(past_key_value) == 2 ), f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states" real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length key_length = real_seq_length if key_value_states is None else key_value_states.shape[1] def shape(states): """projection""" return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) def unshape(states): """reshape""" return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim) def project(hidden_states, proj_layer, key_value_states, past_key_value): """projects hidden states correctly to key/query states""" if key_value_states is None: # self-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(hidden_states)) elif past_key_value is None: # cross-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(key_value_states)) if past_key_value is not None: if key_value_states is None: # self-attn # (batch_size, n_heads, key_length, dim_per_head) hidden_states = torch.cat([past_key_value, hidden_states], dim=2) else: # cross-attn hidden_states = past_key_value return hidden_states # get query states query_states = shape(self.q(hidden_states)) # (batch_size, n_heads, seq_length, dim_per_head) # get key/value states key_states = project( hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None ) value_states = project( hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None ) # compute scores scores = torch.matmul( query_states, key_states.transpose(3, 2) ) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 if position_bias is None: if not self.has_relative_attention_bias: position_bias = torch.zeros( (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype ) if self.gradient_checkpointing and self.training: position_bias.requires_grad = True else: position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device) # if key and values are already calculated # we want only the last query position bias if past_key_value is not None: position_bias = position_bias[:, :, -hidden_states.size(1) :, :] if mask is not None: position_bias = position_bias + mask # (batch_size, n_heads, seq_length, key_length) if self.pruned_heads: mask = torch.ones(position_bias.shape[1]) mask[list(self.pruned_heads)] = 0 position_bias_masked = position_bias[:, mask.bool()] else: position_bias_masked = position_bias scores += position_bias_masked attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as( scores ) # (batch_size, n_heads, seq_length, key_length) attn_weights = nn.functional.dropout( attn_weights, p=self.dropout, training=self.training ) # (batch_size, n_heads, seq_length, key_length) # Mask heads if we want to if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask attn_output = unshape(torch.matmul(attn_weights, value_states)) # (batch_size, seq_length, dim) attn_output = self.o(attn_output) present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None outputs = (attn_output,) + (present_key_value_state,) + (position_bias,) if output_attentions: outputs = outputs + (attn_weights,) return outputs class T5LayerSelfAttention(nn.Module): def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias) self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.SelfAttention( normed_hidden_states, mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = hidden_states + self.dropout(attention_output[0]) outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them return outputs class T5LayerCrossAttention(nn.Module): def __init__(self, config): super().__init__() self.EncDecAttention = T5Attention(config, has_relative_attention_bias=False) self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, key_value_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, query_length=None, output_attentions=False, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.EncDecAttention( normed_hidden_states, mask=attention_mask, key_value_states=key_value_states, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, query_length=query_length, output_attentions=output_attentions, ) layer_output = hidden_states + self.dropout(attention_output[0]) outputs = (layer_output,) + attention_output[1:] # add attentions if we output them return outputs class T5Block(nn.Module): def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.is_decoder = config.is_decoder self.layer = nn.ModuleList() self.layer.append(T5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias)) if self.is_decoder: self.layer.append(T5LayerCrossAttention(config)) self.layer.append(T5LayerFF(config)) def forward( self, hidden_states, attention_mask=None, position_bias=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, return_dict=True, ): if past_key_value is not None: if not self.is_decoder: logger.warning("`past_key_values` is passed to the encoder. Please make sure this is intended.") expected_num_past_key_values = 2 if encoder_hidden_states is None else 4 if len(past_key_value) != expected_num_past_key_values: raise ValueError( f"There should be {expected_num_past_key_values} past states. " f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}" f"Got {len(past_key_value)} past key / value states" ) self_attn_past_key_value = past_key_value[:2] cross_attn_past_key_value = past_key_value[2:] else: self_attn_past_key_value, cross_attn_past_key_value = None, None self_attention_outputs = self.layer[0]( hidden_states, attention_mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=self_attn_past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states, present_key_value_state = self_attention_outputs[:2] attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) do_cross_attention = self.is_decoder and encoder_hidden_states is not None if do_cross_attention: # the actual query length is unknown for cross attention # if using past key value states. Need to inject it here if present_key_value_state is not None: query_length = present_key_value_state[0].shape[2] else: query_length = None cross_attention_outputs = self.layer[1]( hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, position_bias=encoder_decoder_position_bias, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, query_length=query_length, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = cross_attention_outputs[0] # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) # Combine self attn and cross attn key value states if present_key_value_state is not None: present_key_value_state = present_key_value_state + cross_attention_outputs[1] # Keep cross-attention outputs and relative position weights attention_outputs = attention_outputs + cross_attention_outputs[2:] # Apply Feed Forward layer hidden_states = self.layer[-1](hidden_states) # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if use_cache: outputs = outputs + (present_key_value_state,) + attention_outputs else: outputs = outputs + attention_outputs return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) class T5PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = T5Config load_tf_weights = load_tf_weights_in_t5 base_model_prefix = "transformer" is_parallelizable = True supports_gradient_checkpointing = True _no_split_modules = ["T5Block"] @property def dummy_inputs(self): input_ids = torch.tensor(DUMMY_INPUTS) input_mask = torch.tensor(DUMMY_MASK) dummy_inputs = { "decoder_input_ids": input_ids, "input_ids": input_ids, "decoder_attention_mask": input_mask, } return dummy_inputs def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor # Used for testing weights initialization if isinstance(module, T5LayerNorm): module.weight.data.fill_(factor * 1.0) elif isinstance(module, (T5Model, T5ForConditionalGeneration, T5EncoderModel)): # Mesh TensorFlow embeddings initialization # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624 module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0) if hasattr(module, "lm_head") and not self.config.tie_word_embeddings: module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, T5DenseActDense): # Mesh TensorFlow FF initialization # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56 # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89 module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi, "bias") and module.wi.bias is not None: module.wi.bias.data.zero_() module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, T5DenseGatedActDense): module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None: module.wi_0.bias.data.zero_() module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None: module.wi_1.bias.data.zero_() module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, T5Attention): # Mesh TensorFlow attention initialization to avoid scaling before softmax # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136 d_model = self.config.d_model key_value_proj_dim = self.config.d_kv n_heads = self.config.num_heads module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5)) module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5)) if module.has_relative_attention_bias: module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5)) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (T5Attention, T5Stack)): module.gradient_checkpointing = value def _shift_right(self, input_ids): decoder_start_token_id = self.config.decoder_start_token_id pad_token_id = self.config.pad_token_id assert decoder_start_token_id is not None, ( "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id." " See T5 docs for more information" ) # shift inputs to the right if is_torch_fx_proxy(input_ids): # Item assignment is not supported natively for proxies. shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id) shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1) else: shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = decoder_start_token_id assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined." # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids class T5Stack(T5PreTrainedModel): def __init__(self, config, embed_tokens=None): super().__init__(config) self.embed_tokens = embed_tokens self.is_decoder = config.is_decoder self.block = nn.ModuleList( [T5Block(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)] ) self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None self.gradient_checkpointing = False @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): # Check validity of device_map self.device_map = ( get_device_map(len(self.block), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.block)) self.model_parallel = True self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys())) self.last_device = "cuda:" + str(max(self.device_map.keys())) # Load onto devices for k, v in self.device_map.items(): for layer in v: cuda_device = "cuda:" + str(k) self.block[layer] = self.block[layer].to(cuda_device) # Set embed_tokens to first layer self.embed_tokens = self.embed_tokens.to(self.first_device) # Set final layer norm to last device self.final_layer_norm = self.final_layer_norm.to(self.last_device) @add_start_docstrings(PARALLELIZE_DOCSTRING) def deparallelize(self): self.model_parallel = False self.device_map = None self.first_device = "cpu" self.last_device = "cpu" for i in range(len(self.block)): self.block[i] = self.block[i].to("cpu") self.embed_tokens = self.embed_tokens.to("cpu") self.final_layer_norm = self.final_layer_norm.to("cpu") torch.cuda.empty_cache() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, new_embeddings): self.embed_tokens = new_embeddings def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): # Model parallel if self.model_parallel: torch.cuda.set_device(self.first_device) self.embed_tokens = self.embed_tokens.to(self.first_device) use_cache = use_cache if use_cache is not None else self.config.use_cache output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError( f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time" ) elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds") if inputs_embeds is None: assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings" inputs_embeds = self.embed_tokens(input_ids) batch_size, seq_length = input_shape # required mask seq length can be calculated via length of past mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length if use_cache is True: assert self.is_decoder, f"`use_cache` can only be set to `True` if {self} is used as a decoder" if attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None: encoder_seq_length = encoder_hidden_states.shape[1] encoder_attention_mask = torch.ones( batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long ) # initialize past_key_values with `None` if past does not exist if past_key_values is None: past_key_values = [None] * len(self.block) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed head_mask = self.get_head_mask(head_mask, self.config.num_layers) cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers) present_key_value_states = () if use_cache else None all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if (output_attentions and self.is_decoder) else None position_bias = None encoder_decoder_position_bias = None hidden_states = self.dropout(inputs_embeds) for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)): layer_head_mask = head_mask[i] cross_attn_layer_head_mask = cross_attn_head_mask[i] # Model parallel if self.model_parallel: torch.cuda.set_device(hidden_states.device) # Ensure that attention_mask is always on the same device as hidden_states if attention_mask is not None: attention_mask = attention_mask.to(hidden_states.device) if position_bias is not None: position_bias = position_bias.to(hidden_states.device) if encoder_hidden_states is not None: encoder_hidden_states = encoder_hidden_states.to(hidden_states.device) if encoder_extended_attention_mask is not None: encoder_extended_attention_mask = encoder_extended_attention_mask.to(hidden_states.device) if encoder_decoder_position_bias is not None: encoder_decoder_position_bias = encoder_decoder_position_bias.to(hidden_states.device) if layer_head_mask is not None: layer_head_mask = layer_head_mask.to(hidden_states.device) if cross_attn_layer_head_mask is not None: cross_attn_layer_head_mask = cross_attn_layer_head_mask.to(hidden_states.device) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return tuple(module(*inputs, use_cache, output_attentions)) return custom_forward layer_outputs = checkpoint( create_custom_forward(layer_module), hidden_states, extended_attention_mask, position_bias, encoder_hidden_states, encoder_extended_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, None, # past_key_value is always None with gradient checkpointing ) else: layer_outputs = layer_module( hidden_states, attention_mask=extended_attention_mask, position_bias=position_bias, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, layer_head_mask=layer_head_mask, cross_attn_layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) # layer_outputs is a tuple with: # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) if use_cache is False: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:] hidden_states, present_key_value_state = layer_outputs[:2] # We share the position biases between the layers - the first layer store them # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), # (cross-attention position bias), (cross-attention weights) position_bias = layer_outputs[2] if self.is_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3] # append next layer key value states if use_cache: present_key_value_states = present_key_value_states + (present_key_value_state,) if output_attentions: all_attentions = all_attentions + (layer_outputs[3],) if self.is_decoder: all_cross_attentions = all_cross_attentions + (layer_outputs[5],) # Model Parallel: If it's the last layer for that device, put things on the next device if self.model_parallel: for k, v in self.device_map.items(): if i == v[-1] and "cuda:" + str(k) != self.last_device: hidden_states = hidden_states.to("cuda:" + str(k + 1)) hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.dropout(hidden_states) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, present_key_value_states, all_hidden_states, all_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_value_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) T5_START_DOCSTRING = r""" The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting. This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`T5Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ T5_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. [What are input IDs?](../glossary#input-ids) To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training). attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5 Training](./t5#training). decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ T5_ENCODER_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training). attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask __HEAD_MASK_WARNING_MSG = """ The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently, `decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions. If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers, num_heads)`. """ @add_start_docstrings( "The bare T5 Model transformer outputting raw hidden-states without any specific head on top.", T5_START_DOCSTRING, ) class T5Model(T5PreTrainedModel): _keys_to_ignore_on_load_missing = [ r"encoder.embed_tokens.weight", r"decoder.embed_tokens.weight", ] _keys_to_ignore_on_load_unexpected = [ r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight", ] def __init__(self, config: T5Config): super().__init__(config) self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) encoder_config.is_decoder = False encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = T5Stack(encoder_config, self.shared) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False decoder_config.num_layers = config.num_decoder_layers self.decoder = T5Stack(decoder_config, self.shared) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): self.device_map = ( get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.encoder.block)) self.encoder.parallelize(self.device_map) self.decoder.parallelize(self.device_map) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): self.encoder.deparallelize() self.decoder.deparallelize() self.encoder = self.encoder.to("cpu") self.decoder = self.decoder.to("cpu") self.model_parallel = False self.device_map = None torch.cuda.empty_cache() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) self.decoder.set_input_embeddings(new_embeddings) def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.Tensor] = None, decoder_inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]: r""" Returns: Example: ```python >>> from transformers import T5Tokenizer, T5Model >>> tokenizer = T5Tokenizer.from_pretrained("t5-small") >>> model = T5Model.from_pretrained("t5-small") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model. >>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg. >>> decoder_input_ids = model._shift_right(decoder_input_ids) >>> # forward pass >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) >>> last_hidden_states = outputs.last_hidden_state ```""" use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask if head_mask is not None and decoder_head_mask is None: if self.config.num_layers == self.config.num_decoder_layers: warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) decoder_head_mask = head_mask # Encode if needed (training, first prediction pass) if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) hidden_states = encoder_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.decoder.first_device) hidden_states = hidden_states.to(self.decoder.first_device) if decoder_input_ids is not None: decoder_input_ids = decoder_input_ids.to(self.decoder.first_device) if attention_mask is not None: attention_mask = attention_mask.to(self.decoder.first_device) if decoder_attention_mask is not None: decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device) # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings("""T5 Model with a `language modeling` head on top.""", T5_START_DOCSTRING) class T5ForConditionalGeneration(T5PreTrainedModel): _keys_to_ignore_on_load_missing = [ r"encoder.embed_tokens.weight", r"decoder.embed_tokens.weight", r"lm_head.weight", ] _keys_to_ignore_on_load_unexpected = [ r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight", ] def __init__(self, config: T5Config): super().__init__(config) self.model_dim = config.d_model self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) encoder_config.is_decoder = False encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = T5Stack(encoder_config, self.shared) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False decoder_config.num_layers = config.num_decoder_layers self.decoder = T5Stack(decoder_config, self.shared) self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): self.device_map = ( get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.encoder.block)) self.encoder.parallelize(self.device_map) self.decoder.parallelize(self.device_map) self.lm_head = self.lm_head.to(self.decoder.first_device) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): self.encoder.deparallelize() self.decoder.deparallelize() self.encoder = self.encoder.to("cpu") self.decoder = self.decoder.to("cpu") self.lm_head = self.lm_head.to("cpu") self.model_parallel = False self.device_map = None torch.cuda.empty_cache() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) self.decoder.set_input_embeddings(new_embeddings) def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def get_output_embeddings(self): return self.lm_head def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` Returns: Examples: ```python >>> from transformers import T5Tokenizer, T5ForConditionalGeneration >>> tokenizer = T5Tokenizer.from_pretrained("t5-small") >>> model = T5ForConditionalGeneration.from_pretrained("t5-small") >>> # training >>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids >>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids >>> outputs = model(input_ids=input_ids, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits >>> # inference >>> input_ids = tokenizer( ... "summarize: studies have shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> outputs = model.generate(input_ids) >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True)) >>> # studies have shown that owning a dog is good for you. ```""" use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask if head_mask is not None and decoder_head_mask is None: if self.config.num_layers == self.config.num_decoder_layers: warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) decoder_head_mask = head_mask # Encode if needed (training, first prediction pass) if encoder_outputs is None: # Convert encoder inputs in embeddings if needed encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) hidden_states = encoder_outputs[0] if self.model_parallel: torch.cuda.set_device(self.decoder.first_device) if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: # get decoder inputs from shifting lm labels to the right decoder_input_ids = self._shift_right(labels) # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.decoder.first_device) hidden_states = hidden_states.to(self.decoder.first_device) if decoder_input_ids is not None: decoder_input_ids = decoder_input_ids.to(self.decoder.first_device) if attention_mask is not None: attention_mask = attention_mask.to(self.decoder.first_device) if decoder_attention_mask is not None: decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device) # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = decoder_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.encoder.first_device) self.lm_head = self.lm_head.to(self.encoder.first_device) sequence_output = sequence_output.to(self.lm_head.weight.device) if self.config.tie_word_embeddings: # Rescale output before projecting on vocab # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586 sequence_output = sequence_output * (self.model_dim**-0.5) lm_logits = self.lm_head(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss(ignore_index=-100) loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666 if not return_dict: output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs return ((loss,) + output) if loss is not None else output return Seq2SeqLMOutput( loss=loss, logits=lm_logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) def prepare_inputs_for_generation( self, input_ids, past=None, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs ): # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return { "decoder_input_ids": input_ids, "past_key_values": past, "encoder_outputs": encoder_outputs, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, } def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return self._shift_right(labels) def _reorder_cache(self, past, beam_idx): # if decoder past is not included in output # speedy decoding is disabled and no need to reorder if past is None: logger.warning("You might want to consider setting `use_cache=True` to speed up decoding") return past reordered_decoder_past = () for layer_past_states in past: # get the correct batch idx from layer past batch dim # batch dim of `past` is at 2nd position reordered_layer_past_states = () for layer_past_state in layer_past_states: # need to set correct `past` for each of the four key / value states reordered_layer_past_states = reordered_layer_past_states + ( layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)), ) assert reordered_layer_past_states[0].shape == layer_past_states[0].shape assert len(reordered_layer_past_states) == len(layer_past_states) reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,) return reordered_decoder_past @add_start_docstrings( "The bare T5 Model transformer outputting encoder's raw hidden-states without any specific head on top.", T5_START_DOCSTRING, ) class T5EncoderModel(T5PreTrainedModel): _keys_to_ignore_on_load_missing = [r"encoder.embed_tokens.weight"] def __init__(self, config: T5Config): super().__init__(config) self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = T5Stack(encoder_config, self.shared) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): self.device_map = ( get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.encoder.block)) self.encoder.parallelize(self.device_map) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): self.encoder.deparallelize() self.encoder = self.encoder.to("cpu") self.model_parallel = False self.device_map = None torch.cuda.empty_cache() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) def get_encoder(self): return self.encoder def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads) @add_start_docstrings_to_model_forward(T5_ENCODER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]: r""" Returns: Example: ```python >>> from transformers import T5Tokenizer, T5EncoderModel >>> tokenizer = T5Tokenizer.from_pretrained("t5-small") >>> model = T5EncoderModel.from_pretrained("t5-small") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> outputs = model(input_ids=input_ids) >>> last_hidden_states = outputs.last_hidden_state ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) return encoder_outputs

# coding=utf-8 # Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch T5 model.""" import copy import math import os import warnings from typing import Optional, Tuple, Union import torch from torch import nn from torch.nn import CrossEntropyLoss from torch.utils.checkpoint import checkpoint from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import ALL_LAYERNORM_LAYERS, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging, replace_return_docstrings, ) from ...utils.model_parallel_utils import assert_device_map, get_device_map from .configuration_t5 import T5Config logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "T5Config" _TOKENIZER_FOR_DOC = "T5Tokenizer" _CHECKPOINT_FOR_DOC = "t5-small" #################################################### # This dict contains ids and associated url # for the pretrained weights provided with the models #################################################### T5_PRETRAINED_MODEL_ARCHIVE_LIST = [ "t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b", # See all T5 models at https://huggingface.co/models?filter=t5 ] #################################################### # This is a conversion method from TF 1.0 to PyTorch # More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28 #################################################### def load_tf_weights_in_t5(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] tf_weights = {} for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) tf_weights[name] = array for txt_name in names: name = txt_name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any( n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name ): logger.info(f"Skipping {'/'.join(name)}") tf_weights.pop(txt_name, None) continue if "_slot_" in name[-1]: logger.info(f"Skipping {'/'.join(name)}") tf_weights.pop(txt_name, None) continue pointer = model array = tf_weights[txt_name] for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] in ["kernel", "scale", "embedding"]: pointer = getattr(pointer, "weight") elif scope_names[0] == "self_attention": pointer = getattr(pointer, "layer") pointer = pointer[0] elif scope_names[0] == "enc_dec_attention": pointer = getattr(pointer, "layer") pointer = pointer[1] elif scope_names[0] == "dense_relu_dense": pointer = getattr(pointer, "layer") pointer = pointer[2] elif scope_names[0] == "rms_norm": if hasattr(pointer, "layer_norm"): pointer = getattr(pointer, "layer_norm") elif hasattr(pointer, "final_layer_norm"): pointer = getattr(pointer, "final_layer_norm") elif scope_names[0] == "scale": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier") elif scope_names[0] == "decoder" and name[1] == "logits": continue elif scope_names[0] == "logits": pointer = getattr(pointer, "lm_head") elif scope_names[0] == "wi" and len(scope_names) > 1 and scope_names[1].isdigit(): pointer = getattr(pointer, f"wi_{scope_names[1]}") continue else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if scope_names[0] not in ["kernel", "scale", "embedding"]: pointer = getattr(pointer, "weight") if scope_names[0] != "embedding": logger.info(f"Transposing numpy weight of shape {array.shape} for {name}") array = np.transpose(array) try: assert ( pointer.shape == array.shape ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array.astype(np.float32)) tf_weights.pop(txt_name, None) logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}.") return model #################################################### # PyTorch Models are constructed by sub-classing # - torch.nn.Module for the layers and # - PreTrainedModel for the models (it-self a sub-class of nn.Module) #################################################### PARALLELIZE_DOCSTRING = r""" This is an experimental feature and is a subject to change at a moment's notice. Uses a device map to distribute attention modules of the model across several devices. If no device map is given, it will evenly distribute blocks across all devices. Args: device_map (`Dict[int, list]`, optional, defaults to None): A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always automatically mapped to the first device (for esoteric reasons). That means that the first device should have fewer attention modules mapped to it than other devices. For reference, the t5 models have the following number of attention modules: - t5-small: 6 - t5-base: 12 - t5-large: 24 - t5-3b: 24 - t5-11b: 24 Example: ```python # Here is an example of a device map on a machine with 4 GPUs using t5-3b, which has a total of 24 attention modules: model = T5ForConditionalGeneration.from_pretrained("t5-3b") device_map = { 0: [0, 1, 2], 1: [3, 4, 5, 6, 7, 8, 9], 2: [10, 11, 12, 13, 14, 15, 16], 3: [17, 18, 19, 20, 21, 22, 23], } model.parallelize(device_map) ``` """ DEPARALLELIZE_DOCSTRING = r""" Moves the model to cpu from a model parallel state. Example: ```python # On a 4 GPU machine with t5-3b: model = T5ForConditionalGeneration.from_pretrained("t5-3b") device_map = { 0: [0, 1, 2], 1: [3, 4, 5, 6, 7, 8, 9], 2: [10, 11, 12, 13, 14, 15, 16], 3: [17, 18, 19, 20, 21, 22, 23], } model.parallelize(device_map) # Splits the model across several devices model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache() ``` """ class T5LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Construct a layernorm module in the T5 style. No bias and no subtraction of mean. """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for # half-precision inputs is done in fp32 variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) # convert into half-precision if necessary if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states try: from apex.normalization import FusedRMSNorm T5LayerNorm = FusedRMSNorm # noqa logger.info("Discovered apex.normalization.FusedRMSNorm - will use it instead of T5LayerNorm") except ImportError: # using the normal T5LayerNorm pass except Exception: logger.warning("discovered apex but it failed to load, falling back to T5LayerNorm") pass ALL_LAYERNORM_LAYERS.append(T5LayerNorm) class T5DenseActDense(nn.Module): def __init__(self, config: T5Config): super().__init__() self.wi = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.act = ACT2FN[config.dense_act_fn] def forward(self, hidden_states): hidden_states = self.wi(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.wo(hidden_states) return hidden_states class T5DenseGatedActDense(nn.Module): def __init__(self, config: T5Config): super().__init__() self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.act = ACT2FN[config.dense_act_fn] def forward(self, hidden_states): hidden_gelu = self.act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) hidden_states = self.wo(hidden_states) return hidden_states class T5LayerFF(nn.Module): def __init__(self, config: T5Config): super().__init__() if config.is_gated_act: self.DenseReluDense = T5DenseGatedActDense(config) else: self.DenseReluDense = T5DenseActDense(config) self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward(self, hidden_states): forwarded_states = self.layer_norm(hidden_states) forwarded_states = self.DenseReluDense(forwarded_states) hidden_states = hidden_states + self.dropout(forwarded_states) return hidden_states class T5Attention(nn.Module): def __init__(self, config: T5Config, has_relative_attention_bias=False): super().__init__() self.is_decoder = config.is_decoder self.has_relative_attention_bias = has_relative_attention_bias self.relative_attention_num_buckets = config.relative_attention_num_buckets self.relative_attention_max_distance = config.relative_attention_max_distance self.d_model = config.d_model self.key_value_proj_dim = config.d_kv self.n_heads = config.num_heads self.dropout = config.dropout_rate self.inner_dim = self.n_heads * self.key_value_proj_dim # Mesh TensorFlow initialization to avoid scaling before softmax self.q = nn.Linear(self.d_model, self.inner_dim, bias=False) self.k = nn.Linear(self.d_model, self.inner_dim, bias=False) self.v = nn.Linear(self.d_model, self.inner_dim, bias=False) self.o = nn.Linear(self.inner_dim, self.d_model, bias=False) if self.has_relative_attention_bias: self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads) self.pruned_heads = set() self.gradient_checkpointing = False def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads ) # Prune linear layers self.q = prune_linear_layer(self.q, index) self.k = prune_linear_layer(self.k, index) self.v = prune_linear_layer(self.v, index) self.o = prune_linear_layer(self.o, index, dim=1) # Update hyper params self.n_heads = self.n_heads - len(heads) self.inner_dim = self.key_value_proj_dim * self.n_heads self.pruned_heads = self.pruned_heads.union(heads) @staticmethod def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ relative_buckets = 0 if bidirectional: num_buckets //= 2 relative_buckets += (relative_position > 0).to(torch.long) * num_buckets relative_position = torch.abs(relative_position) else: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) # now relative_position is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = relative_position < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance relative_position_if_large = max_exact + ( torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) relative_position_if_large = torch.min( relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) ) relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) return relative_buckets def compute_bias(self, query_length, key_length, device=None): """Compute binned relative position bias""" if device is None: device = self.relative_attention_bias.weight.device context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] relative_position = memory_position - context_position # shape (query_length, key_length) relative_position_bucket = self._relative_position_bucket( relative_position, # shape (query_length, key_length) bidirectional=(not self.is_decoder), num_buckets=self.relative_attention_num_buckets, max_distance=self.relative_attention_max_distance, ) values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads) values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) return values def forward( self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, output_attentions=False, ): """ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). """ # Input is (batch_size, seq_length, dim) # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length) # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head) batch_size, seq_length = hidden_states.shape[:2] real_seq_length = seq_length if past_key_value is not None: assert ( len(past_key_value) == 2 ), f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states" real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length key_length = real_seq_length if key_value_states is None else key_value_states.shape[1] def shape(states): """projection""" return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) def unshape(states): """reshape""" return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim) def project(hidden_states, proj_layer, key_value_states, past_key_value): """projects hidden states correctly to key/query states""" if key_value_states is None: # self-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(hidden_states)) elif past_key_value is None: # cross-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(key_value_states)) if past_key_value is not None: if key_value_states is None: # self-attn # (batch_size, n_heads, key_length, dim_per_head) hidden_states = torch.cat([past_key_value, hidden_states], dim=2) else: # cross-attn hidden_states = past_key_value return hidden_states # get query states query_states = shape(self.q(hidden_states)) # (batch_size, n_heads, seq_length, dim_per_head) # get key/value states key_states = project( hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None ) value_states = project( hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None ) # compute scores scores = torch.matmul( query_states, key_states.transpose(3, 2) ) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 if position_bias is None: if not self.has_relative_attention_bias: position_bias = torch.zeros( (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype ) if self.gradient_checkpointing and self.training: position_bias.requires_grad = True else: position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device) # if key and values are already calculated # we want only the last query position bias if past_key_value is not None: position_bias = position_bias[:, :, -hidden_states.size(1) :, :] if mask is not None: position_bias = position_bias + mask # (batch_size, n_heads, seq_length, key_length) if self.pruned_heads: mask = torch.ones(position_bias.shape[1]) mask[list(self.pruned_heads)] = 0 position_bias_masked = position_bias[:, mask.bool()] else: position_bias_masked = position_bias scores += position_bias_masked attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as( scores ) # (batch_size, n_heads, seq_length, key_length) attn_weights = nn.functional.dropout( attn_weights, p=self.dropout, training=self.training ) # (batch_size, n_heads, seq_length, key_length) # Mask heads if we want to if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask attn_output = unshape(torch.matmul(attn_weights, value_states)) # (batch_size, seq_length, dim) attn_output = self.o(attn_output) present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None outputs = (attn_output,) + (present_key_value_state,) + (position_bias,) if output_attentions: outputs = outputs + (attn_weights,) return outputs class T5LayerSelfAttention(nn.Module): def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias) self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.SelfAttention( normed_hidden_states, mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = hidden_states + self.dropout(attention_output[0]) outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them return outputs class T5LayerCrossAttention(nn.Module): def __init__(self, config): super().__init__() self.EncDecAttention = T5Attention(config, has_relative_attention_bias=False) self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, key_value_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, query_length=None, output_attentions=False, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.EncDecAttention( normed_hidden_states, mask=attention_mask, key_value_states=key_value_states, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, query_length=query_length, output_attentions=output_attentions, ) layer_output = hidden_states + self.dropout(attention_output[0]) outputs = (layer_output,) + attention_output[1:] # add attentions if we output them return outputs class T5Block(nn.Module): def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.is_decoder = config.is_decoder self.layer = nn.ModuleList() self.layer.append(T5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias)) if self.is_decoder: self.layer.append(T5LayerCrossAttention(config)) self.layer.append(T5LayerFF(config)) def forward( self, hidden_states, attention_mask=None, position_bias=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, return_dict=True, ): if past_key_value is not None: if not self.is_decoder: logger.warning("`past_key_values` is passed to the encoder. Please make sure this is intended.") expected_num_past_key_values = 2 if encoder_hidden_states is None else 4 if len(past_key_value) != expected_num_past_key_values: raise ValueError( f"There should be {expected_num_past_key_values} past states. " f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}" f"Got {len(past_key_value)} past key / value states" ) self_attn_past_key_value = past_key_value[:2] cross_attn_past_key_value = past_key_value[2:] else: self_attn_past_key_value, cross_attn_past_key_value = None, None self_attention_outputs = self.layer[0]( hidden_states, attention_mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=self_attn_past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states, present_key_value_state = self_attention_outputs[:2] attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) do_cross_attention = self.is_decoder and encoder_hidden_states is not None if do_cross_attention: # the actual query length is unknown for cross attention # if using past key value states. Need to inject it here if present_key_value_state is not None: query_length = present_key_value_state[0].shape[2] else: query_length = None cross_attention_outputs = self.layer[1]( hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, position_bias=encoder_decoder_position_bias, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, query_length=query_length, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = cross_attention_outputs[0] # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) # Combine self attn and cross attn key value states if present_key_value_state is not None: present_key_value_state = present_key_value_state + cross_attention_outputs[1] # Keep cross-attention outputs and relative position weights attention_outputs = attention_outputs + cross_attention_outputs[2:] # Apply Feed Forward layer hidden_states = self.layer[-1](hidden_states) # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if use_cache: outputs = outputs + (present_key_value_state,) + attention_outputs else: outputs = outputs + attention_outputs return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) class T5PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = T5Config load_tf_weights = load_tf_weights_in_t5 base_model_prefix = "transformer" is_parallelizable = True supports_gradient_checkpointing = True _no_split_modules = ["T5Block"] @property def dummy_inputs(self): input_ids = torch.tensor(DUMMY_INPUTS) input_mask = torch.tensor(DUMMY_MASK) dummy_inputs = { "decoder_input_ids": input_ids, "input_ids": input_ids, "decoder_attention_mask": input_mask, } return dummy_inputs def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor # Used for testing weights initialization if isinstance(module, T5LayerNorm): module.weight.data.fill_(factor * 1.0) elif isinstance(module, (T5Model, T5ForConditionalGeneration, T5EncoderModel)): # Mesh TensorFlow embeddings initialization # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624 module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0) if hasattr(module, "lm_head") and not self.config.tie_word_embeddings: module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, T5DenseActDense): # Mesh TensorFlow FF initialization # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56 # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89 module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi, "bias") and module.wi.bias is not None: module.wi.bias.data.zero_() module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, T5DenseGatedActDense): module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None: module.wi_0.bias.data.zero_() module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None: module.wi_1.bias.data.zero_() module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, T5Attention): # Mesh TensorFlow attention initialization to avoid scaling before softmax # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136 d_model = self.config.d_model key_value_proj_dim = self.config.d_kv n_heads = self.config.num_heads module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5)) module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5)) if module.has_relative_attention_bias: module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5)) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (T5Attention, T5Stack)): module.gradient_checkpointing = value def _shift_right(self, input_ids): decoder_start_token_id = self.config.decoder_start_token_id pad_token_id = self.config.pad_token_id assert decoder_start_token_id is not None, ( "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id." " See T5 docs for more information" ) # shift inputs to the right if is_torch_fx_proxy(input_ids): # Item assignment is not supported natively for proxies. shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id) shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1) else: shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = decoder_start_token_id assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined." # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids class T5Stack(T5PreTrainedModel): def __init__(self, config, embed_tokens=None): super().__init__(config) self.embed_tokens = embed_tokens self.is_decoder = config.is_decoder self.block = nn.ModuleList( [T5Block(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)] ) self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None self.gradient_checkpointing = False @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): # Check validity of device_map self.device_map = ( get_device_map(len(self.block), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.block)) self.model_parallel = True self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys())) self.last_device = "cuda:" + str(max(self.device_map.keys())) # Load onto devices for k, v in self.device_map.items(): for layer in v: cuda_device = "cuda:" + str(k) self.block[layer] = self.block[layer].to(cuda_device) # Set embed_tokens to first layer self.embed_tokens = self.embed_tokens.to(self.first_device) # Set final layer norm to last device self.final_layer_norm = self.final_layer_norm.to(self.last_device) @add_start_docstrings(PARALLELIZE_DOCSTRING) def deparallelize(self): self.model_parallel = False self.device_map = None self.first_device = "cpu" self.last_device = "cpu" for i in range(len(self.block)): self.block[i] = self.block[i].to("cpu") self.embed_tokens = self.embed_tokens.to("cpu") self.final_layer_norm = self.final_layer_norm.to("cpu") torch.cuda.empty_cache() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, new_embeddings): self.embed_tokens = new_embeddings def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): # Model parallel if self.model_parallel: torch.cuda.set_device(self.first_device) self.embed_tokens = self.embed_tokens.to(self.first_device) use_cache = use_cache if use_cache is not None else self.config.use_cache output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError( f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time" ) elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds") if inputs_embeds is None: assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings" inputs_embeds = self.embed_tokens(input_ids) batch_size, seq_length = input_shape # required mask seq length can be calculated via length of past mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length if use_cache is True: assert self.is_decoder, f"`use_cache` can only be set to `True` if {self} is used as a decoder" if attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None: encoder_seq_length = encoder_hidden_states.shape[1] encoder_attention_mask = torch.ones( batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long ) # initialize past_key_values with `None` if past does not exist if past_key_values is None: past_key_values = [None] * len(self.block) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed head_mask = self.get_head_mask(head_mask, self.config.num_layers) cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers) present_key_value_states = () if use_cache else None all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if (output_attentions and self.is_decoder) else None position_bias = None encoder_decoder_position_bias = None hidden_states = self.dropout(inputs_embeds) for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)): layer_head_mask = head_mask[i] cross_attn_layer_head_mask = cross_attn_head_mask[i] # Model parallel if self.model_parallel: torch.cuda.set_device(hidden_states.device) # Ensure that attention_mask is always on the same device as hidden_states if attention_mask is not None: attention_mask = attention_mask.to(hidden_states.device) if position_bias is not None: position_bias = position_bias.to(hidden_states.device) if encoder_hidden_states is not None: encoder_hidden_states = encoder_hidden_states.to(hidden_states.device) if encoder_extended_attention_mask is not None: encoder_extended_attention_mask = encoder_extended_attention_mask.to(hidden_states.device) if encoder_decoder_position_bias is not None: encoder_decoder_position_bias = encoder_decoder_position_bias.to(hidden_states.device) if layer_head_mask is not None: layer_head_mask = layer_head_mask.to(hidden_states.device) if cross_attn_layer_head_mask is not None: cross_attn_layer_head_mask = cross_attn_layer_head_mask.to(hidden_states.device) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return tuple(module(*inputs, use_cache, output_attentions)) return custom_forward layer_outputs = checkpoint( create_custom_forward(layer_module), hidden_states, extended_attention_mask, position_bias, encoder_hidden_states, encoder_extended_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, None, # past_key_value is always None with gradient checkpointing ) else: layer_outputs = layer_module( hidden_states, attention_mask=extended_attention_mask, position_bias=position_bias, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, layer_head_mask=layer_head_mask, cross_attn_layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) # layer_outputs is a tuple with: # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) if use_cache is False: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:] hidden_states, present_key_value_state = layer_outputs[:2] # We share the position biases between the layers - the first layer store them # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), # (cross-attention position bias), (cross-attention weights) position_bias = layer_outputs[2] if self.is_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3] # append next layer key value states if use_cache: present_key_value_states = present_key_value_states + (present_key_value_state,) if output_attentions: all_attentions = all_attentions + (layer_outputs[3],) if self.is_decoder: all_cross_attentions = all_cross_attentions + (layer_outputs[5],) # Model Parallel: If it's the last layer for that device, put things on the next device if self.model_parallel: for k, v in self.device_map.items(): if i == v[-1] and "cuda:" + str(k) != self.last_device: hidden_states = hidden_states.to("cuda:" + str(k + 1)) hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.dropout(hidden_states) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, present_key_value_states, all_hidden_states, all_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_value_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) T5_START_DOCSTRING = r""" The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting. This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`T5Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ T5_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. [What are input IDs?](../glossary#input-ids) To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training). attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5 Training](./t5#training). decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ T5_ENCODER_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training). attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask __HEAD_MASK_WARNING_MSG = """ The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently, `decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions. If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers, num_heads)`. """ @add_start_docstrings( "The bare T5 Model transformer outputting raw hidden-states without any specific head on top.", T5_START_DOCSTRING, ) class T5Model(T5PreTrainedModel): _keys_to_ignore_on_load_missing = [ r"encoder.embed_tokens.weight", r"decoder.embed_tokens.weight", ] _keys_to_ignore_on_load_unexpected = [ r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight", ] def __init__(self, config: T5Config): super().__init__(config) self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) encoder_config.is_decoder = False encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = T5Stack(encoder_config, self.shared) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False decoder_config.num_layers = config.num_decoder_layers self.decoder = T5Stack(decoder_config, self.shared) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): self.device_map = ( get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.encoder.block)) self.encoder.parallelize(self.device_map) self.decoder.parallelize(self.device_map) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): self.encoder.deparallelize() self.decoder.deparallelize() self.encoder = self.encoder.to("cpu") self.decoder = self.decoder.to("cpu") self.model_parallel = False self.device_map = None torch.cuda.empty_cache() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) self.decoder.set_input_embeddings(new_embeddings) def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.Tensor] = None, decoder_inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]: r""" Returns: Example: ```python >>> from transformers import T5Tokenizer, T5Model >>> tokenizer = T5Tokenizer.from_pretrained("t5-small") >>> model = T5Model.from_pretrained("t5-small") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model. >>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg. >>> decoder_input_ids = model._shift_right(decoder_input_ids) >>> # forward pass >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) >>> last_hidden_states = outputs.last_hidden_state ```""" use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask if head_mask is not None and decoder_head_mask is None: if self.config.num_layers == self.config.num_decoder_layers: warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) decoder_head_mask = head_mask # Encode if needed (training, first prediction pass) if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) hidden_states = encoder_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.decoder.first_device) hidden_states = hidden_states.to(self.decoder.first_device) if decoder_input_ids is not None: decoder_input_ids = decoder_input_ids.to(self.decoder.first_device) if attention_mask is not None: attention_mask = attention_mask.to(self.decoder.first_device) if decoder_attention_mask is not None: decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device) # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings("""T5 Model with a `language modeling` head on top.""", T5_START_DOCSTRING) class T5ForConditionalGeneration(T5PreTrainedModel): _keys_to_ignore_on_load_missing = [ r"encoder.embed_tokens.weight", r"decoder.embed_tokens.weight", r"lm_head.weight", ] _keys_to_ignore_on_load_unexpected = [ r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight", ] def __init__(self, config: T5Config): super().__init__(config) self.model_dim = config.d_model self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) encoder_config.is_decoder = False encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = T5Stack(encoder_config, self.shared) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False decoder_config.num_layers = config.num_decoder_layers self.decoder = T5Stack(decoder_config, self.shared) self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): self.device_map = ( get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.encoder.block)) self.encoder.parallelize(self.device_map) self.decoder.parallelize(self.device_map) self.lm_head = self.lm_head.to(self.decoder.first_device) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): self.encoder.deparallelize() self.decoder.deparallelize() self.encoder = self.encoder.to("cpu") self.decoder = self.decoder.to("cpu") self.lm_head = self.lm_head.to("cpu") self.model_parallel = False self.device_map = None torch.cuda.empty_cache() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) self.decoder.set_input_embeddings(new_embeddings) def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def get_output_embeddings(self): return self.lm_head def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` Returns: Examples: ```python >>> from transformers import T5Tokenizer, T5ForConditionalGeneration >>> tokenizer = T5Tokenizer.from_pretrained("t5-small") >>> model = T5ForConditionalGeneration.from_pretrained("t5-small") >>> # training >>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids >>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids >>> outputs = model(input_ids=input_ids, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits >>> # inference >>> input_ids = tokenizer( ... "summarize: studies have shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> outputs = model.generate(input_ids) >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True)) >>> # studies have shown that owning a dog is good for you. ```""" use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask if head_mask is not None and decoder_head_mask is None: if self.config.num_layers == self.config.num_decoder_layers: warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) decoder_head_mask = head_mask # Encode if needed (training, first prediction pass) if encoder_outputs is None: # Convert encoder inputs in embeddings if needed encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) hidden_states = encoder_outputs[0] if self.model_parallel: torch.cuda.set_device(self.decoder.first_device) if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: # get decoder inputs from shifting lm labels to the right decoder_input_ids = self._shift_right(labels) # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.decoder.first_device) hidden_states = hidden_states.to(self.decoder.first_device) if decoder_input_ids is not None: decoder_input_ids = decoder_input_ids.to(self.decoder.first_device) if attention_mask is not None: attention_mask = attention_mask.to(self.decoder.first_device) if decoder_attention_mask is not None: decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device) # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = decoder_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.encoder.first_device) self.lm_head = self.lm_head.to(self.encoder.first_device) sequence_output = sequence_output.to(self.lm_head.weight.device) if self.config.tie_word_embeddings: # Rescale output before projecting on vocab # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586 sequence_output = sequence_output * (self.model_dim**-0.5) lm_logits = self.lm_head(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss(ignore_index=-100) loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666 if not return_dict: output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs return ((loss,) + output) if loss is not None else output return Seq2SeqLMOutput( loss=loss, logits=lm_logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) def prepare_inputs_for_generation( self, input_ids, past=None, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs ): # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return { "decoder_input_ids": input_ids, "past_key_values": past, "encoder_outputs": encoder_outputs, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, } def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return self._shift_right(labels) def _reorder_cache(self, past, beam_idx): # if decoder past is not included in output # speedy decoding is disabled and no need to reorder if past is None: logger.warning("You might want to consider setting `use_cache=True` to speed up decoding") return past reordered_decoder_past = () for layer_past_states in past: # get the correct batch idx from layer past batch dim # batch dim of `past` is at 2nd position reordered_layer_past_states = () for layer_past_state in layer_past_states: # need to set correct `past` for each of the four key / value states reordered_layer_past_states = reordered_layer_past_states + ( layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)), ) assert reordered_layer_past_states[0].shape == layer_past_states[0].shape assert len(reordered_layer_past_states) == len(layer_past_states) reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,) return reordered_decoder_past @add_start_docstrings( "The bare T5 Model transformer outputting encoder's raw hidden-states without any specific head on top.", T5_START_DOCSTRING, ) class T5EncoderModel(T5PreTrainedModel): _keys_to_ignore_on_load_missing = [r"encoder.embed_tokens.weight"] def __init__(self, config: T5Config): super().__init__(config) self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = T5Stack(encoder_config, self.shared) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): self.device_map = ( get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.encoder.block)) self.encoder.parallelize(self.device_map) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): self.encoder.deparallelize() self.encoder = self.encoder.to("cpu") self.model_parallel = False self.device_map = None torch.cuda.empty_cache() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) def get_encoder(self): return self.encoder def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads) @add_start_docstrings_to_model_forward(T5_ENCODER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]: r""" Returns: Example: ```python >>> from transformers import T5Tokenizer, T5EncoderModel >>> tokenizer = T5Tokenizer.from_pretrained("t5-small") >>> model = T5EncoderModel.from_pretrained("t5-small") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> outputs = model(input_ids=input_ids) >>> last_hidden_states = outputs.last_hidden_state ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) return encoder_outputs

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./utils/custom_init_isort.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import re PATH_TO_TRANSFORMERS = "src/transformers" # Pattern that looks at the indentation in a line. _re_indent = re.compile(r"^(\s*)\S") # Pattern that matches `"key":" and puts `key` in group 0. _re_direct_key = re.compile(r'^\s*"([^"]+)":') # Pattern that matches `_import_structure["key"]` and puts `key` in group 0. _re_indirect_key = re.compile(r'^\s*_import_structure\["([^"]+)"\]') # Pattern that matches `"key",` and puts `key` in group 0. _re_strip_line = re.compile(r'^\s*"([^"]+)",\s*$') # Pattern that matches any `[stuff]` and puts `stuff` in group 0. _re_bracket_content = re.compile(r"\[([^\]]+)\]") def get_indent(line): """Returns the indent in `line`.""" search = _re_indent.search(line) return "" if search is None else search.groups()[0] def split_code_in_indented_blocks(code, indent_level="", start_prompt=None, end_prompt=None): """ Split `code` into its indented blocks, starting at `indent_level`. If provided, begins splitting after `start_prompt` and stops at `end_prompt` (but returns what's before `start_prompt` as a first block and what's after `end_prompt` as a last block, so `code` is always the same as joining the result of this function). """ # Let's split the code into lines and move to start_index. index = 0 lines = code.split("\n") if start_prompt is not None: while not lines[index].startswith(start_prompt): index += 1 blocks = ["\n".join(lines[:index])] else: blocks = [] # We split into blocks until we get to the `end_prompt` (or the end of the block). current_block = [lines[index]] index += 1 while index < len(lines) and (end_prompt is None or not lines[index].startswith(end_prompt)): if len(lines[index]) > 0 and get_indent(lines[index]) == indent_level: if len(current_block) > 0 and get_indent(current_block[-1]).startswith(indent_level + " "): current_block.append(lines[index]) blocks.append("\n".join(current_block)) if index < len(lines) - 1: current_block = [lines[index + 1]] index += 1 else: current_block = [] else: blocks.append("\n".join(current_block)) current_block = [lines[index]] else: current_block.append(lines[index]) index += 1 # Adds current block if it's nonempty. if len(current_block) > 0: blocks.append("\n".join(current_block)) # Add final block after end_prompt if provided. if end_prompt is not None and index < len(lines): blocks.append("\n".join(lines[index:])) return blocks def ignore_underscore(key): "Wraps a `key` (that maps an object to string) to lower case and remove underscores." def _inner(x): return key(x).lower().replace("_", "") return _inner def sort_objects(objects, key=None): "Sort a list of `objects` following the rules of isort. `key` optionally maps an object to a str." # If no key is provided, we use a noop. def noop(x): return x if key is None: key = noop # Constants are all uppercase, they go first. constants = [obj for obj in objects if key(obj).isupper()] # Classes are not all uppercase but start with a capital, they go second. classes = [obj for obj in objects if key(obj)[0].isupper() and not key(obj).isupper()] # Functions begin with a lowercase, they go last. functions = [obj for obj in objects if not key(obj)[0].isupper()] key1 = ignore_underscore(key) return sorted(constants, key=key1) + sorted(classes, key=key1) + sorted(functions, key=key1) def sort_objects_in_import(import_statement): """ Return the same `import_statement` but with objects properly sorted. """ # This inner function sort imports between [ ]. def _replace(match): imports = match.groups()[0] if "," not in imports: return f"[{imports}]" keys = [part.strip().replace('"', "") for part in imports.split(",")] # We will have a final empty element if the line finished with a comma. if len(keys[-1]) == 0: keys = keys[:-1] return "[" + ", ".join([f'"{k}"' for k in sort_objects(keys)]) + "]" lines = import_statement.split("\n") if len(lines) > 3: # Here we have to sort internal imports that are on several lines (one per name): # key: [ # "object1", # "object2", # ... # ] # We may have to ignore one or two lines on each side. idx = 2 if lines[1].strip() == "[" else 1 keys_to_sort = [(i, _re_strip_line.search(line).groups()[0]) for i, line in enumerate(lines[idx:-idx])] sorted_indices = sort_objects(keys_to_sort, key=lambda x: x[1]) sorted_lines = [lines[x[0] + idx] for x in sorted_indices] return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]) elif len(lines) == 3: # Here we have to sort internal imports that are on one separate line: # key: [ # "object1", "object2", ... # ] if _re_bracket_content.search(lines[1]) is not None: lines[1] = _re_bracket_content.sub(_replace, lines[1]) else: keys = [part.strip().replace('"', "") for part in lines[1].split(",")] # We will have a final empty element if the line finished with a comma. if len(keys[-1]) == 0: keys = keys[:-1] lines[1] = get_indent(lines[1]) + ", ".join([f'"{k}"' for k in sort_objects(keys)]) return "\n".join(lines) else: # Finally we have to deal with imports fitting on one line import_statement = _re_bracket_content.sub(_replace, import_statement) return import_statement def sort_imports(file, check_only=True): """ Sort `_import_structure` imports in `file`, `check_only` determines if we only check or overwrite. """ with open(file, encoding="utf-8") as f: code = f.read() if "_import_structure" not in code: return # Blocks of indent level 0 main_blocks = split_code_in_indented_blocks( code, start_prompt="_import_structure = {", end_prompt="if TYPE_CHECKING:" ) # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt). for block_idx in range(1, len(main_blocks) - 1): # Check if the block contains some `_import_structure`s thingy to sort. block = main_blocks[block_idx] block_lines = block.split("\n") # Get to the start of the imports. line_idx = 0 while line_idx < len(block_lines) and "_import_structure" not in block_lines[line_idx]: # Skip dummy import blocks if "import dummy" in block_lines[line_idx]: line_idx = len(block_lines) else: line_idx += 1 if line_idx >= len(block_lines): continue # Ignore beginning and last line: they don't contain anything. internal_block_code = "\n".join(block_lines[line_idx:-1]) indent = get_indent(block_lines[1]) # Slit the internal block into blocks of indent level 1. internal_blocks = split_code_in_indented_blocks(internal_block_code, indent_level=indent) # We have two categories of import key: list or _import_structu[key].append/extend pattern = _re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key # Grab the keys, but there is a trap: some lines are empty or jsut comments. keys = [(pattern.search(b).groups()[0] if pattern.search(b) is not None else None) for b in internal_blocks] # We only sort the lines with a key. keys_to_sort = [(i, key) for i, key in enumerate(keys) if key is not None] sorted_indices = [x[0] for x in sorted(keys_to_sort, key=lambda x: x[1])] # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest. count = 0 reorderded_blocks = [] for i in range(len(internal_blocks)): if keys[i] is None: reorderded_blocks.append(internal_blocks[i]) else: block = sort_objects_in_import(internal_blocks[sorted_indices[count]]) reorderded_blocks.append(block) count += 1 # And we put our main block back together with its first and last line. main_blocks[block_idx] = "\n".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]) if code != "\n".join(main_blocks): if check_only: return True else: print(f"Overwriting {file}.") with open(file, "w", encoding="utf-8") as f: f.write("\n".join(main_blocks)) def sort_imports_in_all_inits(check_only=True): failures = [] for root, _, files in os.walk(PATH_TO_TRANSFORMERS): if "__init__.py" in files: result = sort_imports(os.path.join(root, "__init__.py"), check_only=check_only) if result: failures = [os.path.join(root, "__init__.py")] if len(failures) > 0: raise ValueError(f"Would overwrite {len(failures)} files, run `make style`.") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.") args = parser.parse_args() sort_imports_in_all_inits(check_only=args.check_only)

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import re PATH_TO_TRANSFORMERS = "src/transformers" # Pattern that looks at the indentation in a line. _re_indent = re.compile(r"^(\s*)\S") # Pattern that matches `"key":" and puts `key` in group 0. _re_direct_key = re.compile(r'^\s*"([^"]+)":') # Pattern that matches `_import_structure["key"]` and puts `key` in group 0. _re_indirect_key = re.compile(r'^\s*_import_structure\["([^"]+)"\]') # Pattern that matches `"key",` and puts `key` in group 0. _re_strip_line = re.compile(r'^\s*"([^"]+)",\s*$') # Pattern that matches any `[stuff]` and puts `stuff` in group 0. _re_bracket_content = re.compile(r"\[([^\]]+)\]") def get_indent(line): """Returns the indent in `line`.""" search = _re_indent.search(line) return "" if search is None else search.groups()[0] def split_code_in_indented_blocks(code, indent_level="", start_prompt=None, end_prompt=None): """ Split `code` into its indented blocks, starting at `indent_level`. If provided, begins splitting after `start_prompt` and stops at `end_prompt` (but returns what's before `start_prompt` as a first block and what's after `end_prompt` as a last block, so `code` is always the same as joining the result of this function). """ # Let's split the code into lines and move to start_index. index = 0 lines = code.split("\n") if start_prompt is not None: while not lines[index].startswith(start_prompt): index += 1 blocks = ["\n".join(lines[:index])] else: blocks = [] # We split into blocks until we get to the `end_prompt` (or the end of the block). current_block = [lines[index]] index += 1 while index < len(lines) and (end_prompt is None or not lines[index].startswith(end_prompt)): if len(lines[index]) > 0 and get_indent(lines[index]) == indent_level: if len(current_block) > 0 and get_indent(current_block[-1]).startswith(indent_level + " "): current_block.append(lines[index]) blocks.append("\n".join(current_block)) if index < len(lines) - 1: current_block = [lines[index + 1]] index += 1 else: current_block = [] else: blocks.append("\n".join(current_block)) current_block = [lines[index]] else: current_block.append(lines[index]) index += 1 # Adds current block if it's nonempty. if len(current_block) > 0: blocks.append("\n".join(current_block)) # Add final block after end_prompt if provided. if end_prompt is not None and index < len(lines): blocks.append("\n".join(lines[index:])) return blocks def ignore_underscore(key): "Wraps a `key` (that maps an object to string) to lower case and remove underscores." def _inner(x): return key(x).lower().replace("_", "") return _inner def sort_objects(objects, key=None): "Sort a list of `objects` following the rules of isort. `key` optionally maps an object to a str." # If no key is provided, we use a noop. def noop(x): return x if key is None: key = noop # Constants are all uppercase, they go first. constants = [obj for obj in objects if key(obj).isupper()] # Classes are not all uppercase but start with a capital, they go second. classes = [obj for obj in objects if key(obj)[0].isupper() and not key(obj).isupper()] # Functions begin with a lowercase, they go last. functions = [obj for obj in objects if not key(obj)[0].isupper()] key1 = ignore_underscore(key) return sorted(constants, key=key1) + sorted(classes, key=key1) + sorted(functions, key=key1) def sort_objects_in_import(import_statement): """ Return the same `import_statement` but with objects properly sorted. """ # This inner function sort imports between [ ]. def _replace(match): imports = match.groups()[0] if "," not in imports: return f"[{imports}]" keys = [part.strip().replace('"', "") for part in imports.split(",")] # We will have a final empty element if the line finished with a comma. if len(keys[-1]) == 0: keys = keys[:-1] return "[" + ", ".join([f'"{k}"' for k in sort_objects(keys)]) + "]" lines = import_statement.split("\n") if len(lines) > 3: # Here we have to sort internal imports that are on several lines (one per name): # key: [ # "object1", # "object2", # ... # ] # We may have to ignore one or two lines on each side. idx = 2 if lines[1].strip() == "[" else 1 keys_to_sort = [(i, _re_strip_line.search(line).groups()[0]) for i, line in enumerate(lines[idx:-idx])] sorted_indices = sort_objects(keys_to_sort, key=lambda x: x[1]) sorted_lines = [lines[x[0] + idx] for x in sorted_indices] return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]) elif len(lines) == 3: # Here we have to sort internal imports that are on one separate line: # key: [ # "object1", "object2", ... # ] if _re_bracket_content.search(lines[1]) is not None: lines[1] = _re_bracket_content.sub(_replace, lines[1]) else: keys = [part.strip().replace('"', "") for part in lines[1].split(",")] # We will have a final empty element if the line finished with a comma. if len(keys[-1]) == 0: keys = keys[:-1] lines[1] = get_indent(lines[1]) + ", ".join([f'"{k}"' for k in sort_objects(keys)]) return "\n".join(lines) else: # Finally we have to deal with imports fitting on one line import_statement = _re_bracket_content.sub(_replace, import_statement) return import_statement def sort_imports(file, check_only=True): """ Sort `_import_structure` imports in `file`, `check_only` determines if we only check or overwrite. """ with open(file, encoding="utf-8") as f: code = f.read() if "_import_structure" not in code: return # Blocks of indent level 0 main_blocks = split_code_in_indented_blocks( code, start_prompt="_import_structure = {", end_prompt="if TYPE_CHECKING:" ) # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt). for block_idx in range(1, len(main_blocks) - 1): # Check if the block contains some `_import_structure`s thingy to sort. block = main_blocks[block_idx] block_lines = block.split("\n") # Get to the start of the imports. line_idx = 0 while line_idx < len(block_lines) and "_import_structure" not in block_lines[line_idx]: # Skip dummy import blocks if "import dummy" in block_lines[line_idx]: line_idx = len(block_lines) else: line_idx += 1 if line_idx >= len(block_lines): continue # Ignore beginning and last line: they don't contain anything. internal_block_code = "\n".join(block_lines[line_idx:-1]) indent = get_indent(block_lines[1]) # Slit the internal block into blocks of indent level 1. internal_blocks = split_code_in_indented_blocks(internal_block_code, indent_level=indent) # We have two categories of import key: list or _import_structu[key].append/extend pattern = _re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key # Grab the keys, but there is a trap: some lines are empty or jsut comments. keys = [(pattern.search(b).groups()[0] if pattern.search(b) is not None else None) for b in internal_blocks] # We only sort the lines with a key. keys_to_sort = [(i, key) for i, key in enumerate(keys) if key is not None] sorted_indices = [x[0] for x in sorted(keys_to_sort, key=lambda x: x[1])] # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest. count = 0 reorderded_blocks = [] for i in range(len(internal_blocks)): if keys[i] is None: reorderded_blocks.append(internal_blocks[i]) else: block = sort_objects_in_import(internal_blocks[sorted_indices[count]]) reorderded_blocks.append(block) count += 1 # And we put our main block back together with its first and last line. main_blocks[block_idx] = "\n".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]) if code != "\n".join(main_blocks): if check_only: return True else: print(f"Overwriting {file}.") with open(file, "w", encoding="utf-8") as f: f.write("\n".join(main_blocks)) def sort_imports_in_all_inits(check_only=True): failures = [] for root, _, files in os.walk(PATH_TO_TRANSFORMERS): if "__init__.py" in files: result = sort_imports(os.path.join(root, "__init__.py"), check_only=check_only) if result: failures = [os.path.join(root, "__init__.py")] if len(failures) > 0: raise ValueError(f"Would overwrite {len(failures)} files, run `make style`.") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.") args = parser.parse_args() sort_imports_in_all_inits(check_only=args.check_only)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/flava/feature_extraction_flava.py

# coding=utf-8 # Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Feature extractor class for FLAVA.""" from ...utils import logging from .image_processing_flava import FlavaImageProcessor logger = logging.get_logger(__name__) FlavaFeatureExtractor = FlavaImageProcessor

# coding=utf-8 # Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Feature extractor class for FLAVA.""" from ...utils import logging from .image_processing_flava import FlavaImageProcessor logger = logging.get_logger(__name__) FlavaFeatureExtractor = FlavaImageProcessor

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/performer/modeling_flax_performer_utils.py

# coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ IMPORTANT: This code was copied from https://github.com/google-research/google-research/blob/master/performer/fast_self_attention/fast_self_attention.py on 6/11/2020. This is very new code, so it might be prone to change soon -> make sure to check the original code and update accordingly Core Fast Attention Module for Flax. Implementation of the approximate fast softmax and generalized attention mechanism leveraging structured random feature maps [RFM] techniques and low rank decomposition of the attention matrix. """ # pylint: disable=invalid-name, missing-function-docstring, line-too-long import abc import functools from collections.abc import Iterable # pylint: disable=g-importing-member import numpy as onp from absl import logging import jax import jax.numpy as jnp from jax import lax, random def nonnegative_softmax_kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data=True, eps=0.0001 ): """ Constructs nonnegative kernel features for fast softmax attention Args: data: input for which features are computes projection_matrix: random matrix used to compute features attention_dims_t: tuple of attention dimensions batch_dims_t: tuple of batch dimensions precision: precision parameter is_query: predicate indicating whether input data corresponds to queries or keys normalize_data: predicate indicating whether data should be normalized, eps: numerical stabilizer Returns: Random features for fast softmax attention. """ del attention_dims_t if normalize_data: # We have e^{qk^T/sqrt{d}} = e^{q_norm k_norm^T}, where # w_norm = w * data_normalizer for w in {q,k}. data_normalizer = 1.0 / (jnp.sqrt(jnp.sqrt(data.shape[-1]))) else: data_normalizer = 1.0 ratio = 1.0 / jnp.sqrt(projection_matrix.shape[0]) data_mod_shape = data.shape[0 : len(batch_dims_t)] + projection_matrix.shape data_thick_random_matrix = jnp.zeros(data_mod_shape) + projection_matrix data_dash = lax.dot_general( data_normalizer * data, data_thick_random_matrix, (((data.ndim - 1,), (data_thick_random_matrix.ndim - 1,)), (batch_dims_t, batch_dims_t)), precision=precision, ) diag_data = jnp.square(data) diag_data = jnp.sum(diag_data, axis=data.ndim - 1) diag_data = (diag_data / 2.0) * data_normalizer * data_normalizer diag_data = jnp.expand_dims(diag_data, axis=data.ndim - 1) if is_query: last_dims_t = (len(data_dash.shape) - 1,) data_dash = ratio * ( jnp.exp(data_dash - diag_data - jnp.max(data_dash, axis=last_dims_t, keepdims=True)) + eps ) else: data_dash = ratio * (jnp.exp(data_dash - diag_data - jnp.max(data_dash)) + eps) return data_dash def sincos_softmax_kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, normalize_data=True ): """ Constructs kernel sin-cos features for fast softmax attention Args: data: input for which features are computes projection_matrix: random matrix used to compute features attention_dims_t: tuple of attention dimensions batch_dims_t: tuple of batch dimensions precision: precision parameter normalize_data: predicate indicating whether data should be normalized Returns: Random features for fast softmax attention. """ if normalize_data: # We have: exp(qk^T/sqrt{d}) = exp(|q|^2/2sqrt{d}) * exp(|k|^2/2sqrt{d}) * # exp(-(|q*c-k*c|^2)/2), where c = 1.0 / sqrt{sqrt{d}}. data_normalizer = 1.0 / (jnp.sqrt(jnp.sqrt(data.shape[-1]))) else: data_normalizer = 1.0 ratio = 1.0 / jnp.sqrt(projection_matrix.shape[0]) data_mod_shape = data.shape[0 : len(batch_dims_t)] + projection_matrix.shape data_thick_random_matrix = jnp.zeros(data_mod_shape) + projection_matrix data_dash = lax.dot_general( data_normalizer * data, data_thick_random_matrix, (((data.ndim - 1,), (data_thick_random_matrix.ndim - 1,)), (batch_dims_t, batch_dims_t)), precision=precision, ) data_dash_cos = ratio * jnp.cos(data_dash) data_dash_sin = ratio * jnp.sin(data_dash) data_dash = jnp.concatenate((data_dash_cos, data_dash_sin), axis=-1) # Constructing D_data and data^{'} diag_data = jnp.square(data) diag_data = jnp.sum(diag_data, axis=data.ndim - 1) diag_data = (diag_data / 2.0) * data_normalizer * data_normalizer diag_data = jnp.expand_dims(diag_data, axis=data.ndim - 1) # Additional renormalization for numerical stability data_renormalizer = jnp.max(diag_data, attention_dims_t, keepdims=True) diag_data -= data_renormalizer diag_data = jnp.exp(diag_data) data_prime = data_dash * diag_data return data_prime def generalized_kernel_feature_creator( data, projection_matrix, batch_dims_t, precision, kernel_fn, kernel_epsilon, normalize_data ): """ Constructs kernel features for fast generalized attention Args: data: input for which features are computes projection_matrix: matrix used to compute features batch_dims_t: tuple of batch dimensions precision: precision parameter kernel_fn: kernel function used kernel_epsilon: additive positive term added to every feature for numerical stability normalize_data: predicate indicating whether data should be normalized Returns: Random features for fast generalized attention. """ if normalize_data: data_normalizer = 1.0 / (jnp.sqrt(jnp.sqrt(data.shape[-1]))) else: data_normalizer = 1.0 if projection_matrix is None: return kernel_fn(data_normalizer * data) + kernel_epsilon else: data_mod_shape = data.shape[0 : len(batch_dims_t)] + projection_matrix.shape data_thick_random_matrix = jnp.zeros(data_mod_shape) + projection_matrix data_dash = lax.dot_general( data_normalizer * data, data_thick_random_matrix, (((data.ndim - 1,), (data_thick_random_matrix.ndim - 1,)), (batch_dims_t, batch_dims_t)), precision=precision, ) data_prime = kernel_fn(data_dash) + kernel_epsilon return data_prime def make_fast_softmax_attention( qkv_dim, renormalize_attention=True, numerical_stabilizer=0.000001, nb_features=256, ortho_features=True, ortho_scaling=0.0, redraw_features=True, unidirectional=False, nonnegative_features=True, lax_scan_unroll=1, ): """Construct a fast softmax attention method.""" logging.info( "Fast softmax attention: %s features and orthogonal=%s, renormalize=%s", nb_features, ortho_features, renormalize_attention, ) if ortho_features: matrix_creator = functools.partial(GaussianOrthogonalRandomMatrix, nb_features, qkv_dim, scaling=ortho_scaling) else: matrix_creator = functools.partial(GaussianUnstructuredRandomMatrix, nb_features, qkv_dim) if nonnegative_features: def kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data=True ): return nonnegative_softmax_kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data, numerical_stabilizer, ) else: def kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data=True ): del is_query return sincos_softmax_kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, normalize_data ) attention_fn = FastAttentionviaLowRankDecomposition( matrix_creator, kernel_feature_creator, renormalize_attention=renormalize_attention, numerical_stabilizer=numerical_stabilizer, redraw_features=redraw_features, unidirectional=unidirectional, lax_scan_unroll=lax_scan_unroll, ).dot_product_attention return attention_fn def make_fast_generalized_attention( qkv_dim, renormalize_attention=True, numerical_stabilizer=0.0, nb_features=256, features_type="deterministic", kernel_fn=jax.nn.relu, kernel_epsilon=0.001, redraw_features=False, unidirectional=False, lax_scan_unroll=1, ): """Construct a fast generalized attention menthod.""" logging.info("Fast generalized attention.: %s features and renormalize=%s", nb_features, renormalize_attention) if features_type == "ortho": matrix_creator = functools.partial(GaussianOrthogonalRandomMatrix, nb_features, qkv_dim, scaling=False) elif features_type == "iid": matrix_creator = functools.partial(GaussianUnstructuredRandomMatrix, nb_features, qkv_dim) elif features_type == "deterministic": matrix_creator = None else: raise ValueError("Unknown feature value type") def kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data=False ): del attention_dims_t del is_query return generalized_kernel_feature_creator( data, projection_matrix, batch_dims_t, precision, kernel_fn, kernel_epsilon, normalize_data ) attention_fn = FastAttentionviaLowRankDecomposition( matrix_creator, kernel_feature_creator, renormalize_attention=renormalize_attention, numerical_stabilizer=numerical_stabilizer, redraw_features=redraw_features, unidirectional=unidirectional, lax_scan_unroll=lax_scan_unroll, ).dot_product_attention return attention_fn class RandomMatrix(object): r""" Abstract class providing a method for constructing 2D random arrays. Class is responsible for constructing 2D random arrays. """ __metaclass__ = abc.ABCMeta @abc.abstractmethod def get_2d_array(self): raise NotImplementedError("Abstract method") class GaussianUnstructuredRandomMatrix(RandomMatrix): def __init__(self, nb_rows, nb_columns, key): self.nb_rows = nb_rows self.nb_columns = nb_columns self.key = key def get_2d_array(self): return random.normal(self.key, (self.nb_rows, self.nb_columns)) class GaussianOrthogonalRandomMatrix(RandomMatrix): r""" Class providing a method to create Gaussian orthogonal matrix. Class is responsible for constructing 2D Gaussian orthogonal arrays. """ def __init__(self, nb_rows, nb_columns, key, scaling=0): self.nb_rows = nb_rows self.nb_columns = nb_columns self.key = key self.scaling = scaling def get_2d_array(self): nb_full_blocks = int(self.nb_rows / self.nb_columns) block_list = [] rng = self.key for _ in range(nb_full_blocks): rng, rng_input = jax.random.split(rng) unstructured_block = random.normal(rng_input, (self.nb_columns, self.nb_columns)) q, _ = jnp.linalg.qr(unstructured_block) q = jnp.transpose(q) block_list.append(q) remaining_rows = self.nb_rows - nb_full_blocks * self.nb_columns if remaining_rows > 0: rng, rng_input = jax.random.split(rng) unstructured_block = random.normal(rng_input, (self.nb_columns, self.nb_columns)) q, _ = jnp.linalg.qr(unstructured_block) q = jnp.transpose(q) block_list.append(q[0:remaining_rows]) final_matrix = jnp.vstack(block_list) if self.scaling == 0: multiplier = jnp.linalg.norm(random.normal(self.key, (self.nb_rows, self.nb_columns)), axis=1) elif self.scaling == 1: multiplier = jnp.sqrt(float(self.nb_columns)) * jnp.ones((self.nb_rows)) else: raise ValueError("Scaling must be one of {0, 1}. Was %s" % self._scaling) return jnp.matmul(jnp.diag(multiplier), final_matrix) class FastAttention(object): r""" Abstract class providing a method for fast attention. Class is responsible for providing a method <dot_product_attention> for fast approximate attention. """ __metaclass__ = abc.ABCMeta @abc.abstractmethod def dot_product_attention( self, query, key, value, dtype=jnp.float32, bias=None, axis=None, broadcast_dropout=True, dropout_rng=None, dropout_rate=0.0, deterministic=False, precision=None, ): """ Computes dot-product attention given query, key, and value. This is the core function for applying fast approximate dot-product attention. It calculates the attention weights given query and key and combines the values using the attention weights. This function supports multi-dimensional inputs Args: query: queries for calculating attention with shape of [batch_size, dim1, dim2, ..., dimN, num_heads, mem_channels]. key: keys for calculating attention with shape of [batch_size, dim1, dim2, ..., dimN, num_heads, mem_channels]. value: values to be used in attention with shape of [batch_size, dim1, dim2,..., dimN, num_heads, value_channels]. dtype: the dtype of the computation (default: float32) bias: bias for the attention weights. This can be used for incorporating autoregressive mask, padding mask, proximity bias. axis: axises over which the attention is applied. broadcast_dropout: bool: use a broadcasted dropout along batch dims. dropout_rng: JAX PRNGKey: to be used for dropout. dropout_rate: dropout rate. deterministic: bool, deterministic or not (to apply dropout). precision: numerical precision of the computation see `jax.lax.Precision` for details Returns: Output of shape [bs, dim1, dim2, ..., dimN,, num_heads, value_channels]. """ raise NotImplementedError("Abstract method") def _numerator(z_slice_shape, precision, unroll=1): def fwd(qs, ks, vs): def body(p, qkv): (q, k, v) = qkv p += jnp.einsum("...m,...d->...md", k, v, precision=precision) X_slice = jnp.einsum("...m,...md->...d", q, p, precision=precision) return p, X_slice init_value = jnp.zeros(z_slice_shape) p, W = lax.scan(body, init_value, (qs, ks, vs), unroll=unroll) return W, (p, qs, ks, vs) def bwd(pqkv, W_ct): def body(carry, qkv_xct): p, p_ct = carry q, k, v, x_ct = qkv_xct q_ct = jnp.einsum("...d,...md->...m", x_ct, p, precision=precision) p_ct += jnp.einsum("...d,...m->...md", x_ct, q, precision=precision) k_ct = jnp.einsum("...md,...d->...m", p_ct, v, precision=precision) v_ct = jnp.einsum("...md,...m->...d", p_ct, k, precision=precision) p -= jnp.einsum("...m,...d->...md", k, v, precision=precision) return (p, p_ct), (q_ct, k_ct, v_ct) p, qs, ks, vs = pqkv _, (qs_ct, ks_ct, vs_ct) = lax.scan( body, (p, jnp.zeros_like(p)), (qs, ks, vs, W_ct), reverse=True, unroll=unroll ) return qs_ct, ks_ct, vs_ct @jax.custom_vjp def _numerator_impl(qs, ks, vs): W, _ = fwd(qs, ks, vs) return W _numerator_impl.defvjp(fwd, bwd) return _numerator_impl def _denominator(t_slice_shape, precision, unroll=1): def fwd(qs, ks): def body(p, qk): q, k = qk p += k x = jnp.einsum("...m,...m->...", q, p, precision=precision) return p, x p = jnp.zeros(t_slice_shape) p, R = lax.scan(body, p, (qs, ks), unroll=unroll) return R, (qs, ks, p) def bwd(qkp, R_ct): def body(carry, qkx): p, p_ct = carry q, k, x_ct = qkx q_ct = jnp.einsum("...,...m->...m", x_ct, p, precision=precision) p_ct += jnp.einsum("...,...m->...m", x_ct, q, precision=precision) k_ct = p_ct p -= k return (p, p_ct), (q_ct, k_ct) qs, ks, p = qkp _, (qs_ct, ks_ct) = lax.scan(body, (p, jnp.zeros_like(p)), (qs, ks, R_ct), reverse=True, unroll=unroll) return (qs_ct, ks_ct) @jax.custom_vjp def _denominator_impl(qs, ks): R, _ = fwd(qs, ks) return R _denominator_impl.defvjp(fwd, bwd) return _denominator_impl class FastAttentionviaLowRankDecomposition(FastAttention): r""" Class providing a method for fast attention via low rank decomposition. Class is responsible for providing a method <dot_product_attention> for fast dot-product attention with the use of low rank decomposition (e.g. with random feature maps). """ def __init__( self, matrix_creator, kernel_feature_creator, renormalize_attention, numerical_stabilizer, redraw_features, unidirectional, lax_scan_unroll=1, ): # For optimal GPU performance, set to 16. rng = random.PRNGKey(0) self.matrix_creator = matrix_creator self.projection_matrix = self.draw_weights(rng) self.kernel_feature_creator = kernel_feature_creator self.renormalize_attention = renormalize_attention self.numerical_stabilizer = numerical_stabilizer self.redraw_features = redraw_features self.unidirectional = unidirectional self.lax_scan_unroll = lax_scan_unroll def draw_weights(self, key): if self.matrix_creator is None: return None matrixrng, _ = random.split(key) projection_matrix = self.matrix_creator(key=matrixrng).get_2d_array() return projection_matrix def dot_product_attention( self, query, key, value, dtype=jnp.float32, bias=None, axis=None, broadcast_dropout=True, dropout_rng=None, dropout_rate=0.0, deterministic=False, precision=None, ): assert key.shape[:-1] == value.shape[:-1] assert query.shape[0:1] == key.shape[0:1] and query.shape[-1] == key.shape[-1] if axis is None: axis = tuple(range(1, key.ndim - 2)) if not isinstance(axis, Iterable): axis = (axis,) assert key.ndim == query.ndim assert key.ndim == value.ndim for ax in axis: if not (query.ndim >= 3 and 1 <= ax < query.ndim - 2): raise ValueError("Attention axis must be between the batch axis and the last-two axes.") n = key.ndim # Constructing projection tensor. if self.redraw_features: # TODO(kchoro): Get rid of the constant below. query_seed = lax.convert_element_type(jnp.ceil(jnp.sum(query) * 10000000.0), jnp.int32) rng = random.PRNGKey(query_seed) self.projection_matrix = self.draw_weights(rng) # batch_dims is <bs, <non-attention dims>, num_heads> batch_dims = tuple(onp.delete(range(n), axis + (n - 1,))) # q & k -> (bs, <non-attention dims>, num_heads, <attention dims>, channels) qk_perm = batch_dims + axis + (n - 1,) k_extra_perm = axis + batch_dims + (n - 1,) key_extra = key.transpose(k_extra_perm) key = key.transpose(qk_perm) query = query.transpose(qk_perm) # v -> (bs, <non-attention dims>, num_heads, <attention dims>, channels) v_perm = batch_dims + axis + (n - 1,) value = value.transpose(v_perm) batch_dims_t = tuple(range(len(batch_dims))) attention_dims_t = tuple(range(len(batch_dims), len(batch_dims) + len(axis))) # Constructing tensors Q^{'} and K^{'}. query_prime = self.kernel_feature_creator( query, self.projection_matrix, attention_dims_t, batch_dims_t, precision, True ) key_prime = self.kernel_feature_creator( key, self.projection_matrix, attention_dims_t, batch_dims_t, precision, False ) if self.unidirectional: index = attention_dims_t[0] z_slice_shape = key_prime.shape[0 : len(batch_dims_t)] + (key_prime.shape[-1],) + (value.shape[-1],) numerator_fn = _numerator(z_slice_shape, precision, self.lax_scan_unroll) W = numerator_fn( jnp.moveaxis(query_prime, index, 0), jnp.moveaxis(key_prime, index, 0), jnp.moveaxis(value, index, 0) ) # Constructing W = (Q^{'}(K^{'})^{T})_{masked}V W = jnp.moveaxis(W, 0, index) if not self.renormalize_attention: # Unidirectional, not-normalized attention. perm_inv = _invert_perm(qk_perm) result = W.transpose(perm_inv) return result else: # Unidirectional, normalized attention. thick_all_ones = jnp.zeros(key.shape[0:-1]) + jnp.ones(key_extra.shape[0 : len(axis)]) index = attention_dims_t[0] t_slice_shape = key_prime.shape[0 : len(batch_dims_t)] + (key_prime.shape[-1],) denominator_fn = _denominator(t_slice_shape, precision, self.lax_scan_unroll) R = denominator_fn(jnp.moveaxis(query_prime, index, 0), jnp.moveaxis(key_prime, index, 0)) R = jnp.moveaxis(R, 0, index) else: contract_query = tuple(range(len(batch_dims) + len(axis), len(batch_dims) + len(axis) + 1)) contract_z = tuple(range(len(batch_dims), len(batch_dims) + 1)) # Constructing Z = (K^{'})^{T}V # Z (bs, <non-attention dims>, num_heads, channels_m, channels_v) Z = lax.dot_general( key_prime, value, ((attention_dims_t, attention_dims_t), (batch_dims_t, batch_dims_t)), precision=precision, ) # Constructing W = Q^{'}Z = Q^{'}(K^{'})^{T}V # q (bs, <non-attention dims>, num_heads, <attention dims>, channels_m) # Z (bs, <non-attention dims>, num_heads, channels_m, channels_v) # W (bs, <non-attention dims>, num_heads, <attention dims>, channels_v) W = lax.dot_general( query_prime, Z, ((contract_query, contract_z), (batch_dims_t, batch_dims_t)), precision=precision ) if not self.renormalize_attention: # Bidirectional, not-normalized attention. perm_inv = _invert_perm(qk_perm) result = W.transpose(perm_inv) return result else: # Bidirectional, normalized attention. thick_all_ones = jnp.zeros(key.shape[0:-1]) + jnp.ones(key_extra.shape[0 : len(axis)]) contract_key = tuple(range(len(batch_dims), len(batch_dims) + len(axis))) contract_thick_all_ones = tuple(range(thick_all_ones.ndim - len(axis), thick_all_ones.ndim)) # Construct T = (K^{'})^{T} 1_L # k (bs, <non-attention dims>, num_heads, <attention dims>, channels) T = lax.dot_general( key_prime, thick_all_ones, ((contract_key, contract_thick_all_ones), (batch_dims_t, batch_dims_t)), precision=precision, ) # Construct partition function: R = Q^{'} T = Q^{'}(K^{'})^{T} 1_L # q_p (bs, <non-attention dims>, num_heads, <attention dims>, channs_m) # T (bs, <non-attention dims>, num_heads, channels_m) R = lax.dot_general( query_prime, T, (((query_prime.ndim - 1,), (T.ndim - 1,)), (batch_dims_t, range(0, len(T.shape) - 1))), precision=precision, ) R = R + 2 * self.numerical_stabilizer * (jnp.abs(R) <= self.numerical_stabilizer) R = jnp.reciprocal(R) R = jnp.expand_dims(R, len(R.shape)) # W (bs, <non-attention dims>, num_heads, <attention dims>, channels_v) # R (bs, <non-attention dims>, num_heads, <attention dims>, extra_channel) result = W * R # back to (bs, dim1, dim2, ..., dimN, num_heads, channels) perm_inv = _invert_perm(qk_perm) result = result.transpose(perm_inv) return result def _invert_perm(perm): perm_inv = [0] * len(perm) for i, j in enumerate(perm): perm_inv[j] = i return tuple(perm_inv)

# coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ IMPORTANT: This code was copied from https://github.com/google-research/google-research/blob/master/performer/fast_self_attention/fast_self_attention.py on 6/11/2020. This is very new code, so it might be prone to change soon -> make sure to check the original code and update accordingly Core Fast Attention Module for Flax. Implementation of the approximate fast softmax and generalized attention mechanism leveraging structured random feature maps [RFM] techniques and low rank decomposition of the attention matrix. """ # pylint: disable=invalid-name, missing-function-docstring, line-too-long import abc import functools from collections.abc import Iterable # pylint: disable=g-importing-member import numpy as onp from absl import logging import jax import jax.numpy as jnp from jax import lax, random def nonnegative_softmax_kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data=True, eps=0.0001 ): """ Constructs nonnegative kernel features for fast softmax attention Args: data: input for which features are computes projection_matrix: random matrix used to compute features attention_dims_t: tuple of attention dimensions batch_dims_t: tuple of batch dimensions precision: precision parameter is_query: predicate indicating whether input data corresponds to queries or keys normalize_data: predicate indicating whether data should be normalized, eps: numerical stabilizer Returns: Random features for fast softmax attention. """ del attention_dims_t if normalize_data: # We have e^{qk^T/sqrt{d}} = e^{q_norm k_norm^T}, where # w_norm = w * data_normalizer for w in {q,k}. data_normalizer = 1.0 / (jnp.sqrt(jnp.sqrt(data.shape[-1]))) else: data_normalizer = 1.0 ratio = 1.0 / jnp.sqrt(projection_matrix.shape[0]) data_mod_shape = data.shape[0 : len(batch_dims_t)] + projection_matrix.shape data_thick_random_matrix = jnp.zeros(data_mod_shape) + projection_matrix data_dash = lax.dot_general( data_normalizer * data, data_thick_random_matrix, (((data.ndim - 1,), (data_thick_random_matrix.ndim - 1,)), (batch_dims_t, batch_dims_t)), precision=precision, ) diag_data = jnp.square(data) diag_data = jnp.sum(diag_data, axis=data.ndim - 1) diag_data = (diag_data / 2.0) * data_normalizer * data_normalizer diag_data = jnp.expand_dims(diag_data, axis=data.ndim - 1) if is_query: last_dims_t = (len(data_dash.shape) - 1,) data_dash = ratio * ( jnp.exp(data_dash - diag_data - jnp.max(data_dash, axis=last_dims_t, keepdims=True)) + eps ) else: data_dash = ratio * (jnp.exp(data_dash - diag_data - jnp.max(data_dash)) + eps) return data_dash def sincos_softmax_kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, normalize_data=True ): """ Constructs kernel sin-cos features for fast softmax attention Args: data: input for which features are computes projection_matrix: random matrix used to compute features attention_dims_t: tuple of attention dimensions batch_dims_t: tuple of batch dimensions precision: precision parameter normalize_data: predicate indicating whether data should be normalized Returns: Random features for fast softmax attention. """ if normalize_data: # We have: exp(qk^T/sqrt{d}) = exp(|q|^2/2sqrt{d}) * exp(|k|^2/2sqrt{d}) * # exp(-(|q*c-k*c|^2)/2), where c = 1.0 / sqrt{sqrt{d}}. data_normalizer = 1.0 / (jnp.sqrt(jnp.sqrt(data.shape[-1]))) else: data_normalizer = 1.0 ratio = 1.0 / jnp.sqrt(projection_matrix.shape[0]) data_mod_shape = data.shape[0 : len(batch_dims_t)] + projection_matrix.shape data_thick_random_matrix = jnp.zeros(data_mod_shape) + projection_matrix data_dash = lax.dot_general( data_normalizer * data, data_thick_random_matrix, (((data.ndim - 1,), (data_thick_random_matrix.ndim - 1,)), (batch_dims_t, batch_dims_t)), precision=precision, ) data_dash_cos = ratio * jnp.cos(data_dash) data_dash_sin = ratio * jnp.sin(data_dash) data_dash = jnp.concatenate((data_dash_cos, data_dash_sin), axis=-1) # Constructing D_data and data^{'} diag_data = jnp.square(data) diag_data = jnp.sum(diag_data, axis=data.ndim - 1) diag_data = (diag_data / 2.0) * data_normalizer * data_normalizer diag_data = jnp.expand_dims(diag_data, axis=data.ndim - 1) # Additional renormalization for numerical stability data_renormalizer = jnp.max(diag_data, attention_dims_t, keepdims=True) diag_data -= data_renormalizer diag_data = jnp.exp(diag_data) data_prime = data_dash * diag_data return data_prime def generalized_kernel_feature_creator( data, projection_matrix, batch_dims_t, precision, kernel_fn, kernel_epsilon, normalize_data ): """ Constructs kernel features for fast generalized attention Args: data: input for which features are computes projection_matrix: matrix used to compute features batch_dims_t: tuple of batch dimensions precision: precision parameter kernel_fn: kernel function used kernel_epsilon: additive positive term added to every feature for numerical stability normalize_data: predicate indicating whether data should be normalized Returns: Random features for fast generalized attention. """ if normalize_data: data_normalizer = 1.0 / (jnp.sqrt(jnp.sqrt(data.shape[-1]))) else: data_normalizer = 1.0 if projection_matrix is None: return kernel_fn(data_normalizer * data) + kernel_epsilon else: data_mod_shape = data.shape[0 : len(batch_dims_t)] + projection_matrix.shape data_thick_random_matrix = jnp.zeros(data_mod_shape) + projection_matrix data_dash = lax.dot_general( data_normalizer * data, data_thick_random_matrix, (((data.ndim - 1,), (data_thick_random_matrix.ndim - 1,)), (batch_dims_t, batch_dims_t)), precision=precision, ) data_prime = kernel_fn(data_dash) + kernel_epsilon return data_prime def make_fast_softmax_attention( qkv_dim, renormalize_attention=True, numerical_stabilizer=0.000001, nb_features=256, ortho_features=True, ortho_scaling=0.0, redraw_features=True, unidirectional=False, nonnegative_features=True, lax_scan_unroll=1, ): """Construct a fast softmax attention method.""" logging.info( "Fast softmax attention: %s features and orthogonal=%s, renormalize=%s", nb_features, ortho_features, renormalize_attention, ) if ortho_features: matrix_creator = functools.partial(GaussianOrthogonalRandomMatrix, nb_features, qkv_dim, scaling=ortho_scaling) else: matrix_creator = functools.partial(GaussianUnstructuredRandomMatrix, nb_features, qkv_dim) if nonnegative_features: def kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data=True ): return nonnegative_softmax_kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data, numerical_stabilizer, ) else: def kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data=True ): del is_query return sincos_softmax_kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, normalize_data ) attention_fn = FastAttentionviaLowRankDecomposition( matrix_creator, kernel_feature_creator, renormalize_attention=renormalize_attention, numerical_stabilizer=numerical_stabilizer, redraw_features=redraw_features, unidirectional=unidirectional, lax_scan_unroll=lax_scan_unroll, ).dot_product_attention return attention_fn def make_fast_generalized_attention( qkv_dim, renormalize_attention=True, numerical_stabilizer=0.0, nb_features=256, features_type="deterministic", kernel_fn=jax.nn.relu, kernel_epsilon=0.001, redraw_features=False, unidirectional=False, lax_scan_unroll=1, ): """Construct a fast generalized attention menthod.""" logging.info("Fast generalized attention.: %s features and renormalize=%s", nb_features, renormalize_attention) if features_type == "ortho": matrix_creator = functools.partial(GaussianOrthogonalRandomMatrix, nb_features, qkv_dim, scaling=False) elif features_type == "iid": matrix_creator = functools.partial(GaussianUnstructuredRandomMatrix, nb_features, qkv_dim) elif features_type == "deterministic": matrix_creator = None else: raise ValueError("Unknown feature value type") def kernel_feature_creator( data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data=False ): del attention_dims_t del is_query return generalized_kernel_feature_creator( data, projection_matrix, batch_dims_t, precision, kernel_fn, kernel_epsilon, normalize_data ) attention_fn = FastAttentionviaLowRankDecomposition( matrix_creator, kernel_feature_creator, renormalize_attention=renormalize_attention, numerical_stabilizer=numerical_stabilizer, redraw_features=redraw_features, unidirectional=unidirectional, lax_scan_unroll=lax_scan_unroll, ).dot_product_attention return attention_fn class RandomMatrix(object): r""" Abstract class providing a method for constructing 2D random arrays. Class is responsible for constructing 2D random arrays. """ __metaclass__ = abc.ABCMeta @abc.abstractmethod def get_2d_array(self): raise NotImplementedError("Abstract method") class GaussianUnstructuredRandomMatrix(RandomMatrix): def __init__(self, nb_rows, nb_columns, key): self.nb_rows = nb_rows self.nb_columns = nb_columns self.key = key def get_2d_array(self): return random.normal(self.key, (self.nb_rows, self.nb_columns)) class GaussianOrthogonalRandomMatrix(RandomMatrix): r""" Class providing a method to create Gaussian orthogonal matrix. Class is responsible for constructing 2D Gaussian orthogonal arrays. """ def __init__(self, nb_rows, nb_columns, key, scaling=0): self.nb_rows = nb_rows self.nb_columns = nb_columns self.key = key self.scaling = scaling def get_2d_array(self): nb_full_blocks = int(self.nb_rows / self.nb_columns) block_list = [] rng = self.key for _ in range(nb_full_blocks): rng, rng_input = jax.random.split(rng) unstructured_block = random.normal(rng_input, (self.nb_columns, self.nb_columns)) q, _ = jnp.linalg.qr(unstructured_block) q = jnp.transpose(q) block_list.append(q) remaining_rows = self.nb_rows - nb_full_blocks * self.nb_columns if remaining_rows > 0: rng, rng_input = jax.random.split(rng) unstructured_block = random.normal(rng_input, (self.nb_columns, self.nb_columns)) q, _ = jnp.linalg.qr(unstructured_block) q = jnp.transpose(q) block_list.append(q[0:remaining_rows]) final_matrix = jnp.vstack(block_list) if self.scaling == 0: multiplier = jnp.linalg.norm(random.normal(self.key, (self.nb_rows, self.nb_columns)), axis=1) elif self.scaling == 1: multiplier = jnp.sqrt(float(self.nb_columns)) * jnp.ones((self.nb_rows)) else: raise ValueError("Scaling must be one of {0, 1}. Was %s" % self._scaling) return jnp.matmul(jnp.diag(multiplier), final_matrix) class FastAttention(object): r""" Abstract class providing a method for fast attention. Class is responsible for providing a method <dot_product_attention> for fast approximate attention. """ __metaclass__ = abc.ABCMeta @abc.abstractmethod def dot_product_attention( self, query, key, value, dtype=jnp.float32, bias=None, axis=None, broadcast_dropout=True, dropout_rng=None, dropout_rate=0.0, deterministic=False, precision=None, ): """ Computes dot-product attention given query, key, and value. This is the core function for applying fast approximate dot-product attention. It calculates the attention weights given query and key and combines the values using the attention weights. This function supports multi-dimensional inputs Args: query: queries for calculating attention with shape of [batch_size, dim1, dim2, ..., dimN, num_heads, mem_channels]. key: keys for calculating attention with shape of [batch_size, dim1, dim2, ..., dimN, num_heads, mem_channels]. value: values to be used in attention with shape of [batch_size, dim1, dim2,..., dimN, num_heads, value_channels]. dtype: the dtype of the computation (default: float32) bias: bias for the attention weights. This can be used for incorporating autoregressive mask, padding mask, proximity bias. axis: axises over which the attention is applied. broadcast_dropout: bool: use a broadcasted dropout along batch dims. dropout_rng: JAX PRNGKey: to be used for dropout. dropout_rate: dropout rate. deterministic: bool, deterministic or not (to apply dropout). precision: numerical precision of the computation see `jax.lax.Precision` for details Returns: Output of shape [bs, dim1, dim2, ..., dimN,, num_heads, value_channels]. """ raise NotImplementedError("Abstract method") def _numerator(z_slice_shape, precision, unroll=1): def fwd(qs, ks, vs): def body(p, qkv): (q, k, v) = qkv p += jnp.einsum("...m,...d->...md", k, v, precision=precision) X_slice = jnp.einsum("...m,...md->...d", q, p, precision=precision) return p, X_slice init_value = jnp.zeros(z_slice_shape) p, W = lax.scan(body, init_value, (qs, ks, vs), unroll=unroll) return W, (p, qs, ks, vs) def bwd(pqkv, W_ct): def body(carry, qkv_xct): p, p_ct = carry q, k, v, x_ct = qkv_xct q_ct = jnp.einsum("...d,...md->...m", x_ct, p, precision=precision) p_ct += jnp.einsum("...d,...m->...md", x_ct, q, precision=precision) k_ct = jnp.einsum("...md,...d->...m", p_ct, v, precision=precision) v_ct = jnp.einsum("...md,...m->...d", p_ct, k, precision=precision) p -= jnp.einsum("...m,...d->...md", k, v, precision=precision) return (p, p_ct), (q_ct, k_ct, v_ct) p, qs, ks, vs = pqkv _, (qs_ct, ks_ct, vs_ct) = lax.scan( body, (p, jnp.zeros_like(p)), (qs, ks, vs, W_ct), reverse=True, unroll=unroll ) return qs_ct, ks_ct, vs_ct @jax.custom_vjp def _numerator_impl(qs, ks, vs): W, _ = fwd(qs, ks, vs) return W _numerator_impl.defvjp(fwd, bwd) return _numerator_impl def _denominator(t_slice_shape, precision, unroll=1): def fwd(qs, ks): def body(p, qk): q, k = qk p += k x = jnp.einsum("...m,...m->...", q, p, precision=precision) return p, x p = jnp.zeros(t_slice_shape) p, R = lax.scan(body, p, (qs, ks), unroll=unroll) return R, (qs, ks, p) def bwd(qkp, R_ct): def body(carry, qkx): p, p_ct = carry q, k, x_ct = qkx q_ct = jnp.einsum("...,...m->...m", x_ct, p, precision=precision) p_ct += jnp.einsum("...,...m->...m", x_ct, q, precision=precision) k_ct = p_ct p -= k return (p, p_ct), (q_ct, k_ct) qs, ks, p = qkp _, (qs_ct, ks_ct) = lax.scan(body, (p, jnp.zeros_like(p)), (qs, ks, R_ct), reverse=True, unroll=unroll) return (qs_ct, ks_ct) @jax.custom_vjp def _denominator_impl(qs, ks): R, _ = fwd(qs, ks) return R _denominator_impl.defvjp(fwd, bwd) return _denominator_impl class FastAttentionviaLowRankDecomposition(FastAttention): r""" Class providing a method for fast attention via low rank decomposition. Class is responsible for providing a method <dot_product_attention> for fast dot-product attention with the use of low rank decomposition (e.g. with random feature maps). """ def __init__( self, matrix_creator, kernel_feature_creator, renormalize_attention, numerical_stabilizer, redraw_features, unidirectional, lax_scan_unroll=1, ): # For optimal GPU performance, set to 16. rng = random.PRNGKey(0) self.matrix_creator = matrix_creator self.projection_matrix = self.draw_weights(rng) self.kernel_feature_creator = kernel_feature_creator self.renormalize_attention = renormalize_attention self.numerical_stabilizer = numerical_stabilizer self.redraw_features = redraw_features self.unidirectional = unidirectional self.lax_scan_unroll = lax_scan_unroll def draw_weights(self, key): if self.matrix_creator is None: return None matrixrng, _ = random.split(key) projection_matrix = self.matrix_creator(key=matrixrng).get_2d_array() return projection_matrix def dot_product_attention( self, query, key, value, dtype=jnp.float32, bias=None, axis=None, broadcast_dropout=True, dropout_rng=None, dropout_rate=0.0, deterministic=False, precision=None, ): assert key.shape[:-1] == value.shape[:-1] assert query.shape[0:1] == key.shape[0:1] and query.shape[-1] == key.shape[-1] if axis is None: axis = tuple(range(1, key.ndim - 2)) if not isinstance(axis, Iterable): axis = (axis,) assert key.ndim == query.ndim assert key.ndim == value.ndim for ax in axis: if not (query.ndim >= 3 and 1 <= ax < query.ndim - 2): raise ValueError("Attention axis must be between the batch axis and the last-two axes.") n = key.ndim # Constructing projection tensor. if self.redraw_features: # TODO(kchoro): Get rid of the constant below. query_seed = lax.convert_element_type(jnp.ceil(jnp.sum(query) * 10000000.0), jnp.int32) rng = random.PRNGKey(query_seed) self.projection_matrix = self.draw_weights(rng) # batch_dims is <bs, <non-attention dims>, num_heads> batch_dims = tuple(onp.delete(range(n), axis + (n - 1,))) # q & k -> (bs, <non-attention dims>, num_heads, <attention dims>, channels) qk_perm = batch_dims + axis + (n - 1,) k_extra_perm = axis + batch_dims + (n - 1,) key_extra = key.transpose(k_extra_perm) key = key.transpose(qk_perm) query = query.transpose(qk_perm) # v -> (bs, <non-attention dims>, num_heads, <attention dims>, channels) v_perm = batch_dims + axis + (n - 1,) value = value.transpose(v_perm) batch_dims_t = tuple(range(len(batch_dims))) attention_dims_t = tuple(range(len(batch_dims), len(batch_dims) + len(axis))) # Constructing tensors Q^{'} and K^{'}. query_prime = self.kernel_feature_creator( query, self.projection_matrix, attention_dims_t, batch_dims_t, precision, True ) key_prime = self.kernel_feature_creator( key, self.projection_matrix, attention_dims_t, batch_dims_t, precision, False ) if self.unidirectional: index = attention_dims_t[0] z_slice_shape = key_prime.shape[0 : len(batch_dims_t)] + (key_prime.shape[-1],) + (value.shape[-1],) numerator_fn = _numerator(z_slice_shape, precision, self.lax_scan_unroll) W = numerator_fn( jnp.moveaxis(query_prime, index, 0), jnp.moveaxis(key_prime, index, 0), jnp.moveaxis(value, index, 0) ) # Constructing W = (Q^{'}(K^{'})^{T})_{masked}V W = jnp.moveaxis(W, 0, index) if not self.renormalize_attention: # Unidirectional, not-normalized attention. perm_inv = _invert_perm(qk_perm) result = W.transpose(perm_inv) return result else: # Unidirectional, normalized attention. thick_all_ones = jnp.zeros(key.shape[0:-1]) + jnp.ones(key_extra.shape[0 : len(axis)]) index = attention_dims_t[0] t_slice_shape = key_prime.shape[0 : len(batch_dims_t)] + (key_prime.shape[-1],) denominator_fn = _denominator(t_slice_shape, precision, self.lax_scan_unroll) R = denominator_fn(jnp.moveaxis(query_prime, index, 0), jnp.moveaxis(key_prime, index, 0)) R = jnp.moveaxis(R, 0, index) else: contract_query = tuple(range(len(batch_dims) + len(axis), len(batch_dims) + len(axis) + 1)) contract_z = tuple(range(len(batch_dims), len(batch_dims) + 1)) # Constructing Z = (K^{'})^{T}V # Z (bs, <non-attention dims>, num_heads, channels_m, channels_v) Z = lax.dot_general( key_prime, value, ((attention_dims_t, attention_dims_t), (batch_dims_t, batch_dims_t)), precision=precision, ) # Constructing W = Q^{'}Z = Q^{'}(K^{'})^{T}V # q (bs, <non-attention dims>, num_heads, <attention dims>, channels_m) # Z (bs, <non-attention dims>, num_heads, channels_m, channels_v) # W (bs, <non-attention dims>, num_heads, <attention dims>, channels_v) W = lax.dot_general( query_prime, Z, ((contract_query, contract_z), (batch_dims_t, batch_dims_t)), precision=precision ) if not self.renormalize_attention: # Bidirectional, not-normalized attention. perm_inv = _invert_perm(qk_perm) result = W.transpose(perm_inv) return result else: # Bidirectional, normalized attention. thick_all_ones = jnp.zeros(key.shape[0:-1]) + jnp.ones(key_extra.shape[0 : len(axis)]) contract_key = tuple(range(len(batch_dims), len(batch_dims) + len(axis))) contract_thick_all_ones = tuple(range(thick_all_ones.ndim - len(axis), thick_all_ones.ndim)) # Construct T = (K^{'})^{T} 1_L # k (bs, <non-attention dims>, num_heads, <attention dims>, channels) T = lax.dot_general( key_prime, thick_all_ones, ((contract_key, contract_thick_all_ones), (batch_dims_t, batch_dims_t)), precision=precision, ) # Construct partition function: R = Q^{'} T = Q^{'}(K^{'})^{T} 1_L # q_p (bs, <non-attention dims>, num_heads, <attention dims>, channs_m) # T (bs, <non-attention dims>, num_heads, channels_m) R = lax.dot_general( query_prime, T, (((query_prime.ndim - 1,), (T.ndim - 1,)), (batch_dims_t, range(0, len(T.shape) - 1))), precision=precision, ) R = R + 2 * self.numerical_stabilizer * (jnp.abs(R) <= self.numerical_stabilizer) R = jnp.reciprocal(R) R = jnp.expand_dims(R, len(R.shape)) # W (bs, <non-attention dims>, num_heads, <attention dims>, channels_v) # R (bs, <non-attention dims>, num_heads, <attention dims>, extra_channel) result = W * R # back to (bs, dim1, dim2, ..., dimN, num_heads, channels) perm_inv = _invert_perm(qk_perm) result = result.transpose(perm_inv) return result def _invert_perm(perm): perm_inv = [0] * len(perm) for i, j in enumerate(perm): perm_inv[j] = i return tuple(perm_inv)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/detr/modeling_detr.py

# coding=utf-8 # Copyright 2021 Facebook AI Research The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch DETR model.""" import math import random from dataclasses import dataclass from typing import Dict, List, Optional, Tuple import torch from torch import Tensor, nn from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import torch_int_div from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_scipy_available, is_timm_available, logging, replace_return_docstrings, requires_backends, ) from .configuration_detr import DetrConfig if is_scipy_available(): from scipy.optimize import linear_sum_assignment if is_timm_available(): from timm import create_model logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "DetrConfig" _CHECKPOINT_FOR_DOC = "facebook/detr-resnet-50" DETR_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/detr-resnet-50", # See all DETR models at https://huggingface.co/models?filter=detr ] @dataclass class DetrDecoderOutput(BaseModelOutputWithCrossAttentions): """ Base class for outputs of the DETR decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. This is useful when training the model with auxiliary decoding losses. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. """ intermediate_hidden_states: Optional[torch.FloatTensor] = None @dataclass class DetrModelOutput(Seq2SeqModelOutput): """ Base class for outputs of the DETR encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. This is useful when training the model with auxiliary decoding losses. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, sequence_length, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. """ intermediate_hidden_states: Optional[torch.FloatTensor] = None @dataclass class DetrObjectDetectionOutput(ModelOutput): """ Output type of [`DetrForObjectDetection`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized scale-invariant IoU loss. loss_dict (`Dict`, *optional*): A dictionary containing the individual losses. Useful for logging. logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): Classification logits (including no-object) for all queries. pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding). You can use [`~DetrFeatureExtractor.post_process_object_detection`] to retrieve the unnormalized bounding boxes. auxiliary_outputs (`list[Dict]`, *optional*): Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and `pred_boxes`) for each decoder layer. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the decoder of the model. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None loss_dict: Optional[Dict] = None logits: torch.FloatTensor = None pred_boxes: torch.FloatTensor = None auxiliary_outputs: Optional[List[Dict]] = None last_hidden_state: Optional[torch.FloatTensor] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class DetrSegmentationOutput(ModelOutput): """ Output type of [`DetrForSegmentation`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized scale-invariant IoU loss. loss_dict (`Dict`, *optional*): A dictionary containing the individual losses. Useful for logging. logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): Classification logits (including no-object) for all queries. pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding). You can use [`~DetrFeatureExtractor.post_process_object_detection`] to retrieve the unnormalized bounding boxes. pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height/4, width/4)`): Segmentation masks logits for all queries. See also [`~DetrFeatureExtractor.post_process_semantic_segmentation`] or [`~DetrFeatureExtractor.post_process_instance_segmentation`] [`~DetrFeatureExtractor.post_process_panoptic_segmentation`] to evaluate semantic, instance and panoptic segmentation masks respectively. auxiliary_outputs (`list[Dict]`, *optional*): Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and `pred_boxes`) for each decoder layer. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the decoder of the model. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None loss_dict: Optional[Dict] = None logits: torch.FloatTensor = None pred_boxes: torch.FloatTensor = None pred_masks: torch.FloatTensor = None auxiliary_outputs: Optional[List[Dict]] = None last_hidden_state: Optional[torch.FloatTensor] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None # BELOW: utilities copied from # https://github.com/facebookresearch/detr/blob/master/backbone.py class DetrFrozenBatchNorm2d(nn.Module): """ BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than torchvision.models.resnet[18,34,50,101] produce nans. """ def __init__(self, n): super().__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) def _load_from_state_dict( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ): num_batches_tracked_key = prefix + "num_batches_tracked" if num_batches_tracked_key in state_dict: del state_dict[num_batches_tracked_key] super()._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ) def forward(self, x): # move reshapes to the beginning # to make it user-friendly weight = self.weight.reshape(1, -1, 1, 1) bias = self.bias.reshape(1, -1, 1, 1) running_var = self.running_var.reshape(1, -1, 1, 1) running_mean = self.running_mean.reshape(1, -1, 1, 1) epsilon = 1e-5 scale = weight * (running_var + epsilon).rsqrt() bias = bias - running_mean * scale return x * scale + bias def replace_batch_norm(m, name=""): for attr_str in dir(m): target_attr = getattr(m, attr_str) if isinstance(target_attr, nn.BatchNorm2d): frozen = DetrFrozenBatchNorm2d(target_attr.num_features) bn = getattr(m, attr_str) frozen.weight.data.copy_(bn.weight) frozen.bias.data.copy_(bn.bias) frozen.running_mean.data.copy_(bn.running_mean) frozen.running_var.data.copy_(bn.running_var) setattr(m, attr_str, frozen) for n, ch in m.named_children(): replace_batch_norm(ch, n) class DetrTimmConvEncoder(nn.Module): """ Convolutional encoder (backbone) from the timm library. nn.BatchNorm2d layers are replaced by DetrFrozenBatchNorm2d as defined above. """ def __init__(self, name: str, dilation: bool, use_pretrained_backbone: bool, num_channels: int = 3): super().__init__() kwargs = {} if dilation: kwargs["output_stride"] = 16 requires_backends(self, ["timm"]) backbone = create_model( name, pretrained=use_pretrained_backbone, features_only=True, out_indices=(1, 2, 3, 4), in_chans=num_channels, **kwargs, ) # replace batch norm by frozen batch norm with torch.no_grad(): replace_batch_norm(backbone) self.model = backbone self.intermediate_channel_sizes = self.model.feature_info.channels() if "resnet" in name: for name, parameter in self.model.named_parameters(): if "layer2" not in name and "layer3" not in name and "layer4" not in name: parameter.requires_grad_(False) def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): # send pixel_values through the model to get list of feature maps features = self.model(pixel_values) out = [] for feature_map in features: # downsample pixel_mask to match shape of corresponding feature_map mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] out.append((feature_map, mask)) return out class DetrConvModel(nn.Module): """ This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder. """ def __init__(self, conv_encoder, position_embedding): super().__init__() self.conv_encoder = conv_encoder self.position_embedding = position_embedding def forward(self, pixel_values, pixel_mask): # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples out = self.conv_encoder(pixel_values, pixel_mask) pos = [] for feature_map, mask in out: # position encoding pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype)) return out, pos def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, target_len: Optional[int] = None): """ Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, target_seq_len, source_seq_len]`. """ batch_size, source_len = mask.size() target_len = target_len if target_len is not None else source_len expanded_mask = mask[:, None, None, :].expand(batch_size, 1, target_len, source_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min) class DetrSinePositionEmbedding(nn.Module): """ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized to work on images. """ def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None): super().__init__() self.embedding_dim = embedding_dim self.temperature = temperature self.normalize = normalize if scale is not None and normalize is False: raise ValueError("normalize should be True if scale is passed") if scale is None: scale = 2 * math.pi self.scale = scale def forward(self, pixel_values, pixel_mask): if pixel_mask is None: raise ValueError("No pixel mask provided") y_embed = pixel_mask.cumsum(1, dtype=torch.float32) x_embed = pixel_mask.cumsum(2, dtype=torch.float32) if self.normalize: y_embed = y_embed / (y_embed[:, -1:, :] + 1e-6) * self.scale x_embed = x_embed / (x_embed[:, :, -1:] + 1e-6) * self.scale dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device) dim_t = self.temperature ** (2 * torch_int_div(dim_t, 2) / self.embedding_dim) pos_x = x_embed[:, :, :, None] / dim_t pos_y = y_embed[:, :, :, None] / dim_t pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) return pos class DetrLearnedPositionEmbedding(nn.Module): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, embedding_dim=256): super().__init__() self.row_embeddings = nn.Embedding(50, embedding_dim) self.column_embeddings = nn.Embedding(50, embedding_dim) def forward(self, pixel_values, pixel_mask=None): height, width = pixel_values.shape[-2:] width_values = torch.arange(width, device=pixel_values.device) height_values = torch.arange(height, device=pixel_values.device) x_emb = self.column_embeddings(width_values) y_emb = self.row_embeddings(height_values) pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1) pos = pos.permute(2, 0, 1) pos = pos.unsqueeze(0) pos = pos.repeat(pixel_values.shape[0], 1, 1, 1) return pos def build_position_encoding(config): n_steps = config.d_model // 2 if config.position_embedding_type == "sine": # TODO find a better way of exposing other arguments position_embedding = DetrSinePositionEmbedding(n_steps, normalize=True) elif config.position_embedding_type == "learned": position_embedding = DetrLearnedPositionEmbedding(n_steps) else: raise ValueError(f"Not supported {config.position_embedding_type}") return position_embedding class DetrAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Here, we add position embeddings to the queries and keys (as explained in the DETR paper). """ def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if self.head_dim * num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {num_heads})." ) self.scaling = self.head_dim**-0.5 self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]): return tensor if position_embeddings is None else tensor + position_embeddings def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_embeddings: Optional[torch.Tensor] = None, key_value_states: Optional[torch.Tensor] = None, key_value_position_embeddings: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size, target_len, embed_dim = hidden_states.size() # add position embeddings to the hidden states before projecting to queries and keys if position_embeddings is not None: hidden_states_original = hidden_states hidden_states = self.with_pos_embed(hidden_states, position_embeddings) # add key-value position embeddings to the key value states if key_value_position_embeddings is not None: key_value_states_original = key_value_states key_value_states = self.with_pos_embed(key_value_states, key_value_position_embeddings) # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, batch_size) value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, batch_size) value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size) proj_shape = (batch_size * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) source_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): raise ValueError( f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (batch_size, 1, target_len, source_len): raise ValueError( f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is" f" {attention_mask.size()}" ) attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(batch_size, target_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped class DetrEncoderLayer(nn.Module): def __init__(self, config: DetrConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = DetrAttention( embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, position_embeddings: torch.Tensor = None, output_attentions: bool = False, ): """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. position_embeddings (`torch.FloatTensor`, *optional*): position embeddings, to be added to hidden_states. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_embeddings=position_embeddings, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) if self.training: if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class DetrDecoderLayer(nn.Module): def __init__(self, config: DetrConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = DetrAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = DetrAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_embeddings: Optional[torch.Tensor] = None, query_position_embeddings: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ): """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. position_embeddings (`torch.FloatTensor`, *optional*): position embeddings that are added to the queries and keys in the cross-attention layer. query_position_embeddings (`torch.FloatTensor`, *optional*): position embeddings that are added to the queries and keys in the self-attention layer. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(seq_len, batch, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, position_embeddings=query_position_embeddings, attention_mask=attention_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, position_embeddings=query_position_embeddings, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, key_value_position_embeddings=position_embeddings, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs class DetrClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float): super().__init__() self.dense = nn.Linear(input_dim, inner_dim) self.dropout = nn.Dropout(p=pooler_dropout) self.out_proj = nn.Linear(inner_dim, num_classes) def forward(self, hidden_states: torch.Tensor): hidden_states = self.dropout(hidden_states) hidden_states = self.dense(hidden_states) hidden_states = torch.tanh(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states class DetrPreTrainedModel(PreTrainedModel): config_class = DetrConfig base_model_prefix = "model" main_input_name = "pixel_values" def _init_weights(self, module): std = self.config.init_std xavier_std = self.config.init_xavier_std if isinstance(module, DetrMHAttentionMap): nn.init.zeros_(module.k_linear.bias) nn.init.zeros_(module.q_linear.bias) nn.init.xavier_uniform_(module.k_linear.weight, gain=xavier_std) nn.init.xavier_uniform_(module.q_linear.weight, gain=xavier_std) elif isinstance(module, DetrLearnedPositionEmbedding): nn.init.uniform_(module.row_embeddings.weight) nn.init.uniform_(module.column_embeddings.weight) if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, DetrDecoder): module.gradient_checkpointing = value DETR_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`DetrConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ DETR_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`DetrFeatureExtractor`]. See [`DetrFeatureExtractor.__call__`] for details. pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, num_queries)`, *optional*): Not used by default. Can be used to mask object queries. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you can choose to directly pass a flattened representation of an image. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an embedded representation. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class DetrEncoder(DetrPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`DetrEncoderLayer`]. The encoder updates the flattened feature map through multiple self-attention layers. Small tweak for DETR: - position_embeddings are added to the forward pass. Args: config: DetrConfig """ def __init__(self, config: DetrConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop self.layers = nn.ModuleList([DetrEncoderLayer(config) for _ in range(config.encoder_layers)]) # in the original DETR, no layernorm is used at the end of the encoder, as "normalize_before" is set to False by default # Initialize weights and apply final processing self.post_init() def forward( self, inputs_embeds=None, attention_mask=None, position_embeddings=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`: - 1 for pixel features that are real (i.e. **not masked**), - 0 for pixel features that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Position embeddings that are added to the queries and keys in each self-attention layer. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = inputs_embeds hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # expand attention_mask if attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for i, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: # we add position_embeddings as extra input to the encoder_layer layer_outputs = encoder_layer( hidden_states, attention_mask, position_embeddings=position_embeddings, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class DetrDecoder(DetrPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetrDecoderLayer`]. The decoder updates the query embeddings through multiple self-attention and cross-attention layers. Some small tweaks for DETR: - position_embeddings and query_position_embeddings are added to the forward pass. - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers. Args: config: DetrConfig """ def __init__(self, config: DetrConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.layers = nn.ModuleList([DetrDecoderLayer(config) for _ in range(config.decoder_layers)]) # in DETR, the decoder uses layernorm after the last decoder layer output self.layernorm = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def forward( self, inputs_embeds=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, position_embeddings=None, query_position_embeddings=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): The query embeddings that are passed into the decoder. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`: - 1 for queries that are **not masked**, - 0 for queries that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Position embeddings that are added to the queries and keys in each cross-attention layer. query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if inputs_embeds is not None: hidden_states = inputs_embeds input_shape = inputs_embeds.size()[:-1] combined_attention_mask = None if attention_mask is not None and combined_attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] combined_attention_mask = combined_attention_mask + _expand_mask( attention_mask, inputs_embeds.dtype, target_len=input_shape[-1] ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] encoder_attention_mask = _expand_mask( encoder_attention_mask, inputs_embeds.dtype, target_len=input_shape[-1] ) # optional intermediate hidden states intermediate = () if self.config.auxiliary_loss else None # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): continue if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), hidden_states, combined_attention_mask, encoder_hidden_states, encoder_attention_mask, None, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=combined_attention_mask, position_embeddings=position_embeddings, query_position_embeddings=query_position_embeddings, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if self.config.auxiliary_loss: hidden_states = self.layernorm(hidden_states) intermediate += (hidden_states,) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # finally, apply layernorm hidden_states = self.layernorm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) # stack intermediate decoder activations if self.config.auxiliary_loss: intermediate = torch.stack(intermediate) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate] if v is not None ) return DetrDecoderOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, intermediate_hidden_states=intermediate, ) @add_start_docstrings( """ The bare DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without any specific head on top. """, DETR_START_DOCSTRING, ) class DetrModel(DetrPreTrainedModel): def __init__(self, config: DetrConfig): super().__init__(config) # Create backbone + positional encoding backbone = DetrTimmConvEncoder( config.backbone, config.dilation, config.use_pretrained_backbone, config.num_channels ) position_embeddings = build_position_encoding(config) self.backbone = DetrConvModel(backbone, position_embeddings) # Create projection layer self.input_projection = nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1) self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model) self.encoder = DetrEncoder(config) self.decoder = DetrDecoder(config) # Initialize weights and apply final processing self.post_init() def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def freeze_backbone(self): for name, param in self.backbone.conv_encoder.model.named_parameters(): param.requires_grad_(False) def unfreeze_backbone(self): for name, param in self.backbone.conv_encoder.model.named_parameters(): param.requires_grad_(True) @add_start_docstrings_to_model_forward(DETR_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DetrModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values, pixel_mask=None, decoder_attention_mask=None, encoder_outputs=None, inputs_embeds=None, decoder_inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Returns: Examples: ```python >>> from transformers import DetrFeatureExtractor, DetrModel >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50") >>> model = DetrModel.from_pretrained("facebook/detr-resnet-50") >>> # prepare image for the model >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> # the last hidden states are the final query embeddings of the Transformer decoder >>> # these are of shape (batch_size, num_queries, hidden_size) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 100, 256] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, num_channels, height, width = pixel_values.shape device = pixel_values.device if pixel_mask is None: pixel_mask = torch.ones(((batch_size, height, width)), device=device) # First, sent pixel_values + pixel_mask through Backbone to obtain the features # pixel_values should be of shape (batch_size, num_channels, height, width) # pixel_mask should be of shape (batch_size, height, width) features, position_embeddings_list = self.backbone(pixel_values, pixel_mask) # get final feature map and downsampled mask feature_map, mask = features[-1] if mask is None: raise ValueError("Backbone does not return downsampled pixel mask") # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) projected_feature_map = self.input_projection(feature_map) # Third, flatten the feature map + position embeddings of shape NxCxHxW to NxCxHW, and permute it to NxHWxC # In other words, turn their shape into (batch_size, sequence_length, hidden_size) flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1) position_embeddings = position_embeddings_list[-1].flatten(2).permute(0, 2, 1) flattened_mask = mask.flatten(1) # Fourth, sent flattened_features + flattened_mask + position embeddings through encoder # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size) # flattened_mask is a Tensor of shape (batch_size, heigth*width) if encoder_outputs is None: encoder_outputs = self.encoder( inputs_embeds=flattened_features, attention_mask=flattened_mask, position_embeddings=position_embeddings, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # Fifth, sent query embeddings + position embeddings through the decoder (which is conditioned on the encoder output) query_position_embeddings = self.query_position_embeddings.weight.unsqueeze(0).repeat(batch_size, 1, 1) queries = torch.zeros_like(query_position_embeddings) # decoder outputs consists of (dec_features, dec_hidden, dec_attn) decoder_outputs = self.decoder( inputs_embeds=queries, attention_mask=None, position_embeddings=position_embeddings, query_position_embeddings=query_position_embeddings, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=flattened_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return DetrModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, ) @add_start_docstrings( """ DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks such as COCO detection. """, DETR_START_DOCSTRING, ) class DetrForObjectDetection(DetrPreTrainedModel): def __init__(self, config: DetrConfig): super().__init__(config) # DETR encoder-decoder model self.model = DetrModel(config) # Object detection heads self.class_labels_classifier = nn.Linear( config.d_model, config.num_labels + 1 ) # We add one for the "no object" class self.bbox_predictor = DetrMLPPredictionHead( input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3 ) # Initialize weights and apply final processing self.post_init() # taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py @torch.jit.unused def _set_aux_loss(self, outputs_class, outputs_coord): # this is a workaround to make torchscript happy, as torchscript # doesn't support dictionary with non-homogeneous values, such # as a dict having both a Tensor and a list. return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])] @add_start_docstrings_to_model_forward(DETR_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DetrObjectDetectionOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values, pixel_mask=None, decoder_attention_mask=None, encoder_outputs=None, inputs_embeds=None, decoder_inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (`List[Dict]` of len `(batch_size,)`, *optional*): Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. Returns: Examples: ```python >>> from transformers import DetrFeatureExtractor, DetrForObjectDetection >>> import torch >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50") >>> model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> # convert outputs (bounding boxes and class logits) to COCO API >>> target_sizes = torch.tensor([image.size[::-1]]) >>> results = feature_extractor.post_process_object_detection( ... outputs, threshold=0.9, target_sizes=target_sizes ... )[0] >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): ... box = [round(i, 2) for i in box.tolist()] ... print( ... f"Detected {model.config.id2label[label.item()]} with confidence " ... f"{round(score.item(), 3)} at location {box}" ... ) Detected remote with confidence 0.998 at location [40.16, 70.81, 175.55, 117.98] Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66] Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76] Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93] Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.72] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict # First, sent images through DETR base model to obtain encoder + decoder outputs outputs = self.model( pixel_values, pixel_mask=pixel_mask, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] # class logits + predicted bounding boxes logits = self.class_labels_classifier(sequence_output) pred_boxes = self.bbox_predictor(sequence_output).sigmoid() loss, loss_dict, auxiliary_outputs = None, None, None if labels is not None: # First: create the matcher matcher = DetrHungarianMatcher( class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost ) # Second: create the criterion losses = ["labels", "boxes", "cardinality"] criterion = DetrLoss( matcher=matcher, num_classes=self.config.num_labels, eos_coef=self.config.eos_coefficient, losses=losses, ) criterion.to(self.device) # Third: compute the losses, based on outputs and labels outputs_loss = {} outputs_loss["logits"] = logits outputs_loss["pred_boxes"] = pred_boxes if self.config.auxiliary_loss: intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4] outputs_class = self.class_labels_classifier(intermediate) outputs_coord = self.bbox_predictor(intermediate).sigmoid() auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord) outputs_loss["auxiliary_outputs"] = auxiliary_outputs loss_dict = criterion(outputs_loss, labels) # Fourth: compute total loss, as a weighted sum of the various losses weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient} weight_dict["loss_giou"] = self.config.giou_loss_coefficient if self.config.auxiliary_loss: aux_weight_dict = {} for i in range(self.config.decoder_layers - 1): aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) weight_dict.update(aux_weight_dict) loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) if not return_dict: if auxiliary_outputs is not None: output = (logits, pred_boxes) + auxiliary_outputs + outputs else: output = (logits, pred_boxes) + outputs return ((loss, loss_dict) + output) if loss is not None else output return DetrObjectDetectionOutput( loss=loss, loss_dict=loss_dict, logits=logits, pred_boxes=pred_boxes, auxiliary_outputs=auxiliary_outputs, last_hidden_state=outputs.last_hidden_state, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ DETR Model (consisting of a backbone and encoder-decoder Transformer) with a segmentation head on top, for tasks such as COCO panoptic. """, DETR_START_DOCSTRING, ) class DetrForSegmentation(DetrPreTrainedModel): def __init__(self, config: DetrConfig): super().__init__(config) # object detection model self.detr = DetrForObjectDetection(config) # segmentation head hidden_size, number_of_heads = config.d_model, config.encoder_attention_heads intermediate_channel_sizes = self.detr.model.backbone.conv_encoder.intermediate_channel_sizes self.mask_head = DetrMaskHeadSmallConv( hidden_size + number_of_heads, intermediate_channel_sizes[::-1][-3:], hidden_size ) self.bbox_attention = DetrMHAttentionMap( hidden_size, hidden_size, number_of_heads, dropout=0.0, std=config.init_xavier_std ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(DETR_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DetrSegmentationOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values, pixel_mask=None, decoder_attention_mask=None, encoder_outputs=None, inputs_embeds=None, decoder_inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (`List[Dict]` of len `(batch_size,)`, *optional*): Labels for computing the bipartite matching loss, DICE/F-1 loss and Focal loss. List of dicts, each dictionary containing at least the following 3 keys: 'class_labels', 'boxes' and 'masks' (the class labels, bounding boxes and segmentation masks of an image in the batch respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes in the image,)`, the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)` and the masks a `torch.FloatTensor` of shape `(number of bounding boxes in the image, height, width)`. Returns: Examples: ```python >>> import io >>> import requests >>> from PIL import Image >>> import torch >>> import numpy >>> from transformers import DetrFeatureExtractor, DetrForSegmentation >>> from transformers.models.detr.feature_extraction_detr import rgb_to_id >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic") >>> model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic") >>> # prepare image for the model >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> # Use the `post_process_panoptic_segmentation` method of `DetrFeatureExtractor` to retrieve post-processed panoptic segmentation maps >>> # Segmentation results are returned as a list of dictionaries >>> result = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[(300, 500)]) >>> # A tensor of shape (height, width) where each value denotes a segment id, filled with -1 if no segment is found >>> panoptic_seg = result[0]["segmentation"] >>> # Get prediction score and segment_id to class_id mapping of each segment >>> panoptic_segments_info = result[0]["segments_info"] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, num_channels, height, width = pixel_values.shape device = pixel_values.device if pixel_mask is None: pixel_mask = torch.ones((batch_size, height, width), device=device) # First, get list of feature maps and position embeddings features, position_embeddings_list = self.detr.model.backbone(pixel_values, pixel_mask=pixel_mask) # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) feature_map, mask = features[-1] batch_size, num_channels, height, width = feature_map.shape projected_feature_map = self.detr.model.input_projection(feature_map) # Third, flatten the feature map + position embeddings of shape NxCxHxW to NxCxHW, and permute it to NxHWxC # In other words, turn their shape into (batch_size, sequence_length, hidden_size) flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1) position_embeddings = position_embeddings_list[-1].flatten(2).permute(0, 2, 1) flattened_mask = mask.flatten(1) # Fourth, sent flattened_features + flattened_mask + position embeddings through encoder # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size) # flattened_mask is a Tensor of shape (batch_size, heigth*width) if encoder_outputs is None: encoder_outputs = self.detr.model.encoder( inputs_embeds=flattened_features, attention_mask=flattened_mask, position_embeddings=position_embeddings, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # Fifth, sent query embeddings + position embeddings through the decoder (which is conditioned on the encoder output) query_position_embeddings = self.detr.model.query_position_embeddings.weight.unsqueeze(0).repeat( batch_size, 1, 1 ) queries = torch.zeros_like(query_position_embeddings) # decoder outputs consists of (dec_features, dec_hidden, dec_attn) decoder_outputs = self.detr.model.decoder( inputs_embeds=queries, attention_mask=None, position_embeddings=position_embeddings, query_position_embeddings=query_position_embeddings, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=flattened_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = decoder_outputs[0] # Sixth, compute logits, pred_boxes and pred_masks logits = self.detr.class_labels_classifier(sequence_output) pred_boxes = self.detr.bbox_predictor(sequence_output).sigmoid() memory = encoder_outputs[0].permute(0, 2, 1).view(batch_size, self.config.d_model, height, width) mask = flattened_mask.view(batch_size, height, width) # FIXME h_boxes takes the last one computed, keep this in mind # important: we need to reverse the mask, since in the original implementation the mask works reversed # bbox_mask is of shape (batch_size, num_queries, number_of_attention_heads in bbox_attention, height/32, width/32) bbox_mask = self.bbox_attention(sequence_output, memory, mask=~mask) seg_masks = self.mask_head(projected_feature_map, bbox_mask, [features[2][0], features[1][0], features[0][0]]) pred_masks = seg_masks.view(batch_size, self.detr.config.num_queries, seg_masks.shape[-2], seg_masks.shape[-1]) loss, loss_dict, auxiliary_outputs = None, None, None if labels is not None: # First: create the matcher matcher = DetrHungarianMatcher( class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost ) # Second: create the criterion losses = ["labels", "boxes", "cardinality", "masks"] criterion = DetrLoss( matcher=matcher, num_classes=self.config.num_labels, eos_coef=self.config.eos_coefficient, losses=losses, ) criterion.to(self.device) # Third: compute the losses, based on outputs and labels outputs_loss = {} outputs_loss["logits"] = logits outputs_loss["pred_boxes"] = pred_boxes outputs_loss["pred_masks"] = pred_masks if self.config.auxiliary_loss: intermediate = decoder_outputs.intermediate_hidden_states if return_dict else decoder_outputs[-1] outputs_class = self.class_labels_classifier(intermediate) outputs_coord = self.bbox_predictor(intermediate).sigmoid() auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord) outputs_loss["auxiliary_outputs"] = auxiliary_outputs loss_dict = criterion(outputs_loss, labels) # Fourth: compute total loss, as a weighted sum of the various losses weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient} weight_dict["loss_giou"] = self.config.giou_loss_coefficient weight_dict["loss_mask"] = self.config.mask_loss_coefficient weight_dict["loss_dice"] = self.config.dice_loss_coefficient if self.config.auxiliary_loss: aux_weight_dict = {} for i in range(self.config.decoder_layers - 1): aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) weight_dict.update(aux_weight_dict) loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) if not return_dict: if auxiliary_outputs is not None: output = (logits, pred_boxes, pred_masks) + auxiliary_outputs + decoder_outputs + encoder_outputs else: output = (logits, pred_boxes, pred_masks) + decoder_outputs + encoder_outputs return ((loss, loss_dict) + output) if loss is not None else output return DetrSegmentationOutput( loss=loss, loss_dict=loss_dict, logits=logits, pred_boxes=pred_boxes, pred_masks=pred_masks, auxiliary_outputs=auxiliary_outputs, last_hidden_state=decoder_outputs.last_hidden_state, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) def _expand(tensor, length: int): return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1) # taken from https://github.com/facebookresearch/detr/blob/master/models/segmentation.py class DetrMaskHeadSmallConv(nn.Module): """ Simple convolutional head, using group norm. Upsampling is done using a FPN approach """ def __init__(self, dim, fpn_dims, context_dim): super().__init__() if dim % 8 != 0: raise ValueError( "The hidden_size + number of attention heads must be divisible by 8 as the number of groups in" " GroupNorm is set to 8" ) inter_dims = [dim, context_dim // 2, context_dim // 4, context_dim // 8, context_dim // 16, context_dim // 64] self.lay1 = nn.Conv2d(dim, dim, 3, padding=1) self.gn1 = nn.GroupNorm(8, dim) self.lay2 = nn.Conv2d(dim, inter_dims[1], 3, padding=1) self.gn2 = nn.GroupNorm(8, inter_dims[1]) self.lay3 = nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1) self.gn3 = nn.GroupNorm(8, inter_dims[2]) self.lay4 = nn.Conv2d(inter_dims[2], inter_dims[3], 3, padding=1) self.gn4 = nn.GroupNorm(8, inter_dims[3]) self.lay5 = nn.Conv2d(inter_dims[3], inter_dims[4], 3, padding=1) self.gn5 = nn.GroupNorm(8, inter_dims[4]) self.out_lay = nn.Conv2d(inter_dims[4], 1, 3, padding=1) self.dim = dim self.adapter1 = nn.Conv2d(fpn_dims[0], inter_dims[1], 1) self.adapter2 = nn.Conv2d(fpn_dims[1], inter_dims[2], 1) self.adapter3 = nn.Conv2d(fpn_dims[2], inter_dims[3], 1) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_uniform_(m.weight, a=1) nn.init.constant_(m.bias, 0) def forward(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor]): # here we concatenate x, the projected feature map, of shape (batch_size, d_model, heigth/32, width/32) with # the bbox_mask = the attention maps of shape (batch_size, n_queries, n_heads, height/32, width/32). # We expand the projected feature map to match the number of heads. x = torch.cat([_expand(x, bbox_mask.shape[1]), bbox_mask.flatten(0, 1)], 1) x = self.lay1(x) x = self.gn1(x) x = nn.functional.relu(x) x = self.lay2(x) x = self.gn2(x) x = nn.functional.relu(x) cur_fpn = self.adapter1(fpns[0]) if cur_fpn.size(0) != x.size(0): cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") x = self.lay3(x) x = self.gn3(x) x = nn.functional.relu(x) cur_fpn = self.adapter2(fpns[1]) if cur_fpn.size(0) != x.size(0): cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") x = self.lay4(x) x = self.gn4(x) x = nn.functional.relu(x) cur_fpn = self.adapter3(fpns[2]) if cur_fpn.size(0) != x.size(0): cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") x = self.lay5(x) x = self.gn5(x) x = nn.functional.relu(x) x = self.out_lay(x) return x class DetrMHAttentionMap(nn.Module): """This is a 2D attention module, which only returns the attention softmax (no multiplication by value)""" def __init__(self, query_dim, hidden_dim, num_heads, dropout=0.0, bias=True, std=None): super().__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.dropout = nn.Dropout(dropout) self.q_linear = nn.Linear(query_dim, hidden_dim, bias=bias) self.k_linear = nn.Linear(query_dim, hidden_dim, bias=bias) self.normalize_fact = float(hidden_dim / self.num_heads) ** -0.5 def forward(self, q, k, mask: Optional[Tensor] = None): q = self.q_linear(q) k = nn.functional.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias) queries_per_head = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads) keys_per_head = k.view(k.shape[0], self.num_heads, self.hidden_dim // self.num_heads, k.shape[-2], k.shape[-1]) weights = torch.einsum("bqnc,bnchw->bqnhw", queries_per_head * self.normalize_fact, keys_per_head) if mask is not None: weights.masked_fill_(mask.unsqueeze(1).unsqueeze(1), torch.finfo(weights.dtype).min) weights = nn.functional.softmax(weights.flatten(2), dim=-1).view(weights.size()) weights = self.dropout(weights) return weights def dice_loss(inputs, targets, num_boxes): """ Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class). """ inputs = inputs.sigmoid() inputs = inputs.flatten(1) numerator = 2 * (inputs * targets).sum(1) denominator = inputs.sum(-1) + targets.sum(-1) loss = 1 - (numerator + 1) / (denominator + 1) return loss.sum() / num_boxes def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2): """ Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs (`torch.FloatTensor` of arbitrary shape): The predictions for each example. targets (`torch.FloatTensor` with the same shape as `inputs`) A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class and 1 for the positive class). alpha (`float`, *optional*, defaults to `0.25`): Optional weighting factor in the range (0,1) to balance positive vs. negative examples. gamma (`int`, *optional*, defaults to `2`): Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples. Returns: Loss tensor """ prob = inputs.sigmoid() ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") # add modulating factor p_t = prob * targets + (1 - prob) * (1 - targets) loss = ce_loss * ((1 - p_t) ** gamma) if alpha >= 0: alpha_t = alpha * targets + (1 - alpha) * (1 - targets) loss = alpha_t * loss return loss.mean(1).sum() / num_boxes # taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py class DetrLoss(nn.Module): """ This class computes the losses for DetrForObjectDetection/DetrForSegmentation. The process happens in two steps: 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched ground-truth / prediction (supervise class and box). A note on the `num_classes` argument (copied from original repo in detr.py): "the naming of the `num_classes` parameter of the criterion is somewhat misleading. It indeed corresponds to `max_obj_id` + 1, where `max_obj_id` is the maximum id for a class in your dataset. For example, COCO has a `max_obj_id` of 90, so we pass `num_classes` to be 91. As another example, for a dataset that has a single class with `id` 1, you should pass `num_classes` to be 2 (`max_obj_id` + 1). For more details on this, check the following discussion https://github.com/facebookresearch/detr/issues/108#issuecomment-650269223" Args: matcher (`DetrHungarianMatcher`): Module able to compute a matching between targets and proposals. num_classes (`int`): Number of object categories, omitting the special no-object category. eos_coef (`float`): Relative classification weight applied to the no-object category. losses (`List[str]`): List of all the losses to be applied. See `get_loss` for a list of all available losses. """ def __init__(self, matcher, num_classes, eos_coef, losses): super().__init__() self.matcher = matcher self.num_classes = num_classes self.eos_coef = eos_coef self.losses = losses empty_weight = torch.ones(self.num_classes + 1) empty_weight[-1] = self.eos_coef self.register_buffer("empty_weight", empty_weight) # removed logging parameter, which was part of the original implementation def loss_labels(self, outputs, targets, indices, num_boxes): """ Classification loss (NLL) targets dicts must contain the key "class_labels" containing a tensor of dim [nb_target_boxes] """ if "logits" not in outputs: raise KeyError("No logits were found in the outputs") source_logits = outputs["logits"] idx = self._get_source_permutation_idx(indices) target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)]) target_classes = torch.full( source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device ) target_classes[idx] = target_classes_o loss_ce = nn.functional.cross_entropy(source_logits.transpose(1, 2), target_classes, self.empty_weight) losses = {"loss_ce": loss_ce} return losses @torch.no_grad() def loss_cardinality(self, outputs, targets, indices, num_boxes): """ Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes. This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients. """ logits = outputs["logits"] device = logits.device target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device) # Count the number of predictions that are NOT "no-object" (which is the last class) card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1) card_err = nn.functional.l1_loss(card_pred.float(), target_lengths.float()) losses = {"cardinality_error": card_err} return losses def loss_boxes(self, outputs, targets, indices, num_boxes): """ Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss. Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. """ if "pred_boxes" not in outputs: raise KeyError("No predicted boxes found in outputs") idx = self._get_source_permutation_idx(indices) source_boxes = outputs["pred_boxes"][idx] target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) loss_bbox = nn.functional.l1_loss(source_boxes, target_boxes, reduction="none") losses = {} losses["loss_bbox"] = loss_bbox.sum() / num_boxes loss_giou = 1 - torch.diag( generalized_box_iou(center_to_corners_format(source_boxes), center_to_corners_format(target_boxes)) ) losses["loss_giou"] = loss_giou.sum() / num_boxes return losses def loss_masks(self, outputs, targets, indices, num_boxes): """ Compute the losses related to the masks: the focal loss and the dice loss. Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]. """ if "pred_masks" not in outputs: raise KeyError("No predicted masks found in outputs") source_idx = self._get_source_permutation_idx(indices) target_idx = self._get_target_permutation_idx(indices) source_masks = outputs["pred_masks"] source_masks = source_masks[source_idx] masks = [t["masks"] for t in targets] # TODO use valid to mask invalid areas due to padding in loss target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() target_masks = target_masks.to(source_masks) target_masks = target_masks[target_idx] # upsample predictions to the target size source_masks = nn.functional.interpolate( source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False ) source_masks = source_masks[:, 0].flatten(1) target_masks = target_masks.flatten(1) target_masks = target_masks.view(source_masks.shape) losses = { "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes), "loss_dice": dice_loss(source_masks, target_masks, num_boxes), } return losses def _get_source_permutation_idx(self, indices): # permute predictions following indices batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)]) source_idx = torch.cat([source for (source, _) in indices]) return batch_idx, source_idx def _get_target_permutation_idx(self, indices): # permute targets following indices batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)]) target_idx = torch.cat([target for (_, target) in indices]) return batch_idx, target_idx def get_loss(self, loss, outputs, targets, indices, num_boxes): loss_map = { "labels": self.loss_labels, "cardinality": self.loss_cardinality, "boxes": self.loss_boxes, "masks": self.loss_masks, } if loss not in loss_map: raise ValueError(f"Loss {loss} not supported") return loss_map[loss](outputs, targets, indices, num_boxes) def forward(self, outputs, targets): """ This performs the loss computation. Args: outputs (`dict`, *optional*): Dictionary of tensors, see the output specification of the model for the format. targets (`List[dict]`, *optional*): List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the losses applied, see each loss' doc. """ outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"} # Retrieve the matching between the outputs of the last layer and the targets indices = self.matcher(outputs_without_aux, targets) # Compute the average number of target boxes across all nodes, for normalization purposes num_boxes = sum(len(t["class_labels"]) for t in targets) num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device) # (Niels): comment out function below, distributed training to be added # if is_dist_avail_and_initialized(): # torch.distributed.all_reduce(num_boxes) # (Niels) in original implementation, num_boxes is divided by get_world_size() num_boxes = torch.clamp(num_boxes, min=1).item() # Compute all the requested losses losses = {} for loss in self.losses: losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes)) # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. if "auxiliary_outputs" in outputs: for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]): indices = self.matcher(auxiliary_outputs, targets) for loss in self.losses: if loss == "masks": # Intermediate masks losses are too costly to compute, we ignore them. continue l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes) l_dict = {k + f"_{i}": v for k, v in l_dict.items()} losses.update(l_dict) return losses # taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py class DetrMLPPredictionHead(nn.Module): """ Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates, height and width of a bounding box w.r.t. an image. Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py """ def __init__(self, input_dim, hidden_dim, output_dim, num_layers): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) def forward(self, x): for i, layer in enumerate(self.layers): x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x # taken from https://github.com/facebookresearch/detr/blob/master/models/matcher.py class DetrHungarianMatcher(nn.Module): """ This class computes an assignment between the targets and the predictions of the network. For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are un-matched (and thus treated as non-objects). Args: class_cost: The relative weight of the classification error in the matching cost. bbox_cost: The relative weight of the L1 error of the bounding box coordinates in the matching cost. giou_cost: The relative weight of the giou loss of the bounding box in the matching cost. """ def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1): super().__init__() requires_backends(self, ["scipy"]) self.class_cost = class_cost self.bbox_cost = bbox_cost self.giou_cost = giou_cost if class_cost == 0 and bbox_cost == 0 and giou_cost == 0: raise ValueError("All costs of the Matcher can't be 0") @torch.no_grad() def forward(self, outputs, targets): """ Args: outputs (`dict`): A dictionary that contains at least these entries: * "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits * "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates. targets (`List[dict]`): A list of targets (len(targets) = batch_size), where each target is a dict containing: * "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth objects in the target) containing the class labels * "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates. Returns: `List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where: - index_i is the indices of the selected predictions (in order) - index_j is the indices of the corresponding selected targets (in order) For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes) """ batch_size, num_queries = outputs["logits"].shape[:2] # We flatten to compute the cost matrices in a batch out_prob = outputs["logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes] out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] # Also concat the target labels and boxes target_ids = torch.cat([v["class_labels"] for v in targets]) target_bbox = torch.cat([v["boxes"] for v in targets]) # Compute the classification cost. Contrary to the loss, we don't use the NLL, # but approximate it in 1 - proba[target class]. # The 1 is a constant that doesn't change the matching, it can be ommitted. class_cost = -out_prob[:, target_ids] # Compute the L1 cost between boxes bbox_cost = torch.cdist(out_bbox, target_bbox, p=1) # Compute the giou cost between boxes giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox)) # Final cost matrix cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu() sizes = [len(v["boxes"]) for v in targets] indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))] return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] # below: bounding box utilities taken from https://github.com/facebookresearch/detr/blob/master/util/box_ops.py def _upcast(t: Tensor) -> Tensor: # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type if t.is_floating_point(): return t if t.dtype in (torch.float32, torch.float64) else t.float() else: return t if t.dtype in (torch.int32, torch.int64) else t.int() def box_area(boxes: Tensor) -> Tensor: """ Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. Args: boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 < x2` and `0 <= y1 < y2`. Returns: `torch.FloatTensor`: a tensor containing the area for each box. """ boxes = _upcast(boxes) return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) # modified from torchvision to also return the union def box_iou(boxes1, boxes2): area1 = box_area(boxes1) area2 = box_area(boxes2) left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] union = area1[:, None] + area2 - inter iou = inter / union return iou, union def generalized_box_iou(boxes1, boxes2): """ Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. Returns: `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) """ # degenerate boxes gives inf / nan results # so do an early check if not (boxes1[:, 2:] >= boxes1[:, :2]).all(): raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}") if not (boxes2[:, 2:] >= boxes2[:, :2]).all(): raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}") iou, union = box_iou(boxes1, boxes2) top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2]) bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2] area = width_height[:, :, 0] * width_height[:, :, 1] return iou - (area - union) / area # Copied from transformers.models.detr.feature_extraction_detr.center_to_corners_format def center_to_corners_format(x): """ Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format (x_0, y_0, x_1, y_1). """ center_x, center_y, width, height = x.unbind(-1) b = [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)] return torch.stack(b, dim=-1) # below: taken from https://github.com/facebookresearch/detr/blob/master/util/misc.py#L306 def _max_by_axis(the_list): # type: (List[List[int]]) -> List[int] maxes = the_list[0] for sublist in the_list[1:]: for index, item in enumerate(sublist): maxes[index] = max(maxes[index], item) return maxes class NestedTensor(object): def __init__(self, tensors, mask: Optional[Tensor]): self.tensors = tensors self.mask = mask def to(self, device): cast_tensor = self.tensors.to(device) mask = self.mask if mask is not None: cast_mask = mask.to(device) else: cast_mask = None return NestedTensor(cast_tensor, cast_mask) def decompose(self): return self.tensors, self.mask def __repr__(self): return str(self.tensors) def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): if tensor_list[0].ndim == 3: max_size = _max_by_axis([list(img.shape) for img in tensor_list]) batch_shape = [len(tensor_list)] + max_size batch_size, num_channels, height, width = batch_shape dtype = tensor_list[0].dtype device = tensor_list[0].device tensor = torch.zeros(batch_shape, dtype=dtype, device=device) mask = torch.ones((batch_size, height, width), dtype=torch.bool, device=device) for img, pad_img, m in zip(tensor_list, tensor, mask): pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) m[: img.shape[1], : img.shape[2]] = False else: raise ValueError("Only 3-dimensional tensors are supported") return NestedTensor(tensor, mask)

# coding=utf-8 # Copyright 2021 Facebook AI Research The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch DETR model.""" import math import random from dataclasses import dataclass from typing import Dict, List, Optional, Tuple import torch from torch import Tensor, nn from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import torch_int_div from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_scipy_available, is_timm_available, logging, replace_return_docstrings, requires_backends, ) from .configuration_detr import DetrConfig if is_scipy_available(): from scipy.optimize import linear_sum_assignment if is_timm_available(): from timm import create_model logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "DetrConfig" _CHECKPOINT_FOR_DOC = "facebook/detr-resnet-50" DETR_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/detr-resnet-50", # See all DETR models at https://huggingface.co/models?filter=detr ] @dataclass class DetrDecoderOutput(BaseModelOutputWithCrossAttentions): """ Base class for outputs of the DETR decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. This is useful when training the model with auxiliary decoding losses. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. """ intermediate_hidden_states: Optional[torch.FloatTensor] = None @dataclass class DetrModelOutput(Seq2SeqModelOutput): """ Base class for outputs of the DETR encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. This is useful when training the model with auxiliary decoding losses. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, sequence_length, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. """ intermediate_hidden_states: Optional[torch.FloatTensor] = None @dataclass class DetrObjectDetectionOutput(ModelOutput): """ Output type of [`DetrForObjectDetection`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized scale-invariant IoU loss. loss_dict (`Dict`, *optional*): A dictionary containing the individual losses. Useful for logging. logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): Classification logits (including no-object) for all queries. pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding). You can use [`~DetrFeatureExtractor.post_process_object_detection`] to retrieve the unnormalized bounding boxes. auxiliary_outputs (`list[Dict]`, *optional*): Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and `pred_boxes`) for each decoder layer. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the decoder of the model. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None loss_dict: Optional[Dict] = None logits: torch.FloatTensor = None pred_boxes: torch.FloatTensor = None auxiliary_outputs: Optional[List[Dict]] = None last_hidden_state: Optional[torch.FloatTensor] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class DetrSegmentationOutput(ModelOutput): """ Output type of [`DetrForSegmentation`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized scale-invariant IoU loss. loss_dict (`Dict`, *optional*): A dictionary containing the individual losses. Useful for logging. logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): Classification logits (including no-object) for all queries. pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding). You can use [`~DetrFeatureExtractor.post_process_object_detection`] to retrieve the unnormalized bounding boxes. pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height/4, width/4)`): Segmentation masks logits for all queries. See also [`~DetrFeatureExtractor.post_process_semantic_segmentation`] or [`~DetrFeatureExtractor.post_process_instance_segmentation`] [`~DetrFeatureExtractor.post_process_panoptic_segmentation`] to evaluate semantic, instance and panoptic segmentation masks respectively. auxiliary_outputs (`list[Dict]`, *optional*): Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and `pred_boxes`) for each decoder layer. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the decoder of the model. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None loss_dict: Optional[Dict] = None logits: torch.FloatTensor = None pred_boxes: torch.FloatTensor = None pred_masks: torch.FloatTensor = None auxiliary_outputs: Optional[List[Dict]] = None last_hidden_state: Optional[torch.FloatTensor] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None # BELOW: utilities copied from # https://github.com/facebookresearch/detr/blob/master/backbone.py class DetrFrozenBatchNorm2d(nn.Module): """ BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than torchvision.models.resnet[18,34,50,101] produce nans. """ def __init__(self, n): super().__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) def _load_from_state_dict( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ): num_batches_tracked_key = prefix + "num_batches_tracked" if num_batches_tracked_key in state_dict: del state_dict[num_batches_tracked_key] super()._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ) def forward(self, x): # move reshapes to the beginning # to make it user-friendly weight = self.weight.reshape(1, -1, 1, 1) bias = self.bias.reshape(1, -1, 1, 1) running_var = self.running_var.reshape(1, -1, 1, 1) running_mean = self.running_mean.reshape(1, -1, 1, 1) epsilon = 1e-5 scale = weight * (running_var + epsilon).rsqrt() bias = bias - running_mean * scale return x * scale + bias def replace_batch_norm(m, name=""): for attr_str in dir(m): target_attr = getattr(m, attr_str) if isinstance(target_attr, nn.BatchNorm2d): frozen = DetrFrozenBatchNorm2d(target_attr.num_features) bn = getattr(m, attr_str) frozen.weight.data.copy_(bn.weight) frozen.bias.data.copy_(bn.bias) frozen.running_mean.data.copy_(bn.running_mean) frozen.running_var.data.copy_(bn.running_var) setattr(m, attr_str, frozen) for n, ch in m.named_children(): replace_batch_norm(ch, n) class DetrTimmConvEncoder(nn.Module): """ Convolutional encoder (backbone) from the timm library. nn.BatchNorm2d layers are replaced by DetrFrozenBatchNorm2d as defined above. """ def __init__(self, name: str, dilation: bool, use_pretrained_backbone: bool, num_channels: int = 3): super().__init__() kwargs = {} if dilation: kwargs["output_stride"] = 16 requires_backends(self, ["timm"]) backbone = create_model( name, pretrained=use_pretrained_backbone, features_only=True, out_indices=(1, 2, 3, 4), in_chans=num_channels, **kwargs, ) # replace batch norm by frozen batch norm with torch.no_grad(): replace_batch_norm(backbone) self.model = backbone self.intermediate_channel_sizes = self.model.feature_info.channels() if "resnet" in name: for name, parameter in self.model.named_parameters(): if "layer2" not in name and "layer3" not in name and "layer4" not in name: parameter.requires_grad_(False) def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): # send pixel_values through the model to get list of feature maps features = self.model(pixel_values) out = [] for feature_map in features: # downsample pixel_mask to match shape of corresponding feature_map mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] out.append((feature_map, mask)) return out class DetrConvModel(nn.Module): """ This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder. """ def __init__(self, conv_encoder, position_embedding): super().__init__() self.conv_encoder = conv_encoder self.position_embedding = position_embedding def forward(self, pixel_values, pixel_mask): # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples out = self.conv_encoder(pixel_values, pixel_mask) pos = [] for feature_map, mask in out: # position encoding pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype)) return out, pos def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, target_len: Optional[int] = None): """ Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, target_seq_len, source_seq_len]`. """ batch_size, source_len = mask.size() target_len = target_len if target_len is not None else source_len expanded_mask = mask[:, None, None, :].expand(batch_size, 1, target_len, source_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min) class DetrSinePositionEmbedding(nn.Module): """ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized to work on images. """ def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None): super().__init__() self.embedding_dim = embedding_dim self.temperature = temperature self.normalize = normalize if scale is not None and normalize is False: raise ValueError("normalize should be True if scale is passed") if scale is None: scale = 2 * math.pi self.scale = scale def forward(self, pixel_values, pixel_mask): if pixel_mask is None: raise ValueError("No pixel mask provided") y_embed = pixel_mask.cumsum(1, dtype=torch.float32) x_embed = pixel_mask.cumsum(2, dtype=torch.float32) if self.normalize: y_embed = y_embed / (y_embed[:, -1:, :] + 1e-6) * self.scale x_embed = x_embed / (x_embed[:, :, -1:] + 1e-6) * self.scale dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device) dim_t = self.temperature ** (2 * torch_int_div(dim_t, 2) / self.embedding_dim) pos_x = x_embed[:, :, :, None] / dim_t pos_y = y_embed[:, :, :, None] / dim_t pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) return pos class DetrLearnedPositionEmbedding(nn.Module): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, embedding_dim=256): super().__init__() self.row_embeddings = nn.Embedding(50, embedding_dim) self.column_embeddings = nn.Embedding(50, embedding_dim) def forward(self, pixel_values, pixel_mask=None): height, width = pixel_values.shape[-2:] width_values = torch.arange(width, device=pixel_values.device) height_values = torch.arange(height, device=pixel_values.device) x_emb = self.column_embeddings(width_values) y_emb = self.row_embeddings(height_values) pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1) pos = pos.permute(2, 0, 1) pos = pos.unsqueeze(0) pos = pos.repeat(pixel_values.shape[0], 1, 1, 1) return pos def build_position_encoding(config): n_steps = config.d_model // 2 if config.position_embedding_type == "sine": # TODO find a better way of exposing other arguments position_embedding = DetrSinePositionEmbedding(n_steps, normalize=True) elif config.position_embedding_type == "learned": position_embedding = DetrLearnedPositionEmbedding(n_steps) else: raise ValueError(f"Not supported {config.position_embedding_type}") return position_embedding class DetrAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Here, we add position embeddings to the queries and keys (as explained in the DETR paper). """ def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if self.head_dim * num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {num_heads})." ) self.scaling = self.head_dim**-0.5 self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]): return tensor if position_embeddings is None else tensor + position_embeddings def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_embeddings: Optional[torch.Tensor] = None, key_value_states: Optional[torch.Tensor] = None, key_value_position_embeddings: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size, target_len, embed_dim = hidden_states.size() # add position embeddings to the hidden states before projecting to queries and keys if position_embeddings is not None: hidden_states_original = hidden_states hidden_states = self.with_pos_embed(hidden_states, position_embeddings) # add key-value position embeddings to the key value states if key_value_position_embeddings is not None: key_value_states_original = key_value_states key_value_states = self.with_pos_embed(key_value_states, key_value_position_embeddings) # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, batch_size) value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, batch_size) value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size) proj_shape = (batch_size * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) source_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): raise ValueError( f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (batch_size, 1, target_len, source_len): raise ValueError( f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is" f" {attention_mask.size()}" ) attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(batch_size, target_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped class DetrEncoderLayer(nn.Module): def __init__(self, config: DetrConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = DetrAttention( embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, position_embeddings: torch.Tensor = None, output_attentions: bool = False, ): """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. position_embeddings (`torch.FloatTensor`, *optional*): position embeddings, to be added to hidden_states. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_embeddings=position_embeddings, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) if self.training: if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class DetrDecoderLayer(nn.Module): def __init__(self, config: DetrConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = DetrAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = DetrAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_embeddings: Optional[torch.Tensor] = None, query_position_embeddings: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ): """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. position_embeddings (`torch.FloatTensor`, *optional*): position embeddings that are added to the queries and keys in the cross-attention layer. query_position_embeddings (`torch.FloatTensor`, *optional*): position embeddings that are added to the queries and keys in the self-attention layer. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(seq_len, batch, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, position_embeddings=query_position_embeddings, attention_mask=attention_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, position_embeddings=query_position_embeddings, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, key_value_position_embeddings=position_embeddings, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs class DetrClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float): super().__init__() self.dense = nn.Linear(input_dim, inner_dim) self.dropout = nn.Dropout(p=pooler_dropout) self.out_proj = nn.Linear(inner_dim, num_classes) def forward(self, hidden_states: torch.Tensor): hidden_states = self.dropout(hidden_states) hidden_states = self.dense(hidden_states) hidden_states = torch.tanh(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states class DetrPreTrainedModel(PreTrainedModel): config_class = DetrConfig base_model_prefix = "model" main_input_name = "pixel_values" def _init_weights(self, module): std = self.config.init_std xavier_std = self.config.init_xavier_std if isinstance(module, DetrMHAttentionMap): nn.init.zeros_(module.k_linear.bias) nn.init.zeros_(module.q_linear.bias) nn.init.xavier_uniform_(module.k_linear.weight, gain=xavier_std) nn.init.xavier_uniform_(module.q_linear.weight, gain=xavier_std) elif isinstance(module, DetrLearnedPositionEmbedding): nn.init.uniform_(module.row_embeddings.weight) nn.init.uniform_(module.column_embeddings.weight) if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, DetrDecoder): module.gradient_checkpointing = value DETR_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`DetrConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ DETR_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`DetrFeatureExtractor`]. See [`DetrFeatureExtractor.__call__`] for details. pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, num_queries)`, *optional*): Not used by default. Can be used to mask object queries. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you can choose to directly pass a flattened representation of an image. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an embedded representation. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class DetrEncoder(DetrPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`DetrEncoderLayer`]. The encoder updates the flattened feature map through multiple self-attention layers. Small tweak for DETR: - position_embeddings are added to the forward pass. Args: config: DetrConfig """ def __init__(self, config: DetrConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop self.layers = nn.ModuleList([DetrEncoderLayer(config) for _ in range(config.encoder_layers)]) # in the original DETR, no layernorm is used at the end of the encoder, as "normalize_before" is set to False by default # Initialize weights and apply final processing self.post_init() def forward( self, inputs_embeds=None, attention_mask=None, position_embeddings=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`: - 1 for pixel features that are real (i.e. **not masked**), - 0 for pixel features that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Position embeddings that are added to the queries and keys in each self-attention layer. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = inputs_embeds hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # expand attention_mask if attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for i, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: # we add position_embeddings as extra input to the encoder_layer layer_outputs = encoder_layer( hidden_states, attention_mask, position_embeddings=position_embeddings, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class DetrDecoder(DetrPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetrDecoderLayer`]. The decoder updates the query embeddings through multiple self-attention and cross-attention layers. Some small tweaks for DETR: - position_embeddings and query_position_embeddings are added to the forward pass. - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers. Args: config: DetrConfig """ def __init__(self, config: DetrConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.layers = nn.ModuleList([DetrDecoderLayer(config) for _ in range(config.decoder_layers)]) # in DETR, the decoder uses layernorm after the last decoder layer output self.layernorm = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def forward( self, inputs_embeds=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, position_embeddings=None, query_position_embeddings=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): The query embeddings that are passed into the decoder. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`: - 1 for queries that are **not masked**, - 0 for queries that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Position embeddings that are added to the queries and keys in each cross-attention layer. query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if inputs_embeds is not None: hidden_states = inputs_embeds input_shape = inputs_embeds.size()[:-1] combined_attention_mask = None if attention_mask is not None and combined_attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] combined_attention_mask = combined_attention_mask + _expand_mask( attention_mask, inputs_embeds.dtype, target_len=input_shape[-1] ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] encoder_attention_mask = _expand_mask( encoder_attention_mask, inputs_embeds.dtype, target_len=input_shape[-1] ) # optional intermediate hidden states intermediate = () if self.config.auxiliary_loss else None # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): continue if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), hidden_states, combined_attention_mask, encoder_hidden_states, encoder_attention_mask, None, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=combined_attention_mask, position_embeddings=position_embeddings, query_position_embeddings=query_position_embeddings, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if self.config.auxiliary_loss: hidden_states = self.layernorm(hidden_states) intermediate += (hidden_states,) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # finally, apply layernorm hidden_states = self.layernorm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) # stack intermediate decoder activations if self.config.auxiliary_loss: intermediate = torch.stack(intermediate) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate] if v is not None ) return DetrDecoderOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, intermediate_hidden_states=intermediate, ) @add_start_docstrings( """ The bare DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without any specific head on top. """, DETR_START_DOCSTRING, ) class DetrModel(DetrPreTrainedModel): def __init__(self, config: DetrConfig): super().__init__(config) # Create backbone + positional encoding backbone = DetrTimmConvEncoder( config.backbone, config.dilation, config.use_pretrained_backbone, config.num_channels ) position_embeddings = build_position_encoding(config) self.backbone = DetrConvModel(backbone, position_embeddings) # Create projection layer self.input_projection = nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1) self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model) self.encoder = DetrEncoder(config) self.decoder = DetrDecoder(config) # Initialize weights and apply final processing self.post_init() def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def freeze_backbone(self): for name, param in self.backbone.conv_encoder.model.named_parameters(): param.requires_grad_(False) def unfreeze_backbone(self): for name, param in self.backbone.conv_encoder.model.named_parameters(): param.requires_grad_(True) @add_start_docstrings_to_model_forward(DETR_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DetrModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values, pixel_mask=None, decoder_attention_mask=None, encoder_outputs=None, inputs_embeds=None, decoder_inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Returns: Examples: ```python >>> from transformers import DetrFeatureExtractor, DetrModel >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50") >>> model = DetrModel.from_pretrained("facebook/detr-resnet-50") >>> # prepare image for the model >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> # the last hidden states are the final query embeddings of the Transformer decoder >>> # these are of shape (batch_size, num_queries, hidden_size) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 100, 256] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, num_channels, height, width = pixel_values.shape device = pixel_values.device if pixel_mask is None: pixel_mask = torch.ones(((batch_size, height, width)), device=device) # First, sent pixel_values + pixel_mask through Backbone to obtain the features # pixel_values should be of shape (batch_size, num_channels, height, width) # pixel_mask should be of shape (batch_size, height, width) features, position_embeddings_list = self.backbone(pixel_values, pixel_mask) # get final feature map and downsampled mask feature_map, mask = features[-1] if mask is None: raise ValueError("Backbone does not return downsampled pixel mask") # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) projected_feature_map = self.input_projection(feature_map) # Third, flatten the feature map + position embeddings of shape NxCxHxW to NxCxHW, and permute it to NxHWxC # In other words, turn their shape into (batch_size, sequence_length, hidden_size) flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1) position_embeddings = position_embeddings_list[-1].flatten(2).permute(0, 2, 1) flattened_mask = mask.flatten(1) # Fourth, sent flattened_features + flattened_mask + position embeddings through encoder # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size) # flattened_mask is a Tensor of shape (batch_size, heigth*width) if encoder_outputs is None: encoder_outputs = self.encoder( inputs_embeds=flattened_features, attention_mask=flattened_mask, position_embeddings=position_embeddings, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # Fifth, sent query embeddings + position embeddings through the decoder (which is conditioned on the encoder output) query_position_embeddings = self.query_position_embeddings.weight.unsqueeze(0).repeat(batch_size, 1, 1) queries = torch.zeros_like(query_position_embeddings) # decoder outputs consists of (dec_features, dec_hidden, dec_attn) decoder_outputs = self.decoder( inputs_embeds=queries, attention_mask=None, position_embeddings=position_embeddings, query_position_embeddings=query_position_embeddings, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=flattened_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return DetrModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, ) @add_start_docstrings( """ DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks such as COCO detection. """, DETR_START_DOCSTRING, ) class DetrForObjectDetection(DetrPreTrainedModel): def __init__(self, config: DetrConfig): super().__init__(config) # DETR encoder-decoder model self.model = DetrModel(config) # Object detection heads self.class_labels_classifier = nn.Linear( config.d_model, config.num_labels + 1 ) # We add one for the "no object" class self.bbox_predictor = DetrMLPPredictionHead( input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3 ) # Initialize weights and apply final processing self.post_init() # taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py @torch.jit.unused def _set_aux_loss(self, outputs_class, outputs_coord): # this is a workaround to make torchscript happy, as torchscript # doesn't support dictionary with non-homogeneous values, such # as a dict having both a Tensor and a list. return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])] @add_start_docstrings_to_model_forward(DETR_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DetrObjectDetectionOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values, pixel_mask=None, decoder_attention_mask=None, encoder_outputs=None, inputs_embeds=None, decoder_inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (`List[Dict]` of len `(batch_size,)`, *optional*): Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. Returns: Examples: ```python >>> from transformers import DetrFeatureExtractor, DetrForObjectDetection >>> import torch >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50") >>> model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> # convert outputs (bounding boxes and class logits) to COCO API >>> target_sizes = torch.tensor([image.size[::-1]]) >>> results = feature_extractor.post_process_object_detection( ... outputs, threshold=0.9, target_sizes=target_sizes ... )[0] >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): ... box = [round(i, 2) for i in box.tolist()] ... print( ... f"Detected {model.config.id2label[label.item()]} with confidence " ... f"{round(score.item(), 3)} at location {box}" ... ) Detected remote with confidence 0.998 at location [40.16, 70.81, 175.55, 117.98] Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66] Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76] Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93] Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.72] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict # First, sent images through DETR base model to obtain encoder + decoder outputs outputs = self.model( pixel_values, pixel_mask=pixel_mask, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] # class logits + predicted bounding boxes logits = self.class_labels_classifier(sequence_output) pred_boxes = self.bbox_predictor(sequence_output).sigmoid() loss, loss_dict, auxiliary_outputs = None, None, None if labels is not None: # First: create the matcher matcher = DetrHungarianMatcher( class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost ) # Second: create the criterion losses = ["labels", "boxes", "cardinality"] criterion = DetrLoss( matcher=matcher, num_classes=self.config.num_labels, eos_coef=self.config.eos_coefficient, losses=losses, ) criterion.to(self.device) # Third: compute the losses, based on outputs and labels outputs_loss = {} outputs_loss["logits"] = logits outputs_loss["pred_boxes"] = pred_boxes if self.config.auxiliary_loss: intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4] outputs_class = self.class_labels_classifier(intermediate) outputs_coord = self.bbox_predictor(intermediate).sigmoid() auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord) outputs_loss["auxiliary_outputs"] = auxiliary_outputs loss_dict = criterion(outputs_loss, labels) # Fourth: compute total loss, as a weighted sum of the various losses weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient} weight_dict["loss_giou"] = self.config.giou_loss_coefficient if self.config.auxiliary_loss: aux_weight_dict = {} for i in range(self.config.decoder_layers - 1): aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) weight_dict.update(aux_weight_dict) loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) if not return_dict: if auxiliary_outputs is not None: output = (logits, pred_boxes) + auxiliary_outputs + outputs else: output = (logits, pred_boxes) + outputs return ((loss, loss_dict) + output) if loss is not None else output return DetrObjectDetectionOutput( loss=loss, loss_dict=loss_dict, logits=logits, pred_boxes=pred_boxes, auxiliary_outputs=auxiliary_outputs, last_hidden_state=outputs.last_hidden_state, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ DETR Model (consisting of a backbone and encoder-decoder Transformer) with a segmentation head on top, for tasks such as COCO panoptic. """, DETR_START_DOCSTRING, ) class DetrForSegmentation(DetrPreTrainedModel): def __init__(self, config: DetrConfig): super().__init__(config) # object detection model self.detr = DetrForObjectDetection(config) # segmentation head hidden_size, number_of_heads = config.d_model, config.encoder_attention_heads intermediate_channel_sizes = self.detr.model.backbone.conv_encoder.intermediate_channel_sizes self.mask_head = DetrMaskHeadSmallConv( hidden_size + number_of_heads, intermediate_channel_sizes[::-1][-3:], hidden_size ) self.bbox_attention = DetrMHAttentionMap( hidden_size, hidden_size, number_of_heads, dropout=0.0, std=config.init_xavier_std ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(DETR_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DetrSegmentationOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values, pixel_mask=None, decoder_attention_mask=None, encoder_outputs=None, inputs_embeds=None, decoder_inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (`List[Dict]` of len `(batch_size,)`, *optional*): Labels for computing the bipartite matching loss, DICE/F-1 loss and Focal loss. List of dicts, each dictionary containing at least the following 3 keys: 'class_labels', 'boxes' and 'masks' (the class labels, bounding boxes and segmentation masks of an image in the batch respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes in the image,)`, the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)` and the masks a `torch.FloatTensor` of shape `(number of bounding boxes in the image, height, width)`. Returns: Examples: ```python >>> import io >>> import requests >>> from PIL import Image >>> import torch >>> import numpy >>> from transformers import DetrFeatureExtractor, DetrForSegmentation >>> from transformers.models.detr.feature_extraction_detr import rgb_to_id >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic") >>> model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic") >>> # prepare image for the model >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> # Use the `post_process_panoptic_segmentation` method of `DetrFeatureExtractor` to retrieve post-processed panoptic segmentation maps >>> # Segmentation results are returned as a list of dictionaries >>> result = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[(300, 500)]) >>> # A tensor of shape (height, width) where each value denotes a segment id, filled with -1 if no segment is found >>> panoptic_seg = result[0]["segmentation"] >>> # Get prediction score and segment_id to class_id mapping of each segment >>> panoptic_segments_info = result[0]["segments_info"] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, num_channels, height, width = pixel_values.shape device = pixel_values.device if pixel_mask is None: pixel_mask = torch.ones((batch_size, height, width), device=device) # First, get list of feature maps and position embeddings features, position_embeddings_list = self.detr.model.backbone(pixel_values, pixel_mask=pixel_mask) # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) feature_map, mask = features[-1] batch_size, num_channels, height, width = feature_map.shape projected_feature_map = self.detr.model.input_projection(feature_map) # Third, flatten the feature map + position embeddings of shape NxCxHxW to NxCxHW, and permute it to NxHWxC # In other words, turn their shape into (batch_size, sequence_length, hidden_size) flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1) position_embeddings = position_embeddings_list[-1].flatten(2).permute(0, 2, 1) flattened_mask = mask.flatten(1) # Fourth, sent flattened_features + flattened_mask + position embeddings through encoder # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size) # flattened_mask is a Tensor of shape (batch_size, heigth*width) if encoder_outputs is None: encoder_outputs = self.detr.model.encoder( inputs_embeds=flattened_features, attention_mask=flattened_mask, position_embeddings=position_embeddings, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # Fifth, sent query embeddings + position embeddings through the decoder (which is conditioned on the encoder output) query_position_embeddings = self.detr.model.query_position_embeddings.weight.unsqueeze(0).repeat( batch_size, 1, 1 ) queries = torch.zeros_like(query_position_embeddings) # decoder outputs consists of (dec_features, dec_hidden, dec_attn) decoder_outputs = self.detr.model.decoder( inputs_embeds=queries, attention_mask=None, position_embeddings=position_embeddings, query_position_embeddings=query_position_embeddings, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=flattened_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = decoder_outputs[0] # Sixth, compute logits, pred_boxes and pred_masks logits = self.detr.class_labels_classifier(sequence_output) pred_boxes = self.detr.bbox_predictor(sequence_output).sigmoid() memory = encoder_outputs[0].permute(0, 2, 1).view(batch_size, self.config.d_model, height, width) mask = flattened_mask.view(batch_size, height, width) # FIXME h_boxes takes the last one computed, keep this in mind # important: we need to reverse the mask, since in the original implementation the mask works reversed # bbox_mask is of shape (batch_size, num_queries, number_of_attention_heads in bbox_attention, height/32, width/32) bbox_mask = self.bbox_attention(sequence_output, memory, mask=~mask) seg_masks = self.mask_head(projected_feature_map, bbox_mask, [features[2][0], features[1][0], features[0][0]]) pred_masks = seg_masks.view(batch_size, self.detr.config.num_queries, seg_masks.shape[-2], seg_masks.shape[-1]) loss, loss_dict, auxiliary_outputs = None, None, None if labels is not None: # First: create the matcher matcher = DetrHungarianMatcher( class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost ) # Second: create the criterion losses = ["labels", "boxes", "cardinality", "masks"] criterion = DetrLoss( matcher=matcher, num_classes=self.config.num_labels, eos_coef=self.config.eos_coefficient, losses=losses, ) criterion.to(self.device) # Third: compute the losses, based on outputs and labels outputs_loss = {} outputs_loss["logits"] = logits outputs_loss["pred_boxes"] = pred_boxes outputs_loss["pred_masks"] = pred_masks if self.config.auxiliary_loss: intermediate = decoder_outputs.intermediate_hidden_states if return_dict else decoder_outputs[-1] outputs_class = self.class_labels_classifier(intermediate) outputs_coord = self.bbox_predictor(intermediate).sigmoid() auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord) outputs_loss["auxiliary_outputs"] = auxiliary_outputs loss_dict = criterion(outputs_loss, labels) # Fourth: compute total loss, as a weighted sum of the various losses weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient} weight_dict["loss_giou"] = self.config.giou_loss_coefficient weight_dict["loss_mask"] = self.config.mask_loss_coefficient weight_dict["loss_dice"] = self.config.dice_loss_coefficient if self.config.auxiliary_loss: aux_weight_dict = {} for i in range(self.config.decoder_layers - 1): aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) weight_dict.update(aux_weight_dict) loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) if not return_dict: if auxiliary_outputs is not None: output = (logits, pred_boxes, pred_masks) + auxiliary_outputs + decoder_outputs + encoder_outputs else: output = (logits, pred_boxes, pred_masks) + decoder_outputs + encoder_outputs return ((loss, loss_dict) + output) if loss is not None else output return DetrSegmentationOutput( loss=loss, loss_dict=loss_dict, logits=logits, pred_boxes=pred_boxes, pred_masks=pred_masks, auxiliary_outputs=auxiliary_outputs, last_hidden_state=decoder_outputs.last_hidden_state, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) def _expand(tensor, length: int): return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1) # taken from https://github.com/facebookresearch/detr/blob/master/models/segmentation.py class DetrMaskHeadSmallConv(nn.Module): """ Simple convolutional head, using group norm. Upsampling is done using a FPN approach """ def __init__(self, dim, fpn_dims, context_dim): super().__init__() if dim % 8 != 0: raise ValueError( "The hidden_size + number of attention heads must be divisible by 8 as the number of groups in" " GroupNorm is set to 8" ) inter_dims = [dim, context_dim // 2, context_dim // 4, context_dim // 8, context_dim // 16, context_dim // 64] self.lay1 = nn.Conv2d(dim, dim, 3, padding=1) self.gn1 = nn.GroupNorm(8, dim) self.lay2 = nn.Conv2d(dim, inter_dims[1], 3, padding=1) self.gn2 = nn.GroupNorm(8, inter_dims[1]) self.lay3 = nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1) self.gn3 = nn.GroupNorm(8, inter_dims[2]) self.lay4 = nn.Conv2d(inter_dims[2], inter_dims[3], 3, padding=1) self.gn4 = nn.GroupNorm(8, inter_dims[3]) self.lay5 = nn.Conv2d(inter_dims[3], inter_dims[4], 3, padding=1) self.gn5 = nn.GroupNorm(8, inter_dims[4]) self.out_lay = nn.Conv2d(inter_dims[4], 1, 3, padding=1) self.dim = dim self.adapter1 = nn.Conv2d(fpn_dims[0], inter_dims[1], 1) self.adapter2 = nn.Conv2d(fpn_dims[1], inter_dims[2], 1) self.adapter3 = nn.Conv2d(fpn_dims[2], inter_dims[3], 1) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_uniform_(m.weight, a=1) nn.init.constant_(m.bias, 0) def forward(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor]): # here we concatenate x, the projected feature map, of shape (batch_size, d_model, heigth/32, width/32) with # the bbox_mask = the attention maps of shape (batch_size, n_queries, n_heads, height/32, width/32). # We expand the projected feature map to match the number of heads. x = torch.cat([_expand(x, bbox_mask.shape[1]), bbox_mask.flatten(0, 1)], 1) x = self.lay1(x) x = self.gn1(x) x = nn.functional.relu(x) x = self.lay2(x) x = self.gn2(x) x = nn.functional.relu(x) cur_fpn = self.adapter1(fpns[0]) if cur_fpn.size(0) != x.size(0): cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") x = self.lay3(x) x = self.gn3(x) x = nn.functional.relu(x) cur_fpn = self.adapter2(fpns[1]) if cur_fpn.size(0) != x.size(0): cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") x = self.lay4(x) x = self.gn4(x) x = nn.functional.relu(x) cur_fpn = self.adapter3(fpns[2]) if cur_fpn.size(0) != x.size(0): cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") x = self.lay5(x) x = self.gn5(x) x = nn.functional.relu(x) x = self.out_lay(x) return x class DetrMHAttentionMap(nn.Module): """This is a 2D attention module, which only returns the attention softmax (no multiplication by value)""" def __init__(self, query_dim, hidden_dim, num_heads, dropout=0.0, bias=True, std=None): super().__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.dropout = nn.Dropout(dropout) self.q_linear = nn.Linear(query_dim, hidden_dim, bias=bias) self.k_linear = nn.Linear(query_dim, hidden_dim, bias=bias) self.normalize_fact = float(hidden_dim / self.num_heads) ** -0.5 def forward(self, q, k, mask: Optional[Tensor] = None): q = self.q_linear(q) k = nn.functional.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias) queries_per_head = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads) keys_per_head = k.view(k.shape[0], self.num_heads, self.hidden_dim // self.num_heads, k.shape[-2], k.shape[-1]) weights = torch.einsum("bqnc,bnchw->bqnhw", queries_per_head * self.normalize_fact, keys_per_head) if mask is not None: weights.masked_fill_(mask.unsqueeze(1).unsqueeze(1), torch.finfo(weights.dtype).min) weights = nn.functional.softmax(weights.flatten(2), dim=-1).view(weights.size()) weights = self.dropout(weights) return weights def dice_loss(inputs, targets, num_boxes): """ Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class). """ inputs = inputs.sigmoid() inputs = inputs.flatten(1) numerator = 2 * (inputs * targets).sum(1) denominator = inputs.sum(-1) + targets.sum(-1) loss = 1 - (numerator + 1) / (denominator + 1) return loss.sum() / num_boxes def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2): """ Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs (`torch.FloatTensor` of arbitrary shape): The predictions for each example. targets (`torch.FloatTensor` with the same shape as `inputs`) A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class and 1 for the positive class). alpha (`float`, *optional*, defaults to `0.25`): Optional weighting factor in the range (0,1) to balance positive vs. negative examples. gamma (`int`, *optional*, defaults to `2`): Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples. Returns: Loss tensor """ prob = inputs.sigmoid() ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") # add modulating factor p_t = prob * targets + (1 - prob) * (1 - targets) loss = ce_loss * ((1 - p_t) ** gamma) if alpha >= 0: alpha_t = alpha * targets + (1 - alpha) * (1 - targets) loss = alpha_t * loss return loss.mean(1).sum() / num_boxes # taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py class DetrLoss(nn.Module): """ This class computes the losses for DetrForObjectDetection/DetrForSegmentation. The process happens in two steps: 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched ground-truth / prediction (supervise class and box). A note on the `num_classes` argument (copied from original repo in detr.py): "the naming of the `num_classes` parameter of the criterion is somewhat misleading. It indeed corresponds to `max_obj_id` + 1, where `max_obj_id` is the maximum id for a class in your dataset. For example, COCO has a `max_obj_id` of 90, so we pass `num_classes` to be 91. As another example, for a dataset that has a single class with `id` 1, you should pass `num_classes` to be 2 (`max_obj_id` + 1). For more details on this, check the following discussion https://github.com/facebookresearch/detr/issues/108#issuecomment-650269223" Args: matcher (`DetrHungarianMatcher`): Module able to compute a matching between targets and proposals. num_classes (`int`): Number of object categories, omitting the special no-object category. eos_coef (`float`): Relative classification weight applied to the no-object category. losses (`List[str]`): List of all the losses to be applied. See `get_loss` for a list of all available losses. """ def __init__(self, matcher, num_classes, eos_coef, losses): super().__init__() self.matcher = matcher self.num_classes = num_classes self.eos_coef = eos_coef self.losses = losses empty_weight = torch.ones(self.num_classes + 1) empty_weight[-1] = self.eos_coef self.register_buffer("empty_weight", empty_weight) # removed logging parameter, which was part of the original implementation def loss_labels(self, outputs, targets, indices, num_boxes): """ Classification loss (NLL) targets dicts must contain the key "class_labels" containing a tensor of dim [nb_target_boxes] """ if "logits" not in outputs: raise KeyError("No logits were found in the outputs") source_logits = outputs["logits"] idx = self._get_source_permutation_idx(indices) target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)]) target_classes = torch.full( source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device ) target_classes[idx] = target_classes_o loss_ce = nn.functional.cross_entropy(source_logits.transpose(1, 2), target_classes, self.empty_weight) losses = {"loss_ce": loss_ce} return losses @torch.no_grad() def loss_cardinality(self, outputs, targets, indices, num_boxes): """ Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes. This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients. """ logits = outputs["logits"] device = logits.device target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device) # Count the number of predictions that are NOT "no-object" (which is the last class) card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1) card_err = nn.functional.l1_loss(card_pred.float(), target_lengths.float()) losses = {"cardinality_error": card_err} return losses def loss_boxes(self, outputs, targets, indices, num_boxes): """ Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss. Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. """ if "pred_boxes" not in outputs: raise KeyError("No predicted boxes found in outputs") idx = self._get_source_permutation_idx(indices) source_boxes = outputs["pred_boxes"][idx] target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) loss_bbox = nn.functional.l1_loss(source_boxes, target_boxes, reduction="none") losses = {} losses["loss_bbox"] = loss_bbox.sum() / num_boxes loss_giou = 1 - torch.diag( generalized_box_iou(center_to_corners_format(source_boxes), center_to_corners_format(target_boxes)) ) losses["loss_giou"] = loss_giou.sum() / num_boxes return losses def loss_masks(self, outputs, targets, indices, num_boxes): """ Compute the losses related to the masks: the focal loss and the dice loss. Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]. """ if "pred_masks" not in outputs: raise KeyError("No predicted masks found in outputs") source_idx = self._get_source_permutation_idx(indices) target_idx = self._get_target_permutation_idx(indices) source_masks = outputs["pred_masks"] source_masks = source_masks[source_idx] masks = [t["masks"] for t in targets] # TODO use valid to mask invalid areas due to padding in loss target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() target_masks = target_masks.to(source_masks) target_masks = target_masks[target_idx] # upsample predictions to the target size source_masks = nn.functional.interpolate( source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False ) source_masks = source_masks[:, 0].flatten(1) target_masks = target_masks.flatten(1) target_masks = target_masks.view(source_masks.shape) losses = { "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes), "loss_dice": dice_loss(source_masks, target_masks, num_boxes), } return losses def _get_source_permutation_idx(self, indices): # permute predictions following indices batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)]) source_idx = torch.cat([source for (source, _) in indices]) return batch_idx, source_idx def _get_target_permutation_idx(self, indices): # permute targets following indices batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)]) target_idx = torch.cat([target for (_, target) in indices]) return batch_idx, target_idx def get_loss(self, loss, outputs, targets, indices, num_boxes): loss_map = { "labels": self.loss_labels, "cardinality": self.loss_cardinality, "boxes": self.loss_boxes, "masks": self.loss_masks, } if loss not in loss_map: raise ValueError(f"Loss {loss} not supported") return loss_map[loss](outputs, targets, indices, num_boxes) def forward(self, outputs, targets): """ This performs the loss computation. Args: outputs (`dict`, *optional*): Dictionary of tensors, see the output specification of the model for the format. targets (`List[dict]`, *optional*): List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the losses applied, see each loss' doc. """ outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"} # Retrieve the matching between the outputs of the last layer and the targets indices = self.matcher(outputs_without_aux, targets) # Compute the average number of target boxes across all nodes, for normalization purposes num_boxes = sum(len(t["class_labels"]) for t in targets) num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device) # (Niels): comment out function below, distributed training to be added # if is_dist_avail_and_initialized(): # torch.distributed.all_reduce(num_boxes) # (Niels) in original implementation, num_boxes is divided by get_world_size() num_boxes = torch.clamp(num_boxes, min=1).item() # Compute all the requested losses losses = {} for loss in self.losses: losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes)) # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. if "auxiliary_outputs" in outputs: for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]): indices = self.matcher(auxiliary_outputs, targets) for loss in self.losses: if loss == "masks": # Intermediate masks losses are too costly to compute, we ignore them. continue l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes) l_dict = {k + f"_{i}": v for k, v in l_dict.items()} losses.update(l_dict) return losses # taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py class DetrMLPPredictionHead(nn.Module): """ Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates, height and width of a bounding box w.r.t. an image. Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py """ def __init__(self, input_dim, hidden_dim, output_dim, num_layers): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) def forward(self, x): for i, layer in enumerate(self.layers): x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x # taken from https://github.com/facebookresearch/detr/blob/master/models/matcher.py class DetrHungarianMatcher(nn.Module): """ This class computes an assignment between the targets and the predictions of the network. For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are un-matched (and thus treated as non-objects). Args: class_cost: The relative weight of the classification error in the matching cost. bbox_cost: The relative weight of the L1 error of the bounding box coordinates in the matching cost. giou_cost: The relative weight of the giou loss of the bounding box in the matching cost. """ def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1): super().__init__() requires_backends(self, ["scipy"]) self.class_cost = class_cost self.bbox_cost = bbox_cost self.giou_cost = giou_cost if class_cost == 0 and bbox_cost == 0 and giou_cost == 0: raise ValueError("All costs of the Matcher can't be 0") @torch.no_grad() def forward(self, outputs, targets): """ Args: outputs (`dict`): A dictionary that contains at least these entries: * "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits * "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates. targets (`List[dict]`): A list of targets (len(targets) = batch_size), where each target is a dict containing: * "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth objects in the target) containing the class labels * "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates. Returns: `List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where: - index_i is the indices of the selected predictions (in order) - index_j is the indices of the corresponding selected targets (in order) For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes) """ batch_size, num_queries = outputs["logits"].shape[:2] # We flatten to compute the cost matrices in a batch out_prob = outputs["logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes] out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] # Also concat the target labels and boxes target_ids = torch.cat([v["class_labels"] for v in targets]) target_bbox = torch.cat([v["boxes"] for v in targets]) # Compute the classification cost. Contrary to the loss, we don't use the NLL, # but approximate it in 1 - proba[target class]. # The 1 is a constant that doesn't change the matching, it can be ommitted. class_cost = -out_prob[:, target_ids] # Compute the L1 cost between boxes bbox_cost = torch.cdist(out_bbox, target_bbox, p=1) # Compute the giou cost between boxes giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox)) # Final cost matrix cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu() sizes = [len(v["boxes"]) for v in targets] indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))] return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] # below: bounding box utilities taken from https://github.com/facebookresearch/detr/blob/master/util/box_ops.py def _upcast(t: Tensor) -> Tensor: # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type if t.is_floating_point(): return t if t.dtype in (torch.float32, torch.float64) else t.float() else: return t if t.dtype in (torch.int32, torch.int64) else t.int() def box_area(boxes: Tensor) -> Tensor: """ Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. Args: boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 < x2` and `0 <= y1 < y2`. Returns: `torch.FloatTensor`: a tensor containing the area for each box. """ boxes = _upcast(boxes) return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) # modified from torchvision to also return the union def box_iou(boxes1, boxes2): area1 = box_area(boxes1) area2 = box_area(boxes2) left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] union = area1[:, None] + area2 - inter iou = inter / union return iou, union def generalized_box_iou(boxes1, boxes2): """ Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. Returns: `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) """ # degenerate boxes gives inf / nan results # so do an early check if not (boxes1[:, 2:] >= boxes1[:, :2]).all(): raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}") if not (boxes2[:, 2:] >= boxes2[:, :2]).all(): raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}") iou, union = box_iou(boxes1, boxes2) top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2]) bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2] area = width_height[:, :, 0] * width_height[:, :, 1] return iou - (area - union) / area # Copied from transformers.models.detr.feature_extraction_detr.center_to_corners_format def center_to_corners_format(x): """ Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format (x_0, y_0, x_1, y_1). """ center_x, center_y, width, height = x.unbind(-1) b = [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)] return torch.stack(b, dim=-1) # below: taken from https://github.com/facebookresearch/detr/blob/master/util/misc.py#L306 def _max_by_axis(the_list): # type: (List[List[int]]) -> List[int] maxes = the_list[0] for sublist in the_list[1:]: for index, item in enumerate(sublist): maxes[index] = max(maxes[index], item) return maxes class NestedTensor(object): def __init__(self, tensors, mask: Optional[Tensor]): self.tensors = tensors self.mask = mask def to(self, device): cast_tensor = self.tensors.to(device) mask = self.mask if mask is not None: cast_mask = mask.to(device) else: cast_mask = None return NestedTensor(cast_tensor, cast_mask) def decompose(self): return self.tensors, self.mask def __repr__(self): return str(self.tensors) def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): if tensor_list[0].ndim == 3: max_size = _max_by_axis([list(img.shape) for img in tensor_list]) batch_shape = [len(tensor_list)] + max_size batch_size, num_channels, height, width = batch_shape dtype = tensor_list[0].dtype device = tensor_list[0].device tensor = torch.zeros(batch_shape, dtype=dtype, device=device) mask = torch.ones((batch_size, height, width), dtype=torch.bool, device=device) for img, pad_img, m in zip(tensor_list, tensor, mask): pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) m[: img.shape[1], : img.shape[2]] = False else: raise ValueError("Only 3-dimensional tensors are supported") return NestedTensor(tensor, mask)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/test_modeling_common.py

# coding=utf-8 # Copyright 2019 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import gc import inspect import json import os import os.path import pickle import random import sys import tempfile import unittest import unittest.mock as mock import warnings from pathlib import Path from typing import Dict, List, Tuple import numpy as np import transformers from huggingface_hub import HfFolder, delete_repo, set_access_token from huggingface_hub.file_download import http_get from requests.exceptions import HTTPError from transformers import ( AutoConfig, AutoModel, AutoModelForSequenceClassification, PretrainedConfig, is_torch_available, logging, ) from transformers.models.auto import get_values from transformers.testing_utils import ( TOKEN, USER, CaptureLogger, TestCasePlus, is_flaky, is_pt_flax_cross_test, is_pt_tf_cross_test, is_staging_test, require_accelerate, require_safetensors, require_torch, require_torch_gpu, require_torch_multi_gpu, require_usr_bin_time, slow, torch_device, ) from transformers.utils import ( SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, is_accelerate_available, is_flax_available, is_tf_available, is_torch_fx_available, ) from transformers.utils.generic import ModelOutput sys.path.append(str(Path(__file__).parent.parent / "utils")) from test_module.custom_configuration import CustomConfig, NoSuperInitConfig # noqa E402 if is_accelerate_available(): from accelerate.utils import compute_module_sizes if is_torch_available(): import torch from torch import nn from test_module.custom_modeling import CustomModel, NoSuperInitModel from transformers import ( BERT_PRETRAINED_MODEL_ARCHIVE_LIST, MODEL_FOR_AUDIO_XVECTOR_MAPPING, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, MODEL_MAPPING, AdaptiveEmbedding, AutoModelForCausalLM, AutoTokenizer, BertConfig, BertModel, PreTrainedModel, T5Config, T5ForConditionalGeneration, ) from transformers.modeling_utils import shard_checkpoint # Fake pretrained models for tests class BaseModel(PreTrainedModel): config_class = PretrainedConfig def __init__(self, config): super().__init__(config) self.linear = nn.Linear(4, 5) self.linear_2 = nn.Linear(5, 6) def forward(self, x): return self.linear_2(self.linear(x)) class ModelWithHead(PreTrainedModel): base_model_prefix = "base" config_class = PretrainedConfig def _init_weights(self, module): pass def __init__(self, config): super().__init__(config) self.base = BaseModel(config) # linear is a common name between Base and Head on purpose. self.linear = nn.Linear(6, 3) self.linear2 = nn.Linear(3, 5) def forward(self, x): return self.linear2(self.linear(self.base(x))) if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp from transformers.modeling_flax_pytorch_utils import ( convert_pytorch_state_dict_to_flax, load_flax_weights_in_pytorch_model, ) if is_torch_fx_available(): from transformers.utils.fx import symbolic_trace def _config_zero_init(config): configs_no_init = copy.deepcopy(config) for key in configs_no_init.__dict__.keys(): if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key: setattr(configs_no_init, key, 1e-10) return configs_no_init TINY_T5 = "patrickvonplaten/t5-tiny-random" TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification" @require_torch class ModelTesterMixin: model_tester = None all_model_classes = () all_generative_model_classes = () fx_compatible = False test_torchscript = True test_pruning = True test_resize_embeddings = True test_resize_position_embeddings = False test_head_masking = True test_mismatched_shapes = True test_missing_keys = True test_model_parallel = False is_encoder_decoder = False has_attentions = True model_split_percents = [0.5, 0.7, 0.9] def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict = { k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous() if isinstance(v, torch.Tensor) and v.ndim > 1 else v for k, v in inputs_dict.items() } elif model_class in get_values(MODEL_FOR_AUDIO_XVECTOR_MAPPING): inputs_dict.pop("attention_mask") if return_labels: if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device) elif model_class in [ *get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING), *get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING), ]: inputs_dict["start_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) inputs_dict["end_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class in [ *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING), *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING), ]: inputs_dict["labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class in [ *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_CAUSAL_LM_MAPPING), *get_values(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING), *get_values(MODEL_FOR_MASKED_LM_MAPPING), *get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING), ]: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) elif model_class in get_values(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING): num_patches = self.model_tester.image_size // self.model_tester.patch_size inputs_dict["bool_masked_pos"] = torch.zeros( (self.model_tester.batch_size, num_patches**2), dtype=torch.long, device=torch_device ) elif model_class in get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING): batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = torch.zeros( [self.model_tester.batch_size, height, width], device=torch_device ).long() return inputs_dict def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) out_2 = outputs[0].cpu().numpy() out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname) model.to(torch_device) with torch.no_grad(): after_outputs = model(**self._prepare_for_class(inputs_dict, model_class)) # Make sure we don't have nans out_1 = after_outputs[0].cpu().numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) def test_save_load_keys_to_ignore_on_save(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) _keys_to_ignore_on_save = getattr(model, "_keys_to_ignore_on_save", None) if _keys_to_ignore_on_save is None: continue # check the keys are in the original state_dict for k in _keys_to_ignore_on_save: self.assertIn(k, model.state_dict().keys(), "\n".join(model.state_dict().keys())) # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME) state_dict_saved = torch.load(output_model_file) for k in _keys_to_ignore_on_save: self.assertNotIn(k, state_dict_saved.keys(), "\n".join(state_dict_saved.keys())) # Test we can load the state dict in the model, necessary for the checkpointing API in Trainer. load_result = model.load_state_dict(state_dict_saved, strict=False) self.assertTrue( len(load_result.missing_keys) == 0 or set(load_result.missing_keys) == set(model._keys_to_ignore_on_save) ) self.assertTrue(len(load_result.unexpected_keys) == 0) def test_gradient_checkpointing_backward_compatibility(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue config.gradient_checkpointing = True model = model_class(config) self.assertTrue(model.is_gradient_checkpointing) def test_gradient_checkpointing_enable_disable(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue # at init model should have gradient checkpointing disabled model = model_class(config) self.assertFalse(model.is_gradient_checkpointing) # check enable works model.gradient_checkpointing_enable() self.assertTrue(model.is_gradient_checkpointing) # check disable works model.gradient_checkpointing_disable() self.assertFalse(model.is_gradient_checkpointing) def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) @is_flaky() def test_save_load_fast_init_from_base(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() base_class = MODEL_MAPPING[config.__class__] if isinstance(base_class, tuple): base_class = base_class[0] for model_class in self.all_model_classes: if model_class == base_class: continue # make a copy of model class to not break future tests # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class class CopyClass(model_class): pass model_class_copy = CopyClass # make sure that all keys are expected for test model_class_copy._keys_to_ignore_on_load_missing = [] # make init deterministic, but make sure that # non-initialized weights throw errors nevertheless model_class_copy._init_weights = self._mock_init_weights model = base_class(config) state_dict = model.state_dict() # this will often delete a single weight of a multi-weight module # to test an edge case random_key_to_del = random.choice(list(state_dict.keys())) del state_dict[random_key_to_del] # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) model_fast_init = model_class_copy.from_pretrained(tmpdirname) model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False) for key in model_fast_init.state_dict().keys(): max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") def test_save_load_fast_init_to_base(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() base_class = MODEL_MAPPING[config.__class__] if isinstance(base_class, tuple): base_class = base_class[0] for model_class in self.all_model_classes: if model_class == base_class: continue # make a copy of model class to not break future tests # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class class CopyClass(base_class): pass base_class_copy = CopyClass # make sure that all keys are expected for test base_class_copy._keys_to_ignore_on_load_missing = [] # make init deterministic, but make sure that # non-initialized weights throw errors nevertheless base_class_copy._init_weights = self._mock_init_weights model = model_class(config) state_dict = model.state_dict() # this will often delete a single weight of a multi-weight module # to test an edge case random_key_to_del = random.choice(list(state_dict.keys())) del state_dict[random_key_to_del] # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.config.save_pretrained(tmpdirname) torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) model_fast_init = base_class_copy.from_pretrained(tmpdirname) model_slow_init = base_class_copy.from_pretrained(tmpdirname, _fast_init=False) for key in model_fast_init.state_dict().keys(): max_diff = torch.max( torch.abs(model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]) ).item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_determinism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): first = model(**self._prepare_for_class(inputs_dict, model_class))[0] second = model(**self._prepare_for_class(inputs_dict, model_class))[0] out_1 = first.cpu().numpy() out_2 = second.cpu().numpy() out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] if model.config.is_encoder_decoder: expected_arg_names = [ "input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend( ["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names else ["encoder_outputs"] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) else: expected_arg_names = ["input_ids"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_training(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True if model_class in get_values(MODEL_MAPPING): continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_cache = False config.return_dict = True if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing: continue model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable() model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) chunk_length = getattr(self.model_tester, "chunk_length", None) if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) if chunk_length is not None: self.assertListEqual( list(attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) out_len = len(outputs) if self.is_encoder_decoder: correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Question Answering model returns start_logits and end_logits if model_class in [ *get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING), *get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING), ]: correct_outlen += 1 # start_logits and end_logits instead of only 1 output if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) if chunk_length is not None: self.assertListEqual( list(self_attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) @slow def test_torchscript_simple(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() self._create_and_check_torchscript(config, inputs_dict) @slow def test_torchscript_output_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_attentions = True self._create_and_check_torchscript(config, inputs_dict) @slow def test_torchscript_output_hidden_state(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True self._create_and_check_torchscript(config, inputs_dict) # This is copied from `torch/testing/_internal/jit_utils.py::clear_class_registry` def clear_torch_jit_class_registry(self): torch._C._jit_clear_class_registry() torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore() torch.jit._state._clear_class_state() def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class) main_input_name = model_class.main_input_name try: if model.config.is_encoder_decoder: model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward main_input = inputs[main_input_name] attention_mask = inputs["attention_mask"] decoder_input_ids = inputs["decoder_input_ids"] decoder_attention_mask = inputs["decoder_attention_mask"] model(main_input, attention_mask, decoder_input_ids, decoder_attention_mask) traced_model = torch.jit.trace( model, (main_input, attention_mask, decoder_input_ids, decoder_attention_mask) ) elif "bbox" in inputs and "image" in inputs: # LayoutLMv2 requires additional inputs input_ids = inputs["input_ids"] bbox = inputs["bbox"] image = inputs["image"].tensor model(input_ids, bbox, image) traced_model = torch.jit.trace( model, (input_ids, bbox, image), check_trace=False ) # when traced model is checked, an error is produced due to name mangling else: main_input = inputs[main_input_name] model(main_input) traced_model = torch.jit.trace(model, main_input) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): if layer_name in loaded_model_state_dict: p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) # Avoid memory leak. Without this, each call increase RAM usage by ~20MB. # (Even with this call, there are still memory leak by ~0.04MB) self.clear_torch_jit_class_registry() def test_torch_fx(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() self._create_and_check_torch_fx_tracing(config, inputs_dict) def test_torch_fx_output_loss(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() self._create_and_check_torch_fx_tracing(config, inputs_dict, output_loss=True) def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False): if not is_torch_fx_available() or not self.fx_compatible: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss) try: if model.config.is_encoder_decoder: model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward labels = inputs.get("labels", None) input_names = [ "attention_mask", "decoder_attention_mask", "decoder_input_ids", "input_features", "input_ids", "input_values", ] if labels is not None: input_names.append("labels") filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names} input_names = list(filtered_inputs.keys()) model_output = model(**filtered_inputs) traced_model = symbolic_trace(model, input_names) traced_output = traced_model(**filtered_inputs) else: input_names = [ "attention_mask", "bbox", "input_features", "input_ids", "input_values", "pixel_values", "token_type_ids", "visual_feats", "visual_pos", ] labels = inputs.get("labels", None) start_positions = inputs.get("start_positions", None) end_positions = inputs.get("end_positions", None) if labels is not None: input_names.append("labels") if start_positions is not None: input_names.append("start_positions") if end_positions is not None: input_names.append("end_positions") filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names} input_names = list(filtered_inputs.keys()) if isinstance(model, tuple(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values())) and ( not hasattr(model.config, "problem_type") or model.config.problem_type is None ): model.config.problem_type = "single_label_classification" traced_model = symbolic_trace(model, input_names) traced_output = traced_model(**filtered_inputs) model_output = model(**filtered_inputs) except Exception as e: self.fail(f"Couldn't trace module: {e}") def flatten_output(output): flatten = [] for x in output: if isinstance(x, (tuple, list)): flatten += flatten_output(x) elif not isinstance(x, torch.Tensor): continue else: flatten.append(x) return flatten model_output = flatten_output(model_output) traced_output = flatten_output(traced_output) num_outputs = len(model_output) for i in range(num_outputs): self.assertTrue( torch.allclose(model_output[i], traced_output[i]), f"traced {i}th output doesn't match model {i}th output for {model_class}", ) # Test that the model can be serialized and restored properly with tempfile.TemporaryDirectory() as tmp_dir_name: pkl_file_name = os.path.join(tmp_dir_name, "model.pkl") try: with open(pkl_file_name, "wb") as f: pickle.dump(traced_model, f) with open(pkl_file_name, "rb") as f: loaded = pickle.load(f) except Exception as e: self.fail(f"Couldn't serialize / deserialize the traced model: {e}") loaded_output = loaded(**filtered_inputs) loaded_output = flatten_output(loaded_output) for i in range(num_outputs): self.assertTrue( torch.allclose(model_output[i], loaded_output[i]), f"serialized model {i}th output doesn't match model {i}th output for {model_class}", ) # Avoid memory leak. Without this, each call increase RAM usage by ~20MB. # (Even with this call, there are still memory leak by ~0.04MB) self.clear_torch_jit_class_registry() def test_headmasking(self): if not self.test_head_masking: return global_rng.seed(42) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() global_rng.seed() inputs_dict["output_attentions"] = True config.output_hidden_states = True configs_no_init = _config_zero_init(config) # To be sure we have no Nan for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() # Prepare head_mask # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior) head_mask = torch.ones( self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device, ) head_mask[0, 0] = 0 head_mask[-1, :-1] = 0 head_mask.requires_grad_(requires_grad=True) inputs = self._prepare_for_class(inputs_dict, model_class).copy() inputs["head_mask"] = head_mask if model.config.is_encoder_decoder: signature = inspect.signature(model.forward) arg_names = [*signature.parameters.keys()] if "decoder_head_mask" in arg_names: # necessary diferentiation because of T5 model inputs["decoder_head_mask"] = head_mask if "cross_attn_head_mask" in arg_names: inputs["cross_attn_head_mask"] = head_mask outputs = model(**inputs, return_dict=True) # Test that we can get a gradient back for importance score computation output = sum(t.sum() for t in outputs[0]) output = output.sum() output.backward() multihead_outputs = head_mask.grad self.assertIsNotNone(multihead_outputs) self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers) def check_attentions_validity(attentions): # Remove Nan for t in attentions: self.assertLess( torch.sum(torch.isnan(t)), t.numel() / 4 ) # Check we don't have more than 25% nans (arbitrary) attentions = [ t.masked_fill(torch.isnan(t), 0.0) for t in attentions ] # remove them (the test is less complete) self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0) self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0) if len(attentions) > 2: # encoder-decoder models have only 2 layers in each module self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0) self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0) self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0) if model.config.is_encoder_decoder: check_attentions_validity(outputs.encoder_attentions) check_attentions_validity(outputs.decoder_attentions) check_attentions_validity(outputs.cross_attentions) else: check_attentions_validity(outputs.attentions) def test_head_pruning(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False model = model_class(config=config) model.to(torch_device) model.eval() heads_to_prune = { 0: list(range(1, self.model_tester.num_attention_heads)), -1: [0], } model.prune_heads(heads_to_prune) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) def test_head_pruning_save_load_from_pretrained(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False model = model_class(config=config) model.to(torch_device) model.eval() heads_to_prune = { 0: list(range(1, self.model_tester.num_attention_heads)), -1: [0], } model.prune_heads(heads_to_prune) with tempfile.TemporaryDirectory() as temp_dir_name: model.save_pretrained(temp_dir_name) model = model_class.from_pretrained(temp_dir_name) model.to(torch_device) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) def test_head_pruning_save_load_from_config_init(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False heads_to_prune = { 0: list(range(1, self.model_tester.num_attention_heads)), -1: [0], } config.pruned_heads = heads_to_prune model = model_class(config=config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) def test_head_pruning_integration(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False heads_to_prune = {0: [0], 1: [1, 2]} config.pruned_heads = heads_to_prune model = model_class(config=config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads) with tempfile.TemporaryDirectory() as temp_dir_name: model.save_pretrained(temp_dir_name) model = model_class.from_pretrained(temp_dir_name) model.to(torch_device) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads) heads_to_prune = {0: [0], 2: [1, 2]} model.prune_heads(heads_to_prune) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2) self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads) self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]}) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1: seq_length = seq_length * self.model_tester.chunk_length else: seq_length = self.model_tester.seq_length self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = self.has_attentions # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0] if config.is_encoder_decoder: # Seq2Seq models encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() decoder_hidden_states = outputs.decoder_hidden_states[0] decoder_hidden_states.retain_grad() if self.has_attentions: encoder_attentions = outputs.encoder_attentions[0] encoder_attentions.retain_grad() decoder_attentions = outputs.decoder_attentions[0] decoder_attentions.retain_grad() cross_attentions = outputs.cross_attentions[0] cross_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(decoder_hidden_states.grad) if self.has_attentions: self.assertIsNotNone(encoder_attentions.grad) self.assertIsNotNone(decoder_attentions.grad) self.assertIsNotNone(cross_attentions.grad) else: # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] hidden_states.retain_grad() if self.has_attentions: attentions = outputs.attentions[0] attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) if self.has_attentions: self.assertIsNotNone(attentions.grad) def test_feed_forward_chunking(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: torch.manual_seed(0) config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) model.eval() hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0] torch.manual_seed(0) config.chunk_size_feed_forward = 1 model = model_class(config) model.to(torch_device) model.eval() hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0] self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3)) def test_resize_position_vector_embeddings(self): if not self.test_resize_position_embeddings: return ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() max_position_embeddings = config.max_position_embeddings # Retrieve the embeddings and clone theme if model.config.is_encoder_decoder: encoder_model_embed, decoder_model_embed = model.get_position_embeddings() encoder_cloned_embeddings = encoder_model_embed.weight.clone() decoder_cloned_embeddings = decoder_model_embed.weight.clone() else: model_embed = model.get_position_embeddings() cloned_embeddings = model_embed.weight.clone() # Check that resizing the position embeddings with a larger max_position_embeddings increases # the model's postion embeddings size model.resize_position_embeddings(max_position_embeddings + 10) self.assertEqual(model.config.max_position_embeddings, max_position_embeddings + 10) # Check that it actually resizes the embeddings matrix if model.config.is_encoder_decoder: encoder_model_embed, decoder_model_embed = model.get_position_embeddings() self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] + 10) self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] + 10) else: model_embed = model.get_position_embeddings() self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the position embeddings with a smaller max_position_embeddings decreases # the model's max_position_embeddings model.resize_position_embeddings(max_position_embeddings - 5) self.assertEqual(model.config.max_position_embeddings, max_position_embeddings - 5) # Check that it actually resizes the embeddings matrix if model.config.is_encoder_decoder: encoder_model_embed, decoder_model_embed = model.get_position_embeddings() self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] - 5) self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] - 5) else: model_embed = model.get_position_embeddings() self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 5) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True if model.config.is_encoder_decoder: for p1, p2 in zip(encoder_cloned_embeddings, encoder_model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False for p1, p2 in zip(decoder_cloned_embeddings, decoder_model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False else: for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_resize_tokens_embeddings(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) # make sure that decoder_input_ids are resized as well if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_resize_embeddings_untied(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return original_config.tie_word_embeddings = False # if model cannot untied embeddings -> leave test if original_config.tie_word_embeddings: return for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Embedding, AdaptiveEmbedding)) model.set_input_embeddings(nn.Embedding(10, 10)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_model_main_input_name(self): for model_class in self.all_model_classes: model_signature = inspect.signature(getattr(model_class, "forward")) # The main input is the name of the argument after `self` observed_main_input_name = list(model_signature.parameters.keys())[1] self.assertEqual(model_class.main_input_name, observed_main_input_name) def test_correct_missing_keys(self): if not self.test_missing_keys: return config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) base_model_prefix = model.base_model_prefix if hasattr(model, base_model_prefix): extra_params = {k: v for k, v in model.named_parameters() if not k.startswith(base_model_prefix)} extra_params.update({k: v for k, v in model.named_buffers() if not k.startswith(base_model_prefix)}) # Some models define this as None if model._keys_to_ignore_on_load_missing: for key in model._keys_to_ignore_on_load_missing: extra_params.pop(key, None) if not extra_params: # In that case, we *are* on a head model, but every # single key is not actual parameters and this is # tested in `test_tied_model_weights_key_ignore` test. continue with tempfile.TemporaryDirectory() as temp_dir_name: model.base_model.save_pretrained(temp_dir_name) model, loading_info = model_class.from_pretrained(temp_dir_name, output_loading_info=True) self.assertGreater(len(loading_info["missing_keys"]), 0, model.__class__.__name__) def test_tie_model_weights(self): if not self.test_torchscript: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_same_values(layer_1, layer_2): equal = True for p1, p2 in zip(layer_1.weight, layer_2.weight): if p1.data.ne(p2.data).sum() > 0: equal = False return equal for model_class in self.all_model_classes: config.torchscript = True model_not_tied = model_class(config) if model_not_tied.get_output_embeddings() is None: continue config_tied = copy.deepcopy(config) config_tied.torchscript = False model_tied = model_class(config_tied) params_tied = list(model_tied.parameters()) # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(check_same_values(embeddings, decoding)) # # Check that after modification, they remain the same. # embeddings.weight.data.div_(2) # # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(embeddings.weight.shape, decoding.weight.shape) # self.assertTrue(check_same_values(embeddings, decoding)) # # Check that after modification, they remain the same. # decoding.weight.data.div_(4) # # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(embeddings.weight.shape, decoding.weight.shape) # self.assertTrue(check_same_values(embeddings, decoding)) # Check that after resize they remain tied. model_tied.resize_token_embeddings(config.vocab_size + 10) params_tied_2 = list(model_tied.parameters()) self.assertEqual(len(params_tied_2), len(params_tied)) # decoding.weight.data.mul_(20) # # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape) # self.assertTrue(check_same_values(model.transformer.wte, model.lm_head)) def test_tied_model_weights_key_ignore(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model_tied = model_class(config) with tempfile.TemporaryDirectory() as d: model_tied.save_pretrained(d) # We are nuking ALL weights on file, so every parameter should # yell on load. We're going to detect if we yell too much, or too little. with open(os.path.join(d, "pytorch_model.bin"), "wb") as f: torch.save({}, f) model_reloaded, infos = model_class.from_pretrained(d, output_loading_info=True) # ! Actually we could use `state_dict()` and check iteratively the tensors which are the same (for instance using `tensor.data_ptr()`). to detect the duplicates. # ```python # model = GPT2LMHeadModel.from_pretrained("gpt2") # "lm_head.weight" in model.state_dict().keys() # True # "lm_head.weight" in model.named_parameters() # False # In [6]: model.lm_head.weight.data_ptr() # Out[6]: 139901378371648 # In [9]: model.transformer.wte.weight.data_ptr() # Out[9]: 139901378371648 # Same PTR, it's the same DATA ! we would need to check for stride too to be 100% accurate. # ``` prefix = f"{model_reloaded.base_model_prefix}." params = dict(model_reloaded.named_parameters()) params.update(dict(model_reloaded.named_buffers())) # param_names = set(k[len(prefix) :] if k.startswith(prefix) else k for k in params.keys()) param_names = set(k[len(prefix) :] if k.startswith(prefix) else k for k in params.keys()) missing_keys = set(infos["missing_keys"]) extra_missing = missing_keys - param_names # missed_missing = param_names - missing_keys self.assertEqual( extra_missing, set(), f"This model {model_class.__name__} might be missing some `keys_to_ignore`: {extra_missing}", ) # self.assertEqual( # missed_missing, # set(), # f"This model {model_class.__name__} ignores keys {missed_missing} but they look like real" # " parameters", # ) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def set_nan_tensor_to_zero(t): t[t != t] = 0 return t def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): with torch.no_grad(): tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (List, Tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif isinstance(tuple_object, Dict): for tuple_iterable_value, dict_iterable_value in zip( tuple_object.values(), dict_object.values() ): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( torch.allclose( set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 ), msg=( "Tuple and dict output are not equal. Difference:" f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) if self.has_attentions: tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence( model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True} ) # Don't copy this method to model specific test file! # TODO: remove this method once the issues are all fixed! def _make_attention_mask_non_null(self, inputs_dict): """Make sure no sequence has all zeros as attention mask""" for k in ["attention_mask", "encoder_attention_mask", "decoder_attention_mask"]: if k in inputs_dict: attention_mask = inputs_dict[k] # Make sure no all 0s attention masks - to avoid failure at this moment. # Put `1` at the beginning of sequences to make it still work when combining causal attention masks. # TODO: remove this line once a fix regarding large negative values for attention mask is done. attention_mask = torch.cat( [torch.ones_like(attention_mask[:, :1], dtype=attention_mask.dtype), attention_mask[:, 1:]], dim=-1 ) # Here we make the first sequence with all 0s as attention mask. # Currently, this will fail for `TFWav2Vec2Model`. This is caused by the different large negative # values, like `1e-4`, `1e-9`, `1e-30` and `-inf` for attention mask across models/frameworks. # TODO: enable this block once the large negative values thing is cleaned up. # (see https://github.com/huggingface/transformers/issues/14859) # attention_mask = torch.cat( # [torch.zeros_like(attention_mask[:1], dtype=attention_mask.dtype), attention_mask[1:]], # dim=0 # ) inputs_dict[k] = attention_mask # Don't copy this method to model specific test file! # TODO: remove this method once the issues are all fixed! def _postprocessing_to_ignore_test_cases(self, tf_outputs, pt_outputs, model_class): """For temporarily ignoring some failed test cases (issues to be fixed)""" tf_keys = set([k for k, v in tf_outputs.items() if v is not None]) pt_keys = set([k for k, v in pt_outputs.items() if v is not None]) key_differences = tf_keys.symmetric_difference(pt_keys) if model_class.__name__ in [ "FlaubertWithLMHeadModel", "FunnelForPreTraining", "ElectraForPreTraining", "XLMWithLMHeadModel", "TransfoXLLMHeadModel", ]: for k in key_differences: if k in ["loss", "losses"]: tf_keys.discard(k) pt_keys.discard(k) elif model_class.__name__.startswith("GPT2"): # `TFGPT2` has `past_key_values` as a tensor while `GPT2` has it as a tuple. tf_keys.discard("past_key_values") pt_keys.discard("past_key_values") # create new outputs from the remaining fields new_tf_outputs = type(tf_outputs)(**{k: tf_outputs[k] for k in tf_keys}) new_pt_outputs = type(pt_outputs)(**{k: pt_outputs[k] for k in pt_keys}) return new_tf_outputs, new_pt_outputs # Copied from tests.test_modeling_tf_common.TFModelTesterMixin.check_pt_tf_outputs def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): """Check the outputs from PyTorch and TensorFlow models are close enough. Checks are done in a recursive way. Args: model_class: The class of the model that is currently testing. For example, `TFBertModel`, TFBertForMaskedLM`, `TFBertForSequenceClassification`, etc. Mainly used for providing more informative error messages. name (`str`): The name of the output. For example, `output.hidden_states`, `output.attentions`, etc. attributes (`Tuple[str]`): The names of the output's element if the output is a tuple/list with each element being a named field in the output. """ self.assertEqual(type(name), str) if attributes is not None: self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`") # Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`). if isinstance(tf_outputs, ModelOutput): self.assertTrue( isinstance(pt_outputs, ModelOutput), f"{name}: `pt_outputs` should an instance of `ModelOutput` when `tf_outputs` is", ) # Don't copy this block to model specific test file! # TODO: remove this method and this line after issues are fixed tf_outputs, pt_outputs = self._postprocessing_to_ignore_test_cases(tf_outputs, pt_outputs, model_class) tf_keys = [k for k, v in tf_outputs.items() if v is not None] pt_keys = [k for k, v in pt_outputs.items() if v is not None] self.assertEqual(tf_keys, pt_keys, f"{name}: Output keys differ between TF and PyTorch") # convert to the case of `tuple` # appending each key to the current (string) `name` attributes = tuple([f"{name}.{k}" for k in tf_keys]) self.check_pt_tf_outputs( tf_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes ) # Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.) elif type(tf_outputs) in [tuple, list]: self.assertEqual(type(tf_outputs), type(pt_outputs), f"{name}: Output types differ between TF and PyTorch") self.assertEqual(len(tf_outputs), len(pt_outputs), f"{name}: Output lengths differ between TF and PyTorch") if attributes is not None: # case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`) self.assertEqual( len(attributes), len(tf_outputs), f"{name}: The tuple `attributes` should have the same length as `tf_outputs`", ) else: # case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name` attributes = tuple([f"{name}_{idx}" for idx in range(len(tf_outputs))]) for tf_output, pt_output, attr in zip(tf_outputs, pt_outputs, attributes): self.check_pt_tf_outputs(tf_output, pt_output, model_class, tol=tol, name=attr) elif isinstance(tf_outputs, tf.Tensor): self.assertTrue( isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `tf_outputs` is" ) tf_outputs = tf_outputs.numpy() pt_outputs = pt_outputs.detach().to("cpu").numpy() self.assertEqual( tf_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between TF and PyTorch" ) # deal with NumPy's scalars to make replacing nan values by 0 work. if np.isscalar(tf_outputs): tf_outputs = np.array([tf_outputs]) pt_outputs = np.array([pt_outputs]) tf_nans = np.isnan(tf_outputs) pt_nans = np.isnan(pt_outputs) pt_outputs[tf_nans] = 0 tf_outputs[tf_nans] = 0 pt_outputs[pt_nans] = 0 tf_outputs[pt_nans] = 0 max_diff = np.amax(np.abs(tf_outputs - pt_outputs)) self.assertLessEqual(max_diff, tol, f"{name}: Difference between PyTorch and TF is {max_diff} (>= {tol}).") else: raise ValueError( "`tf_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `tf.Tensor`. Got" f" {type(tf_outputs)} instead." ) def prepare_tf_inputs_from_pt_inputs(self, pt_inputs_dict): tf_inputs_dict = {} for key, tensor in pt_inputs_dict.items(): # skip key that does not exist in tf if type(tensor) == bool: tf_inputs_dict[key] = tensor elif key == "input_values": tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) elif key == "pixel_values": tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) elif key == "input_features": tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) # other general float inputs elif tensor.is_floating_point(): tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) else: tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32) return tf_inputs_dict def check_pt_tf_models(self, tf_model, pt_model, pt_inputs_dict): tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict) # send pytorch inputs to the correct device pt_inputs_dict = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs_dict.items() } # send pytorch model to the correct device pt_model.to(torch_device) # Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences pt_model.eval() with torch.no_grad(): pt_outputs = pt_model(**pt_inputs_dict) tf_outputs = tf_model(tf_inputs_dict) # tf models returned loss is usually a tensor rather than a scalar. # (see `hf_compute_loss`: it uses `tf.keras.losses.Reduction.NONE`) # Change it here to a scalar to match PyTorch models' loss tf_loss = getattr(tf_outputs, "loss", None) if tf_loss is not None: tf_outputs.loss = tf.math.reduce_mean(tf_loss) self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(pt_model)) @is_pt_tf_cross_test def test_pt_tf_model_equivalence(self): import transformers for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() tf_model_class_name = "TF" + model_class.__name__ # Add the "TF" at the beginning if not hasattr(transformers, tf_model_class_name): # transformers does not have this model in TF version yet return # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions # Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency # of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`. # TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it. self._make_attention_mask_non_null(inputs_dict) tf_model_class = getattr(transformers, tf_model_class_name) pt_model = model_class(config) tf_model = tf_model_class(config) pt_inputs_dict = self._prepare_for_class(inputs_dict, model_class) pt_inputs_dict_with_labels = self._prepare_for_class( inputs_dict, model_class, # Not all models accept "labels" in the forward pass (yet :) ) return_labels=True if "labels" in inspect.signature(model_class.forward).parameters.keys() else False, ) # make sure only tf inputs are forward that actually exist in function args tf_input_keys = set(inspect.signature(tf_model.call).parameters.keys()) # remove all head masks tf_input_keys.discard("head_mask") tf_input_keys.discard("cross_attn_head_mask") tf_input_keys.discard("decoder_head_mask") pt_inputs_dict = {k: v for k, v in pt_inputs_dict.items() if k in tf_input_keys} pt_inputs_dict_with_labels = {k: v for k, v in pt_inputs_dict_with_labels.items() if k in tf_input_keys} # For some models (e.g. base models), there is no label returned. # Set the input dict to `None` to avoid check outputs twice for the same input dicts. if set(pt_inputs_dict_with_labels.keys()).symmetric_difference(pt_inputs_dict.keys()): pt_inputs_dict_with_labels = None # Check we can load pt model in tf and vice-versa with model => model functions # Here requires `tf_inputs_dict` to build `tf_model` tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict) tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict) pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model) # Original test: check without `labels` self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict) # check with `labels` if pt_inputs_dict_with_labels: self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict_with_labels) # Check we can load pt model in tf and vice-versa with checkpoint => model functions with tempfile.TemporaryDirectory() as tmpdirname: pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin") torch.save(pt_model.state_dict(), pt_checkpoint_path) tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path) tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5") tf_model.save_weights(tf_checkpoint_path) pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path) # Original test: check without `labels` self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict) # check with `labels` if pt_inputs_dict_with_labels: self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict_with_labels) def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float): diff = np.abs((a - b)).max() self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).") def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): """ Args: model_class: The class of the model that is currently testing. For example, ..., etc. Currently unused, but it could make debugging easier and faster. names: A string, or a list of strings. These specify what fx_outputs/pt_outputs represent in the model outputs. Currently unused, but in the future, we could use this information to make the error message clearer by giving the name(s) of the output tensor(s) with large difference(s) between PT and Flax. """ self.assertEqual(type(name), str) if attributes is not None: self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`") # Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`). if isinstance(fx_outputs, ModelOutput): self.assertTrue( isinstance(pt_outputs, ModelOutput), f"{name}: `pt_outputs` should an instance of `ModelOutput` when `fx_outputs` is", ) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys, f"{name}: Output keys differ between Flax and PyTorch") # convert to the case of `tuple` # appending each key to the current (string) `name` attributes = tuple([f"{name}.{k}" for k in fx_keys]) self.check_pt_flax_outputs( fx_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes ) # Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.) elif type(fx_outputs) in [tuple, list]: self.assertEqual( type(fx_outputs), type(pt_outputs), f"{name}: Output types differ between Flax and PyTorch" ) self.assertEqual( len(fx_outputs), len(pt_outputs), f"{name}: Output lengths differ between Flax and PyTorch" ) if attributes is not None: # case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`) self.assertEqual( len(attributes), len(fx_outputs), f"{name}: The tuple `attributes` should have the same length as `fx_outputs`", ) else: # case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name` attributes = tuple([f"{name}_{idx}" for idx in range(len(fx_outputs))]) for fx_output, pt_output, attr in zip(fx_outputs, pt_outputs, attributes): self.check_pt_flax_outputs(fx_output, pt_output, model_class, tol=tol, name=attr) elif isinstance(fx_outputs, jnp.ndarray): self.assertTrue( isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `fx_outputs` is" ) # Using `np.asarray` gives `ValueError: assignment destination is read-only` at the line `fx_outputs[fx_nans] = 0`. fx_outputs = np.array(fx_outputs) pt_outputs = pt_outputs.detach().to("cpu").numpy() self.assertEqual( fx_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between Flax and PyTorch" ) # deal with NumPy's scalars to make replacing nan values by 0 work. if np.isscalar(fx_outputs): fx_outputs = np.array([fx_outputs]) pt_outputs = np.array([pt_outputs]) fx_nans = np.isnan(fx_outputs) pt_nans = np.isnan(pt_outputs) pt_outputs[fx_nans] = 0 fx_outputs[fx_nans] = 0 pt_outputs[pt_nans] = 0 fx_outputs[pt_nans] = 0 max_diff = np.amax(np.abs(fx_outputs - pt_outputs)) self.assertLessEqual( max_diff, tol, f"{name}: Difference between PyTorch and Flax is {max_diff} (>= {tol})." ) else: raise ValueError( "`fx_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `jnp.ndarray`. Got" f" {type(fx_outputs)} instead." ) @is_pt_flax_cross_test def test_equivalence_pt_to_flax(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): fx_model_class_name = "Flax" + model_class.__name__ if not hasattr(transformers, fx_model_class_name): # no flax model exists for this class return # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions fx_model_class = getattr(transformers, fx_model_class_name) # load PyTorch class pt_model = model_class(config).eval() # Flax models don't use the `use_cache` option and cache is not returned as a default. # So we disable `use_cache` here for PyTorch model. pt_model.config.use_cache = False # load Flax class fx_model = fx_model_class(config, dtype=jnp.float32) # make sure only flax inputs are forward that actually exist in function args fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys() # prepare inputs pt_inputs = self._prepare_for_class(inputs_dict, model_class) # remove function args that don't exist in Flax pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys} # send pytorch inputs to the correct device pt_inputs = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items() } # convert inputs to Flax fx_inputs = {k: np.array(v) for k, v in pt_inputs.items() if torch.is_tensor(v)} fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model) fx_model.params = fx_state # send pytorch model to the correct device pt_model.to(torch_device) with torch.no_grad(): pt_outputs = pt_model(**pt_inputs) fx_outputs = fx_model(**fx_inputs) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class) with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**fx_inputs) fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class) @is_pt_flax_cross_test def test_equivalence_flax_to_pt(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): fx_model_class_name = "Flax" + model_class.__name__ if not hasattr(transformers, fx_model_class_name): # no flax model exists for this class return # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions fx_model_class = getattr(transformers, fx_model_class_name) # load PyTorch class pt_model = model_class(config).eval() # Flax models don't use the `use_cache` option and cache is not returned as a default. # So we disable `use_cache` here for PyTorch model. pt_model.config.use_cache = False # load Flax class fx_model = fx_model_class(config, dtype=jnp.float32) # make sure only flax inputs are forward that actually exist in function args fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys() # prepare inputs pt_inputs = self._prepare_for_class(inputs_dict, model_class) # remove function args that don't exist in Flax pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys} # send pytorch inputs to the correct device pt_inputs = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items() } # convert inputs to Flax fx_inputs = {k: np.array(v) for k, v in pt_inputs.items() if torch.is_tensor(v)} pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params) # make sure weights are tied in PyTorch pt_model.tie_weights() # send pytorch model to the correct device pt_model.to(torch_device) with torch.no_grad(): pt_outputs = pt_model(**pt_inputs) fx_outputs = fx_model(**fx_inputs) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class) with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True) # send pytorch model to the correct device pt_model_loaded.to(torch_device) pt_model_loaded.eval() with torch.no_grad(): pt_outputs_loaded = pt_model_loaded(**pt_inputs) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class) def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) if not self.is_encoder_decoder: input_ids = inputs["input_ids"] del inputs["input_ids"] else: encoder_input_ids = inputs["input_ids"] decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids) del inputs["input_ids"] inputs.pop("decoder_input_ids", None) wte = model.get_input_embeddings() if not self.is_encoder_decoder: inputs["inputs_embeds"] = wte(input_ids) else: inputs["inputs_embeds"] = wte(encoder_input_ids) inputs["decoder_inputs_embeds"] = wte(decoder_input_ids) with torch.no_grad(): model(**inputs)[0] @require_torch_multi_gpu def test_multi_gpu_data_parallel_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # some params shouldn't be scattered by nn.DataParallel # so just remove them if they are present. blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"] for k in blacklist_non_batched_params: inputs_dict.pop(k, None) # move input tensors to cuda:O for k, v in inputs_dict.items(): if torch.is_tensor(v): inputs_dict[k] = v.to(0) for model_class in self.all_model_classes: model = model_class(config=config) model.to(0) model.eval() # Wrap model in nn.DataParallel model = nn.DataParallel(model) with torch.no_grad(): _ = model(**self._prepare_for_class(inputs_dict, model_class)) @require_torch_multi_gpu def test_model_parallelization(self): if not self.test_model_parallel: return # a candidate for testing_utils def get_current_gpu_memory_use(): """returns a list of cuda memory allocations per GPU in MBs""" per_device_memory = [] for id in range(torch.cuda.device_count()): with torch.cuda.device(id): per_device_memory.append(torch.cuda.memory_allocated() >> 20) return per_device_memory # Needs a large model to see the difference. config = self.model_tester.get_large_model_config() for model_class in self.all_parallelizable_model_classes: torch.cuda.empty_cache() # 1. single gpu memory load + unload + memory measurements # Retrieve initial memory usage (can easily be ~0.6-1.5GB if cuda-kernels have been preloaded by previous tests) memory_at_start = get_current_gpu_memory_use() # Put model on device 0 and take a memory snapshot model = model_class(config) model.to("cuda:0") memory_after_model_load = get_current_gpu_memory_use() # The memory use on device 0 should be higher than it was initially. self.assertGreater(memory_after_model_load[0], memory_at_start[0]) del model gc.collect() torch.cuda.empty_cache() # 2. MP test # it's essential to re-calibrate the usage before the next stage memory_at_start = get_current_gpu_memory_use() # Spread model layers over multiple devices model = model_class(config) model.parallelize() memory_after_parallelization = get_current_gpu_memory_use() # Assert that the memory use on all devices is higher than it was when loaded only on CPU for n in range(len(model.device_map.keys())): self.assertGreater(memory_after_parallelization[n], memory_at_start[n]) # Assert that the memory use of device 0 is lower than it was when the entire model was loaded on it self.assertLess(memory_after_parallelization[0], memory_after_model_load[0]) # Assert that the memory use of device 1 is higher than it was when the entire model was loaded # on device 0 and device 1 wasn't used at all self.assertGreater(memory_after_parallelization[1], memory_after_model_load[1]) del model gc.collect() torch.cuda.empty_cache() @require_torch_multi_gpu def test_model_parallel_equal_results(self): if not self.test_model_parallel: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_parallelizable_model_classes: inputs_dict = self._prepare_for_class(inputs_dict, model_class) def cast_to_device(dictionary, device): output = {} for k, v in dictionary.items(): if isinstance(v, torch.Tensor): output[k] = v.to(device) else: output[k] = v return output model = model_class(config) output = model(**cast_to_device(inputs_dict, "cpu")) model.parallelize() parallel_output = model(**cast_to_device(inputs_dict, "cuda:0")) for value, parallel_value in zip(output, parallel_output): if isinstance(value, torch.Tensor): self.assertTrue(torch.allclose(value, parallel_value.to("cpu"), atol=1e-7)) elif isinstance(value, (Tuple, List)): for value_, parallel_value_ in zip(value, parallel_value): self.assertTrue(torch.allclose(value_, parallel_value_.to("cpu"), atol=1e-7)) @require_torch_multi_gpu def test_model_parallel_beam_search(self): if not self.test_model_parallel: return all_generative_and_parallelizable_model_classes = tuple( set(self.all_generative_model_classes).intersection(self.all_parallelizable_model_classes) ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in all_generative_and_parallelizable_model_classes: inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) def cast_to_device(dictionary, device): output = {} for k, v in dictionary.items(): if isinstance(v, torch.Tensor): output[k] = v.to(device) else: output[k] = v return output model.parallelize() model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2) def check_device_map_is_respected(self, model, device_map): for param_name, param in model.named_parameters(): # Find device in device_map while len(param_name) > 0 and param_name not in device_map: param_name = ".".join(param_name.split(".")[:-1]) if param_name not in device_map: raise ValueError("device map is incomplete, it does not contain any device for `param_name`.") param_device = device_map[param_name] if param_device in ["cpu", "disk"]: self.assertEqual(param.device, torch.device("meta")) else: self.assertEqual(param.device, torch.device(param_device)) @require_accelerate @require_torch_gpu def test_disk_offload(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._no_split_modules is None: continue inputs_dict_class = self._prepare_for_class(inputs_dict, model_class) model = model_class(config).eval() model = model.to(torch_device) torch.manual_seed(0) base_output = model(**inputs_dict_class) model_size = compute_module_sizes(model)[""] max_size = int(self.model_split_percents[0] * model_size) with tempfile.TemporaryDirectory() as tmp_dir: model.cpu().save_pretrained(tmp_dir) max_memory = {0: max_size, "cpu": max_size} with self.assertRaises(ValueError): # This errors out cause it's missing an offload folder new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) new_model = model_class.from_pretrained( tmp_dir, device_map="auto", max_memory=max_memory, offload_folder=tmp_dir ) self.check_device_map_is_respected(new_model, new_model.hf_device_map) torch.manual_seed(0) new_output = new_model(**inputs_dict_class) self.assertTrue(torch.allclose(base_output[0], new_output[0])) @require_accelerate @require_torch_gpu def test_cpu_offload(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._no_split_modules is None: continue inputs_dict_class = self._prepare_for_class(inputs_dict, model_class) model = model_class(config).eval() model = model.to(torch_device) torch.manual_seed(0) base_output = model(**inputs_dict_class) model_size = compute_module_sizes(model)[""] # We test several splits of sizes to make sure it works. max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents] with tempfile.TemporaryDirectory() as tmp_dir: model.cpu().save_pretrained(tmp_dir) for max_size in max_gpu_sizes: max_memory = {0: max_size, "cpu": model_size * 2} new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) # Making sure part of the model will actually end up offloaded self.assertSetEqual(set(new_model.hf_device_map.values()), {0, "cpu"}) self.check_device_map_is_respected(new_model, new_model.hf_device_map) torch.manual_seed(0) new_output = new_model(**inputs_dict_class) self.assertTrue(torch.allclose(base_output[0], new_output[0])) @require_accelerate @require_torch_multi_gpu def test_model_parallelism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._no_split_modules is None: continue inputs_dict_class = self._prepare_for_class(inputs_dict, model_class) model = model_class(config).eval() model = model.to(torch_device) torch.manual_seed(0) base_output = model(**inputs_dict_class) model_size = compute_module_sizes(model)[""] # We test several splits of sizes to make sure it works. max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents] with tempfile.TemporaryDirectory() as tmp_dir: model.cpu().save_pretrained(tmp_dir) for max_size in max_gpu_sizes: max_memory = {0: max_size, 1: model_size * 2, "cpu": model_size * 2} new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) # Making sure part of the model will actually end up offloaded self.assertSetEqual(set(new_model.hf_device_map.values()), {0, 1}) self.check_device_map_is_respected(new_model, new_model.hf_device_map) torch.manual_seed(0) new_output = new_model(**inputs_dict_class) self.assertTrue(torch.allclose(base_output[0], new_output[0])) def test_problem_types(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() problem_types = [ {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float}, {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long}, {"title": "regression", "num_labels": 1, "dtype": torch.float}, ] for model_class in self.all_model_classes: if model_class not in [ *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING), ]: continue for problem_type in problem_types: with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"): config.problem_type = problem_type["title"] config.num_labels = problem_type["num_labels"] model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) if problem_type["num_labels"] > 1: inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"]) inputs["labels"] = inputs["labels"].to(problem_type["dtype"]) # This tests that we do not trigger the warning form PyTorch "Using a target size that is different # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure # they have the same size." which is a symptom something in wrong for the regression problem. # See https://github.com/huggingface/transformers/issues/11780 with warnings.catch_warnings(record=True) as warning_list: loss = model(**inputs).loss for w in warning_list: if "Using a target size that is different to the input size" in str(w.message): raise ValueError( f"Something is going wrong in the regression problem: intercepted {w.message}" ) loss.backward() def test_load_with_mismatched_shapes(self): if not self.test_mismatched_shapes: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING): continue with self.subTest(msg=f"Testing {model_class}"): with tempfile.TemporaryDirectory() as tmp_dir: model = model_class(config) model.save_pretrained(tmp_dir) # Fails when we don't set ignore_mismatched_sizes=True with self.assertRaises(RuntimeError): new_model = AutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42) with self.assertRaises(RuntimeError): new_model_without_prefix = AutoModel.from_pretrained(tmp_dir, vocab_size=10) logger = logging.get_logger("transformers.modeling_utils") with CaptureLogger(logger) as cl: new_model = AutoModelForSequenceClassification.from_pretrained( tmp_dir, num_labels=42, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) new_model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) logits = new_model(**inputs).logits self.assertEqual(logits.shape[1], 42) with CaptureLogger(logger) as cl: new_model_without_prefix = AutoModel.from_pretrained( tmp_dir, vocab_size=10, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) input_ids = ids_tensor((2, 8), 10) new_model_without_prefix.to(torch_device) if self.is_encoder_decoder: new_model_without_prefix(input_ids, decoder_input_ids=input_ids) else: new_model_without_prefix(input_ids) global_rng = random.Random() def ids_tensor(shape, vocab_size, rng=None, name=None): # Creates a random int32 tensor of the shape within the vocab size if rng is None: rng = global_rng total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous() def random_attention_mask(shape, rng=None, name=None): attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None) # make sure that at least one token is attended to for each batch attn_mask[:, -1] = 1 return attn_mask def floats_tensor(shape, scale=1.0, rng=None, name=None): """Creates a random float32 tensor""" if rng is None: rng = global_rng total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.random() * scale) return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous() def check_models_equal(model1, model2): models_are_equal = True for model1_p, model2_p in zip(model1.parameters(), model2.parameters()): if model1_p.data.ne(model2_p.data).sum() > 0: models_are_equal = False return models_are_equal @require_torch class ModelUtilsTest(TestCasePlus): @slow def test_model_from_pretrained(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = BertConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, PretrainedConfig) model = BertModel.from_pretrained(model_name) model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, PreTrainedModel) self.assertEqual(len(loading_info["missing_keys"]), 0) self.assertEqual(len(loading_info["unexpected_keys"]), 8) self.assertEqual(len(loading_info["mismatched_keys"]), 0) self.assertEqual(len(loading_info["error_msgs"]), 0) config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True) # Not sure this is the intended behavior. TODO fix Lysandre & Thom config.name_or_path = model_name model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True) self.assertEqual(model.config.output_hidden_states, True) self.assertEqual(model.config, config) def test_model_from_pretrained_subfolder(self): config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") model = BertModel(config) subfolder = "bert" with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(tmp_dir, subfolder)) with self.assertRaises(OSError): _ = BertModel.from_pretrained(tmp_dir) model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder) self.assertTrue(check_models_equal(model, model_loaded)) def test_model_from_pretrained_subfolder_sharded(self): config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") model = BertModel(config) subfolder = "bert" with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB") with self.assertRaises(OSError): _ = BertModel.from_pretrained(tmp_dir) model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder) self.assertTrue(check_models_equal(model, model_loaded)) def test_model_from_pretrained_hub_subfolder(self): subfolder = "bert" model_id = "hf-internal-testing/tiny-random-bert-subfolder" with self.assertRaises(OSError): _ = BertModel.from_pretrained(model_id) model = BertModel.from_pretrained(model_id, subfolder=subfolder) self.assertIsNotNone(model) def test_model_from_pretrained_hub_subfolder_sharded(self): subfolder = "bert" model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder" with self.assertRaises(OSError): _ = BertModel.from_pretrained(model_id) model = BertModel.from_pretrained(model_id, subfolder=subfolder) self.assertIsNotNone(model) def test_model_from_pretrained_with_different_pretrained_model_name(self): model = T5ForConditionalGeneration.from_pretrained(TINY_T5) self.assertIsNotNone(model) logger = logging.get_logger("transformers.configuration_utils") with CaptureLogger(logger) as cl: BertModel.from_pretrained(TINY_T5) self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out) @require_torch def test_model_from_config_torch_dtype(self): # test that the model can be instantiated with dtype of user's choice - as long as it's a # float dtype. To make it happen config.torch_dtype needs to be set before instantiating the # model from the config object. config = T5Config.from_pretrained(TINY_T5) model = AutoModel.from_config(config) # XXX: isn't supported # model = T5ForConditionalGeneration.from_config(config) self.assertEqual(model.dtype, torch.float32) model = AutoModel.from_config(config, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # torch.set_default_dtype() supports only float dtypes, so will fail with non-float type with self.assertRaises(ValueError): model = AutoModel.from_config(config, torch_dtype=torch.int64) @require_torch def test_model_from_pretrained_torch_dtype(self): # test that the model can be instantiated with dtype of either # 1. explicit from_pretrained's torch_dtype argument # 2. via autodiscovery by looking at model weights (torch_dtype="auto") # so if a model.half() was saved, we want it to be instantiated as such. # # test an explicit model class, but also AutoModel separately as the latter goes through a different code path model_path = self.get_auto_remove_tmp_dir() # baseline - we know TINY_T5 is fp32 model model = T5ForConditionalGeneration.from_pretrained(TINY_T5) self.assertEqual(model.dtype, torch.float32) # test the default fp32 save_pretrained => from_pretrained cycle model.save_pretrained(model_path) model = T5ForConditionalGeneration.from_pretrained(model_path) self.assertEqual(model.dtype, torch.float32) # test with auto-detection model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto") self.assertEqual(model.dtype, torch.float32) # test forced loading in fp16 (even though the weights are in fp32) model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # test fp16 save_pretrained, loaded with auto-detection model = model.half() model.save_pretrained(model_path) model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto") self.assertEqual(model.config.torch_dtype, torch.float16) self.assertEqual(model.dtype, torch.float16) # tests `config.torch_dtype` saving with open(f"{model_path}/config.json") as f: config_dict = json.load(f) self.assertEqual(config_dict["torch_dtype"], "float16") # test fp16 save_pretrained, loaded with the explicit fp16 model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # test AutoModel separately as it goes through a different path # test auto-detection model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto") self.assertEqual(model.dtype, torch.float32) # test forcing an explicit dtype model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # test model whose first param is not of a floating type, but int model = AutoModel.from_pretrained(TINY_BERT_FOR_TOKEN_CLASSIFICATION, torch_dtype="auto") self.assertEqual(model.dtype, torch.float32) def test_no_super_init_config_and_model(self): config = NoSuperInitConfig(attribute=32) model = NoSuperInitModel(config) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = NoSuperInitModel.from_pretrained(tmp_dir) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_shard_checkpoint(self): # This is the model we will use, total size 340,000 bytes. model = torch.nn.Sequential( torch.nn.Linear(100, 200, bias=False), # size 80,000 torch.nn.Linear(200, 200, bias=False), # size 160,000 torch.nn.Linear(200, 100, bias=False), # size 80,000 torch.nn.Linear(100, 50, bias=False), # size 20,000 ) state_dict = model.state_dict() with self.subTest("No shard when max size is bigger than model size"): shards, index = shard_checkpoint(state_dict) self.assertIsNone(index) self.assertDictEqual(shards, {WEIGHTS_NAME: state_dict}) with self.subTest("Test sharding, no weights bigger than max size"): shards, index = shard_checkpoint(state_dict, max_shard_size="300kB") # Split is first two layers then last two. self.assertDictEqual( index, { "metadata": {"total_size": 340000}, "weight_map": { "0.weight": "pytorch_model-00001-of-00002.bin", "1.weight": "pytorch_model-00001-of-00002.bin", "2.weight": "pytorch_model-00002-of-00002.bin", "3.weight": "pytorch_model-00002-of-00002.bin", }, }, ) shard1 = {"0.weight": state_dict["0.weight"], "1.weight": state_dict["1.weight"]} shard2 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]} self.assertDictEqual( shards, {"pytorch_model-00001-of-00002.bin": shard1, "pytorch_model-00002-of-00002.bin": shard2} ) with self.subTest("Test sharding with weights bigger than max size"): shards, index = shard_checkpoint(state_dict, max_shard_size="100kB") # Split is first layer, second layer then last 2. self.assertDictEqual( index, { "metadata": {"total_size": 340000}, "weight_map": { "0.weight": "pytorch_model-00001-of-00003.bin", "1.weight": "pytorch_model-00002-of-00003.bin", "2.weight": "pytorch_model-00003-of-00003.bin", "3.weight": "pytorch_model-00003-of-00003.bin", }, }, ) shard1 = {"0.weight": state_dict["0.weight"]} shard2 = {"1.weight": state_dict["1.weight"]} shard3 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]} self.assertDictEqual( shards, { "pytorch_model-00001-of-00003.bin": shard1, "pytorch_model-00002-of-00003.bin": shard2, "pytorch_model-00003-of-00003.bin": shard3, }, ) def test_checkpoint_sharding_local(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: # We use the same folder for various sizes to make sure a new save erases the old checkpoint. for max_size in ["50kB", "50kiB", "100kB", "100kiB", "200kB", "200kiB"]: model.save_pretrained(tmp_dir, max_shard_size=max_size) # Get each shard file and its size shard_to_size = {} for shard in os.listdir(tmp_dir): if shard.endswith(".bin"): shard_file = os.path.join(tmp_dir, shard) shard_to_size[shard_file] = os.path.getsize(shard_file) index_file = os.path.join(tmp_dir, WEIGHTS_INDEX_NAME) # Check there is an index but no regular weight file self.assertTrue(os.path.isfile(index_file)) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) # Check a file is bigger than max_size only when it has a single weight for shard_file, size in shard_to_size.items(): if max_size.endswith("kiB"): max_size_int = int(max_size[:-3]) * 2**10 else: max_size_int = int(max_size[:-2]) * 10**3 # Note: pickle adds some junk so the weight of the file can end up being slightly bigger than # the size asked for (since we count parameters) if size >= max_size_int + 50000: state_dict = torch.load(shard_file) self.assertEqual(len(state_dict), 1) # Check the index and the shard files found match with open(index_file, "r", encoding="utf-8") as f: index = json.loads(f.read()) all_shards = set(index["weight_map"].values()) shards_found = set(f for f in os.listdir(tmp_dir) if f.endswith(".bin")) self.assertSetEqual(all_shards, shards_found) # Finally, check the model can be reloaded new_model = BertModel.from_pretrained(tmp_dir) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) def test_checkpoint_sharding_from_hub(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded") # the model above is the same as the model below, just a sharded version. ref_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") for p1, p2 in zip(model.parameters(), ref_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) @require_accelerate def test_from_pretrained_low_cpu_mem_usage_functional(self): # test that we can use `from_pretrained(..., low_cpu_mem_usage=True)` with normal and # sharded models mnames = [ "hf-internal-testing/tiny-random-bert-sharded", "hf-internal-testing/tiny-random-bert", ] for mname in mnames: _ = BertModel.from_pretrained(mname, low_cpu_mem_usage=True) @require_usr_bin_time @require_accelerate def test_from_pretrained_low_cpu_mem_usage_measured(self): # test that `from_pretrained(..., low_cpu_mem_usage=True)` uses less cpu memory than default mname = "bert-base-cased" preamble = "from transformers import AutoModel" one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=False)' max_rss_normal = self.python_one_liner_max_rss(one_liner_str) # print(f"{max_rss_normal=}") one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=True)' max_rss_low_mem = self.python_one_liner_max_rss(one_liner_str) # print(f"{max_rss_low_mem=}") diff_bytes = max_rss_normal - max_rss_low_mem diff_percent = diff_bytes / max_rss_low_mem # print(f"{diff_bytes=}, {diff_percent=}") # ideally we would compare that the diff is close to ~1x checkpoint size in bytes, but # measuring cpu memory on linux is very tricky and inconsistent, so instead let's check that # it's at least 15% less cpu memory consumed self.assertGreater( diff_percent, 0.15, "should use less CPU memory for low_cpu_mem_usage=True, " f"but got max_rss_normal={max_rss_normal} and max_rss_low_mem={max_rss_low_mem}", ) # if you want to compare things manually, let's first look at the size of the model in bytes # model = BertModel.from_pretrained(mname, low_cpu_mem_usage=False) # total_numel = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values()) # total_bytes = total_numel * 4 # 420MB # Now the diff_bytes should be very close to total_bytes, but the reports are inconsistent. # The easiest way to test this is to switch the model and torch.load to do all the work on # gpu - that way one can measure exactly the total and peak memory used. Perhaps once we add # functionality to load models directly on gpu, this test can be rewritten to use torch's # cuda memory tracking and then we should be able to do a much more precise test. @require_accelerate @require_torch_multi_gpu @slow def test_model_parallelism_gpt2(self): device_map = {"transformer.wte": 0, "transformer.wpe": 0, "lm_head": 0, "transformer.ln_f": 1} for i in range(12): device_map[f"transformer.h.{i}"] = 0 if i <= 5 else 1 model = AutoModelForCausalLM.from_pretrained("gpt2", device_map=device_map) tokenizer = AutoTokenizer.from_pretrained("gpt2") inputs = tokenizer("Hello, my name is", return_tensors="pt") output = model.generate(inputs["input_ids"].to(0)) text_output = tokenizer.decode(output[0].tolist()) self.assertEqual(text_output, "Hello, my name is John. I'm a writer, and I'm a writer. I'm") @require_accelerate @require_torch_gpu def test_from_pretrained_disk_offload_task_model(self): model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-gpt2") device_map = { "transformer.wte": 0, "transformer.wpe": 0, "transformer.h.0": "cpu", "transformer.h.1": "cpu", "transformer.h.2": "cpu", "transformer.h.3": "disk", "transformer.h.4": "disk", "transformer.ln_f": 0, "lm_head": 0, } with tempfile.TemporaryDirectory() as tmp_dir: inputs = torch.tensor([[1, 2, 3]]).to(0) model.save_pretrained(tmp_dir) new_model = AutoModelForCausalLM.from_pretrained(tmp_dir).to(0) outputs1 = new_model.to(0)(inputs) offload_folder = os.path.join(tmp_dir, "offload") new_model_with_offload = AutoModelForCausalLM.from_pretrained( tmp_dir, device_map=device_map, offload_folder=offload_folder ) outputs2 = new_model_with_offload(inputs) self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu())) # With state dict temp offload offload_folder = os.path.join(tmp_dir, "offload") new_model_with_offload = AutoModelForCausalLM.from_pretrained( tmp_dir, device_map=device_map, offload_folder=offload_folder, offload_state_dict=True, ) outputs2 = new_model_with_offload(inputs) self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu())) def test_cached_files_are_used_when_internet_is_down(self): # A mock response for an HTTP head request to emulate server down response_mock = mock.Mock() response_mock.status_code = 500 response_mock.headers = {} response_mock.raise_for_status.side_effect = HTTPError response_mock.json.return_value = {} # Download this model to make sure it's in the cache. _ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Under the mock environment we get a 500 error when trying to reach the model. with mock.patch("requests.request", return_value=response_mock) as mock_head: _ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # This check we did call the fake head request mock_head.assert_called() def test_load_from_one_file(self): try: tmp_file = tempfile.mktemp() with open(tmp_file, "wb") as f: http_get( "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", f ) config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") _ = BertModel.from_pretrained(tmp_file, config=config) finally: os.remove(tmp_file) def test_legacy_load_from_url(self): # This test is for deprecated behavior and can be removed in v5 config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") _ = BertModel.from_pretrained( "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", config=config ) @require_safetensors def test_safetensors_save_and_load(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True) # No pytorch_model.bin file, only a model.safetensors self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) new_model = BertModel.from_pretrained(tmp_dir) # Check models are equal for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) @require_safetensors def test_safetensors_load_from_hub(self): safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors") pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Check models are equal for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) @require_safetensors def test_safetensors_save_and_load_sharded(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True, max_shard_size="100kB") # No pytorch_model.bin index file, only a model.safetensors index self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME))) self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME))) # No regular weights file self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) new_model = BertModel.from_pretrained(tmp_dir) # Check models are equal for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) @require_safetensors def test_safetensors_load_from_hub_sharded(self): safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded-safetensors") pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded") # Check models are equal for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) def test_base_model_to_head_model_load(self): base_model = BaseModel(PretrainedConfig()) with tempfile.TemporaryDirectory() as tmp_dir: base_model.save_pretrained(tmp_dir) # Can load a base model in a model with head model = ModelWithHead.from_pretrained(tmp_dir) for p1, p2 in zip(model.base.parameters(), base_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) # It doesn't work if the state dict has a mix of keys of the head and base without prefix though. base_state_dict = base_model.state_dict() head_state_dict = model.state_dict() base_state_dict["linear2.weight"] = head_state_dict["linear2.weight"] base_state_dict["linear2.bias"] = head_state_dict["linear2.bias"] torch.save(base_state_dict, os.path.join(tmp_dir, WEIGHTS_NAME)) with self.assertRaisesRegex( ValueError, "The state dictionary of the model you are trying to load is corrupted." ): _ = ModelWithHead.from_pretrained(tmp_dir) @require_torch @is_staging_test class ModelPushToHubTester(unittest.TestCase): @classmethod def setUpClass(cls): cls._token = TOKEN set_access_token(TOKEN) HfFolder.save_token(TOKEN) @classmethod def tearDownClass(cls): try: delete_repo(token=cls._token, repo_id="test-model") except HTTPError: pass try: delete_repo(token=cls._token, repo_id="valid_org/test-model-org") except HTTPError: pass try: delete_repo(token=cls._token, repo_id="test-dynamic-model") except HTTPError: pass def test_push_to_hub(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertModel(config) model.push_to_hub("test-model", use_auth_token=self._token) new_model = BertModel.from_pretrained(f"{USER}/test-model") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) # Reset repo delete_repo(token=self._token, repo_id="test-model") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, repo_id="test-model", push_to_hub=True, use_auth_token=self._token) new_model = BertModel.from_pretrained(f"{USER}/test-model") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_push_to_hub_in_organization(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertModel(config) model.push_to_hub("valid_org/test-model-org", use_auth_token=self._token) new_model = BertModel.from_pretrained("valid_org/test-model-org") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) # Reset repo delete_repo(token=self._token, repo_id="valid_org/test-model-org") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained( tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-org" ) new_model = BertModel.from_pretrained("valid_org/test-model-org") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_push_to_hub_dynamic_model(self): CustomConfig.register_for_auto_class() CustomModel.register_for_auto_class() config = CustomConfig(hidden_size=32) model = CustomModel(config) model.push_to_hub("test-dynamic-model", use_auth_token=self._token) # checks self.assertDictEqual( config.auto_map, {"AutoConfig": "custom_configuration.CustomConfig", "AutoModel": "custom_modeling.CustomModel"}, ) new_model = AutoModel.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True) # Can't make an isinstance check because the new_model is from the CustomModel class of a dynamic module self.assertEqual(new_model.__class__.__name__, "CustomModel") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True) new_model = AutoModel.from_config(config, trust_remote_code=True) self.assertEqual(new_model.__class__.__name__, "CustomModel")

# coding=utf-8 # Copyright 2019 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import gc import inspect import json import os import os.path import pickle import random import sys import tempfile import unittest import unittest.mock as mock import warnings from pathlib import Path from typing import Dict, List, Tuple import numpy as np import transformers from huggingface_hub import HfFolder, delete_repo, set_access_token from huggingface_hub.file_download import http_get from requests.exceptions import HTTPError from transformers import ( AutoConfig, AutoModel, AutoModelForSequenceClassification, PretrainedConfig, is_torch_available, logging, ) from transformers.models.auto import get_values from transformers.testing_utils import ( TOKEN, USER, CaptureLogger, TestCasePlus, is_flaky, is_pt_flax_cross_test, is_pt_tf_cross_test, is_staging_test, require_accelerate, require_safetensors, require_torch, require_torch_gpu, require_torch_multi_gpu, require_usr_bin_time, slow, torch_device, ) from transformers.utils import ( SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, is_accelerate_available, is_flax_available, is_tf_available, is_torch_fx_available, ) from transformers.utils.generic import ModelOutput sys.path.append(str(Path(__file__).parent.parent / "utils")) from test_module.custom_configuration import CustomConfig, NoSuperInitConfig # noqa E402 if is_accelerate_available(): from accelerate.utils import compute_module_sizes if is_torch_available(): import torch from torch import nn from test_module.custom_modeling import CustomModel, NoSuperInitModel from transformers import ( BERT_PRETRAINED_MODEL_ARCHIVE_LIST, MODEL_FOR_AUDIO_XVECTOR_MAPPING, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, MODEL_MAPPING, AdaptiveEmbedding, AutoModelForCausalLM, AutoTokenizer, BertConfig, BertModel, PreTrainedModel, T5Config, T5ForConditionalGeneration, ) from transformers.modeling_utils import shard_checkpoint # Fake pretrained models for tests class BaseModel(PreTrainedModel): config_class = PretrainedConfig def __init__(self, config): super().__init__(config) self.linear = nn.Linear(4, 5) self.linear_2 = nn.Linear(5, 6) def forward(self, x): return self.linear_2(self.linear(x)) class ModelWithHead(PreTrainedModel): base_model_prefix = "base" config_class = PretrainedConfig def _init_weights(self, module): pass def __init__(self, config): super().__init__(config) self.base = BaseModel(config) # linear is a common name between Base and Head on purpose. self.linear = nn.Linear(6, 3) self.linear2 = nn.Linear(3, 5) def forward(self, x): return self.linear2(self.linear(self.base(x))) if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp from transformers.modeling_flax_pytorch_utils import ( convert_pytorch_state_dict_to_flax, load_flax_weights_in_pytorch_model, ) if is_torch_fx_available(): from transformers.utils.fx import symbolic_trace def _config_zero_init(config): configs_no_init = copy.deepcopy(config) for key in configs_no_init.__dict__.keys(): if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key: setattr(configs_no_init, key, 1e-10) return configs_no_init TINY_T5 = "patrickvonplaten/t5-tiny-random" TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification" @require_torch class ModelTesterMixin: model_tester = None all_model_classes = () all_generative_model_classes = () fx_compatible = False test_torchscript = True test_pruning = True test_resize_embeddings = True test_resize_position_embeddings = False test_head_masking = True test_mismatched_shapes = True test_missing_keys = True test_model_parallel = False is_encoder_decoder = False has_attentions = True model_split_percents = [0.5, 0.7, 0.9] def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict = { k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous() if isinstance(v, torch.Tensor) and v.ndim > 1 else v for k, v in inputs_dict.items() } elif model_class in get_values(MODEL_FOR_AUDIO_XVECTOR_MAPPING): inputs_dict.pop("attention_mask") if return_labels: if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device) elif model_class in [ *get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING), *get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING), ]: inputs_dict["start_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) inputs_dict["end_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class in [ *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING), *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING), ]: inputs_dict["labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class in [ *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_CAUSAL_LM_MAPPING), *get_values(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING), *get_values(MODEL_FOR_MASKED_LM_MAPPING), *get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING), ]: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) elif model_class in get_values(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING): num_patches = self.model_tester.image_size // self.model_tester.patch_size inputs_dict["bool_masked_pos"] = torch.zeros( (self.model_tester.batch_size, num_patches**2), dtype=torch.long, device=torch_device ) elif model_class in get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING): batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = torch.zeros( [self.model_tester.batch_size, height, width], device=torch_device ).long() return inputs_dict def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) out_2 = outputs[0].cpu().numpy() out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname) model.to(torch_device) with torch.no_grad(): after_outputs = model(**self._prepare_for_class(inputs_dict, model_class)) # Make sure we don't have nans out_1 = after_outputs[0].cpu().numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) def test_save_load_keys_to_ignore_on_save(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) _keys_to_ignore_on_save = getattr(model, "_keys_to_ignore_on_save", None) if _keys_to_ignore_on_save is None: continue # check the keys are in the original state_dict for k in _keys_to_ignore_on_save: self.assertIn(k, model.state_dict().keys(), "\n".join(model.state_dict().keys())) # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME) state_dict_saved = torch.load(output_model_file) for k in _keys_to_ignore_on_save: self.assertNotIn(k, state_dict_saved.keys(), "\n".join(state_dict_saved.keys())) # Test we can load the state dict in the model, necessary for the checkpointing API in Trainer. load_result = model.load_state_dict(state_dict_saved, strict=False) self.assertTrue( len(load_result.missing_keys) == 0 or set(load_result.missing_keys) == set(model._keys_to_ignore_on_save) ) self.assertTrue(len(load_result.unexpected_keys) == 0) def test_gradient_checkpointing_backward_compatibility(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue config.gradient_checkpointing = True model = model_class(config) self.assertTrue(model.is_gradient_checkpointing) def test_gradient_checkpointing_enable_disable(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue # at init model should have gradient checkpointing disabled model = model_class(config) self.assertFalse(model.is_gradient_checkpointing) # check enable works model.gradient_checkpointing_enable() self.assertTrue(model.is_gradient_checkpointing) # check disable works model.gradient_checkpointing_disable() self.assertFalse(model.is_gradient_checkpointing) def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) @is_flaky() def test_save_load_fast_init_from_base(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() base_class = MODEL_MAPPING[config.__class__] if isinstance(base_class, tuple): base_class = base_class[0] for model_class in self.all_model_classes: if model_class == base_class: continue # make a copy of model class to not break future tests # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class class CopyClass(model_class): pass model_class_copy = CopyClass # make sure that all keys are expected for test model_class_copy._keys_to_ignore_on_load_missing = [] # make init deterministic, but make sure that # non-initialized weights throw errors nevertheless model_class_copy._init_weights = self._mock_init_weights model = base_class(config) state_dict = model.state_dict() # this will often delete a single weight of a multi-weight module # to test an edge case random_key_to_del = random.choice(list(state_dict.keys())) del state_dict[random_key_to_del] # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) model_fast_init = model_class_copy.from_pretrained(tmpdirname) model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False) for key in model_fast_init.state_dict().keys(): max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") def test_save_load_fast_init_to_base(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() base_class = MODEL_MAPPING[config.__class__] if isinstance(base_class, tuple): base_class = base_class[0] for model_class in self.all_model_classes: if model_class == base_class: continue # make a copy of model class to not break future tests # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class class CopyClass(base_class): pass base_class_copy = CopyClass # make sure that all keys are expected for test base_class_copy._keys_to_ignore_on_load_missing = [] # make init deterministic, but make sure that # non-initialized weights throw errors nevertheless base_class_copy._init_weights = self._mock_init_weights model = model_class(config) state_dict = model.state_dict() # this will often delete a single weight of a multi-weight module # to test an edge case random_key_to_del = random.choice(list(state_dict.keys())) del state_dict[random_key_to_del] # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.config.save_pretrained(tmpdirname) torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) model_fast_init = base_class_copy.from_pretrained(tmpdirname) model_slow_init = base_class_copy.from_pretrained(tmpdirname, _fast_init=False) for key in model_fast_init.state_dict().keys(): max_diff = torch.max( torch.abs(model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]) ).item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_determinism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): first = model(**self._prepare_for_class(inputs_dict, model_class))[0] second = model(**self._prepare_for_class(inputs_dict, model_class))[0] out_1 = first.cpu().numpy() out_2 = second.cpu().numpy() out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] if model.config.is_encoder_decoder: expected_arg_names = [ "input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend( ["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names else ["encoder_outputs"] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) else: expected_arg_names = ["input_ids"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_training(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True if model_class in get_values(MODEL_MAPPING): continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_cache = False config.return_dict = True if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing: continue model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable() model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) chunk_length = getattr(self.model_tester, "chunk_length", None) if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) if chunk_length is not None: self.assertListEqual( list(attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) out_len = len(outputs) if self.is_encoder_decoder: correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Question Answering model returns start_logits and end_logits if model_class in [ *get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING), *get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING), ]: correct_outlen += 1 # start_logits and end_logits instead of only 1 output if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) if chunk_length is not None: self.assertListEqual( list(self_attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) @slow def test_torchscript_simple(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() self._create_and_check_torchscript(config, inputs_dict) @slow def test_torchscript_output_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_attentions = True self._create_and_check_torchscript(config, inputs_dict) @slow def test_torchscript_output_hidden_state(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True self._create_and_check_torchscript(config, inputs_dict) # This is copied from `torch/testing/_internal/jit_utils.py::clear_class_registry` def clear_torch_jit_class_registry(self): torch._C._jit_clear_class_registry() torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore() torch.jit._state._clear_class_state() def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class) main_input_name = model_class.main_input_name try: if model.config.is_encoder_decoder: model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward main_input = inputs[main_input_name] attention_mask = inputs["attention_mask"] decoder_input_ids = inputs["decoder_input_ids"] decoder_attention_mask = inputs["decoder_attention_mask"] model(main_input, attention_mask, decoder_input_ids, decoder_attention_mask) traced_model = torch.jit.trace( model, (main_input, attention_mask, decoder_input_ids, decoder_attention_mask) ) elif "bbox" in inputs and "image" in inputs: # LayoutLMv2 requires additional inputs input_ids = inputs["input_ids"] bbox = inputs["bbox"] image = inputs["image"].tensor model(input_ids, bbox, image) traced_model = torch.jit.trace( model, (input_ids, bbox, image), check_trace=False ) # when traced model is checked, an error is produced due to name mangling else: main_input = inputs[main_input_name] model(main_input) traced_model = torch.jit.trace(model, main_input) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): if layer_name in loaded_model_state_dict: p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) # Avoid memory leak. Without this, each call increase RAM usage by ~20MB. # (Even with this call, there are still memory leak by ~0.04MB) self.clear_torch_jit_class_registry() def test_torch_fx(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() self._create_and_check_torch_fx_tracing(config, inputs_dict) def test_torch_fx_output_loss(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() self._create_and_check_torch_fx_tracing(config, inputs_dict, output_loss=True) def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False): if not is_torch_fx_available() or not self.fx_compatible: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss) try: if model.config.is_encoder_decoder: model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward labels = inputs.get("labels", None) input_names = [ "attention_mask", "decoder_attention_mask", "decoder_input_ids", "input_features", "input_ids", "input_values", ] if labels is not None: input_names.append("labels") filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names} input_names = list(filtered_inputs.keys()) model_output = model(**filtered_inputs) traced_model = symbolic_trace(model, input_names) traced_output = traced_model(**filtered_inputs) else: input_names = [ "attention_mask", "bbox", "input_features", "input_ids", "input_values", "pixel_values", "token_type_ids", "visual_feats", "visual_pos", ] labels = inputs.get("labels", None) start_positions = inputs.get("start_positions", None) end_positions = inputs.get("end_positions", None) if labels is not None: input_names.append("labels") if start_positions is not None: input_names.append("start_positions") if end_positions is not None: input_names.append("end_positions") filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names} input_names = list(filtered_inputs.keys()) if isinstance(model, tuple(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values())) and ( not hasattr(model.config, "problem_type") or model.config.problem_type is None ): model.config.problem_type = "single_label_classification" traced_model = symbolic_trace(model, input_names) traced_output = traced_model(**filtered_inputs) model_output = model(**filtered_inputs) except Exception as e: self.fail(f"Couldn't trace module: {e}") def flatten_output(output): flatten = [] for x in output: if isinstance(x, (tuple, list)): flatten += flatten_output(x) elif not isinstance(x, torch.Tensor): continue else: flatten.append(x) return flatten model_output = flatten_output(model_output) traced_output = flatten_output(traced_output) num_outputs = len(model_output) for i in range(num_outputs): self.assertTrue( torch.allclose(model_output[i], traced_output[i]), f"traced {i}th output doesn't match model {i}th output for {model_class}", ) # Test that the model can be serialized and restored properly with tempfile.TemporaryDirectory() as tmp_dir_name: pkl_file_name = os.path.join(tmp_dir_name, "model.pkl") try: with open(pkl_file_name, "wb") as f: pickle.dump(traced_model, f) with open(pkl_file_name, "rb") as f: loaded = pickle.load(f) except Exception as e: self.fail(f"Couldn't serialize / deserialize the traced model: {e}") loaded_output = loaded(**filtered_inputs) loaded_output = flatten_output(loaded_output) for i in range(num_outputs): self.assertTrue( torch.allclose(model_output[i], loaded_output[i]), f"serialized model {i}th output doesn't match model {i}th output for {model_class}", ) # Avoid memory leak. Without this, each call increase RAM usage by ~20MB. # (Even with this call, there are still memory leak by ~0.04MB) self.clear_torch_jit_class_registry() def test_headmasking(self): if not self.test_head_masking: return global_rng.seed(42) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() global_rng.seed() inputs_dict["output_attentions"] = True config.output_hidden_states = True configs_no_init = _config_zero_init(config) # To be sure we have no Nan for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() # Prepare head_mask # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior) head_mask = torch.ones( self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device, ) head_mask[0, 0] = 0 head_mask[-1, :-1] = 0 head_mask.requires_grad_(requires_grad=True) inputs = self._prepare_for_class(inputs_dict, model_class).copy() inputs["head_mask"] = head_mask if model.config.is_encoder_decoder: signature = inspect.signature(model.forward) arg_names = [*signature.parameters.keys()] if "decoder_head_mask" in arg_names: # necessary diferentiation because of T5 model inputs["decoder_head_mask"] = head_mask if "cross_attn_head_mask" in arg_names: inputs["cross_attn_head_mask"] = head_mask outputs = model(**inputs, return_dict=True) # Test that we can get a gradient back for importance score computation output = sum(t.sum() for t in outputs[0]) output = output.sum() output.backward() multihead_outputs = head_mask.grad self.assertIsNotNone(multihead_outputs) self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers) def check_attentions_validity(attentions): # Remove Nan for t in attentions: self.assertLess( torch.sum(torch.isnan(t)), t.numel() / 4 ) # Check we don't have more than 25% nans (arbitrary) attentions = [ t.masked_fill(torch.isnan(t), 0.0) for t in attentions ] # remove them (the test is less complete) self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0) self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0) if len(attentions) > 2: # encoder-decoder models have only 2 layers in each module self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0) self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0) self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0) if model.config.is_encoder_decoder: check_attentions_validity(outputs.encoder_attentions) check_attentions_validity(outputs.decoder_attentions) check_attentions_validity(outputs.cross_attentions) else: check_attentions_validity(outputs.attentions) def test_head_pruning(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False model = model_class(config=config) model.to(torch_device) model.eval() heads_to_prune = { 0: list(range(1, self.model_tester.num_attention_heads)), -1: [0], } model.prune_heads(heads_to_prune) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) def test_head_pruning_save_load_from_pretrained(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False model = model_class(config=config) model.to(torch_device) model.eval() heads_to_prune = { 0: list(range(1, self.model_tester.num_attention_heads)), -1: [0], } model.prune_heads(heads_to_prune) with tempfile.TemporaryDirectory() as temp_dir_name: model.save_pretrained(temp_dir_name) model = model_class.from_pretrained(temp_dir_name) model.to(torch_device) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) def test_head_pruning_save_load_from_config_init(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False heads_to_prune = { 0: list(range(1, self.model_tester.num_attention_heads)), -1: [0], } config.pruned_heads = heads_to_prune model = model_class(config=config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) def test_head_pruning_integration(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False heads_to_prune = {0: [0], 1: [1, 2]} config.pruned_heads = heads_to_prune model = model_class(config=config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads) with tempfile.TemporaryDirectory() as temp_dir_name: model.save_pretrained(temp_dir_name) model = model_class.from_pretrained(temp_dir_name) model.to(torch_device) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads) heads_to_prune = {0: [0], 2: [1, 2]} model.prune_heads(heads_to_prune) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2) self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads) self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]}) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1: seq_length = seq_length * self.model_tester.chunk_length else: seq_length = self.model_tester.seq_length self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = self.has_attentions # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0] if config.is_encoder_decoder: # Seq2Seq models encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() decoder_hidden_states = outputs.decoder_hidden_states[0] decoder_hidden_states.retain_grad() if self.has_attentions: encoder_attentions = outputs.encoder_attentions[0] encoder_attentions.retain_grad() decoder_attentions = outputs.decoder_attentions[0] decoder_attentions.retain_grad() cross_attentions = outputs.cross_attentions[0] cross_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(decoder_hidden_states.grad) if self.has_attentions: self.assertIsNotNone(encoder_attentions.grad) self.assertIsNotNone(decoder_attentions.grad) self.assertIsNotNone(cross_attentions.grad) else: # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] hidden_states.retain_grad() if self.has_attentions: attentions = outputs.attentions[0] attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) if self.has_attentions: self.assertIsNotNone(attentions.grad) def test_feed_forward_chunking(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: torch.manual_seed(0) config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) model.eval() hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0] torch.manual_seed(0) config.chunk_size_feed_forward = 1 model = model_class(config) model.to(torch_device) model.eval() hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0] self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3)) def test_resize_position_vector_embeddings(self): if not self.test_resize_position_embeddings: return ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() max_position_embeddings = config.max_position_embeddings # Retrieve the embeddings and clone theme if model.config.is_encoder_decoder: encoder_model_embed, decoder_model_embed = model.get_position_embeddings() encoder_cloned_embeddings = encoder_model_embed.weight.clone() decoder_cloned_embeddings = decoder_model_embed.weight.clone() else: model_embed = model.get_position_embeddings() cloned_embeddings = model_embed.weight.clone() # Check that resizing the position embeddings with a larger max_position_embeddings increases # the model's postion embeddings size model.resize_position_embeddings(max_position_embeddings + 10) self.assertEqual(model.config.max_position_embeddings, max_position_embeddings + 10) # Check that it actually resizes the embeddings matrix if model.config.is_encoder_decoder: encoder_model_embed, decoder_model_embed = model.get_position_embeddings() self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] + 10) self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] + 10) else: model_embed = model.get_position_embeddings() self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the position embeddings with a smaller max_position_embeddings decreases # the model's max_position_embeddings model.resize_position_embeddings(max_position_embeddings - 5) self.assertEqual(model.config.max_position_embeddings, max_position_embeddings - 5) # Check that it actually resizes the embeddings matrix if model.config.is_encoder_decoder: encoder_model_embed, decoder_model_embed = model.get_position_embeddings() self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] - 5) self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] - 5) else: model_embed = model.get_position_embeddings() self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 5) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True if model.config.is_encoder_decoder: for p1, p2 in zip(encoder_cloned_embeddings, encoder_model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False for p1, p2 in zip(decoder_cloned_embeddings, decoder_model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False else: for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_resize_tokens_embeddings(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) # make sure that decoder_input_ids are resized as well if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_resize_embeddings_untied(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return original_config.tie_word_embeddings = False # if model cannot untied embeddings -> leave test if original_config.tie_word_embeddings: return for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Embedding, AdaptiveEmbedding)) model.set_input_embeddings(nn.Embedding(10, 10)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_model_main_input_name(self): for model_class in self.all_model_classes: model_signature = inspect.signature(getattr(model_class, "forward")) # The main input is the name of the argument after `self` observed_main_input_name = list(model_signature.parameters.keys())[1] self.assertEqual(model_class.main_input_name, observed_main_input_name) def test_correct_missing_keys(self): if not self.test_missing_keys: return config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) base_model_prefix = model.base_model_prefix if hasattr(model, base_model_prefix): extra_params = {k: v for k, v in model.named_parameters() if not k.startswith(base_model_prefix)} extra_params.update({k: v for k, v in model.named_buffers() if not k.startswith(base_model_prefix)}) # Some models define this as None if model._keys_to_ignore_on_load_missing: for key in model._keys_to_ignore_on_load_missing: extra_params.pop(key, None) if not extra_params: # In that case, we *are* on a head model, but every # single key is not actual parameters and this is # tested in `test_tied_model_weights_key_ignore` test. continue with tempfile.TemporaryDirectory() as temp_dir_name: model.base_model.save_pretrained(temp_dir_name) model, loading_info = model_class.from_pretrained(temp_dir_name, output_loading_info=True) self.assertGreater(len(loading_info["missing_keys"]), 0, model.__class__.__name__) def test_tie_model_weights(self): if not self.test_torchscript: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_same_values(layer_1, layer_2): equal = True for p1, p2 in zip(layer_1.weight, layer_2.weight): if p1.data.ne(p2.data).sum() > 0: equal = False return equal for model_class in self.all_model_classes: config.torchscript = True model_not_tied = model_class(config) if model_not_tied.get_output_embeddings() is None: continue config_tied = copy.deepcopy(config) config_tied.torchscript = False model_tied = model_class(config_tied) params_tied = list(model_tied.parameters()) # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(check_same_values(embeddings, decoding)) # # Check that after modification, they remain the same. # embeddings.weight.data.div_(2) # # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(embeddings.weight.shape, decoding.weight.shape) # self.assertTrue(check_same_values(embeddings, decoding)) # # Check that after modification, they remain the same. # decoding.weight.data.div_(4) # # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(embeddings.weight.shape, decoding.weight.shape) # self.assertTrue(check_same_values(embeddings, decoding)) # Check that after resize they remain tied. model_tied.resize_token_embeddings(config.vocab_size + 10) params_tied_2 = list(model_tied.parameters()) self.assertEqual(len(params_tied_2), len(params_tied)) # decoding.weight.data.mul_(20) # # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape) # self.assertTrue(check_same_values(model.transformer.wte, model.lm_head)) def test_tied_model_weights_key_ignore(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model_tied = model_class(config) with tempfile.TemporaryDirectory() as d: model_tied.save_pretrained(d) # We are nuking ALL weights on file, so every parameter should # yell on load. We're going to detect if we yell too much, or too little. with open(os.path.join(d, "pytorch_model.bin"), "wb") as f: torch.save({}, f) model_reloaded, infos = model_class.from_pretrained(d, output_loading_info=True) # ! Actually we could use `state_dict()` and check iteratively the tensors which are the same (for instance using `tensor.data_ptr()`). to detect the duplicates. # ```python # model = GPT2LMHeadModel.from_pretrained("gpt2") # "lm_head.weight" in model.state_dict().keys() # True # "lm_head.weight" in model.named_parameters() # False # In [6]: model.lm_head.weight.data_ptr() # Out[6]: 139901378371648 # In [9]: model.transformer.wte.weight.data_ptr() # Out[9]: 139901378371648 # Same PTR, it's the same DATA ! we would need to check for stride too to be 100% accurate. # ``` prefix = f"{model_reloaded.base_model_prefix}." params = dict(model_reloaded.named_parameters()) params.update(dict(model_reloaded.named_buffers())) # param_names = set(k[len(prefix) :] if k.startswith(prefix) else k for k in params.keys()) param_names = set(k[len(prefix) :] if k.startswith(prefix) else k for k in params.keys()) missing_keys = set(infos["missing_keys"]) extra_missing = missing_keys - param_names # missed_missing = param_names - missing_keys self.assertEqual( extra_missing, set(), f"This model {model_class.__name__} might be missing some `keys_to_ignore`: {extra_missing}", ) # self.assertEqual( # missed_missing, # set(), # f"This model {model_class.__name__} ignores keys {missed_missing} but they look like real" # " parameters", # ) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def set_nan_tensor_to_zero(t): t[t != t] = 0 return t def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): with torch.no_grad(): tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (List, Tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif isinstance(tuple_object, Dict): for tuple_iterable_value, dict_iterable_value in zip( tuple_object.values(), dict_object.values() ): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( torch.allclose( set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 ), msg=( "Tuple and dict output are not equal. Difference:" f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) if self.has_attentions: tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence( model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True} ) # Don't copy this method to model specific test file! # TODO: remove this method once the issues are all fixed! def _make_attention_mask_non_null(self, inputs_dict): """Make sure no sequence has all zeros as attention mask""" for k in ["attention_mask", "encoder_attention_mask", "decoder_attention_mask"]: if k in inputs_dict: attention_mask = inputs_dict[k] # Make sure no all 0s attention masks - to avoid failure at this moment. # Put `1` at the beginning of sequences to make it still work when combining causal attention masks. # TODO: remove this line once a fix regarding large negative values for attention mask is done. attention_mask = torch.cat( [torch.ones_like(attention_mask[:, :1], dtype=attention_mask.dtype), attention_mask[:, 1:]], dim=-1 ) # Here we make the first sequence with all 0s as attention mask. # Currently, this will fail for `TFWav2Vec2Model`. This is caused by the different large negative # values, like `1e-4`, `1e-9`, `1e-30` and `-inf` for attention mask across models/frameworks. # TODO: enable this block once the large negative values thing is cleaned up. # (see https://github.com/huggingface/transformers/issues/14859) # attention_mask = torch.cat( # [torch.zeros_like(attention_mask[:1], dtype=attention_mask.dtype), attention_mask[1:]], # dim=0 # ) inputs_dict[k] = attention_mask # Don't copy this method to model specific test file! # TODO: remove this method once the issues are all fixed! def _postprocessing_to_ignore_test_cases(self, tf_outputs, pt_outputs, model_class): """For temporarily ignoring some failed test cases (issues to be fixed)""" tf_keys = set([k for k, v in tf_outputs.items() if v is not None]) pt_keys = set([k for k, v in pt_outputs.items() if v is not None]) key_differences = tf_keys.symmetric_difference(pt_keys) if model_class.__name__ in [ "FlaubertWithLMHeadModel", "FunnelForPreTraining", "ElectraForPreTraining", "XLMWithLMHeadModel", "TransfoXLLMHeadModel", ]: for k in key_differences: if k in ["loss", "losses"]: tf_keys.discard(k) pt_keys.discard(k) elif model_class.__name__.startswith("GPT2"): # `TFGPT2` has `past_key_values` as a tensor while `GPT2` has it as a tuple. tf_keys.discard("past_key_values") pt_keys.discard("past_key_values") # create new outputs from the remaining fields new_tf_outputs = type(tf_outputs)(**{k: tf_outputs[k] for k in tf_keys}) new_pt_outputs = type(pt_outputs)(**{k: pt_outputs[k] for k in pt_keys}) return new_tf_outputs, new_pt_outputs # Copied from tests.test_modeling_tf_common.TFModelTesterMixin.check_pt_tf_outputs def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): """Check the outputs from PyTorch and TensorFlow models are close enough. Checks are done in a recursive way. Args: model_class: The class of the model that is currently testing. For example, `TFBertModel`, TFBertForMaskedLM`, `TFBertForSequenceClassification`, etc. Mainly used for providing more informative error messages. name (`str`): The name of the output. For example, `output.hidden_states`, `output.attentions`, etc. attributes (`Tuple[str]`): The names of the output's element if the output is a tuple/list with each element being a named field in the output. """ self.assertEqual(type(name), str) if attributes is not None: self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`") # Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`). if isinstance(tf_outputs, ModelOutput): self.assertTrue( isinstance(pt_outputs, ModelOutput), f"{name}: `pt_outputs` should an instance of `ModelOutput` when `tf_outputs` is", ) # Don't copy this block to model specific test file! # TODO: remove this method and this line after issues are fixed tf_outputs, pt_outputs = self._postprocessing_to_ignore_test_cases(tf_outputs, pt_outputs, model_class) tf_keys = [k for k, v in tf_outputs.items() if v is not None] pt_keys = [k for k, v in pt_outputs.items() if v is not None] self.assertEqual(tf_keys, pt_keys, f"{name}: Output keys differ between TF and PyTorch") # convert to the case of `tuple` # appending each key to the current (string) `name` attributes = tuple([f"{name}.{k}" for k in tf_keys]) self.check_pt_tf_outputs( tf_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes ) # Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.) elif type(tf_outputs) in [tuple, list]: self.assertEqual(type(tf_outputs), type(pt_outputs), f"{name}: Output types differ between TF and PyTorch") self.assertEqual(len(tf_outputs), len(pt_outputs), f"{name}: Output lengths differ between TF and PyTorch") if attributes is not None: # case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`) self.assertEqual( len(attributes), len(tf_outputs), f"{name}: The tuple `attributes` should have the same length as `tf_outputs`", ) else: # case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name` attributes = tuple([f"{name}_{idx}" for idx in range(len(tf_outputs))]) for tf_output, pt_output, attr in zip(tf_outputs, pt_outputs, attributes): self.check_pt_tf_outputs(tf_output, pt_output, model_class, tol=tol, name=attr) elif isinstance(tf_outputs, tf.Tensor): self.assertTrue( isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `tf_outputs` is" ) tf_outputs = tf_outputs.numpy() pt_outputs = pt_outputs.detach().to("cpu").numpy() self.assertEqual( tf_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between TF and PyTorch" ) # deal with NumPy's scalars to make replacing nan values by 0 work. if np.isscalar(tf_outputs): tf_outputs = np.array([tf_outputs]) pt_outputs = np.array([pt_outputs]) tf_nans = np.isnan(tf_outputs) pt_nans = np.isnan(pt_outputs) pt_outputs[tf_nans] = 0 tf_outputs[tf_nans] = 0 pt_outputs[pt_nans] = 0 tf_outputs[pt_nans] = 0 max_diff = np.amax(np.abs(tf_outputs - pt_outputs)) self.assertLessEqual(max_diff, tol, f"{name}: Difference between PyTorch and TF is {max_diff} (>= {tol}).") else: raise ValueError( "`tf_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `tf.Tensor`. Got" f" {type(tf_outputs)} instead." ) def prepare_tf_inputs_from_pt_inputs(self, pt_inputs_dict): tf_inputs_dict = {} for key, tensor in pt_inputs_dict.items(): # skip key that does not exist in tf if type(tensor) == bool: tf_inputs_dict[key] = tensor elif key == "input_values": tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) elif key == "pixel_values": tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) elif key == "input_features": tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) # other general float inputs elif tensor.is_floating_point(): tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) else: tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32) return tf_inputs_dict def check_pt_tf_models(self, tf_model, pt_model, pt_inputs_dict): tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict) # send pytorch inputs to the correct device pt_inputs_dict = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs_dict.items() } # send pytorch model to the correct device pt_model.to(torch_device) # Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences pt_model.eval() with torch.no_grad(): pt_outputs = pt_model(**pt_inputs_dict) tf_outputs = tf_model(tf_inputs_dict) # tf models returned loss is usually a tensor rather than a scalar. # (see `hf_compute_loss`: it uses `tf.keras.losses.Reduction.NONE`) # Change it here to a scalar to match PyTorch models' loss tf_loss = getattr(tf_outputs, "loss", None) if tf_loss is not None: tf_outputs.loss = tf.math.reduce_mean(tf_loss) self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(pt_model)) @is_pt_tf_cross_test def test_pt_tf_model_equivalence(self): import transformers for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() tf_model_class_name = "TF" + model_class.__name__ # Add the "TF" at the beginning if not hasattr(transformers, tf_model_class_name): # transformers does not have this model in TF version yet return # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions # Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency # of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`. # TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it. self._make_attention_mask_non_null(inputs_dict) tf_model_class = getattr(transformers, tf_model_class_name) pt_model = model_class(config) tf_model = tf_model_class(config) pt_inputs_dict = self._prepare_for_class(inputs_dict, model_class) pt_inputs_dict_with_labels = self._prepare_for_class( inputs_dict, model_class, # Not all models accept "labels" in the forward pass (yet :) ) return_labels=True if "labels" in inspect.signature(model_class.forward).parameters.keys() else False, ) # make sure only tf inputs are forward that actually exist in function args tf_input_keys = set(inspect.signature(tf_model.call).parameters.keys()) # remove all head masks tf_input_keys.discard("head_mask") tf_input_keys.discard("cross_attn_head_mask") tf_input_keys.discard("decoder_head_mask") pt_inputs_dict = {k: v for k, v in pt_inputs_dict.items() if k in tf_input_keys} pt_inputs_dict_with_labels = {k: v for k, v in pt_inputs_dict_with_labels.items() if k in tf_input_keys} # For some models (e.g. base models), there is no label returned. # Set the input dict to `None` to avoid check outputs twice for the same input dicts. if set(pt_inputs_dict_with_labels.keys()).symmetric_difference(pt_inputs_dict.keys()): pt_inputs_dict_with_labels = None # Check we can load pt model in tf and vice-versa with model => model functions # Here requires `tf_inputs_dict` to build `tf_model` tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict) tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict) pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model) # Original test: check without `labels` self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict) # check with `labels` if pt_inputs_dict_with_labels: self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict_with_labels) # Check we can load pt model in tf and vice-versa with checkpoint => model functions with tempfile.TemporaryDirectory() as tmpdirname: pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin") torch.save(pt_model.state_dict(), pt_checkpoint_path) tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path) tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5") tf_model.save_weights(tf_checkpoint_path) pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path) # Original test: check without `labels` self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict) # check with `labels` if pt_inputs_dict_with_labels: self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict_with_labels) def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float): diff = np.abs((a - b)).max() self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).") def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): """ Args: model_class: The class of the model that is currently testing. For example, ..., etc. Currently unused, but it could make debugging easier and faster. names: A string, or a list of strings. These specify what fx_outputs/pt_outputs represent in the model outputs. Currently unused, but in the future, we could use this information to make the error message clearer by giving the name(s) of the output tensor(s) with large difference(s) between PT and Flax. """ self.assertEqual(type(name), str) if attributes is not None: self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`") # Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`). if isinstance(fx_outputs, ModelOutput): self.assertTrue( isinstance(pt_outputs, ModelOutput), f"{name}: `pt_outputs` should an instance of `ModelOutput` when `fx_outputs` is", ) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys, f"{name}: Output keys differ between Flax and PyTorch") # convert to the case of `tuple` # appending each key to the current (string) `name` attributes = tuple([f"{name}.{k}" for k in fx_keys]) self.check_pt_flax_outputs( fx_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes ) # Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.) elif type(fx_outputs) in [tuple, list]: self.assertEqual( type(fx_outputs), type(pt_outputs), f"{name}: Output types differ between Flax and PyTorch" ) self.assertEqual( len(fx_outputs), len(pt_outputs), f"{name}: Output lengths differ between Flax and PyTorch" ) if attributes is not None: # case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`) self.assertEqual( len(attributes), len(fx_outputs), f"{name}: The tuple `attributes` should have the same length as `fx_outputs`", ) else: # case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name` attributes = tuple([f"{name}_{idx}" for idx in range(len(fx_outputs))]) for fx_output, pt_output, attr in zip(fx_outputs, pt_outputs, attributes): self.check_pt_flax_outputs(fx_output, pt_output, model_class, tol=tol, name=attr) elif isinstance(fx_outputs, jnp.ndarray): self.assertTrue( isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `fx_outputs` is" ) # Using `np.asarray` gives `ValueError: assignment destination is read-only` at the line `fx_outputs[fx_nans] = 0`. fx_outputs = np.array(fx_outputs) pt_outputs = pt_outputs.detach().to("cpu").numpy() self.assertEqual( fx_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between Flax and PyTorch" ) # deal with NumPy's scalars to make replacing nan values by 0 work. if np.isscalar(fx_outputs): fx_outputs = np.array([fx_outputs]) pt_outputs = np.array([pt_outputs]) fx_nans = np.isnan(fx_outputs) pt_nans = np.isnan(pt_outputs) pt_outputs[fx_nans] = 0 fx_outputs[fx_nans] = 0 pt_outputs[pt_nans] = 0 fx_outputs[pt_nans] = 0 max_diff = np.amax(np.abs(fx_outputs - pt_outputs)) self.assertLessEqual( max_diff, tol, f"{name}: Difference between PyTorch and Flax is {max_diff} (>= {tol})." ) else: raise ValueError( "`fx_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `jnp.ndarray`. Got" f" {type(fx_outputs)} instead." ) @is_pt_flax_cross_test def test_equivalence_pt_to_flax(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): fx_model_class_name = "Flax" + model_class.__name__ if not hasattr(transformers, fx_model_class_name): # no flax model exists for this class return # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions fx_model_class = getattr(transformers, fx_model_class_name) # load PyTorch class pt_model = model_class(config).eval() # Flax models don't use the `use_cache` option and cache is not returned as a default. # So we disable `use_cache` here for PyTorch model. pt_model.config.use_cache = False # load Flax class fx_model = fx_model_class(config, dtype=jnp.float32) # make sure only flax inputs are forward that actually exist in function args fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys() # prepare inputs pt_inputs = self._prepare_for_class(inputs_dict, model_class) # remove function args that don't exist in Flax pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys} # send pytorch inputs to the correct device pt_inputs = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items() } # convert inputs to Flax fx_inputs = {k: np.array(v) for k, v in pt_inputs.items() if torch.is_tensor(v)} fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model) fx_model.params = fx_state # send pytorch model to the correct device pt_model.to(torch_device) with torch.no_grad(): pt_outputs = pt_model(**pt_inputs) fx_outputs = fx_model(**fx_inputs) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class) with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**fx_inputs) fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class) @is_pt_flax_cross_test def test_equivalence_flax_to_pt(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): fx_model_class_name = "Flax" + model_class.__name__ if not hasattr(transformers, fx_model_class_name): # no flax model exists for this class return # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions fx_model_class = getattr(transformers, fx_model_class_name) # load PyTorch class pt_model = model_class(config).eval() # Flax models don't use the `use_cache` option and cache is not returned as a default. # So we disable `use_cache` here for PyTorch model. pt_model.config.use_cache = False # load Flax class fx_model = fx_model_class(config, dtype=jnp.float32) # make sure only flax inputs are forward that actually exist in function args fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys() # prepare inputs pt_inputs = self._prepare_for_class(inputs_dict, model_class) # remove function args that don't exist in Flax pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys} # send pytorch inputs to the correct device pt_inputs = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items() } # convert inputs to Flax fx_inputs = {k: np.array(v) for k, v in pt_inputs.items() if torch.is_tensor(v)} pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params) # make sure weights are tied in PyTorch pt_model.tie_weights() # send pytorch model to the correct device pt_model.to(torch_device) with torch.no_grad(): pt_outputs = pt_model(**pt_inputs) fx_outputs = fx_model(**fx_inputs) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class) with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True) # send pytorch model to the correct device pt_model_loaded.to(torch_device) pt_model_loaded.eval() with torch.no_grad(): pt_outputs_loaded = pt_model_loaded(**pt_inputs) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class) def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) if not self.is_encoder_decoder: input_ids = inputs["input_ids"] del inputs["input_ids"] else: encoder_input_ids = inputs["input_ids"] decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids) del inputs["input_ids"] inputs.pop("decoder_input_ids", None) wte = model.get_input_embeddings() if not self.is_encoder_decoder: inputs["inputs_embeds"] = wte(input_ids) else: inputs["inputs_embeds"] = wte(encoder_input_ids) inputs["decoder_inputs_embeds"] = wte(decoder_input_ids) with torch.no_grad(): model(**inputs)[0] @require_torch_multi_gpu def test_multi_gpu_data_parallel_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # some params shouldn't be scattered by nn.DataParallel # so just remove them if they are present. blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"] for k in blacklist_non_batched_params: inputs_dict.pop(k, None) # move input tensors to cuda:O for k, v in inputs_dict.items(): if torch.is_tensor(v): inputs_dict[k] = v.to(0) for model_class in self.all_model_classes: model = model_class(config=config) model.to(0) model.eval() # Wrap model in nn.DataParallel model = nn.DataParallel(model) with torch.no_grad(): _ = model(**self._prepare_for_class(inputs_dict, model_class)) @require_torch_multi_gpu def test_model_parallelization(self): if not self.test_model_parallel: return # a candidate for testing_utils def get_current_gpu_memory_use(): """returns a list of cuda memory allocations per GPU in MBs""" per_device_memory = [] for id in range(torch.cuda.device_count()): with torch.cuda.device(id): per_device_memory.append(torch.cuda.memory_allocated() >> 20) return per_device_memory # Needs a large model to see the difference. config = self.model_tester.get_large_model_config() for model_class in self.all_parallelizable_model_classes: torch.cuda.empty_cache() # 1. single gpu memory load + unload + memory measurements # Retrieve initial memory usage (can easily be ~0.6-1.5GB if cuda-kernels have been preloaded by previous tests) memory_at_start = get_current_gpu_memory_use() # Put model on device 0 and take a memory snapshot model = model_class(config) model.to("cuda:0") memory_after_model_load = get_current_gpu_memory_use() # The memory use on device 0 should be higher than it was initially. self.assertGreater(memory_after_model_load[0], memory_at_start[0]) del model gc.collect() torch.cuda.empty_cache() # 2. MP test # it's essential to re-calibrate the usage before the next stage memory_at_start = get_current_gpu_memory_use() # Spread model layers over multiple devices model = model_class(config) model.parallelize() memory_after_parallelization = get_current_gpu_memory_use() # Assert that the memory use on all devices is higher than it was when loaded only on CPU for n in range(len(model.device_map.keys())): self.assertGreater(memory_after_parallelization[n], memory_at_start[n]) # Assert that the memory use of device 0 is lower than it was when the entire model was loaded on it self.assertLess(memory_after_parallelization[0], memory_after_model_load[0]) # Assert that the memory use of device 1 is higher than it was when the entire model was loaded # on device 0 and device 1 wasn't used at all self.assertGreater(memory_after_parallelization[1], memory_after_model_load[1]) del model gc.collect() torch.cuda.empty_cache() @require_torch_multi_gpu def test_model_parallel_equal_results(self): if not self.test_model_parallel: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_parallelizable_model_classes: inputs_dict = self._prepare_for_class(inputs_dict, model_class) def cast_to_device(dictionary, device): output = {} for k, v in dictionary.items(): if isinstance(v, torch.Tensor): output[k] = v.to(device) else: output[k] = v return output model = model_class(config) output = model(**cast_to_device(inputs_dict, "cpu")) model.parallelize() parallel_output = model(**cast_to_device(inputs_dict, "cuda:0")) for value, parallel_value in zip(output, parallel_output): if isinstance(value, torch.Tensor): self.assertTrue(torch.allclose(value, parallel_value.to("cpu"), atol=1e-7)) elif isinstance(value, (Tuple, List)): for value_, parallel_value_ in zip(value, parallel_value): self.assertTrue(torch.allclose(value_, parallel_value_.to("cpu"), atol=1e-7)) @require_torch_multi_gpu def test_model_parallel_beam_search(self): if not self.test_model_parallel: return all_generative_and_parallelizable_model_classes = tuple( set(self.all_generative_model_classes).intersection(self.all_parallelizable_model_classes) ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in all_generative_and_parallelizable_model_classes: inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) def cast_to_device(dictionary, device): output = {} for k, v in dictionary.items(): if isinstance(v, torch.Tensor): output[k] = v.to(device) else: output[k] = v return output model.parallelize() model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2) def check_device_map_is_respected(self, model, device_map): for param_name, param in model.named_parameters(): # Find device in device_map while len(param_name) > 0 and param_name not in device_map: param_name = ".".join(param_name.split(".")[:-1]) if param_name not in device_map: raise ValueError("device map is incomplete, it does not contain any device for `param_name`.") param_device = device_map[param_name] if param_device in ["cpu", "disk"]: self.assertEqual(param.device, torch.device("meta")) else: self.assertEqual(param.device, torch.device(param_device)) @require_accelerate @require_torch_gpu def test_disk_offload(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._no_split_modules is None: continue inputs_dict_class = self._prepare_for_class(inputs_dict, model_class) model = model_class(config).eval() model = model.to(torch_device) torch.manual_seed(0) base_output = model(**inputs_dict_class) model_size = compute_module_sizes(model)[""] max_size = int(self.model_split_percents[0] * model_size) with tempfile.TemporaryDirectory() as tmp_dir: model.cpu().save_pretrained(tmp_dir) max_memory = {0: max_size, "cpu": max_size} with self.assertRaises(ValueError): # This errors out cause it's missing an offload folder new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) new_model = model_class.from_pretrained( tmp_dir, device_map="auto", max_memory=max_memory, offload_folder=tmp_dir ) self.check_device_map_is_respected(new_model, new_model.hf_device_map) torch.manual_seed(0) new_output = new_model(**inputs_dict_class) self.assertTrue(torch.allclose(base_output[0], new_output[0])) @require_accelerate @require_torch_gpu def test_cpu_offload(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._no_split_modules is None: continue inputs_dict_class = self._prepare_for_class(inputs_dict, model_class) model = model_class(config).eval() model = model.to(torch_device) torch.manual_seed(0) base_output = model(**inputs_dict_class) model_size = compute_module_sizes(model)[""] # We test several splits of sizes to make sure it works. max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents] with tempfile.TemporaryDirectory() as tmp_dir: model.cpu().save_pretrained(tmp_dir) for max_size in max_gpu_sizes: max_memory = {0: max_size, "cpu": model_size * 2} new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) # Making sure part of the model will actually end up offloaded self.assertSetEqual(set(new_model.hf_device_map.values()), {0, "cpu"}) self.check_device_map_is_respected(new_model, new_model.hf_device_map) torch.manual_seed(0) new_output = new_model(**inputs_dict_class) self.assertTrue(torch.allclose(base_output[0], new_output[0])) @require_accelerate @require_torch_multi_gpu def test_model_parallelism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._no_split_modules is None: continue inputs_dict_class = self._prepare_for_class(inputs_dict, model_class) model = model_class(config).eval() model = model.to(torch_device) torch.manual_seed(0) base_output = model(**inputs_dict_class) model_size = compute_module_sizes(model)[""] # We test several splits of sizes to make sure it works. max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents] with tempfile.TemporaryDirectory() as tmp_dir: model.cpu().save_pretrained(tmp_dir) for max_size in max_gpu_sizes: max_memory = {0: max_size, 1: model_size * 2, "cpu": model_size * 2} new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) # Making sure part of the model will actually end up offloaded self.assertSetEqual(set(new_model.hf_device_map.values()), {0, 1}) self.check_device_map_is_respected(new_model, new_model.hf_device_map) torch.manual_seed(0) new_output = new_model(**inputs_dict_class) self.assertTrue(torch.allclose(base_output[0], new_output[0])) def test_problem_types(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() problem_types = [ {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float}, {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long}, {"title": "regression", "num_labels": 1, "dtype": torch.float}, ] for model_class in self.all_model_classes: if model_class not in [ *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING), ]: continue for problem_type in problem_types: with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"): config.problem_type = problem_type["title"] config.num_labels = problem_type["num_labels"] model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) if problem_type["num_labels"] > 1: inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"]) inputs["labels"] = inputs["labels"].to(problem_type["dtype"]) # This tests that we do not trigger the warning form PyTorch "Using a target size that is different # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure # they have the same size." which is a symptom something in wrong for the regression problem. # See https://github.com/huggingface/transformers/issues/11780 with warnings.catch_warnings(record=True) as warning_list: loss = model(**inputs).loss for w in warning_list: if "Using a target size that is different to the input size" in str(w.message): raise ValueError( f"Something is going wrong in the regression problem: intercepted {w.message}" ) loss.backward() def test_load_with_mismatched_shapes(self): if not self.test_mismatched_shapes: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING): continue with self.subTest(msg=f"Testing {model_class}"): with tempfile.TemporaryDirectory() as tmp_dir: model = model_class(config) model.save_pretrained(tmp_dir) # Fails when we don't set ignore_mismatched_sizes=True with self.assertRaises(RuntimeError): new_model = AutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42) with self.assertRaises(RuntimeError): new_model_without_prefix = AutoModel.from_pretrained(tmp_dir, vocab_size=10) logger = logging.get_logger("transformers.modeling_utils") with CaptureLogger(logger) as cl: new_model = AutoModelForSequenceClassification.from_pretrained( tmp_dir, num_labels=42, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) new_model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) logits = new_model(**inputs).logits self.assertEqual(logits.shape[1], 42) with CaptureLogger(logger) as cl: new_model_without_prefix = AutoModel.from_pretrained( tmp_dir, vocab_size=10, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) input_ids = ids_tensor((2, 8), 10) new_model_without_prefix.to(torch_device) if self.is_encoder_decoder: new_model_without_prefix(input_ids, decoder_input_ids=input_ids) else: new_model_without_prefix(input_ids) global_rng = random.Random() def ids_tensor(shape, vocab_size, rng=None, name=None): # Creates a random int32 tensor of the shape within the vocab size if rng is None: rng = global_rng total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous() def random_attention_mask(shape, rng=None, name=None): attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None) # make sure that at least one token is attended to for each batch attn_mask[:, -1] = 1 return attn_mask def floats_tensor(shape, scale=1.0, rng=None, name=None): """Creates a random float32 tensor""" if rng is None: rng = global_rng total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.random() * scale) return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous() def check_models_equal(model1, model2): models_are_equal = True for model1_p, model2_p in zip(model1.parameters(), model2.parameters()): if model1_p.data.ne(model2_p.data).sum() > 0: models_are_equal = False return models_are_equal @require_torch class ModelUtilsTest(TestCasePlus): @slow def test_model_from_pretrained(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = BertConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, PretrainedConfig) model = BertModel.from_pretrained(model_name) model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, PreTrainedModel) self.assertEqual(len(loading_info["missing_keys"]), 0) self.assertEqual(len(loading_info["unexpected_keys"]), 8) self.assertEqual(len(loading_info["mismatched_keys"]), 0) self.assertEqual(len(loading_info["error_msgs"]), 0) config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True) # Not sure this is the intended behavior. TODO fix Lysandre & Thom config.name_or_path = model_name model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True) self.assertEqual(model.config.output_hidden_states, True) self.assertEqual(model.config, config) def test_model_from_pretrained_subfolder(self): config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") model = BertModel(config) subfolder = "bert" with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(tmp_dir, subfolder)) with self.assertRaises(OSError): _ = BertModel.from_pretrained(tmp_dir) model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder) self.assertTrue(check_models_equal(model, model_loaded)) def test_model_from_pretrained_subfolder_sharded(self): config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") model = BertModel(config) subfolder = "bert" with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB") with self.assertRaises(OSError): _ = BertModel.from_pretrained(tmp_dir) model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder) self.assertTrue(check_models_equal(model, model_loaded)) def test_model_from_pretrained_hub_subfolder(self): subfolder = "bert" model_id = "hf-internal-testing/tiny-random-bert-subfolder" with self.assertRaises(OSError): _ = BertModel.from_pretrained(model_id) model = BertModel.from_pretrained(model_id, subfolder=subfolder) self.assertIsNotNone(model) def test_model_from_pretrained_hub_subfolder_sharded(self): subfolder = "bert" model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder" with self.assertRaises(OSError): _ = BertModel.from_pretrained(model_id) model = BertModel.from_pretrained(model_id, subfolder=subfolder) self.assertIsNotNone(model) def test_model_from_pretrained_with_different_pretrained_model_name(self): model = T5ForConditionalGeneration.from_pretrained(TINY_T5) self.assertIsNotNone(model) logger = logging.get_logger("transformers.configuration_utils") with CaptureLogger(logger) as cl: BertModel.from_pretrained(TINY_T5) self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out) @require_torch def test_model_from_config_torch_dtype(self): # test that the model can be instantiated with dtype of user's choice - as long as it's a # float dtype. To make it happen config.torch_dtype needs to be set before instantiating the # model from the config object. config = T5Config.from_pretrained(TINY_T5) model = AutoModel.from_config(config) # XXX: isn't supported # model = T5ForConditionalGeneration.from_config(config) self.assertEqual(model.dtype, torch.float32) model = AutoModel.from_config(config, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # torch.set_default_dtype() supports only float dtypes, so will fail with non-float type with self.assertRaises(ValueError): model = AutoModel.from_config(config, torch_dtype=torch.int64) @require_torch def test_model_from_pretrained_torch_dtype(self): # test that the model can be instantiated with dtype of either # 1. explicit from_pretrained's torch_dtype argument # 2. via autodiscovery by looking at model weights (torch_dtype="auto") # so if a model.half() was saved, we want it to be instantiated as such. # # test an explicit model class, but also AutoModel separately as the latter goes through a different code path model_path = self.get_auto_remove_tmp_dir() # baseline - we know TINY_T5 is fp32 model model = T5ForConditionalGeneration.from_pretrained(TINY_T5) self.assertEqual(model.dtype, torch.float32) # test the default fp32 save_pretrained => from_pretrained cycle model.save_pretrained(model_path) model = T5ForConditionalGeneration.from_pretrained(model_path) self.assertEqual(model.dtype, torch.float32) # test with auto-detection model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto") self.assertEqual(model.dtype, torch.float32) # test forced loading in fp16 (even though the weights are in fp32) model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # test fp16 save_pretrained, loaded with auto-detection model = model.half() model.save_pretrained(model_path) model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto") self.assertEqual(model.config.torch_dtype, torch.float16) self.assertEqual(model.dtype, torch.float16) # tests `config.torch_dtype` saving with open(f"{model_path}/config.json") as f: config_dict = json.load(f) self.assertEqual(config_dict["torch_dtype"], "float16") # test fp16 save_pretrained, loaded with the explicit fp16 model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # test AutoModel separately as it goes through a different path # test auto-detection model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto") self.assertEqual(model.dtype, torch.float32) # test forcing an explicit dtype model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # test model whose first param is not of a floating type, but int model = AutoModel.from_pretrained(TINY_BERT_FOR_TOKEN_CLASSIFICATION, torch_dtype="auto") self.assertEqual(model.dtype, torch.float32) def test_no_super_init_config_and_model(self): config = NoSuperInitConfig(attribute=32) model = NoSuperInitModel(config) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = NoSuperInitModel.from_pretrained(tmp_dir) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_shard_checkpoint(self): # This is the model we will use, total size 340,000 bytes. model = torch.nn.Sequential( torch.nn.Linear(100, 200, bias=False), # size 80,000 torch.nn.Linear(200, 200, bias=False), # size 160,000 torch.nn.Linear(200, 100, bias=False), # size 80,000 torch.nn.Linear(100, 50, bias=False), # size 20,000 ) state_dict = model.state_dict() with self.subTest("No shard when max size is bigger than model size"): shards, index = shard_checkpoint(state_dict) self.assertIsNone(index) self.assertDictEqual(shards, {WEIGHTS_NAME: state_dict}) with self.subTest("Test sharding, no weights bigger than max size"): shards, index = shard_checkpoint(state_dict, max_shard_size="300kB") # Split is first two layers then last two. self.assertDictEqual( index, { "metadata": {"total_size": 340000}, "weight_map": { "0.weight": "pytorch_model-00001-of-00002.bin", "1.weight": "pytorch_model-00001-of-00002.bin", "2.weight": "pytorch_model-00002-of-00002.bin", "3.weight": "pytorch_model-00002-of-00002.bin", }, }, ) shard1 = {"0.weight": state_dict["0.weight"], "1.weight": state_dict["1.weight"]} shard2 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]} self.assertDictEqual( shards, {"pytorch_model-00001-of-00002.bin": shard1, "pytorch_model-00002-of-00002.bin": shard2} ) with self.subTest("Test sharding with weights bigger than max size"): shards, index = shard_checkpoint(state_dict, max_shard_size="100kB") # Split is first layer, second layer then last 2. self.assertDictEqual( index, { "metadata": {"total_size": 340000}, "weight_map": { "0.weight": "pytorch_model-00001-of-00003.bin", "1.weight": "pytorch_model-00002-of-00003.bin", "2.weight": "pytorch_model-00003-of-00003.bin", "3.weight": "pytorch_model-00003-of-00003.bin", }, }, ) shard1 = {"0.weight": state_dict["0.weight"]} shard2 = {"1.weight": state_dict["1.weight"]} shard3 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]} self.assertDictEqual( shards, { "pytorch_model-00001-of-00003.bin": shard1, "pytorch_model-00002-of-00003.bin": shard2, "pytorch_model-00003-of-00003.bin": shard3, }, ) def test_checkpoint_sharding_local(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: # We use the same folder for various sizes to make sure a new save erases the old checkpoint. for max_size in ["50kB", "50kiB", "100kB", "100kiB", "200kB", "200kiB"]: model.save_pretrained(tmp_dir, max_shard_size=max_size) # Get each shard file and its size shard_to_size = {} for shard in os.listdir(tmp_dir): if shard.endswith(".bin"): shard_file = os.path.join(tmp_dir, shard) shard_to_size[shard_file] = os.path.getsize(shard_file) index_file = os.path.join(tmp_dir, WEIGHTS_INDEX_NAME) # Check there is an index but no regular weight file self.assertTrue(os.path.isfile(index_file)) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) # Check a file is bigger than max_size only when it has a single weight for shard_file, size in shard_to_size.items(): if max_size.endswith("kiB"): max_size_int = int(max_size[:-3]) * 2**10 else: max_size_int = int(max_size[:-2]) * 10**3 # Note: pickle adds some junk so the weight of the file can end up being slightly bigger than # the size asked for (since we count parameters) if size >= max_size_int + 50000: state_dict = torch.load(shard_file) self.assertEqual(len(state_dict), 1) # Check the index and the shard files found match with open(index_file, "r", encoding="utf-8") as f: index = json.loads(f.read()) all_shards = set(index["weight_map"].values()) shards_found = set(f for f in os.listdir(tmp_dir) if f.endswith(".bin")) self.assertSetEqual(all_shards, shards_found) # Finally, check the model can be reloaded new_model = BertModel.from_pretrained(tmp_dir) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) def test_checkpoint_sharding_from_hub(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded") # the model above is the same as the model below, just a sharded version. ref_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") for p1, p2 in zip(model.parameters(), ref_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) @require_accelerate def test_from_pretrained_low_cpu_mem_usage_functional(self): # test that we can use `from_pretrained(..., low_cpu_mem_usage=True)` with normal and # sharded models mnames = [ "hf-internal-testing/tiny-random-bert-sharded", "hf-internal-testing/tiny-random-bert", ] for mname in mnames: _ = BertModel.from_pretrained(mname, low_cpu_mem_usage=True) @require_usr_bin_time @require_accelerate def test_from_pretrained_low_cpu_mem_usage_measured(self): # test that `from_pretrained(..., low_cpu_mem_usage=True)` uses less cpu memory than default mname = "bert-base-cased" preamble = "from transformers import AutoModel" one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=False)' max_rss_normal = self.python_one_liner_max_rss(one_liner_str) # print(f"{max_rss_normal=}") one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=True)' max_rss_low_mem = self.python_one_liner_max_rss(one_liner_str) # print(f"{max_rss_low_mem=}") diff_bytes = max_rss_normal - max_rss_low_mem diff_percent = diff_bytes / max_rss_low_mem # print(f"{diff_bytes=}, {diff_percent=}") # ideally we would compare that the diff is close to ~1x checkpoint size in bytes, but # measuring cpu memory on linux is very tricky and inconsistent, so instead let's check that # it's at least 15% less cpu memory consumed self.assertGreater( diff_percent, 0.15, "should use less CPU memory for low_cpu_mem_usage=True, " f"but got max_rss_normal={max_rss_normal} and max_rss_low_mem={max_rss_low_mem}", ) # if you want to compare things manually, let's first look at the size of the model in bytes # model = BertModel.from_pretrained(mname, low_cpu_mem_usage=False) # total_numel = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values()) # total_bytes = total_numel * 4 # 420MB # Now the diff_bytes should be very close to total_bytes, but the reports are inconsistent. # The easiest way to test this is to switch the model and torch.load to do all the work on # gpu - that way one can measure exactly the total and peak memory used. Perhaps once we add # functionality to load models directly on gpu, this test can be rewritten to use torch's # cuda memory tracking and then we should be able to do a much more precise test. @require_accelerate @require_torch_multi_gpu @slow def test_model_parallelism_gpt2(self): device_map = {"transformer.wte": 0, "transformer.wpe": 0, "lm_head": 0, "transformer.ln_f": 1} for i in range(12): device_map[f"transformer.h.{i}"] = 0 if i <= 5 else 1 model = AutoModelForCausalLM.from_pretrained("gpt2", device_map=device_map) tokenizer = AutoTokenizer.from_pretrained("gpt2") inputs = tokenizer("Hello, my name is", return_tensors="pt") output = model.generate(inputs["input_ids"].to(0)) text_output = tokenizer.decode(output[0].tolist()) self.assertEqual(text_output, "Hello, my name is John. I'm a writer, and I'm a writer. I'm") @require_accelerate @require_torch_gpu def test_from_pretrained_disk_offload_task_model(self): model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-gpt2") device_map = { "transformer.wte": 0, "transformer.wpe": 0, "transformer.h.0": "cpu", "transformer.h.1": "cpu", "transformer.h.2": "cpu", "transformer.h.3": "disk", "transformer.h.4": "disk", "transformer.ln_f": 0, "lm_head": 0, } with tempfile.TemporaryDirectory() as tmp_dir: inputs = torch.tensor([[1, 2, 3]]).to(0) model.save_pretrained(tmp_dir) new_model = AutoModelForCausalLM.from_pretrained(tmp_dir).to(0) outputs1 = new_model.to(0)(inputs) offload_folder = os.path.join(tmp_dir, "offload") new_model_with_offload = AutoModelForCausalLM.from_pretrained( tmp_dir, device_map=device_map, offload_folder=offload_folder ) outputs2 = new_model_with_offload(inputs) self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu())) # With state dict temp offload offload_folder = os.path.join(tmp_dir, "offload") new_model_with_offload = AutoModelForCausalLM.from_pretrained( tmp_dir, device_map=device_map, offload_folder=offload_folder, offload_state_dict=True, ) outputs2 = new_model_with_offload(inputs) self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu())) def test_cached_files_are_used_when_internet_is_down(self): # A mock response for an HTTP head request to emulate server down response_mock = mock.Mock() response_mock.status_code = 500 response_mock.headers = {} response_mock.raise_for_status.side_effect = HTTPError response_mock.json.return_value = {} # Download this model to make sure it's in the cache. _ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Under the mock environment we get a 500 error when trying to reach the model. with mock.patch("requests.request", return_value=response_mock) as mock_head: _ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # This check we did call the fake head request mock_head.assert_called() def test_load_from_one_file(self): try: tmp_file = tempfile.mktemp() with open(tmp_file, "wb") as f: http_get( "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", f ) config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") _ = BertModel.from_pretrained(tmp_file, config=config) finally: os.remove(tmp_file) def test_legacy_load_from_url(self): # This test is for deprecated behavior and can be removed in v5 config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") _ = BertModel.from_pretrained( "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", config=config ) @require_safetensors def test_safetensors_save_and_load(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True) # No pytorch_model.bin file, only a model.safetensors self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) new_model = BertModel.from_pretrained(tmp_dir) # Check models are equal for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) @require_safetensors def test_safetensors_load_from_hub(self): safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors") pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Check models are equal for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) @require_safetensors def test_safetensors_save_and_load_sharded(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True, max_shard_size="100kB") # No pytorch_model.bin index file, only a model.safetensors index self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME))) self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME))) # No regular weights file self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) new_model = BertModel.from_pretrained(tmp_dir) # Check models are equal for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) @require_safetensors def test_safetensors_load_from_hub_sharded(self): safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded-safetensors") pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded") # Check models are equal for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) def test_base_model_to_head_model_load(self): base_model = BaseModel(PretrainedConfig()) with tempfile.TemporaryDirectory() as tmp_dir: base_model.save_pretrained(tmp_dir) # Can load a base model in a model with head model = ModelWithHead.from_pretrained(tmp_dir) for p1, p2 in zip(model.base.parameters(), base_model.parameters()): self.assertTrue(torch.allclose(p1, p2)) # It doesn't work if the state dict has a mix of keys of the head and base without prefix though. base_state_dict = base_model.state_dict() head_state_dict = model.state_dict() base_state_dict["linear2.weight"] = head_state_dict["linear2.weight"] base_state_dict["linear2.bias"] = head_state_dict["linear2.bias"] torch.save(base_state_dict, os.path.join(tmp_dir, WEIGHTS_NAME)) with self.assertRaisesRegex( ValueError, "The state dictionary of the model you are trying to load is corrupted." ): _ = ModelWithHead.from_pretrained(tmp_dir) @require_torch @is_staging_test class ModelPushToHubTester(unittest.TestCase): @classmethod def setUpClass(cls): cls._token = TOKEN set_access_token(TOKEN) HfFolder.save_token(TOKEN) @classmethod def tearDownClass(cls): try: delete_repo(token=cls._token, repo_id="test-model") except HTTPError: pass try: delete_repo(token=cls._token, repo_id="valid_org/test-model-org") except HTTPError: pass try: delete_repo(token=cls._token, repo_id="test-dynamic-model") except HTTPError: pass def test_push_to_hub(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertModel(config) model.push_to_hub("test-model", use_auth_token=self._token) new_model = BertModel.from_pretrained(f"{USER}/test-model") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) # Reset repo delete_repo(token=self._token, repo_id="test-model") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, repo_id="test-model", push_to_hub=True, use_auth_token=self._token) new_model = BertModel.from_pretrained(f"{USER}/test-model") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_push_to_hub_in_organization(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertModel(config) model.push_to_hub("valid_org/test-model-org", use_auth_token=self._token) new_model = BertModel.from_pretrained("valid_org/test-model-org") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) # Reset repo delete_repo(token=self._token, repo_id="valid_org/test-model-org") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained( tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-org" ) new_model = BertModel.from_pretrained("valid_org/test-model-org") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_push_to_hub_dynamic_model(self): CustomConfig.register_for_auto_class() CustomModel.register_for_auto_class() config = CustomConfig(hidden_size=32) model = CustomModel(config) model.push_to_hub("test-dynamic-model", use_auth_token=self._token) # checks self.assertDictEqual( config.auto_map, {"AutoConfig": "custom_configuration.CustomConfig", "AutoModel": "custom_modeling.CustomModel"}, ) new_model = AutoModel.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True) # Can't make an isinstance check because the new_model is from the CustomModel class of a dynamic module self.assertEqual(new_model.__class__.__name__, "CustomModel") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True) new_model = AutoModel.from_config(config, trust_remote_code=True) self.assertEqual(new_model.__class__.__name__, "CustomModel")

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/speech_to_text/configuration_speech_to_text.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Speech2Text model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/s2t-small-librispeech-asr": ( "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json" ), # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text } class Speech2TextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Speech2TextModel`]. It is used to instantiate an Speech2Text model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Speech2Text [facebook/s2t-small-librispeech-asr](https://huggingface.co/facebook/s2t-small-librispeech-asr) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50265): Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Speech2TextModel`] d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). max_source_positions (`int`, *optional*, defaults to 6000): The maximum sequence length of log-mel filter-bank features that this model might ever be used with. max_target_positions (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). num_conv_layers (`int`, *optional*, defaults to 2): Number of 1D convolutional layers in the conv module. conv_kernel_sizes (`Tuple[int]`, *optional*, defaults to `(5, 5)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the conv module. The length of `conv_kernel_sizes` has to match `num_conv_layers`. conv_channels (`int`, *optional*, defaults to 1024): An integer defining the number of output channels of each convolution layers except the final one in the conv module. input_feat_per_channel (`int`, *optional*, defaults to 80): An integer specifying the size of feature vector. This is also the dimensions of log-mel filter-bank features. input_channels (`int`, *optional*, defaults to 1): An integer specifying number of input channels of the input feature vector. Example: ```python >>> from transformers import Speech2TextConfig, Speech2TextModel >>> # Initializing a Speech2Text s2t_transformer_s style configuration >>> configuration = Speech2TextConfig() >>> # Initializing a model (with random weights) from the s2t_transformer_s style configuration >>> model = Speech2TextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "speech_to_text" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=10000, encoder_layers=12, encoder_ffn_dim=2048, encoder_attention_heads=4, decoder_layers=6, decoder_ffn_dim=2048, decoder_attention_heads=4, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function="relu", d_model=256, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=2, classifier_dropout=0.0, scale_embedding=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, max_source_positions=6000, max_target_positions=1024, num_conv_layers=2, conv_kernel_sizes=(5, 5), conv_channels=1024, input_feat_per_channel=80, input_channels=1, **kwargs ): self.vocab_size = vocab_size self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions self.num_conv_layers = num_conv_layers self.conv_kernel_sizes = list(conv_kernel_sizes) self.conv_channels = conv_channels self.input_feat_per_channel = input_feat_per_channel self.input_channels = input_channels if len(self.conv_kernel_sizes) != self.num_conv_layers: raise ValueError( "Configuration for convolutional module is incorrect. " "It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` " f"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, " f"`config.num_conv_layers = {self.num_conv_layers}`." ) super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, **kwargs, )

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Speech2Text model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/s2t-small-librispeech-asr": ( "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json" ), # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text } class Speech2TextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Speech2TextModel`]. It is used to instantiate an Speech2Text model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Speech2Text [facebook/s2t-small-librispeech-asr](https://huggingface.co/facebook/s2t-small-librispeech-asr) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50265): Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Speech2TextModel`] d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). max_source_positions (`int`, *optional*, defaults to 6000): The maximum sequence length of log-mel filter-bank features that this model might ever be used with. max_target_positions (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). num_conv_layers (`int`, *optional*, defaults to 2): Number of 1D convolutional layers in the conv module. conv_kernel_sizes (`Tuple[int]`, *optional*, defaults to `(5, 5)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the conv module. The length of `conv_kernel_sizes` has to match `num_conv_layers`. conv_channels (`int`, *optional*, defaults to 1024): An integer defining the number of output channels of each convolution layers except the final one in the conv module. input_feat_per_channel (`int`, *optional*, defaults to 80): An integer specifying the size of feature vector. This is also the dimensions of log-mel filter-bank features. input_channels (`int`, *optional*, defaults to 1): An integer specifying number of input channels of the input feature vector. Example: ```python >>> from transformers import Speech2TextConfig, Speech2TextModel >>> # Initializing a Speech2Text s2t_transformer_s style configuration >>> configuration = Speech2TextConfig() >>> # Initializing a model (with random weights) from the s2t_transformer_s style configuration >>> model = Speech2TextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "speech_to_text" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=10000, encoder_layers=12, encoder_ffn_dim=2048, encoder_attention_heads=4, decoder_layers=6, decoder_ffn_dim=2048, decoder_attention_heads=4, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function="relu", d_model=256, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=2, classifier_dropout=0.0, scale_embedding=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, max_source_positions=6000, max_target_positions=1024, num_conv_layers=2, conv_kernel_sizes=(5, 5), conv_channels=1024, input_feat_per_channel=80, input_channels=1, **kwargs ): self.vocab_size = vocab_size self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions self.num_conv_layers = num_conv_layers self.conv_kernel_sizes = list(conv_kernel_sizes) self.conv_channels = conv_channels self.input_feat_per_channel = input_feat_per_channel self.input_channels = input_channels if len(self.conv_kernel_sizes) != self.num_conv_layers: raise ValueError( "Configuration for convolutional module is incorrect. " "It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` " f"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, " f"`config.num_conv_layers = {self.num_conv_layers}`." ) super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, **kwargs, )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/gpt2/modeling_tf_gpt2.py

# coding=utf-8 # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 OpenAI GPT-2 model.""" from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutputWithPastAndCrossAttentions, TFCausalLMOutputWithCrossAttentions, TFSequenceClassifierOutputWithPast, ) from ...modeling_tf_utils import ( TFCausalLanguageModelingLoss, TFConv1D, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, TFSequenceSummary, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import ( DUMMY_INPUTS, ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_gpt2 import GPT2Config logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "gpt2" _CONFIG_FOR_DOC = "GPT2Config" _TOKENIZER_FOR_DOC = "GPT2Tokenizer" TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "gpt2", "gpt2-medium", "gpt2-large", "gpt2-xl", "distilgpt2", # See all GPT-2 models at https://huggingface.co/models?filter=gpt2 ] class TFAttention(tf.keras.layers.Layer): def __init__(self, nx, config, scale=False, is_cross_attention=False, **kwargs): super().__init__(**kwargs) n_state = nx # in Attention: n_state=768 (nx=n_embd) # [switch nx => n_state from Block to Attention to keep identical to TF implementation] assert n_state % config.n_head == 0 self.n_head = config.n_head self.split_size = n_state self.scale = scale self.output_attentions = config.output_attentions self.is_cross_attention = is_cross_attention if self.is_cross_attention: self.c_attn = TFConv1D(n_state * 2, nx, initializer_range=config.initializer_range, name="c_attn") self.q_attn = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="q_attn") else: self.c_attn = TFConv1D(n_state * 3, nx, initializer_range=config.initializer_range, name="c_attn") self.c_proj = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_proj") self.attn_dropout = tf.keras.layers.Dropout(config.attn_pdrop) self.resid_dropout = tf.keras.layers.Dropout(config.resid_pdrop) self.pruned_heads = set() def prune_heads(self, heads): pass @staticmethod def causal_attention_mask(nd, ns, dtype): """ 1's in the lower triangle, counting from the lower right corner. Same as tf.matrix_band_part(tf.ones([nd, ns]), -1, ns-nd), but doesn't produce garbage on TPUs. """ i = tf.range(nd)[:, None] j = tf.range(ns) m = i >= j - ns + nd return tf.cast(m, dtype) def _attn(self, q, k, v, attention_mask, head_mask, output_attentions, training=False): # q, k, v have shape [batch, heads, sequence, features] w = tf.matmul(q, k, transpose_b=True) if self.scale: dk = tf.cast(shape_list(k)[-1], dtype=w.dtype) # scale attention_scores w = w / tf.math.sqrt(dk) if not self.is_cross_attention: # if only "normal" attention layer implements causal mask # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst. _, _, nd, ns = shape_list(w) b = self.causal_attention_mask(nd, ns, dtype=w.dtype) b = tf.reshape(b, [1, 1, nd, ns]) w = w * b - 1e4 * (1 - b) if attention_mask is not None: # Apply the attention mask attention_mask = tf.cast(attention_mask, dtype=w.dtype) w = w + attention_mask w = stable_softmax(w, axis=-1) w = self.attn_dropout(w, training=training) # Mask heads if we want to if head_mask is not None: w = w * head_mask outputs = [tf.matmul(w, v)] if output_attentions: outputs.append(w) return outputs def merge_heads(self, x): x = tf.transpose(x, [0, 2, 1, 3]) x_shape = shape_list(x) new_x_shape = x_shape[:-2] + [x_shape[-2] * x_shape[-1]] return tf.reshape(x, new_x_shape) def split_heads(self, x): x_shape = shape_list(x) new_x_shape = x_shape[:-1] + [self.n_head, x_shape[-1] // self.n_head] x = tf.reshape(x, new_x_shape) return tf.transpose(x, (0, 2, 1, 3)) # (batch, head, seq_length, head_features) def call( self, x, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, training=False, ): if encoder_hidden_states is not None: if not hasattr(self, "q_attn"): raise ValueError( "If class is used as cross attention, the weights `q_attn` have to be defined. " "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`." ) query = self.q_attn(x) kv_out = self.c_attn(encoder_hidden_states) key, value = tf.split(kv_out, 2, axis=2) attention_mask = encoder_attention_mask else: x = self.c_attn(x) query, key, value = tf.split(x, 3, axis=2) query = self.split_heads(query) key = self.split_heads(key) value = self.split_heads(value) if layer_past is not None: past_key, past_value = tf.unstack(layer_past, axis=0) key = tf.concat([past_key, key], axis=-2) value = tf.concat([past_value, value], axis=-2) # to cope with keras serialization if use_cache: present = tf.stack([key, value], axis=0) else: present = (None,) attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions, training=training) a = attn_outputs[0] a = self.merge_heads(a) a = self.c_proj(a) a = self.resid_dropout(a, training=training) outputs = [a, present] + attn_outputs[1:] return outputs # a, present, (attentions) class TFMLP(tf.keras.layers.Layer): def __init__(self, n_state, config, **kwargs): super().__init__(**kwargs) nx = config.n_embd self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_fc") self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initializer_range, name="c_proj") self.act = get_tf_activation(config.activation_function) self.dropout = tf.keras.layers.Dropout(config.resid_pdrop) def call(self, x, training=False): h = self.act(self.c_fc(x)) h2 = self.c_proj(h) h2 = self.dropout(h2, training=training) return h2 class TFBlock(tf.keras.layers.Layer): def __init__(self, config, scale=False, **kwargs): super().__init__(**kwargs) nx = config.n_embd inner_dim = config.n_inner if config.n_inner is not None else 4 * nx self.ln_1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1") self.attn = TFAttention(nx, config, scale, name="attn") self.ln_2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_2") if config.add_cross_attention: self.crossattention = TFAttention(nx, config, scale, name="crossattention", is_cross_attention=True) self.ln_cross_attn = tf.keras.layers.LayerNormalization( epsilon=config.layer_norm_epsilon, name="ln_cross_attn" ) self.mlp = TFMLP(inner_dim, config, name="mlp") def call( self, x, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, training=False, ): a = self.ln_1(x) output_attn = self.attn( a, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=None, encoder_attention_mask=None, use_cache=use_cache, output_attentions=output_attentions, training=training, ) a = output_attn[0] # output_attn: a, present, (attentions) outputs = output_attn[1:] x = x + a # Cross-Attention Block if encoder_hidden_states is not None: # add one self-attention block for cross-attention if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with " "cross-attention layers by setting `config.add_cross_attention=True`" ) ca = self.ln_cross_attn(x) output_cross_attn = self.crossattention( ca, layer_past=None, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=False, output_attentions=output_attentions, training=training, ) ca = output_cross_attn[0] # output_attn: a, present, (cross_attentions) x = x + ca outputs = outputs + output_cross_attn[2:] # add cross attentions if we output attention weights m = self.ln_2(x) m = self.mlp(m, training=training) x = x + m outputs = [x] + outputs return outputs # x, present, (attentions, cross_attentions) @keras_serializable class TFGPT2MainLayer(tf.keras.layers.Layer): config_class = GPT2Config def __init__(self, config, *inputs, **kwargs): super().__init__(*inputs, **kwargs) self.config = config self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.use_cache = config.use_cache self.return_dict = config.use_return_dict self.num_hidden_layers = config.n_layer self.vocab_size = config.vocab_size self.n_embd = config.n_embd self.n_positions = config.n_positions self.initializer_range = config.initializer_range self.wte = TFSharedEmbeddings( config.vocab_size, config.hidden_size, initializer_range=config.initializer_range, name="wte" ) self.drop = tf.keras.layers.Dropout(config.embd_pdrop) self.h = [TFBlock(config, scale=True, name=f"h_._{i}") for i in range(config.n_layer)] self.ln_f = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_f") def build(self, input_shape): with tf.name_scope("wpe"): self.wpe = self.add_weight( name="embeddings", shape=[self.n_positions, self.n_embd], initializer=get_initializer(self.initializer_range), ) super().build(input_shape) def get_input_embeddings(self): return self.wte def set_input_embeddings(self, value): self.wte.weight = value self.wte.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} """ raise NotImplementedError @unpack_inputs def call( self, input_ids: Optional[TFModelInputType] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) input_ids = tf.reshape(input_ids, [-1, input_shape[-1]]) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if past_key_values is None: past_length = 0 past_key_values = [None] * len(self.h) else: past_length = shape_list(past_key_values[0][0])[-2] if position_ids is None: position_ids = tf.expand_dims(tf.range(past_length, input_shape[-1] + past_length), axis=0) if attention_mask is not None: # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask_shape = shape_list(attention_mask) attention_mask = tf.reshape(attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1])) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. one_cst = tf.constant(1.0) attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype) attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), tf.constant(-10000.0)) # Copied from `modeling_tf_t5.py` with -1e9 -> -10000 if self.config.add_cross_attention and encoder_attention_mask is not None: # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=encoder_hidden_states.dtype) num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask)) if num_dims_encoder_attention_mask == 3: encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] if num_dims_encoder_attention_mask == 2: encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask, # tf.transpose(encoder_extended_attention_mask, perm=(-1, -2))) encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 else: encoder_extended_attention_mask = None encoder_attention_mask = encoder_extended_attention_mask # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.num_hidden_layers # head_mask = tf.constant([0] * self.num_hidden_layers) position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]]) if inputs_embeds is None: # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound # indices on GPU, returning zeros instead. This is a dangerous silent behavior. tf.debugging.assert_less( input_ids, tf.cast(self.vocab_size, dtype=input_ids.dtype), message=( "input_ids must be smaller than the embedding layer's input dimension (got" f" {tf.math.reduce_max(input_ids)} >= {self.vocab_size})" ), ) inputs_embeds = self.wte(input_ids, mode="embedding") position_embeds = tf.gather(self.wpe, position_ids) if token_type_ids is not None: token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]]) token_type_embeds = self.wte(token_type_ids, mode="embedding") else: token_type_embeds = tf.constant(0.0) position_embeds = tf.cast(position_embeds, dtype=inputs_embeds.dtype) token_type_embeds = tf.cast(token_type_embeds, dtype=inputs_embeds.dtype) hidden_states = inputs_embeds + position_embeds + token_type_embeds hidden_states = self.drop(hidden_states, training=training) output_shape = input_shape + [shape_list(hidden_states)[-1]] presents = () if use_cache else None all_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None all_hidden_states = () if output_hidden_states else None for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),) outputs = block( hidden_states, layer_past, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, training=training, ) hidden_states, present = outputs[:2] if use_cache: presents = presents + (present,) if output_attentions: all_attentions = all_attentions + (outputs[2],) if self.config.add_cross_attention and encoder_hidden_states is not None: all_cross_attentions = all_cross_attentions + (outputs[3],) hidden_states = self.ln_f(hidden_states) hidden_states = tf.reshape(hidden_states, output_shape) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if output_attentions: # let the number of heads free (-1) so we can extract attention even after head pruning attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:] all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions) if not return_dict: return tuple( v for v in [hidden_states, presents, all_hidden_states, all_attentions, all_cross_attentions] if v is not None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) class TFGPT2PreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPT2Config base_model_prefix = "transformer" # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"h.\d+.attn.bias", r"h.\d+.crossattention.bias"] @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: `Dict[str, tf.Tensor]`: The dummy inputs. """ dummy = {"input_ids": tf.constant(DUMMY_INPUTS)} # Add `encoder_hidden_states` to make the cross-attention layers' weights initialized if self.config.add_cross_attention: batch_size, seq_len = tf.constant(DUMMY_INPUTS).shape shape = (batch_size, seq_len) + (self.config.hidden_size,) h = tf.random.uniform(shape=shape) dummy["encoder_hidden_states"] = h return dummy @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None), tf.int64, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.int64, name="attention_mask"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) @dataclass class TFGPT2DoubleHeadsModelOutput(ModelOutput): """ Base class for outputs of models predicting if two sentences are consecutive or not. Args: logits (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). mc_logits (`tf.Tensor` of shape `(batch_size, num_choices)`): Prediction scores of the multiple choice classification head (scores for each choice before SoftMax). past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: tf.Tensor = None mc_logits: tf.Tensor = None past_key_values: Optional[List[tf.Tensor]] = None hidden_states: Optional[Tuple[tf.Tensor]] = None attentions: Optional[Tuple[tf.Tensor]] = None GPT2_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`GPT2Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ GPT2_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, input_ids_length)`): `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0].shape[-2]` (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. If `past_key_values` is used, only input IDs that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) past_key_values (`List[tf.Tensor]` of length `config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see `past_key_values` output below). Can be used to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. attention_mask (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. If `past_key_values` is used, `attention_mask` needs to contain the masking strategy that was used for `past_key_values`. In other words, the `attention_mask` always has to have the length: `len(past_key_values) + len(input_ids)` [What are attention masks?](../glossary#attention-mask) token_type_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.", GPT2_START_DOCSTRING, ) class TFGPT2Model(TFGPT2PreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFGPT2MainLayer(config, name="transformer") @unpack_inputs @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past`). Set to `False` during training, `True` during generation """ outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def serving_output(self, output): pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None cross_attns = ( tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions and self.config.add_cross_attention and output.cross_attentions is not None else None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=output.last_hidden_state, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns, ) @add_start_docstrings( """ The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, GPT2_START_DOCSTRING, ) class TFGPT2LMHeadModel(TFGPT2PreTrainedModel, TFCausalLanguageModelingLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFGPT2MainLayer(config, name="transformer") def get_output_embeddings(self): return self.get_input_embeddings() def set_output_embeddings(self, value): self.set_input_embeddings(value) def prepare_inputs_for_generation(self, inputs, past=None, use_cache=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # only last token for inputs_ids if past is defined in kwargs if past: inputs = tf.expand_dims(inputs[:, -1], -1) if token_type_ids is not None: token_type_ids = tf.expand_dims(token_type_ids[:, -1], -1) position_ids = kwargs.get("position_ids", None) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None and position_ids is None: position_ids = tf.math.cumsum(attention_mask, axis=-1, exclusive=True) if past: position_ids = tf.expand_dims(position_ids[:, -1], -1) return { "input_ids": inputs, "attention_mask": attention_mask, "position_ids": position_ids, "past_key_values": past, "use_cache": use_cache, "token_type_ids": token_type_ids, } @unpack_inputs @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past`). Set to `False` during training, `True` during generation labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`. """ transformer_outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) hidden_states = transformer_outputs[0] logits = self.transformer.wte(hidden_states, mode="linear") loss = None if labels is not None: # shift labels to the left and cut last logit token shifted_logits = logits[:, :-1] labels = labels[:, 1:] loss = self.hf_compute_loss(labels, shifted_logits) if not return_dict: output = (logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return TFCausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, cross_attentions=transformer_outputs.cross_attentions, ) def serving_output(self, output): pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None cross_attns = ( tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions and self.config.add_cross_attention and output.cross_attentions is not None else None ) return TFCausalLMOutputWithCrossAttentions( logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns ) @add_start_docstrings( """ The GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. for RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the input embeddings, the classification head takes as input the input of a specified classification token index in the input sequence). """, GPT2_START_DOCSTRING, ) class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) config.num_labels = 1 self.transformer = TFGPT2MainLayer(config, name="transformer") self.multiple_choice_head = TFSequenceSummary( config, initializer_range=config.initializer_range, name="multiple_choice_head" ) @unpack_inputs @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFGPT2DoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: Optional[TFModelInputType] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, mc_token_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFGPT2DoubleHeadsModelOutput, Tuple[tf.Tensor]]: r""" mc_token_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input): Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) - 1]`. Return: Examples: ```python >>> import tensorflow as tf >>> from transformers import GPT2Tokenizer, TFGPT2DoubleHeadsModel >>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2") >>> model = TFGPT2DoubleHeadsModel.from_pretrained("gpt2") >>> # Add a [CLS] to the vocabulary (we should train it also!) >>> num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"}) >>> embedding_layer = model.resize_token_embeddings( ... len(tokenizer) ... ) # Update the model embeddings with the new vocabulary size >>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"] >>> encoded_choices = [tokenizer.encode(s) for s in choices] >>> cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices] >>> input_ids = tf.constant(encoded_choices)[None, :] # Batch size: 1, number of choices: 2 >>> mc_token_ids = tf.constant([cls_token_location]) # Batch size: 1 >>> outputs = model(input_ids, mc_token_ids=mc_token_ids) >>> lm_prediction_scores, mc_prediction_scores = outputs[:2] ```""" if input_ids is not None: input_shapes = shape_list(input_ids) else: input_shapes = shape_list(inputs_embeds)[:-1] seq_length = input_shapes[-1] flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None transformer_outputs = self.transformer( input_ids=flat_input_ids, past_key_values=past_key_values, attention_mask=flat_attention_mask, token_type_ids=flat_token_type_ids, position_ids=flat_position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=None, encoder_attention_mask=None, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) hidden_states = transformer_outputs[0] hidden_states = tf.reshape(hidden_states, input_shapes + shape_list(hidden_states)[-1:]) lm_logits = self.transformer.wte(hidden_states, mode="linear") mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids, training=training) mc_logits = tf.squeeze(mc_logits, axis=-1) if not return_dict: return (lm_logits, mc_logits) + transformer_outputs[1:] return TFGPT2DoubleHeadsModelOutput( logits=lm_logits, mc_logits=mc_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"), "mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="mc_token_ids"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) def serving_output(self, output): pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFGPT2DoubleHeadsModelOutput( logits=output.logits, mc_logits=output.mc_logits, past_key_values=pkv, hidden_states=hs, attentions=attns, ) @add_start_docstrings( """ The GPT2 Model transformer with a sequence classification head on top (linear layer). [`TFGPT2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, GPT2_START_DOCSTRING, ) class TFGPT2ForSequenceClassification(TFGPT2PreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.score = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="score", use_bias=False, ) self.transformer = TFGPT2MainLayer(config, name="transformer") @unpack_inputs @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="microsoft/DialogRPT-updown", output_type=TFSequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutputWithPast, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`. """ transformer_outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) logits_shape = shape_list(logits) in_logits = None if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = ( tf.reduce_sum( tf.cast( tf.math.not_equal(input_ids, self.config.pad_token_id), dtype=input_ids.dtype, ), -1, keepdims=False, ) - 1 ) in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=1) else: sequence_lengths = -1 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) loss = None if labels is not None: assert ( self.config.pad_token_id is not None or logits_shape[0] == 1 ), "Cannot handle batch sizes > 1 if no padding token is defined." if not tf.is_tensor(sequence_lengths): in_logits = logits[0 : logits_shape[0], sequence_lengths] loss = self.hf_compute_loss(tf.reshape(labels, [-1]), tf.reshape(in_logits, [-1, self.num_labels])) pooled_logits = in_logits if in_logits is not None else logits if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def serving_output(self, output): pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFSequenceClassifierOutputWithPast( logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns )

# coding=utf-8 # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 OpenAI GPT-2 model.""" from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutputWithPastAndCrossAttentions, TFCausalLMOutputWithCrossAttentions, TFSequenceClassifierOutputWithPast, ) from ...modeling_tf_utils import ( TFCausalLanguageModelingLoss, TFConv1D, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, TFSequenceSummary, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import ( DUMMY_INPUTS, ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_gpt2 import GPT2Config logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "gpt2" _CONFIG_FOR_DOC = "GPT2Config" _TOKENIZER_FOR_DOC = "GPT2Tokenizer" TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "gpt2", "gpt2-medium", "gpt2-large", "gpt2-xl", "distilgpt2", # See all GPT-2 models at https://huggingface.co/models?filter=gpt2 ] class TFAttention(tf.keras.layers.Layer): def __init__(self, nx, config, scale=False, is_cross_attention=False, **kwargs): super().__init__(**kwargs) n_state = nx # in Attention: n_state=768 (nx=n_embd) # [switch nx => n_state from Block to Attention to keep identical to TF implementation] assert n_state % config.n_head == 0 self.n_head = config.n_head self.split_size = n_state self.scale = scale self.output_attentions = config.output_attentions self.is_cross_attention = is_cross_attention if self.is_cross_attention: self.c_attn = TFConv1D(n_state * 2, nx, initializer_range=config.initializer_range, name="c_attn") self.q_attn = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="q_attn") else: self.c_attn = TFConv1D(n_state * 3, nx, initializer_range=config.initializer_range, name="c_attn") self.c_proj = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_proj") self.attn_dropout = tf.keras.layers.Dropout(config.attn_pdrop) self.resid_dropout = tf.keras.layers.Dropout(config.resid_pdrop) self.pruned_heads = set() def prune_heads(self, heads): pass @staticmethod def causal_attention_mask(nd, ns, dtype): """ 1's in the lower triangle, counting from the lower right corner. Same as tf.matrix_band_part(tf.ones([nd, ns]), -1, ns-nd), but doesn't produce garbage on TPUs. """ i = tf.range(nd)[:, None] j = tf.range(ns) m = i >= j - ns + nd return tf.cast(m, dtype) def _attn(self, q, k, v, attention_mask, head_mask, output_attentions, training=False): # q, k, v have shape [batch, heads, sequence, features] w = tf.matmul(q, k, transpose_b=True) if self.scale: dk = tf.cast(shape_list(k)[-1], dtype=w.dtype) # scale attention_scores w = w / tf.math.sqrt(dk) if not self.is_cross_attention: # if only "normal" attention layer implements causal mask # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst. _, _, nd, ns = shape_list(w) b = self.causal_attention_mask(nd, ns, dtype=w.dtype) b = tf.reshape(b, [1, 1, nd, ns]) w = w * b - 1e4 * (1 - b) if attention_mask is not None: # Apply the attention mask attention_mask = tf.cast(attention_mask, dtype=w.dtype) w = w + attention_mask w = stable_softmax(w, axis=-1) w = self.attn_dropout(w, training=training) # Mask heads if we want to if head_mask is not None: w = w * head_mask outputs = [tf.matmul(w, v)] if output_attentions: outputs.append(w) return outputs def merge_heads(self, x): x = tf.transpose(x, [0, 2, 1, 3]) x_shape = shape_list(x) new_x_shape = x_shape[:-2] + [x_shape[-2] * x_shape[-1]] return tf.reshape(x, new_x_shape) def split_heads(self, x): x_shape = shape_list(x) new_x_shape = x_shape[:-1] + [self.n_head, x_shape[-1] // self.n_head] x = tf.reshape(x, new_x_shape) return tf.transpose(x, (0, 2, 1, 3)) # (batch, head, seq_length, head_features) def call( self, x, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, training=False, ): if encoder_hidden_states is not None: if not hasattr(self, "q_attn"): raise ValueError( "If class is used as cross attention, the weights `q_attn` have to be defined. " "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`." ) query = self.q_attn(x) kv_out = self.c_attn(encoder_hidden_states) key, value = tf.split(kv_out, 2, axis=2) attention_mask = encoder_attention_mask else: x = self.c_attn(x) query, key, value = tf.split(x, 3, axis=2) query = self.split_heads(query) key = self.split_heads(key) value = self.split_heads(value) if layer_past is not None: past_key, past_value = tf.unstack(layer_past, axis=0) key = tf.concat([past_key, key], axis=-2) value = tf.concat([past_value, value], axis=-2) # to cope with keras serialization if use_cache: present = tf.stack([key, value], axis=0) else: present = (None,) attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions, training=training) a = attn_outputs[0] a = self.merge_heads(a) a = self.c_proj(a) a = self.resid_dropout(a, training=training) outputs = [a, present] + attn_outputs[1:] return outputs # a, present, (attentions) class TFMLP(tf.keras.layers.Layer): def __init__(self, n_state, config, **kwargs): super().__init__(**kwargs) nx = config.n_embd self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_fc") self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initializer_range, name="c_proj") self.act = get_tf_activation(config.activation_function) self.dropout = tf.keras.layers.Dropout(config.resid_pdrop) def call(self, x, training=False): h = self.act(self.c_fc(x)) h2 = self.c_proj(h) h2 = self.dropout(h2, training=training) return h2 class TFBlock(tf.keras.layers.Layer): def __init__(self, config, scale=False, **kwargs): super().__init__(**kwargs) nx = config.n_embd inner_dim = config.n_inner if config.n_inner is not None else 4 * nx self.ln_1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1") self.attn = TFAttention(nx, config, scale, name="attn") self.ln_2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_2") if config.add_cross_attention: self.crossattention = TFAttention(nx, config, scale, name="crossattention", is_cross_attention=True) self.ln_cross_attn = tf.keras.layers.LayerNormalization( epsilon=config.layer_norm_epsilon, name="ln_cross_attn" ) self.mlp = TFMLP(inner_dim, config, name="mlp") def call( self, x, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, training=False, ): a = self.ln_1(x) output_attn = self.attn( a, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=None, encoder_attention_mask=None, use_cache=use_cache, output_attentions=output_attentions, training=training, ) a = output_attn[0] # output_attn: a, present, (attentions) outputs = output_attn[1:] x = x + a # Cross-Attention Block if encoder_hidden_states is not None: # add one self-attention block for cross-attention if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with " "cross-attention layers by setting `config.add_cross_attention=True`" ) ca = self.ln_cross_attn(x) output_cross_attn = self.crossattention( ca, layer_past=None, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=False, output_attentions=output_attentions, training=training, ) ca = output_cross_attn[0] # output_attn: a, present, (cross_attentions) x = x + ca outputs = outputs + output_cross_attn[2:] # add cross attentions if we output attention weights m = self.ln_2(x) m = self.mlp(m, training=training) x = x + m outputs = [x] + outputs return outputs # x, present, (attentions, cross_attentions) @keras_serializable class TFGPT2MainLayer(tf.keras.layers.Layer): config_class = GPT2Config def __init__(self, config, *inputs, **kwargs): super().__init__(*inputs, **kwargs) self.config = config self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.use_cache = config.use_cache self.return_dict = config.use_return_dict self.num_hidden_layers = config.n_layer self.vocab_size = config.vocab_size self.n_embd = config.n_embd self.n_positions = config.n_positions self.initializer_range = config.initializer_range self.wte = TFSharedEmbeddings( config.vocab_size, config.hidden_size, initializer_range=config.initializer_range, name="wte" ) self.drop = tf.keras.layers.Dropout(config.embd_pdrop) self.h = [TFBlock(config, scale=True, name=f"h_._{i}") for i in range(config.n_layer)] self.ln_f = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_f") def build(self, input_shape): with tf.name_scope("wpe"): self.wpe = self.add_weight( name="embeddings", shape=[self.n_positions, self.n_embd], initializer=get_initializer(self.initializer_range), ) super().build(input_shape) def get_input_embeddings(self): return self.wte def set_input_embeddings(self, value): self.wte.weight = value self.wte.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} """ raise NotImplementedError @unpack_inputs def call( self, input_ids: Optional[TFModelInputType] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) input_ids = tf.reshape(input_ids, [-1, input_shape[-1]]) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if past_key_values is None: past_length = 0 past_key_values = [None] * len(self.h) else: past_length = shape_list(past_key_values[0][0])[-2] if position_ids is None: position_ids = tf.expand_dims(tf.range(past_length, input_shape[-1] + past_length), axis=0) if attention_mask is not None: # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask_shape = shape_list(attention_mask) attention_mask = tf.reshape(attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1])) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. one_cst = tf.constant(1.0) attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype) attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), tf.constant(-10000.0)) # Copied from `modeling_tf_t5.py` with -1e9 -> -10000 if self.config.add_cross_attention and encoder_attention_mask is not None: # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=encoder_hidden_states.dtype) num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask)) if num_dims_encoder_attention_mask == 3: encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] if num_dims_encoder_attention_mask == 2: encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask, # tf.transpose(encoder_extended_attention_mask, perm=(-1, -2))) encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 else: encoder_extended_attention_mask = None encoder_attention_mask = encoder_extended_attention_mask # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.num_hidden_layers # head_mask = tf.constant([0] * self.num_hidden_layers) position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]]) if inputs_embeds is None: # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound # indices on GPU, returning zeros instead. This is a dangerous silent behavior. tf.debugging.assert_less( input_ids, tf.cast(self.vocab_size, dtype=input_ids.dtype), message=( "input_ids must be smaller than the embedding layer's input dimension (got" f" {tf.math.reduce_max(input_ids)} >= {self.vocab_size})" ), ) inputs_embeds = self.wte(input_ids, mode="embedding") position_embeds = tf.gather(self.wpe, position_ids) if token_type_ids is not None: token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]]) token_type_embeds = self.wte(token_type_ids, mode="embedding") else: token_type_embeds = tf.constant(0.0) position_embeds = tf.cast(position_embeds, dtype=inputs_embeds.dtype) token_type_embeds = tf.cast(token_type_embeds, dtype=inputs_embeds.dtype) hidden_states = inputs_embeds + position_embeds + token_type_embeds hidden_states = self.drop(hidden_states, training=training) output_shape = input_shape + [shape_list(hidden_states)[-1]] presents = () if use_cache else None all_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None all_hidden_states = () if output_hidden_states else None for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),) outputs = block( hidden_states, layer_past, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, training=training, ) hidden_states, present = outputs[:2] if use_cache: presents = presents + (present,) if output_attentions: all_attentions = all_attentions + (outputs[2],) if self.config.add_cross_attention and encoder_hidden_states is not None: all_cross_attentions = all_cross_attentions + (outputs[3],) hidden_states = self.ln_f(hidden_states) hidden_states = tf.reshape(hidden_states, output_shape) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if output_attentions: # let the number of heads free (-1) so we can extract attention even after head pruning attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:] all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions) if not return_dict: return tuple( v for v in [hidden_states, presents, all_hidden_states, all_attentions, all_cross_attentions] if v is not None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) class TFGPT2PreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPT2Config base_model_prefix = "transformer" # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"h.\d+.attn.bias", r"h.\d+.crossattention.bias"] @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: `Dict[str, tf.Tensor]`: The dummy inputs. """ dummy = {"input_ids": tf.constant(DUMMY_INPUTS)} # Add `encoder_hidden_states` to make the cross-attention layers' weights initialized if self.config.add_cross_attention: batch_size, seq_len = tf.constant(DUMMY_INPUTS).shape shape = (batch_size, seq_len) + (self.config.hidden_size,) h = tf.random.uniform(shape=shape) dummy["encoder_hidden_states"] = h return dummy @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None), tf.int64, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.int64, name="attention_mask"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) @dataclass class TFGPT2DoubleHeadsModelOutput(ModelOutput): """ Base class for outputs of models predicting if two sentences are consecutive or not. Args: logits (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). mc_logits (`tf.Tensor` of shape `(batch_size, num_choices)`): Prediction scores of the multiple choice classification head (scores for each choice before SoftMax). past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: tf.Tensor = None mc_logits: tf.Tensor = None past_key_values: Optional[List[tf.Tensor]] = None hidden_states: Optional[Tuple[tf.Tensor]] = None attentions: Optional[Tuple[tf.Tensor]] = None GPT2_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`GPT2Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ GPT2_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, input_ids_length)`): `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0].shape[-2]` (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. If `past_key_values` is used, only input IDs that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) past_key_values (`List[tf.Tensor]` of length `config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see `past_key_values` output below). Can be used to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. attention_mask (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. If `past_key_values` is used, `attention_mask` needs to contain the masking strategy that was used for `past_key_values`. In other words, the `attention_mask` always has to have the length: `len(past_key_values) + len(input_ids)` [What are attention masks?](../glossary#attention-mask) token_type_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.", GPT2_START_DOCSTRING, ) class TFGPT2Model(TFGPT2PreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFGPT2MainLayer(config, name="transformer") @unpack_inputs @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past`). Set to `False` during training, `True` during generation """ outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def serving_output(self, output): pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None cross_attns = ( tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions and self.config.add_cross_attention and output.cross_attentions is not None else None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=output.last_hidden_state, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns, ) @add_start_docstrings( """ The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, GPT2_START_DOCSTRING, ) class TFGPT2LMHeadModel(TFGPT2PreTrainedModel, TFCausalLanguageModelingLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFGPT2MainLayer(config, name="transformer") def get_output_embeddings(self): return self.get_input_embeddings() def set_output_embeddings(self, value): self.set_input_embeddings(value) def prepare_inputs_for_generation(self, inputs, past=None, use_cache=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # only last token for inputs_ids if past is defined in kwargs if past: inputs = tf.expand_dims(inputs[:, -1], -1) if token_type_ids is not None: token_type_ids = tf.expand_dims(token_type_ids[:, -1], -1) position_ids = kwargs.get("position_ids", None) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None and position_ids is None: position_ids = tf.math.cumsum(attention_mask, axis=-1, exclusive=True) if past: position_ids = tf.expand_dims(position_ids[:, -1], -1) return { "input_ids": inputs, "attention_mask": attention_mask, "position_ids": position_ids, "past_key_values": past, "use_cache": use_cache, "token_type_ids": token_type_ids, } @unpack_inputs @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past`). Set to `False` during training, `True` during generation labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`. """ transformer_outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) hidden_states = transformer_outputs[0] logits = self.transformer.wte(hidden_states, mode="linear") loss = None if labels is not None: # shift labels to the left and cut last logit token shifted_logits = logits[:, :-1] labels = labels[:, 1:] loss = self.hf_compute_loss(labels, shifted_logits) if not return_dict: output = (logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return TFCausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, cross_attentions=transformer_outputs.cross_attentions, ) def serving_output(self, output): pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None cross_attns = ( tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions and self.config.add_cross_attention and output.cross_attentions is not None else None ) return TFCausalLMOutputWithCrossAttentions( logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns ) @add_start_docstrings( """ The GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. for RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the input embeddings, the classification head takes as input the input of a specified classification token index in the input sequence). """, GPT2_START_DOCSTRING, ) class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) config.num_labels = 1 self.transformer = TFGPT2MainLayer(config, name="transformer") self.multiple_choice_head = TFSequenceSummary( config, initializer_range=config.initializer_range, name="multiple_choice_head" ) @unpack_inputs @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFGPT2DoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: Optional[TFModelInputType] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, mc_token_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFGPT2DoubleHeadsModelOutput, Tuple[tf.Tensor]]: r""" mc_token_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input): Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) - 1]`. Return: Examples: ```python >>> import tensorflow as tf >>> from transformers import GPT2Tokenizer, TFGPT2DoubleHeadsModel >>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2") >>> model = TFGPT2DoubleHeadsModel.from_pretrained("gpt2") >>> # Add a [CLS] to the vocabulary (we should train it also!) >>> num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"}) >>> embedding_layer = model.resize_token_embeddings( ... len(tokenizer) ... ) # Update the model embeddings with the new vocabulary size >>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"] >>> encoded_choices = [tokenizer.encode(s) for s in choices] >>> cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices] >>> input_ids = tf.constant(encoded_choices)[None, :] # Batch size: 1, number of choices: 2 >>> mc_token_ids = tf.constant([cls_token_location]) # Batch size: 1 >>> outputs = model(input_ids, mc_token_ids=mc_token_ids) >>> lm_prediction_scores, mc_prediction_scores = outputs[:2] ```""" if input_ids is not None: input_shapes = shape_list(input_ids) else: input_shapes = shape_list(inputs_embeds)[:-1] seq_length = input_shapes[-1] flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None transformer_outputs = self.transformer( input_ids=flat_input_ids, past_key_values=past_key_values, attention_mask=flat_attention_mask, token_type_ids=flat_token_type_ids, position_ids=flat_position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=None, encoder_attention_mask=None, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) hidden_states = transformer_outputs[0] hidden_states = tf.reshape(hidden_states, input_shapes + shape_list(hidden_states)[-1:]) lm_logits = self.transformer.wte(hidden_states, mode="linear") mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids, training=training) mc_logits = tf.squeeze(mc_logits, axis=-1) if not return_dict: return (lm_logits, mc_logits) + transformer_outputs[1:] return TFGPT2DoubleHeadsModelOutput( logits=lm_logits, mc_logits=mc_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"), "mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="mc_token_ids"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) def serving_output(self, output): pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFGPT2DoubleHeadsModelOutput( logits=output.logits, mc_logits=output.mc_logits, past_key_values=pkv, hidden_states=hs, attentions=attns, ) @add_start_docstrings( """ The GPT2 Model transformer with a sequence classification head on top (linear layer). [`TFGPT2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, GPT2_START_DOCSTRING, ) class TFGPT2ForSequenceClassification(TFGPT2PreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.score = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="score", use_bias=False, ) self.transformer = TFGPT2MainLayer(config, name="transformer") @unpack_inputs @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="microsoft/DialogRPT-updown", output_type=TFSequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutputWithPast, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`. """ transformer_outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) logits_shape = shape_list(logits) in_logits = None if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = ( tf.reduce_sum( tf.cast( tf.math.not_equal(input_ids, self.config.pad_token_id), dtype=input_ids.dtype, ), -1, keepdims=False, ) - 1 ) in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=1) else: sequence_lengths = -1 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) loss = None if labels is not None: assert ( self.config.pad_token_id is not None or logits_shape[0] == 1 ), "Cannot handle batch sizes > 1 if no padding token is defined." if not tf.is_tensor(sequence_lengths): in_logits = logits[0 : logits_shape[0], sequence_lengths] loss = self.hf_compute_loss(tf.reshape(labels, [-1]), tf.reshape(in_logits, [-1, self.num_labels])) pooled_logits = in_logits if in_logits is not None else logits if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def serving_output(self, output): pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFSequenceClassifierOutputWithPast( logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/pipelines/image_segmentation.py

from typing import Any, Dict, List, Union import numpy as np from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): from ..models.auto.modeling_auto import ( MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, ) logger = logging.get_logger(__name__) Prediction = Dict[str, Any] Predictions = List[Prediction] @add_end_docstrings(PIPELINE_INIT_ARGS) class ImageSegmentationPipeline(Pipeline): """ Image segmentation pipeline using any `AutoModelForXXXSegmentation`. This pipeline predicts masks of objects and their classes. This image segmentation pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"image-segmentation"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=image-segmentation). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if self.framework == "tf": raise ValueError(f"The {self.__class__} is only available in PyTorch.") requires_backends(self, "vision") self.check_model_type( dict( MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items() + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items() + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items() ) ) def _sanitize_parameters(self, **kwargs): postprocess_kwargs = {} if "subtask" in kwargs: postprocess_kwargs["subtask"] = kwargs["subtask"] if "threshold" in kwargs: postprocess_kwargs["threshold"] = kwargs["threshold"] if "mask_threshold" in kwargs: postprocess_kwargs["mask_threshold"] = kwargs["mask_threshold"] if "overlap_mask_area_threshold" in kwargs: postprocess_kwargs["overlap_mask_area_threshold"] = kwargs["overlap_mask_area_threshold"] return {}, {}, postprocess_kwargs def __call__(self, images, **kwargs) -> Union[Predictions, List[Prediction]]: """ Perform segmentation (detect masks & classes) in the image(s) passed as inputs. Args: images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`): The pipeline handles three types of images: - A string containing an HTTP(S) link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the same format: all as HTTP(S) links, all as local paths, or all as PIL images. subtask (`str`, *optional*): Segmentation task to be performed, choose [`semantic`, `instance` and `panoptic`] depending on model capabilities. If not set, the pipeline will attempt tp resolve in the following order: `panoptic`, `instance`, `semantic`. threshold (`float`, *optional*, defaults to 0.9): Probability threshold to filter out predicted masks. mask_threshold (`float`, *optional*, defaults to 0.5): Threshold to use when turning the predicted masks into binary values. overlap_mask_area_threshold (`float`, *optional*, defaults to 0.5): Mask overlap threshold to eliminate small, disconnected segments. Return: A dictionary or a list of dictionaries containing the result. If the input is a single image, will return a list of dictionaries, if the input is a list of several images, will return a list of list of dictionaries corresponding to each image. The dictionaries contain the mask, label and score (where applicable) of each detected object and contains the following keys: - **label** (`str`) -- The class label identified by the model. - **mask** (`PIL.Image`) -- A binary mask of the detected object as a Pil Image of shape (width, height) of the original image. Returns a mask filled with zeros if no object is found. - **score** (*optional* `float`) -- Optionally, when the model is capable of estimating a confidence of the "object" described by the label and the mask. """ return super().__call__(images, **kwargs) def preprocess(self, image): image = load_image(image) target_size = [(image.height, image.width)] inputs = self.feature_extractor(images=[image], return_tensors="pt") inputs["target_size"] = target_size return inputs def _forward(self, model_inputs): target_size = model_inputs.pop("target_size") model_outputs = self.model(**model_inputs) model_outputs["target_size"] = target_size return model_outputs def postprocess( self, model_outputs, subtask=None, threshold=0.9, mask_threshold=0.5, overlap_mask_area_threshold=0.5 ): fn = None if subtask in {"panoptic", None} and hasattr(self.feature_extractor, "post_process_panoptic_segmentation"): fn = self.feature_extractor.post_process_panoptic_segmentation elif subtask in {"instance", None} and hasattr(self.feature_extractor, "post_process_instance_segmentation"): fn = self.feature_extractor.post_process_instance_segmentation if fn is not None: outputs = fn( model_outputs, threshold=threshold, mask_threshold=mask_threshold, overlap_mask_area_threshold=overlap_mask_area_threshold, target_sizes=model_outputs["target_size"], )[0] annotation = [] segmentation = outputs["segmentation"] for segment in outputs["segments_info"]: mask = (segmentation == segment["id"]) * 255 mask = Image.fromarray(mask.numpy().astype(np.uint8), mode="L") label = self.model.config.id2label[segment["label_id"]] score = segment["score"] annotation.append({"score": score, "label": label, "mask": mask}) elif subtask in {"semantic", None} and hasattr(self.feature_extractor, "post_process_semantic_segmentation"): outputs = self.feature_extractor.post_process_semantic_segmentation( model_outputs, target_sizes=model_outputs["target_size"] )[0] annotation = [] segmentation = outputs.numpy() labels = np.unique(segmentation) for label in labels: mask = (segmentation == label) * 255 mask = Image.fromarray(mask.astype(np.uint8), mode="L") label = self.model.config.id2label[label] annotation.append({"score": None, "label": label, "mask": mask}) else: raise ValueError(f"Subtask {subtask} is not supported for model {type(self.model)}") return annotation

from typing import Any, Dict, List, Union import numpy as np from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): from ..models.auto.modeling_auto import ( MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, ) logger = logging.get_logger(__name__) Prediction = Dict[str, Any] Predictions = List[Prediction] @add_end_docstrings(PIPELINE_INIT_ARGS) class ImageSegmentationPipeline(Pipeline): """ Image segmentation pipeline using any `AutoModelForXXXSegmentation`. This pipeline predicts masks of objects and their classes. This image segmentation pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"image-segmentation"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=image-segmentation). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if self.framework == "tf": raise ValueError(f"The {self.__class__} is only available in PyTorch.") requires_backends(self, "vision") self.check_model_type( dict( MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items() + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items() + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items() ) ) def _sanitize_parameters(self, **kwargs): postprocess_kwargs = {} if "subtask" in kwargs: postprocess_kwargs["subtask"] = kwargs["subtask"] if "threshold" in kwargs: postprocess_kwargs["threshold"] = kwargs["threshold"] if "mask_threshold" in kwargs: postprocess_kwargs["mask_threshold"] = kwargs["mask_threshold"] if "overlap_mask_area_threshold" in kwargs: postprocess_kwargs["overlap_mask_area_threshold"] = kwargs["overlap_mask_area_threshold"] return {}, {}, postprocess_kwargs def __call__(self, images, **kwargs) -> Union[Predictions, List[Prediction]]: """ Perform segmentation (detect masks & classes) in the image(s) passed as inputs. Args: images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`): The pipeline handles three types of images: - A string containing an HTTP(S) link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the same format: all as HTTP(S) links, all as local paths, or all as PIL images. subtask (`str`, *optional*): Segmentation task to be performed, choose [`semantic`, `instance` and `panoptic`] depending on model capabilities. If not set, the pipeline will attempt tp resolve in the following order: `panoptic`, `instance`, `semantic`. threshold (`float`, *optional*, defaults to 0.9): Probability threshold to filter out predicted masks. mask_threshold (`float`, *optional*, defaults to 0.5): Threshold to use when turning the predicted masks into binary values. overlap_mask_area_threshold (`float`, *optional*, defaults to 0.5): Mask overlap threshold to eliminate small, disconnected segments. Return: A dictionary or a list of dictionaries containing the result. If the input is a single image, will return a list of dictionaries, if the input is a list of several images, will return a list of list of dictionaries corresponding to each image. The dictionaries contain the mask, label and score (where applicable) of each detected object and contains the following keys: - **label** (`str`) -- The class label identified by the model. - **mask** (`PIL.Image`) -- A binary mask of the detected object as a Pil Image of shape (width, height) of the original image. Returns a mask filled with zeros if no object is found. - **score** (*optional* `float`) -- Optionally, when the model is capable of estimating a confidence of the "object" described by the label and the mask. """ return super().__call__(images, **kwargs) def preprocess(self, image): image = load_image(image) target_size = [(image.height, image.width)] inputs = self.feature_extractor(images=[image], return_tensors="pt") inputs["target_size"] = target_size return inputs def _forward(self, model_inputs): target_size = model_inputs.pop("target_size") model_outputs = self.model(**model_inputs) model_outputs["target_size"] = target_size return model_outputs def postprocess( self, model_outputs, subtask=None, threshold=0.9, mask_threshold=0.5, overlap_mask_area_threshold=0.5 ): fn = None if subtask in {"panoptic", None} and hasattr(self.feature_extractor, "post_process_panoptic_segmentation"): fn = self.feature_extractor.post_process_panoptic_segmentation elif subtask in {"instance", None} and hasattr(self.feature_extractor, "post_process_instance_segmentation"): fn = self.feature_extractor.post_process_instance_segmentation if fn is not None: outputs = fn( model_outputs, threshold=threshold, mask_threshold=mask_threshold, overlap_mask_area_threshold=overlap_mask_area_threshold, target_sizes=model_outputs["target_size"], )[0] annotation = [] segmentation = outputs["segmentation"] for segment in outputs["segments_info"]: mask = (segmentation == segment["id"]) * 255 mask = Image.fromarray(mask.numpy().astype(np.uint8), mode="L") label = self.model.config.id2label[segment["label_id"]] score = segment["score"] annotation.append({"score": score, "label": label, "mask": mask}) elif subtask in {"semantic", None} and hasattr(self.feature_extractor, "post_process_semantic_segmentation"): outputs = self.feature_extractor.post_process_semantic_segmentation( model_outputs, target_sizes=model_outputs["target_size"] )[0] annotation = [] segmentation = outputs.numpy() labels = np.unique(segmentation) for label in labels: mask = (segmentation == label) * 255 mask = Image.fromarray(mask.astype(np.uint8), mode="L") label = self.model.config.id2label[label] annotation.append({"score": None, "label": label, "mask": mask}) else: raise ValueError(f"Subtask {subtask} is not supported for model {type(self.model)}") return annotation

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a text file or a dataset. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=fill-mask """ import logging import os import sys import time from collections import defaultdict from dataclasses import dataclass, field # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. from pathlib import Path from typing import Dict, List, Optional, Tuple import datasets import numpy as np from datasets import load_dataset from tqdm import tqdm import flax import jax import jax.numpy as jnp import optax from flax import jax_utils, traverse_util from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard from transformers import ( CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoTokenizer, FlaxAutoModelForMaskedLM, HfArgumentParser, PreTrainedTokenizerBase, TensorType, TrainingArguments, is_tensorboard_available, set_seed, ) if datasets.__version__ <= "1.8.0": raise ValueError("Make sure to upgrade `datasets` to a version >= 1.9.0 to use dataset streaming") MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) dtype: Optional[str] = field( default="float32", metadata={ "help": ( "Floating-point format in which the model weights should be initialized and trained. Choose one of" " `[float32, float16, bfloat16]`." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) line_by_line: bool = field( default=False, metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the column to retrieve the training text."} ) shuffle_buffer_size: int = field( default=10000, metadata={"help": "The number of examples to pre-load for shuffling."} ) num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."}) num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"}) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." @flax.struct.dataclass class FlaxDataCollatorForLanguageModeling: """ Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they are not all of the same length. Args: tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): The tokenizer used for encoding the data. mlm_probability (:obj:`float`, `optional`, defaults to 0.15): The probability with which to (randomly) mask tokens in the input. .. note:: For best performance, this data collator should be used with a dataset having items that are dictionaries or BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the argument :obj:`return_special_tokens_mask=True`. """ tokenizer: PreTrainedTokenizerBase mlm_probability: float = 0.15 def __post_init__(self): if self.tokenizer.mask_token is None: raise ValueError( "This tokenizer does not have a mask token which is necessary for masked language modeling. " "You should pass `mlm=False` to train on causal language modeling instead." ) def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]: # Handle dict or lists with proper padding and conversion to tensor. batch = self.tokenizer.pad(examples, return_tensors=TensorType.NUMPY) # If special token mask has been preprocessed, pop it from the dict. special_tokens_mask = batch.pop("special_tokens_mask", None) batch["input_ids"], batch["labels"] = self.mask_tokens( batch["input_ids"], special_tokens_mask=special_tokens_mask ) return batch def mask_tokens( self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray] ) -> Tuple[jnp.ndarray, jnp.ndarray]: """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """ labels = inputs.copy() # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`) probability_matrix = np.full(labels.shape, self.mlm_probability) special_tokens_mask = special_tokens_mask.astype("bool") probability_matrix[special_tokens_mask] = 0.0 masked_indices = np.random.binomial(1, probability_matrix).astype("bool") labels[~masked_indices] = -100 # We only compute loss on masked tokens # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token) # 10% of the time, we replace masked input tokens with random word indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool") indices_random &= masked_indices & ~indices_replaced random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4") inputs[indices_random] = random_words[indices_random] # The rest of the time (10% of the time) we keep the masked input tokens unchanged return inputs, labels def generate_batch_splits(samples_idx: np.ndarray, batch_size: int) -> np.ndarray: num_samples = len(samples_idx) samples_to_remove = num_samples % batch_size if samples_to_remove != 0: samples_idx = samples_idx[:-samples_to_remove] sections_split = num_samples // batch_size batch_idx = np.split(samples_idx, sections_split) return batch_idx def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length): """ The training iterator is advanced so that after groupifying the samples, `num_samples` of length `max_seq_length` are returned. """ num_total_tokens = max_seq_length * num_samples samples = defaultdict(list) i = 0 while i < num_total_tokens: tokenized_samples = next(train_iterator) i += len(tokenized_samples["input_ids"]) # concatenate tokenized samples to list (excluding "id" and "text") samples = { k: samples[k] + tokenized_samples[k] for k in ["input_ids", "attention_mask", "special_tokens_mask"] } # Concatenated tokens are split to lists of length `max_seq_length`. # Note that remainedr of % max_seq_length are thrown away. def group_texts(examples): result = { k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)] for k, t in examples.items() } return result grouped_samples = group_texts(samples) return grouped_samples def write_train_metric(summary_writer, train_metrics, train_time, step): summary_writer.scalar("train_time", train_time, step) train_metrics = get_metrics(train_metrics) for key, vals in train_metrics.items(): tag = f"train_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) def write_eval_metric(summary_writer, eval_metrics, step): for metric_name, value in eval_metrics.items(): summary_writer.scalar(f"eval_{metric_name}", value, step) if __name__ == "__main__": # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty." "Use --overwrite_output_dir to overcome." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level="INFO", datefmt="[%X]", ) # Log on each process the small summary: logger = logging.getLogger(__name__) logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, streaming=True, split="train", ) if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more # efficient when it receives the `special_tokens_mask`. def tokenize_function(examples): return tokenizer(examples[data_args.text_column_name], return_special_tokens_mask=True) tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=list(dataset.features.keys())) shuffle_seed = training_args.seed tokenized_datasets = tokenized_datasets.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed) has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) # Data collator # This one will take care of randomly masking the tokens. data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) # Initialize our training rng = jax.random.PRNGKey(training_args.seed) dropout_rngs = jax.random.split(rng, jax.local_device_count()) if model_args.model_name_or_path: model = FlaxAutoModelForMaskedLM.from_pretrained( model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) else: model = FlaxAutoModelForMaskedLM.from_config( config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) # Store some constant num_epochs = int(training_args.num_train_epochs) train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count() # define number steps per stream epoch num_train_steps = data_args.num_train_steps # Create learning rate schedule warmup_fn = optax.linear_schedule( init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps ) decay_fn = optax.linear_schedule( init_value=training_args.learning_rate, end_value=0, transition_steps=num_train_steps - training_args.warmup_steps, ) linear_decay_lr_schedule_fn = optax.join_schedules( schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps] ) # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. # Note that this mask is specifically adapted for FlaxBERT-like models. # For other models, one should correct the layer norm parameter naming # accordingly. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params} return traverse_util.unflatten_dict(flat_mask) # create adam optimizer adamw = optax.adamw( learning_rate=linear_decay_lr_schedule_fn, b1=training_args.adam_beta1, b2=training_args.adam_beta2, eps=training_args.adam_epsilon, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) # Setup train state state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw) # Define gradient update step fn def train_step(state, batch, dropout_rng): dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) def loss_fn(params): labels = batch.pop("labels") logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0] # compute loss, ignore padded input tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # take average loss = loss.sum() / label_mask.sum() return loss grad_fn = jax.value_and_grad(loss_fn) loss, grad = grad_fn(state.params) grad = jax.lax.pmean(grad, "batch") new_state = state.apply_gradients(grads=grad) metrics = jax.lax.pmean( {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch" ) return new_state, metrics, new_dropout_rng # Create parallel version of the train step p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) # Define eval fn def eval_step(params, batch): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] # compute loss, ignore padded input tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # compute accuracy accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask # summarize metrics metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()} metrics = jax.lax.psum(metrics, axis_name="batch") return metrics p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,)) # Replicate the train state on each device state = jax_utils.replicate(state) train_time = 0 train_start = time.time() train_metrics = [] eval_metrics = [] training_iter = iter(tokenized_datasets) max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) eval_samples = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length) steps = tqdm(range(num_train_steps), desc="Training...", position=0) for step in range(num_train_steps): # ======================== Training ================================ try: samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length) except StopIteration: # Once the end of the dataset stream is reached, the training iterator # is reinitialized and reshuffled and a new eval dataset is randomely chosen. shuffle_seed += 1 tokenized_datasets.set_epoch(shuffle_seed) training_iter = iter(tokenized_datasets) eval_dataset = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length) samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length) # process input samples model_inputs = data_collator(samples) # Model forward model_inputs = shard(model_inputs.data) state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs) train_metrics.append(train_metric) if step % training_args.logging_steps == 0 and step > 0: steps.write( f"Step... ({step} | Loss: {train_metric['loss'].mean()}, Learning Rate:" f" {train_metric['learning_rate'].mean()})" ) train_time += time.time() - train_start if has_tensorboard and jax.process_index() == 0: write_train_metric(summary_writer, train_metrics, train_time, step) train_metrics = [] # ======================== Evaluating ============================== if step % training_args.eval_steps == 0 and step > 0: # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(data_args.num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=1)): # process input samples batch_eval_samples = {k: [v[idx] for idx in batch_idx] for k, v in eval_samples.items()} model_inputs = data_collator(batch_eval_samples) # Model forward model_inputs = shard(model_inputs.data) metrics = p_eval_step(state.params, model_inputs) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) # Update progress bar steps.desc = ( f"Step... ({step + 1}/{num_train_steps} | Loss: {eval_metrics['loss']}, Acc:" f" {eval_metrics['accuracy']})" ) if has_tensorboard and jax.process_index() == 0: write_eval_metric(summary_writer, eval_metrics, step) eval_metrics = [] # save checkpoint after each epoch and push checkpoint to the hub if jax.process_index() == 0: params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) model.save_pretrained( training_args.output_dir, params=params, push_to_hub=training_args.push_to_hub, commit_message=f"Saving weights and logs of step {step+1}", ) # update tqdm bar steps.update(1)

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a text file or a dataset. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=fill-mask """ import logging import os import sys import time from collections import defaultdict from dataclasses import dataclass, field # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. from pathlib import Path from typing import Dict, List, Optional, Tuple import datasets import numpy as np from datasets import load_dataset from tqdm import tqdm import flax import jax import jax.numpy as jnp import optax from flax import jax_utils, traverse_util from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard from transformers import ( CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoTokenizer, FlaxAutoModelForMaskedLM, HfArgumentParser, PreTrainedTokenizerBase, TensorType, TrainingArguments, is_tensorboard_available, set_seed, ) if datasets.__version__ <= "1.8.0": raise ValueError("Make sure to upgrade `datasets` to a version >= 1.9.0 to use dataset streaming") MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) dtype: Optional[str] = field( default="float32", metadata={ "help": ( "Floating-point format in which the model weights should be initialized and trained. Choose one of" " `[float32, float16, bfloat16]`." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) line_by_line: bool = field( default=False, metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the column to retrieve the training text."} ) shuffle_buffer_size: int = field( default=10000, metadata={"help": "The number of examples to pre-load for shuffling."} ) num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."}) num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"}) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." @flax.struct.dataclass class FlaxDataCollatorForLanguageModeling: """ Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they are not all of the same length. Args: tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): The tokenizer used for encoding the data. mlm_probability (:obj:`float`, `optional`, defaults to 0.15): The probability with which to (randomly) mask tokens in the input. .. note:: For best performance, this data collator should be used with a dataset having items that are dictionaries or BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the argument :obj:`return_special_tokens_mask=True`. """ tokenizer: PreTrainedTokenizerBase mlm_probability: float = 0.15 def __post_init__(self): if self.tokenizer.mask_token is None: raise ValueError( "This tokenizer does not have a mask token which is necessary for masked language modeling. " "You should pass `mlm=False` to train on causal language modeling instead." ) def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]: # Handle dict or lists with proper padding and conversion to tensor. batch = self.tokenizer.pad(examples, return_tensors=TensorType.NUMPY) # If special token mask has been preprocessed, pop it from the dict. special_tokens_mask = batch.pop("special_tokens_mask", None) batch["input_ids"], batch["labels"] = self.mask_tokens( batch["input_ids"], special_tokens_mask=special_tokens_mask ) return batch def mask_tokens( self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray] ) -> Tuple[jnp.ndarray, jnp.ndarray]: """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """ labels = inputs.copy() # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`) probability_matrix = np.full(labels.shape, self.mlm_probability) special_tokens_mask = special_tokens_mask.astype("bool") probability_matrix[special_tokens_mask] = 0.0 masked_indices = np.random.binomial(1, probability_matrix).astype("bool") labels[~masked_indices] = -100 # We only compute loss on masked tokens # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token) # 10% of the time, we replace masked input tokens with random word indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool") indices_random &= masked_indices & ~indices_replaced random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4") inputs[indices_random] = random_words[indices_random] # The rest of the time (10% of the time) we keep the masked input tokens unchanged return inputs, labels def generate_batch_splits(samples_idx: np.ndarray, batch_size: int) -> np.ndarray: num_samples = len(samples_idx) samples_to_remove = num_samples % batch_size if samples_to_remove != 0: samples_idx = samples_idx[:-samples_to_remove] sections_split = num_samples // batch_size batch_idx = np.split(samples_idx, sections_split) return batch_idx def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length): """ The training iterator is advanced so that after groupifying the samples, `num_samples` of length `max_seq_length` are returned. """ num_total_tokens = max_seq_length * num_samples samples = defaultdict(list) i = 0 while i < num_total_tokens: tokenized_samples = next(train_iterator) i += len(tokenized_samples["input_ids"]) # concatenate tokenized samples to list (excluding "id" and "text") samples = { k: samples[k] + tokenized_samples[k] for k in ["input_ids", "attention_mask", "special_tokens_mask"] } # Concatenated tokens are split to lists of length `max_seq_length`. # Note that remainedr of % max_seq_length are thrown away. def group_texts(examples): result = { k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)] for k, t in examples.items() } return result grouped_samples = group_texts(samples) return grouped_samples def write_train_metric(summary_writer, train_metrics, train_time, step): summary_writer.scalar("train_time", train_time, step) train_metrics = get_metrics(train_metrics) for key, vals in train_metrics.items(): tag = f"train_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) def write_eval_metric(summary_writer, eval_metrics, step): for metric_name, value in eval_metrics.items(): summary_writer.scalar(f"eval_{metric_name}", value, step) if __name__ == "__main__": # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty." "Use --overwrite_output_dir to overcome." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level="INFO", datefmt="[%X]", ) # Log on each process the small summary: logger = logging.getLogger(__name__) logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, streaming=True, split="train", ) if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more # efficient when it receives the `special_tokens_mask`. def tokenize_function(examples): return tokenizer(examples[data_args.text_column_name], return_special_tokens_mask=True) tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=list(dataset.features.keys())) shuffle_seed = training_args.seed tokenized_datasets = tokenized_datasets.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed) has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) # Data collator # This one will take care of randomly masking the tokens. data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) # Initialize our training rng = jax.random.PRNGKey(training_args.seed) dropout_rngs = jax.random.split(rng, jax.local_device_count()) if model_args.model_name_or_path: model = FlaxAutoModelForMaskedLM.from_pretrained( model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) else: model = FlaxAutoModelForMaskedLM.from_config( config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) # Store some constant num_epochs = int(training_args.num_train_epochs) train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count() # define number steps per stream epoch num_train_steps = data_args.num_train_steps # Create learning rate schedule warmup_fn = optax.linear_schedule( init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps ) decay_fn = optax.linear_schedule( init_value=training_args.learning_rate, end_value=0, transition_steps=num_train_steps - training_args.warmup_steps, ) linear_decay_lr_schedule_fn = optax.join_schedules( schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps] ) # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. # Note that this mask is specifically adapted for FlaxBERT-like models. # For other models, one should correct the layer norm parameter naming # accordingly. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params} return traverse_util.unflatten_dict(flat_mask) # create adam optimizer adamw = optax.adamw( learning_rate=linear_decay_lr_schedule_fn, b1=training_args.adam_beta1, b2=training_args.adam_beta2, eps=training_args.adam_epsilon, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) # Setup train state state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw) # Define gradient update step fn def train_step(state, batch, dropout_rng): dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) def loss_fn(params): labels = batch.pop("labels") logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0] # compute loss, ignore padded input tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # take average loss = loss.sum() / label_mask.sum() return loss grad_fn = jax.value_and_grad(loss_fn) loss, grad = grad_fn(state.params) grad = jax.lax.pmean(grad, "batch") new_state = state.apply_gradients(grads=grad) metrics = jax.lax.pmean( {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch" ) return new_state, metrics, new_dropout_rng # Create parallel version of the train step p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) # Define eval fn def eval_step(params, batch): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] # compute loss, ignore padded input tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # compute accuracy accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask # summarize metrics metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()} metrics = jax.lax.psum(metrics, axis_name="batch") return metrics p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,)) # Replicate the train state on each device state = jax_utils.replicate(state) train_time = 0 train_start = time.time() train_metrics = [] eval_metrics = [] training_iter = iter(tokenized_datasets) max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) eval_samples = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length) steps = tqdm(range(num_train_steps), desc="Training...", position=0) for step in range(num_train_steps): # ======================== Training ================================ try: samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length) except StopIteration: # Once the end of the dataset stream is reached, the training iterator # is reinitialized and reshuffled and a new eval dataset is randomely chosen. shuffle_seed += 1 tokenized_datasets.set_epoch(shuffle_seed) training_iter = iter(tokenized_datasets) eval_dataset = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length) samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length) # process input samples model_inputs = data_collator(samples) # Model forward model_inputs = shard(model_inputs.data) state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs) train_metrics.append(train_metric) if step % training_args.logging_steps == 0 and step > 0: steps.write( f"Step... ({step} | Loss: {train_metric['loss'].mean()}, Learning Rate:" f" {train_metric['learning_rate'].mean()})" ) train_time += time.time() - train_start if has_tensorboard and jax.process_index() == 0: write_train_metric(summary_writer, train_metrics, train_time, step) train_metrics = [] # ======================== Evaluating ============================== if step % training_args.eval_steps == 0 and step > 0: # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(data_args.num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=1)): # process input samples batch_eval_samples = {k: [v[idx] for idx in batch_idx] for k, v in eval_samples.items()} model_inputs = data_collator(batch_eval_samples) # Model forward model_inputs = shard(model_inputs.data) metrics = p_eval_step(state.params, model_inputs) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) # Update progress bar steps.desc = ( f"Step... ({step + 1}/{num_train_steps} | Loss: {eval_metrics['loss']}, Acc:" f" {eval_metrics['accuracy']})" ) if has_tensorboard and jax.process_index() == 0: write_eval_metric(summary_writer, eval_metrics, step) eval_metrics = [] # save checkpoint after each epoch and push checkpoint to the hub if jax.process_index() == 0: params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) model.save_pretrained( training_args.output_dir, params=params, push_to_hub=training_args.push_to_hub, commit_message=f"Saving weights and logs of step {step+1}", ) # update tqdm bar steps.update(1)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/funnel/convert_funnel_original_tf_checkpoint_to_pytorch.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Funnel checkpoint.""" import argparse import torch from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel from transformers.utils import logging logging.set_verbosity_info() def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path, base_model): # Initialise PyTorch model config = FunnelConfig.from_json_file(config_file) print(f"Building PyTorch model from configuration: {config}") model = FunnelBaseModel(config) if base_model else FunnelModel(config) # Load weights from tf checkpoint load_tf_weights_in_funnel(model, config, tf_checkpoint_path) # Save pytorch-model print(f"Save PyTorch model to {pytorch_dump_path}") torch.save(model.state_dict(), pytorch_dump_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." ) parser.add_argument( "--config_file", default=None, type=str, required=True, help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.", ) parser.add_argument( "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not." ) args = parser.parse_args() convert_tf_checkpoint_to_pytorch( args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model )

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Funnel checkpoint.""" import argparse import torch from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel from transformers.utils import logging logging.set_verbosity_info() def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path, base_model): # Initialise PyTorch model config = FunnelConfig.from_json_file(config_file) print(f"Building PyTorch model from configuration: {config}") model = FunnelBaseModel(config) if base_model else FunnelModel(config) # Load weights from tf checkpoint load_tf_weights_in_funnel(model, config, tf_checkpoint_path) # Save pytorch-model print(f"Save PyTorch model to {pytorch_dump_path}") torch.save(model.state_dict(), pytorch_dump_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." ) parser.add_argument( "--config_file", default=None, type=str, required=True, help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.", ) parser.add_argument( "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not." ) args = parser.parse_args() convert_tf_checkpoint_to_pytorch( args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/maskformer/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available _import_structure = {"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"]} try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_maskformer"] = ["MaskFormerFeatureExtractor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_maskformer"] = [ "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "MaskFormerForInstanceSegmentation", "MaskFormerModel", "MaskFormerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_maskformer import MaskFormerFeatureExtractor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_maskformer import ( MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, MaskFormerForInstanceSegmentation, MaskFormerModel, MaskFormerPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available _import_structure = {"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"]} try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_maskformer"] = ["MaskFormerFeatureExtractor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_maskformer"] = [ "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "MaskFormerForInstanceSegmentation", "MaskFormerModel", "MaskFormerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_maskformer import MaskFormerFeatureExtractor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_maskformer import ( MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, MaskFormerForInstanceSegmentation, MaskFormerModel, MaskFormerPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Wav2Vec2-Conformer model. """ import math import unittest import numpy as np from datasets import load_dataset from transformers import Wav2Vec2ConformerConfig, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) if is_torch_available(): import torch from transformers import ( Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForCTC, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForXVector, Wav2Vec2ConformerModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import ( Wav2Vec2ConformerGumbelVectorQuantizer, _compute_mask_indices, _sample_negative_indices, ) class Wav2Vec2ConformerModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=16, feat_extract_norm="group", feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(32, 32, 32), conv_stride=(4, 4, 4), conv_kernel=(8, 8, 8), conv_bias=False, num_conv_pos_embeddings=16, num_conv_pos_embedding_groups=2, num_hidden_layers=4, num_attention_heads=2, hidden_dropout_prob=0.1, intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, mask_time_prob=0.5, mask_time_length=2, vocab_size=32, do_stable_layer_norm=False, num_adapter_layers=1, adapter_stride=2, tdnn_dim=(32, 32), tdnn_kernel=(5, 3), tdnn_dilation=(1, 2), xvector_output_dim=32, position_embeddings_type="relative", scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.num_adapter_layers = num_adapter_layers self.adapter_stride = adapter_stride self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.scope = scope self.tdnn_dim = tdnn_dim self.tdnn_kernel = tdnn_kernel self.tdnn_dilation = tdnn_dilation self.xvector_output_dim = xvector_output_dim self.position_embeddings_type = position_embeddings_type output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1 def prepare_config_and_inputs(self, position_embeddings_type="relative"): input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config(position_embeddings_type=position_embeddings_type) return config, input_values, attention_mask def get_config(self, position_embeddings_type="relative"): return Wav2Vec2ConformerConfig( hidden_size=self.hidden_size, feat_extract_norm=self.feat_extract_norm, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, mask_time_prob=self.mask_time_prob, mask_time_length=self.mask_time_length, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout_prob=self.hidden_dropout_prob, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, do_stable_layer_norm=self.do_stable_layer_norm, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, num_adapter_layers=self.num_adapter_layers, adapter_stride=self.adapter_stride, tdnn_dim=self.tdnn_dim, tdnn_kernel=self.tdnn_kernel, tdnn_dilation=self.tdnn_dilation, xvector_output_dim=self.xvector_output_dim, position_embeddings_type=position_embeddings_type, ) def create_and_check_model(self, config, input_values, attention_mask): model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter(self, config, input_values, attention_mask): config.add_adapter = True model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter_for_ctc(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 2 * config.hidden_size model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size) ) def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 8 model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, config.output_hidden_size), ) def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i : i + 1, : input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i : i + 1, : output.shape[1]] self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) def check_ctc_loss(self, config, input_values, *args): model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_seq_classifier_loss(self, config, input_values, *args): model = Wav2Vec2ConformerForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_encoder() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lenghts are at least # one shorter than logit lenghts to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_xvector_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForXVector(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = Wav2Vec2ConformerForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with self.parent.assertRaises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class Wav2Vec2ConformerModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( ( Wav2Vec2ConformerForCTC, Wav2Vec2ConformerModel, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForXVector, ) if is_torch_available() else () ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = Wav2Vec2ConformerModelTester(self) self.config_tester = ConfigTester(self, config_class=Wav2Vec2ConformerConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_relative(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_rotary(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_no_rel_pos(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type=None) self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_adapter(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter(*config_and_inputs) def test_model_with_adapter_for_ctc(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_for_ctc(*config_and_inputs) def test_model_with_adapter_proj_dim(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_xvector_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_xvector_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # Wav2Vec2Conformer has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # Wav2Vec2Conformer cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # Wav2Vec2Conformer has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass @is_pt_flax_cross_test # non-robust architecture does not exist in Flax def test_equivalence_flax_to_pt(self): pass @is_pt_flax_cross_test # non-robust architecture does not exist in Flax def test_equivalence_pt_to_flax(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): uniform_init_parms = [ "conv.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", "project_hid.weight", "project_hid.bias", "project_q.weight", "project_q.bias", "pos_bias_v", "pos_bias_u", "pointwise_conv1", "pointwise_conv2", "feature_projection.projection.weight", "feature_projection.projection.bias", "objective.weight", ] if param.requires_grad: if any([x in name for x in uniform_init_parms]): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "pos_bias_u") and module.pos_bias_u is not None: module.pos_bias_u.data.fill_(3) if hasattr(module, "pos_bias_v") and module.pos_bias_v is not None: module.pos_bias_v.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = Wav2Vec2ConformerForCTC.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = Wav2Vec2ConformerForCTC.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = Wav2Vec2ConformerModel.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") self.assertIsNotNone(model) @require_torch class Wav2Vec2ConformerUtilsTest(unittest.TestCase): def test_compute_mask_indices(self): batch_size = 4 sequence_length = 60 mask_prob = 0.5 mask_length = 1 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)]) def test_compute_mask_indices_low_prob(self): # with these settings num_masked_spans=0.5, which means probabilistic rounding # ensures that in 5 out of 10 method calls, num_masked_spans=0, and in # the other 5 out of 10, cases num_masked_spans=1 n_trials = 100 batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 count_dimensions_masked = 0 count_dimensions_not_masked = 0 for _ in range(n_trials): mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) num_masks = torch.sum(mask).item() if num_masks > 0: count_dimensions_masked += 1 else: count_dimensions_not_masked += 1 # as we test for at least 10 masked dimension and at least # 10 non-masked dimension, this test could fail with probability: # P(100 coin flips, at most 9 heads) = 1.66e-18 self.assertGreater(count_dimensions_masked, int(n_trials * 0.1)) self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1)) def test_compute_mask_indices_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) def test_compute_mask_indices_attn_mask_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) attention_mask[:2, sequence_length // 2 :] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask ) mask = torch.from_numpy(mask).to(torch_device) for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0) def test_compute_mask_indices_short_audio(self): batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) # force one example to be heavily padded attention_mask[0, 5:] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2 ) # make sure that non-padded examples cannot be padded self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any()) def test_compute_perplexity(self): probs = torch.arange(100, device=torch_device).reshape(2, 5, 10) / 100 ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs) self.assertTrue(abs(ppl.item() - 141.4291) < 1e-3) # mask half of the input mask = torch.ones((2,), device=torch_device, dtype=torch.bool) mask[0] = 0 ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs, mask) self.assertTrue(abs(ppl.item() - 58.6757) < 1e-3) def test_sample_negatives(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consits of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # sample negative indices sampled_negative_indices = _sample_negative_indices((batch_size, sequence_length), num_negatives, None) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) def test_sample_negatives_with_mask(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 # second half of last input tensor is padded mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) mask[-1, sequence_length // 2 :] = 0 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consits of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # replace masked feature vectors with -100 to test that those are not sampled features = torch.where(mask[:, :, None].expand(features.shape).bool(), features, -100) # sample negative indices sampled_negative_indices = _sample_negative_indices( (batch_size, sequence_length), num_negatives, mask.cpu().numpy() ) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue((negatives >= 0).all().item()) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) @require_torch @slow class Wav2Vec2ConformerModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").filter(lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]) speech_samples = speech_samples[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_inference_ctc_normal_batched_rel_pos(self): model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-960h-ft") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained( "facebook/wav2vec2-conformer-rel-pos-large-960h-ft", do_lower_case=True ) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loincloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_ctc_normal_batched_rope(self): model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained( "facebook/wav2vec2-conformer-rope-large-960h-ft", do_lower_case=True ) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_pretrained(self): model = Wav2Vec2ConformerForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-conformer-rel-pos-large", return_attention_mask=True ) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) batch_size = inputs_dict["input_values"].shape[0] feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1])) features_shape = (batch_size, feature_seq_length) torch.manual_seed(0) mask_time_indices = _compute_mask_indices( features_shape, model.config.mask_time_prob, model.config.mask_time_length, min_masks=2, ) mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1) # retrieve cosine sim of masked features cosine_sim_masked = cosine_sim[mask_time_indices] # ... now compare to randomly initialized model config = Wav2Vec2ConformerConfig.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model_rand = Wav2Vec2ConformerForPreTraining(config).to(torch_device).eval() with torch.no_grad(): outputs_rand = model_rand( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim_rand = torch.cosine_similarity( outputs_rand.projected_states, outputs_rand.projected_quantized_states, dim=-1 ) # retrieve cosine sim of masked features cosine_sim_masked_rand = cosine_sim_rand[mask_time_indices] # a pretrained wav2vec2_conformer model has learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states > 0.5 # a random wav2vec2_conformer model has not learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states is very likely < 0.1 self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Wav2Vec2-Conformer model. """ import math import unittest import numpy as np from datasets import load_dataset from transformers import Wav2Vec2ConformerConfig, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) if is_torch_available(): import torch from transformers import ( Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForCTC, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForXVector, Wav2Vec2ConformerModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import ( Wav2Vec2ConformerGumbelVectorQuantizer, _compute_mask_indices, _sample_negative_indices, ) class Wav2Vec2ConformerModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=16, feat_extract_norm="group", feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(32, 32, 32), conv_stride=(4, 4, 4), conv_kernel=(8, 8, 8), conv_bias=False, num_conv_pos_embeddings=16, num_conv_pos_embedding_groups=2, num_hidden_layers=4, num_attention_heads=2, hidden_dropout_prob=0.1, intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, mask_time_prob=0.5, mask_time_length=2, vocab_size=32, do_stable_layer_norm=False, num_adapter_layers=1, adapter_stride=2, tdnn_dim=(32, 32), tdnn_kernel=(5, 3), tdnn_dilation=(1, 2), xvector_output_dim=32, position_embeddings_type="relative", scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.num_adapter_layers = num_adapter_layers self.adapter_stride = adapter_stride self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.scope = scope self.tdnn_dim = tdnn_dim self.tdnn_kernel = tdnn_kernel self.tdnn_dilation = tdnn_dilation self.xvector_output_dim = xvector_output_dim self.position_embeddings_type = position_embeddings_type output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1 def prepare_config_and_inputs(self, position_embeddings_type="relative"): input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config(position_embeddings_type=position_embeddings_type) return config, input_values, attention_mask def get_config(self, position_embeddings_type="relative"): return Wav2Vec2ConformerConfig( hidden_size=self.hidden_size, feat_extract_norm=self.feat_extract_norm, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, mask_time_prob=self.mask_time_prob, mask_time_length=self.mask_time_length, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout_prob=self.hidden_dropout_prob, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, do_stable_layer_norm=self.do_stable_layer_norm, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, num_adapter_layers=self.num_adapter_layers, adapter_stride=self.adapter_stride, tdnn_dim=self.tdnn_dim, tdnn_kernel=self.tdnn_kernel, tdnn_dilation=self.tdnn_dilation, xvector_output_dim=self.xvector_output_dim, position_embeddings_type=position_embeddings_type, ) def create_and_check_model(self, config, input_values, attention_mask): model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter(self, config, input_values, attention_mask): config.add_adapter = True model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter_for_ctc(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 2 * config.hidden_size model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size) ) def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 8 model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, config.output_hidden_size), ) def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i : i + 1, : input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i : i + 1, : output.shape[1]] self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) def check_ctc_loss(self, config, input_values, *args): model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_seq_classifier_loss(self, config, input_values, *args): model = Wav2Vec2ConformerForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_encoder() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lenghts are at least # one shorter than logit lenghts to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_xvector_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForXVector(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = Wav2Vec2ConformerForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with self.parent.assertRaises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class Wav2Vec2ConformerModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( ( Wav2Vec2ConformerForCTC, Wav2Vec2ConformerModel, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForXVector, ) if is_torch_available() else () ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = Wav2Vec2ConformerModelTester(self) self.config_tester = ConfigTester(self, config_class=Wav2Vec2ConformerConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_relative(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_rotary(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_no_rel_pos(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type=None) self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_adapter(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter(*config_and_inputs) def test_model_with_adapter_for_ctc(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_for_ctc(*config_and_inputs) def test_model_with_adapter_proj_dim(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_xvector_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_xvector_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # Wav2Vec2Conformer has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # Wav2Vec2Conformer cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # Wav2Vec2Conformer has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass @is_pt_flax_cross_test # non-robust architecture does not exist in Flax def test_equivalence_flax_to_pt(self): pass @is_pt_flax_cross_test # non-robust architecture does not exist in Flax def test_equivalence_pt_to_flax(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): uniform_init_parms = [ "conv.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", "project_hid.weight", "project_hid.bias", "project_q.weight", "project_q.bias", "pos_bias_v", "pos_bias_u", "pointwise_conv1", "pointwise_conv2", "feature_projection.projection.weight", "feature_projection.projection.bias", "objective.weight", ] if param.requires_grad: if any([x in name for x in uniform_init_parms]): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "pos_bias_u") and module.pos_bias_u is not None: module.pos_bias_u.data.fill_(3) if hasattr(module, "pos_bias_v") and module.pos_bias_v is not None: module.pos_bias_v.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = Wav2Vec2ConformerForCTC.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = Wav2Vec2ConformerForCTC.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = Wav2Vec2ConformerModel.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") self.assertIsNotNone(model) @require_torch class Wav2Vec2ConformerUtilsTest(unittest.TestCase): def test_compute_mask_indices(self): batch_size = 4 sequence_length = 60 mask_prob = 0.5 mask_length = 1 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)]) def test_compute_mask_indices_low_prob(self): # with these settings num_masked_spans=0.5, which means probabilistic rounding # ensures that in 5 out of 10 method calls, num_masked_spans=0, and in # the other 5 out of 10, cases num_masked_spans=1 n_trials = 100 batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 count_dimensions_masked = 0 count_dimensions_not_masked = 0 for _ in range(n_trials): mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) num_masks = torch.sum(mask).item() if num_masks > 0: count_dimensions_masked += 1 else: count_dimensions_not_masked += 1 # as we test for at least 10 masked dimension and at least # 10 non-masked dimension, this test could fail with probability: # P(100 coin flips, at most 9 heads) = 1.66e-18 self.assertGreater(count_dimensions_masked, int(n_trials * 0.1)) self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1)) def test_compute_mask_indices_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) def test_compute_mask_indices_attn_mask_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) attention_mask[:2, sequence_length // 2 :] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask ) mask = torch.from_numpy(mask).to(torch_device) for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0) def test_compute_mask_indices_short_audio(self): batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) # force one example to be heavily padded attention_mask[0, 5:] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2 ) # make sure that non-padded examples cannot be padded self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any()) def test_compute_perplexity(self): probs = torch.arange(100, device=torch_device).reshape(2, 5, 10) / 100 ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs) self.assertTrue(abs(ppl.item() - 141.4291) < 1e-3) # mask half of the input mask = torch.ones((2,), device=torch_device, dtype=torch.bool) mask[0] = 0 ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs, mask) self.assertTrue(abs(ppl.item() - 58.6757) < 1e-3) def test_sample_negatives(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consits of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # sample negative indices sampled_negative_indices = _sample_negative_indices((batch_size, sequence_length), num_negatives, None) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) def test_sample_negatives_with_mask(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 # second half of last input tensor is padded mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) mask[-1, sequence_length // 2 :] = 0 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consits of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # replace masked feature vectors with -100 to test that those are not sampled features = torch.where(mask[:, :, None].expand(features.shape).bool(), features, -100) # sample negative indices sampled_negative_indices = _sample_negative_indices( (batch_size, sequence_length), num_negatives, mask.cpu().numpy() ) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue((negatives >= 0).all().item()) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) @require_torch @slow class Wav2Vec2ConformerModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").filter(lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]) speech_samples = speech_samples[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_inference_ctc_normal_batched_rel_pos(self): model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-960h-ft") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained( "facebook/wav2vec2-conformer-rel-pos-large-960h-ft", do_lower_case=True ) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loincloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_ctc_normal_batched_rope(self): model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained( "facebook/wav2vec2-conformer-rope-large-960h-ft", do_lower_case=True ) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_pretrained(self): model = Wav2Vec2ConformerForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-conformer-rel-pos-large", return_attention_mask=True ) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) batch_size = inputs_dict["input_values"].shape[0] feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1])) features_shape = (batch_size, feature_seq_length) torch.manual_seed(0) mask_time_indices = _compute_mask_indices( features_shape, model.config.mask_time_prob, model.config.mask_time_length, min_masks=2, ) mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1) # retrieve cosine sim of masked features cosine_sim_masked = cosine_sim[mask_time_indices] # ... now compare to randomly initialized model config = Wav2Vec2ConformerConfig.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model_rand = Wav2Vec2ConformerForPreTraining(config).to(torch_device).eval() with torch.no_grad(): outputs_rand = model_rand( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim_rand = torch.cosine_similarity( outputs_rand.projected_states, outputs_rand.projected_quantized_states, dim=-1 ) # retrieve cosine sim of masked features cosine_sim_masked_rand = cosine_sim_rand[mask_time_indices] # a pretrained wav2vec2_conformer model has learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states > 0.5 # a random wav2vec2_conformer model has not learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states is very likely < 0.1 self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/imagegpt/test_feature_extraction_imagegpt.py

# coding=utf-8 # Copyright 2021 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import tempfile import unittest import numpy as np from datasets import load_dataset from transformers.testing_utils import require_torch, require_vision, slow from transformers.utils import is_torch_available, is_vision_available from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import ImageGPTFeatureExtractor class ImageGPTFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, ): size = size if size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_normalize = do_normalize def prepare_feat_extract_dict(self): return { # here we create 2 clusters for the sake of simplicity "clusters": np.asarray( [ [0.8866443634033203, 0.6618829369544983, 0.3891746401786804], [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296], ] ), "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, } @require_torch @require_vision class ImageGPTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = ImageGPTFeatureExtractor if is_vision_available() else None def setUp(self): self.feature_extract_tester = ImageGPTFeatureExtractionTester(self) @property def feat_extract_dict(self): return self.feature_extract_tester.prepare_feat_extract_dict() def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "clusters")) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "size")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) def test_feat_extract_to_json_string(self): feat_extract = self.feature_extraction_class(**self.feat_extract_dict) obj = json.loads(feat_extract.to_json_string()) for key, value in self.feat_extract_dict.items(): if key == "clusters": self.assertTrue(np.array_equal(value, obj[key])) else: self.assertEqual(obj[key], value) def test_feat_extract_to_json_file(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: json_file_path = os.path.join(tmpdirname, "feat_extract.json") feat_extract_first.to_json_file(json_file_path) feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path).to_dict() feat_extract_first = feat_extract_first.to_dict() for key, value in feat_extract_first.items(): if key == "clusters": self.assertTrue(np.array_equal(value, feat_extract_second[key])) else: self.assertEqual(feat_extract_first[key], value) def test_feat_extract_from_and_save_pretrained(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: feat_extract_first.save_pretrained(tmpdirname) feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname).to_dict() feat_extract_first = feat_extract_first.to_dict() for key, value in feat_extract_first.items(): if key == "clusters": self.assertTrue(np.array_equal(value, feat_extract_second[key])) else: self.assertEqual(feat_extract_first[key], value) @unittest.skip("ImageGPT requires clusters at initialization") def test_init_without_params(self): pass def prepare_images(): dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test") image1 = Image.open(dataset[4]["file"]) image2 = Image.open(dataset[5]["file"]) images = [image1, image2] return images @require_vision @require_torch class ImageGPTFeatureExtractorIntegrationTest(unittest.TestCase): @slow def test_image(self): feature_extractor = ImageGPTFeatureExtractor.from_pretrained("openai/imagegpt-small") images = prepare_images() # test non-batched encoding = feature_extractor(images[0], return_tensors="pt") self.assertIsInstance(encoding.input_ids, torch.LongTensor) self.assertEqual(encoding.input_ids.shape, (1, 1024)) expected_slice = [306, 191, 191] self.assertEqual(encoding.input_ids[0, :3].tolist(), expected_slice) # test batched encoding = feature_extractor(images, return_tensors="pt") self.assertIsInstance(encoding.input_ids, torch.LongTensor) self.assertEqual(encoding.input_ids.shape, (2, 1024)) expected_slice = [303, 13, 13] self.assertEqual(encoding.input_ids[1, -3:].tolist(), expected_slice)

# coding=utf-8 # Copyright 2021 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import tempfile import unittest import numpy as np from datasets import load_dataset from transformers.testing_utils import require_torch, require_vision, slow from transformers.utils import is_torch_available, is_vision_available from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import ImageGPTFeatureExtractor class ImageGPTFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, ): size = size if size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_normalize = do_normalize def prepare_feat_extract_dict(self): return { # here we create 2 clusters for the sake of simplicity "clusters": np.asarray( [ [0.8866443634033203, 0.6618829369544983, 0.3891746401786804], [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296], ] ), "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, } @require_torch @require_vision class ImageGPTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = ImageGPTFeatureExtractor if is_vision_available() else None def setUp(self): self.feature_extract_tester = ImageGPTFeatureExtractionTester(self) @property def feat_extract_dict(self): return self.feature_extract_tester.prepare_feat_extract_dict() def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "clusters")) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "size")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) def test_feat_extract_to_json_string(self): feat_extract = self.feature_extraction_class(**self.feat_extract_dict) obj = json.loads(feat_extract.to_json_string()) for key, value in self.feat_extract_dict.items(): if key == "clusters": self.assertTrue(np.array_equal(value, obj[key])) else: self.assertEqual(obj[key], value) def test_feat_extract_to_json_file(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: json_file_path = os.path.join(tmpdirname, "feat_extract.json") feat_extract_first.to_json_file(json_file_path) feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path).to_dict() feat_extract_first = feat_extract_first.to_dict() for key, value in feat_extract_first.items(): if key == "clusters": self.assertTrue(np.array_equal(value, feat_extract_second[key])) else: self.assertEqual(feat_extract_first[key], value) def test_feat_extract_from_and_save_pretrained(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: feat_extract_first.save_pretrained(tmpdirname) feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname).to_dict() feat_extract_first = feat_extract_first.to_dict() for key, value in feat_extract_first.items(): if key == "clusters": self.assertTrue(np.array_equal(value, feat_extract_second[key])) else: self.assertEqual(feat_extract_first[key], value) @unittest.skip("ImageGPT requires clusters at initialization") def test_init_without_params(self): pass def prepare_images(): dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test") image1 = Image.open(dataset[4]["file"]) image2 = Image.open(dataset[5]["file"]) images = [image1, image2] return images @require_vision @require_torch class ImageGPTFeatureExtractorIntegrationTest(unittest.TestCase): @slow def test_image(self): feature_extractor = ImageGPTFeatureExtractor.from_pretrained("openai/imagegpt-small") images = prepare_images() # test non-batched encoding = feature_extractor(images[0], return_tensors="pt") self.assertIsInstance(encoding.input_ids, torch.LongTensor) self.assertEqual(encoding.input_ids.shape, (1, 1024)) expected_slice = [306, 191, 191] self.assertEqual(encoding.input_ids[0, :3].tolist(), expected_slice) # test batched encoding = feature_extractor(images, return_tensors="pt") self.assertIsInstance(encoding.input_ids, torch.LongTensor) self.assertEqual(encoding.input_ids.shape, (2, 1024)) expected_slice = [303, 13, 13] self.assertEqual(encoding.input_ids[1, -3:].tolist(), expected_slice)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/levit/feature_extraction_levit.py

# coding=utf-8 # Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Feature extractor class for LeViT.""" from ...utils import logging from .image_processing_levit import LevitImageProcessor logger = logging.get_logger(__name__) # Feature extractor for Levit is being replaced by image processor LevitFeatureExtractor = LevitImageProcessor

# coding=utf-8 # Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Feature extractor class for LeViT.""" from ...utils import logging from .image_processing_levit import LevitImageProcessor logger = logging.get_logger(__name__) # Feature extractor for Levit is being replaced by image processor LevitFeatureExtractor = LevitImageProcessor

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/transfo_xl/modeling_transfo_xl_utilities.py

# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities for PyTorch Transformer XL model. Directly adapted from https://github.com/kimiyoung/transformer-xl. """ import torch from torch import nn # CUDA_MAJOR = int(torch.version.cuda.split('.')[0]) # CUDA_MINOR = int(torch.version.cuda.split('.')[1]) class ProjectedAdaptiveLogSoftmax(nn.Module): def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, keep_order=False): super().__init__() self.n_token = n_token self.d_embed = d_embed self.d_proj = d_proj self.cutoffs = cutoffs + [n_token] self.cutoff_ends = [0] + self.cutoffs self.div_val = div_val self.shortlist_size = self.cutoffs[0] self.n_clusters = len(self.cutoffs) - 1 self.head_size = self.shortlist_size + self.n_clusters if self.n_clusters > 0: self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed)) self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters)) self.out_layers = nn.ModuleList() self.out_projs = nn.ParameterList() if div_val == 1: for i in range(len(self.cutoffs)): if d_proj != d_embed: self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed))) else: self.out_projs.append(None) self.out_layers.append(nn.Linear(d_embed, n_token)) else: for i in range(len(self.cutoffs)): l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] d_emb_i = d_embed // (div_val**i) self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_emb_i))) self.out_layers.append(nn.Linear(d_emb_i, r_idx - l_idx)) self.keep_order = keep_order def _compute_logit(self, hidden, weight, bias, proj): if proj is None: logit = nn.functional.linear(hidden, weight, bias=bias) else: # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1: proj_hid = nn.functional.linear(hidden, proj.t().contiguous()) logit = nn.functional.linear(proj_hid, weight, bias=bias) # else: # logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t())) # if bias is not None: # logit = logit + bias return logit def forward(self, hidden, labels=None, keep_order=False): """ Params: hidden :: [len*bsz x d_proj] labels :: [len*bsz Return: if labels is None: out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabulary else: out :: [(len-1)*bsz] Negative log likelihood. We could replace this implementation by the native PyTorch one if theirs had an option to set bias on all clusters in the native one. here: https://github.com/pytorch/pytorch/blob/dbe6a7a9ff1a364a8706bf5df58a1ca96d2fd9da/torch/nn/modules/adaptive.py#L138 """ if labels is not None: # Shift so that tokens < n predict n hidden = hidden[..., :-1, :].contiguous() labels = labels[..., 1:].contiguous() hidden = hidden.view(-1, hidden.size(-1)) labels = labels.view(-1) if hidden.size(0) != labels.size(0): raise RuntimeError("Input and labels should have the same size in the batch dimension.") else: hidden = hidden.view(-1, hidden.size(-1)) if self.n_clusters == 0: logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0]) if labels is not None: out = -nn.functional.log_softmax(logit, dim=-1).gather(1, labels.unsqueeze(1)).squeeze(1) else: out = nn.functional.log_softmax(logit, dim=-1) else: # construct weights and biases weights, biases = [], [] for i in range(len(self.cutoffs)): if self.div_val == 1: l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] weight_i = self.out_layers[0].weight[l_idx:r_idx] bias_i = self.out_layers[0].bias[l_idx:r_idx] else: weight_i = self.out_layers[i].weight bias_i = self.out_layers[i].bias if i == 0: weight_i = torch.cat([weight_i, self.cluster_weight], dim=0) bias_i = torch.cat([bias_i, self.cluster_bias], dim=0) weights.append(weight_i) biases.append(bias_i) head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0] head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj) head_logprob = nn.functional.log_softmax(head_logit, dim=1) if labels is None: out = hidden.new_empty((head_logit.size(0), self.n_token)) else: out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device) offset = 0 cutoff_values = [0] + self.cutoffs for i in range(len(cutoff_values) - 1): l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1] if labels is not None: mask_i = (labels >= l_idx) & (labels < r_idx) indices_i = mask_i.nonzero().squeeze() if indices_i.numel() == 0: continue target_i = labels.index_select(0, indices_i) - l_idx head_logprob_i = head_logprob.index_select(0, indices_i) hidden_i = hidden.index_select(0, indices_i) else: hidden_i = hidden if i == 0: if labels is not None: logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1) else: out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]] else: weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i] tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i) tail_logprob_i = nn.functional.log_softmax(tail_logit_i, dim=1) cluster_prob_idx = self.cutoffs[0] + i - 1 # No probability for the head cluster if labels is not None: logprob_i = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather( 1, target_i[:, None] ).squeeze(1) else: logprob_i = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i out[:, l_idx:r_idx] = logprob_i if labels is not None: if (hasattr(self, "keep_order") and self.keep_order) or keep_order: out.index_copy_(0, indices_i, -logprob_i) else: out[offset : offset + logprob_i.size(0)].copy_(-logprob_i) offset += logprob_i.size(0) return out def log_prob(self, hidden): r""" Computes log probabilities for all \\(n\_classes\\) From: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/adaptive.p Args: hidden (Tensor): a minibatch of example Returns: log-probabilities of for each class \\(c\\) in range \\(0 <= c <= n\_classes\\), where \\(n\_classes\\) is a parameter passed to `AdaptiveLogSoftmaxWithLoss` constructor. Shape: - Input: \\((N, in\_features)\\) - Output: \\((N, n\_classes)\\) """ if self.n_clusters == 0: logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0]) return nn.functional.log_softmax(logit, dim=-1) else: # construct weights and biases weights, biases = [], [] for i in range(len(self.cutoffs)): if self.div_val == 1: l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] weight_i = self.out_layers[0].weight[l_idx:r_idx] bias_i = self.out_layers[0].bias[l_idx:r_idx] else: weight_i = self.out_layers[i].weight bias_i = self.out_layers[i].bias if i == 0: weight_i = torch.cat([weight_i, self.cluster_weight], dim=0) bias_i = torch.cat([bias_i, self.cluster_bias], dim=0) weights.append(weight_i) biases.append(bias_i) head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0] head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj) out = hidden.new_empty((head_logit.size(0), self.n_token)) head_logprob = nn.functional.log_softmax(head_logit, dim=1) cutoff_values = [0] + self.cutoffs for i in range(len(cutoff_values) - 1): start_idx, stop_idx = cutoff_values[i], cutoff_values[i + 1] if i == 0: out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]] else: weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i] tail_logit_i = self._compute_logit(hidden, weight_i, bias_i, proj_i) tail_logprob_i = nn.functional.log_softmax(tail_logit_i, dim=1) logprob_i = head_logprob[:, -i] + tail_logprob_i out[:, start_idx, stop_idx] = logprob_i return out

# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities for PyTorch Transformer XL model. Directly adapted from https://github.com/kimiyoung/transformer-xl. """ import torch from torch import nn # CUDA_MAJOR = int(torch.version.cuda.split('.')[0]) # CUDA_MINOR = int(torch.version.cuda.split('.')[1]) class ProjectedAdaptiveLogSoftmax(nn.Module): def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, keep_order=False): super().__init__() self.n_token = n_token self.d_embed = d_embed self.d_proj = d_proj self.cutoffs = cutoffs + [n_token] self.cutoff_ends = [0] + self.cutoffs self.div_val = div_val self.shortlist_size = self.cutoffs[0] self.n_clusters = len(self.cutoffs) - 1 self.head_size = self.shortlist_size + self.n_clusters if self.n_clusters > 0: self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed)) self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters)) self.out_layers = nn.ModuleList() self.out_projs = nn.ParameterList() if div_val == 1: for i in range(len(self.cutoffs)): if d_proj != d_embed: self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed))) else: self.out_projs.append(None) self.out_layers.append(nn.Linear(d_embed, n_token)) else: for i in range(len(self.cutoffs)): l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] d_emb_i = d_embed // (div_val**i) self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_emb_i))) self.out_layers.append(nn.Linear(d_emb_i, r_idx - l_idx)) self.keep_order = keep_order def _compute_logit(self, hidden, weight, bias, proj): if proj is None: logit = nn.functional.linear(hidden, weight, bias=bias) else: # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1: proj_hid = nn.functional.linear(hidden, proj.t().contiguous()) logit = nn.functional.linear(proj_hid, weight, bias=bias) # else: # logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t())) # if bias is not None: # logit = logit + bias return logit def forward(self, hidden, labels=None, keep_order=False): """ Params: hidden :: [len*bsz x d_proj] labels :: [len*bsz Return: if labels is None: out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabulary else: out :: [(len-1)*bsz] Negative log likelihood. We could replace this implementation by the native PyTorch one if theirs had an option to set bias on all clusters in the native one. here: https://github.com/pytorch/pytorch/blob/dbe6a7a9ff1a364a8706bf5df58a1ca96d2fd9da/torch/nn/modules/adaptive.py#L138 """ if labels is not None: # Shift so that tokens < n predict n hidden = hidden[..., :-1, :].contiguous() labels = labels[..., 1:].contiguous() hidden = hidden.view(-1, hidden.size(-1)) labels = labels.view(-1) if hidden.size(0) != labels.size(0): raise RuntimeError("Input and labels should have the same size in the batch dimension.") else: hidden = hidden.view(-1, hidden.size(-1)) if self.n_clusters == 0: logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0]) if labels is not None: out = -nn.functional.log_softmax(logit, dim=-1).gather(1, labels.unsqueeze(1)).squeeze(1) else: out = nn.functional.log_softmax(logit, dim=-1) else: # construct weights and biases weights, biases = [], [] for i in range(len(self.cutoffs)): if self.div_val == 1: l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] weight_i = self.out_layers[0].weight[l_idx:r_idx] bias_i = self.out_layers[0].bias[l_idx:r_idx] else: weight_i = self.out_layers[i].weight bias_i = self.out_layers[i].bias if i == 0: weight_i = torch.cat([weight_i, self.cluster_weight], dim=0) bias_i = torch.cat([bias_i, self.cluster_bias], dim=0) weights.append(weight_i) biases.append(bias_i) head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0] head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj) head_logprob = nn.functional.log_softmax(head_logit, dim=1) if labels is None: out = hidden.new_empty((head_logit.size(0), self.n_token)) else: out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device) offset = 0 cutoff_values = [0] + self.cutoffs for i in range(len(cutoff_values) - 1): l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1] if labels is not None: mask_i = (labels >= l_idx) & (labels < r_idx) indices_i = mask_i.nonzero().squeeze() if indices_i.numel() == 0: continue target_i = labels.index_select(0, indices_i) - l_idx head_logprob_i = head_logprob.index_select(0, indices_i) hidden_i = hidden.index_select(0, indices_i) else: hidden_i = hidden if i == 0: if labels is not None: logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1) else: out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]] else: weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i] tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i) tail_logprob_i = nn.functional.log_softmax(tail_logit_i, dim=1) cluster_prob_idx = self.cutoffs[0] + i - 1 # No probability for the head cluster if labels is not None: logprob_i = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather( 1, target_i[:, None] ).squeeze(1) else: logprob_i = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i out[:, l_idx:r_idx] = logprob_i if labels is not None: if (hasattr(self, "keep_order") and self.keep_order) or keep_order: out.index_copy_(0, indices_i, -logprob_i) else: out[offset : offset + logprob_i.size(0)].copy_(-logprob_i) offset += logprob_i.size(0) return out def log_prob(self, hidden): r""" Computes log probabilities for all \\(n\_classes\\) From: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/adaptive.p Args: hidden (Tensor): a minibatch of example Returns: log-probabilities of for each class \\(c\\) in range \\(0 <= c <= n\_classes\\), where \\(n\_classes\\) is a parameter passed to `AdaptiveLogSoftmaxWithLoss` constructor. Shape: - Input: \\((N, in\_features)\\) - Output: \\((N, n\_classes)\\) """ if self.n_clusters == 0: logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0]) return nn.functional.log_softmax(logit, dim=-1) else: # construct weights and biases weights, biases = [], [] for i in range(len(self.cutoffs)): if self.div_val == 1: l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] weight_i = self.out_layers[0].weight[l_idx:r_idx] bias_i = self.out_layers[0].bias[l_idx:r_idx] else: weight_i = self.out_layers[i].weight bias_i = self.out_layers[i].bias if i == 0: weight_i = torch.cat([weight_i, self.cluster_weight], dim=0) bias_i = torch.cat([bias_i, self.cluster_bias], dim=0) weights.append(weight_i) biases.append(bias_i) head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0] head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj) out = hidden.new_empty((head_logit.size(0), self.n_token)) head_logprob = nn.functional.log_softmax(head_logit, dim=1) cutoff_values = [0] + self.cutoffs for i in range(len(cutoff_values) - 1): start_idx, stop_idx = cutoff_values[i], cutoff_values[i + 1] if i == 0: out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]] else: weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i] tail_logit_i = self._compute_logit(hidden, weight_i, bias_i, proj_i) tail_logprob_i = nn.functional.log_softmax(tail_logit_i, dim=1) logprob_i = head_logprob[:, -i] + tail_logprob_i out[:, start_idx, stop_idx] = logprob_i return out

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch XLM RoBERTa xl,xxl model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN, gelu from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_xlm_roberta_xl import XLMRobertaXLConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "xlm-roberta-xlarge" _CONFIG_FOR_DOC = "XLMRobertaXLConfig" _TOKENIZER_FOR_DOC = "XLMRobertaTokenizer" XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/xlm-roberta-xl", "facebook/xlm-roberta-xxl", # See all RoBERTa models at https://huggingface.co/models?filter=xlm-roberta-xl ] class XLMRobertaXLEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) # End copy self.padding_idx = config.pad_token_id self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx ) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.dropout(embeddings) return embeddings # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings.create_position_ids_from_inputs_embeds def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) # Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->XLMRobertaXL class XLMRobertaXLSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.is_decoder = config.is_decoder def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) use_cache = past_key_value is not None if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in XLMRobertaXLModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs class XLMRobertaXLSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class XLMRobertaXLAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self_attn_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.self = XLMRobertaXLSelfAttention(config, position_embedding_type=position_embedding_type) self.output = XLMRobertaXLSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): intermediate = self.self_attn_layer_norm(hidden_states) self_outputs = self.self( intermediate, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate class XLMRobertaXLIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class XLMRobertaXLOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class XLMRobertaXLLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = XLMRobertaXLAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = XLMRobertaXLAttention(config, position_embedding_type="absolute") self.intermediate = XLMRobertaXLIntermediate(config) self.output = XLMRobertaXLOutput(config) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.LayerNorm(attention_output) intermediate_output = self.intermediate(intermediate_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class XLMRobertaXLEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([XLMRobertaXLLayer(config) for _ in range(config.num_hidden_layers)]) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.gradient_checkpointing = False def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) hidden_states = self.LayerNorm(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertPooler class XLMRobertaXLPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class XLMRobertaXLPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = XLMRobertaXLConfig base_model_prefix = "roberta" # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def update_keys_to_ignore(self, config, del_keys_to_ignore): """Remove some keys from ignore list""" if not config.tie_word_embeddings: # must make a new list, or the class variable gets modified! self._keys_to_ignore_on_save = [k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore] self._keys_to_ignore_on_load_missing = [ k for k in self._keys_to_ignore_on_load_missing if k not in del_keys_to_ignore ] XLM_ROBERTA_XL_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`XLMRobertaXLConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ XLM_ROBERTA_XL_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare XLM-RoBERTa-xlarge Model transformer outputting raw hidden-states without any specific head on top.", XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLModel(XLMRobertaXLPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in *Attention is all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 """ _keys_to_ignore_on_load_missing = [r"position_ids"] # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->XLMRobertaXL def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = XLMRobertaXLEmbeddings(config) self.encoder = XLMRobertaXLEncoder(config) self.pooler = XLMRobertaXLPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_bert.BertModel.forward def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings( """XLM-RoBERTa-xlarge Model with a `language modeling` head on top for CLM fine-tuning.""", XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLForCausalLM(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if not config.is_decoder: logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`") self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False) self.lm_head = XLMRobertaXLLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) self.init_weights() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Returns: Example: ```python >>> from transformers import RobertaTokenizer, RobertaForCausalLM, RobertaConfig >>> import torch >>> tokenizer = RobertaTokenizer.from_pretrained("roberta-base") >>> config = RobertaConfig.from_pretrained("roberta-base") >>> config.is_decoder = True >>> model = RobertaForCausalLM.from_pretrained("roberta-base", config=config) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past} def _reorder_cache(self, past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past @add_start_docstrings( """XLM-RoBERTa-xlarge Model with a `language modeling` head on top.""", XLM_ROBERTA_XL_START_DOCSTRING ) class XLMRobertaXLForMaskedLM(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False) self.lm_head = XLMRobertaXLLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) self.init_weights() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class XLMRobertaXLLMHead(nn.Module): """XLM-Roberta-xlarge Head for masked language modeling.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.decoder = nn.Linear(config.hidden_size, config.vocab_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.decoder.bias = self.bias def forward(self, features, **kwargs): x = self.dense(features) x = gelu(x) x = self.layer_norm(x) # project back to size of vocabulary with bias x = self.decoder(x) return x def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) self.bias = self.decoder.bias @add_start_docstrings( """ XLM-RoBERTa-xlarge Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLForSequenceClassification(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False) self.classifier = XLMRobertaXLClassificationHead(config) self.init_weights() @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ XLM-Roberta-xlarge Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLForMultipleChoice(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.roberta = XLMRobertaXLModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) self.init_weights() @add_start_docstrings_to_model_forward( XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.roberta( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ XLM-Roberta-xlarge Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLForTokenClassification(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.init_weights() @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() # Only keep active parts of the loss if attention_mask is not None: active_loss = attention_mask.view(-1) == 1 active_logits = logits.view(-1, self.num_labels) active_labels = torch.where( active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels) ) loss = loss_fct(active_logits, active_labels) else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class XLMRobertaXLClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) return x @add_start_docstrings( """ XLM-Roberta-xlarge Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLForQuestionAnswering(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.init_weights() @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch XLM RoBERTa xl,xxl model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN, gelu from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_xlm_roberta_xl import XLMRobertaXLConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "xlm-roberta-xlarge" _CONFIG_FOR_DOC = "XLMRobertaXLConfig" _TOKENIZER_FOR_DOC = "XLMRobertaTokenizer" XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/xlm-roberta-xl", "facebook/xlm-roberta-xxl", # See all RoBERTa models at https://huggingface.co/models?filter=xlm-roberta-xl ] class XLMRobertaXLEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) # End copy self.padding_idx = config.pad_token_id self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx ) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.dropout(embeddings) return embeddings # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings.create_position_ids_from_inputs_embeds def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) # Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->XLMRobertaXL class XLMRobertaXLSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.is_decoder = config.is_decoder def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) use_cache = past_key_value is not None if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in XLMRobertaXLModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs class XLMRobertaXLSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class XLMRobertaXLAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self_attn_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.self = XLMRobertaXLSelfAttention(config, position_embedding_type=position_embedding_type) self.output = XLMRobertaXLSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): intermediate = self.self_attn_layer_norm(hidden_states) self_outputs = self.self( intermediate, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate class XLMRobertaXLIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class XLMRobertaXLOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class XLMRobertaXLLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = XLMRobertaXLAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = XLMRobertaXLAttention(config, position_embedding_type="absolute") self.intermediate = XLMRobertaXLIntermediate(config) self.output = XLMRobertaXLOutput(config) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.LayerNorm(attention_output) intermediate_output = self.intermediate(intermediate_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class XLMRobertaXLEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([XLMRobertaXLLayer(config) for _ in range(config.num_hidden_layers)]) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.gradient_checkpointing = False def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) hidden_states = self.LayerNorm(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertPooler class XLMRobertaXLPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class XLMRobertaXLPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = XLMRobertaXLConfig base_model_prefix = "roberta" # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def update_keys_to_ignore(self, config, del_keys_to_ignore): """Remove some keys from ignore list""" if not config.tie_word_embeddings: # must make a new list, or the class variable gets modified! self._keys_to_ignore_on_save = [k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore] self._keys_to_ignore_on_load_missing = [ k for k in self._keys_to_ignore_on_load_missing if k not in del_keys_to_ignore ] XLM_ROBERTA_XL_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`XLMRobertaXLConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ XLM_ROBERTA_XL_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare XLM-RoBERTa-xlarge Model transformer outputting raw hidden-states without any specific head on top.", XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLModel(XLMRobertaXLPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in *Attention is all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 """ _keys_to_ignore_on_load_missing = [r"position_ids"] # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->XLMRobertaXL def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = XLMRobertaXLEmbeddings(config) self.encoder = XLMRobertaXLEncoder(config) self.pooler = XLMRobertaXLPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_bert.BertModel.forward def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings( """XLM-RoBERTa-xlarge Model with a `language modeling` head on top for CLM fine-tuning.""", XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLForCausalLM(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if not config.is_decoder: logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`") self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False) self.lm_head = XLMRobertaXLLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) self.init_weights() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Returns: Example: ```python >>> from transformers import RobertaTokenizer, RobertaForCausalLM, RobertaConfig >>> import torch >>> tokenizer = RobertaTokenizer.from_pretrained("roberta-base") >>> config = RobertaConfig.from_pretrained("roberta-base") >>> config.is_decoder = True >>> model = RobertaForCausalLM.from_pretrained("roberta-base", config=config) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past} def _reorder_cache(self, past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past @add_start_docstrings( """XLM-RoBERTa-xlarge Model with a `language modeling` head on top.""", XLM_ROBERTA_XL_START_DOCSTRING ) class XLMRobertaXLForMaskedLM(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False) self.lm_head = XLMRobertaXLLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) self.init_weights() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class XLMRobertaXLLMHead(nn.Module): """XLM-Roberta-xlarge Head for masked language modeling.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.decoder = nn.Linear(config.hidden_size, config.vocab_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.decoder.bias = self.bias def forward(self, features, **kwargs): x = self.dense(features) x = gelu(x) x = self.layer_norm(x) # project back to size of vocabulary with bias x = self.decoder(x) return x def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) self.bias = self.decoder.bias @add_start_docstrings( """ XLM-RoBERTa-xlarge Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLForSequenceClassification(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False) self.classifier = XLMRobertaXLClassificationHead(config) self.init_weights() @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ XLM-Roberta-xlarge Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLForMultipleChoice(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.roberta = XLMRobertaXLModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) self.init_weights() @add_start_docstrings_to_model_forward( XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.roberta( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ XLM-Roberta-xlarge Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLForTokenClassification(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.init_weights() @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() # Only keep active parts of the loss if attention_mask is not None: active_loss = attention_mask.view(-1) == 1 active_logits = logits.view(-1, self.num_labels) active_labels = torch.where( active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels) ) loss = loss_fct(active_logits, active_labels) else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class XLMRobertaXLClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) return x @add_start_docstrings( """ XLM-Roberta-xlarge Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, XLM_ROBERTA_XL_START_DOCSTRING, ) class XLMRobertaXLForQuestionAnswering(XLMRobertaXLPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.init_weights() @add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/legacy/token-classification/tasks.py

import logging import os from typing import List, TextIO, Union from conllu import parse_incr from utils_ner import InputExample, Split, TokenClassificationTask logger = logging.getLogger(__name__) class NER(TokenClassificationTask): def __init__(self, label_idx=-1): # in NER datasets, the last column is usually reserved for NER label self.label_idx = label_idx def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]: if isinstance(mode, Split): mode = mode.value file_path = os.path.join(data_dir, f"{mode}.txt") guid_index = 1 examples = [] with open(file_path, encoding="utf-8") as f: words = [] labels = [] for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": if words: examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) guid_index += 1 words = [] labels = [] else: splits = line.split(" ") words.append(splits[0]) if len(splits) > 1: labels.append(splits[self.label_idx].replace("\n", "")) else: # Examples could have no label for mode = "test" labels.append("O") if words: examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) return examples def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List): example_id = 0 for line in test_input_reader: if line.startswith("-DOCSTART-") or line == "" or line == "\n": writer.write(line) if not preds_list[example_id]: example_id += 1 elif preds_list[example_id]: output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n" writer.write(output_line) else: logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]) def get_labels(self, path: str) -> List[str]: if path: with open(path, "r") as f: labels = f.read().splitlines() if "O" not in labels: labels = ["O"] + labels return labels else: return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"] class Chunk(NER): def __init__(self): # in CONLL2003 dataset chunk column is second-to-last super().__init__(label_idx=-2) def get_labels(self, path: str) -> List[str]: if path: with open(path, "r") as f: labels = f.read().splitlines() if "O" not in labels: labels = ["O"] + labels return labels else: return [ "O", "B-ADVP", "B-INTJ", "B-LST", "B-PRT", "B-NP", "B-SBAR", "B-VP", "B-ADJP", "B-CONJP", "B-PP", "I-ADVP", "I-INTJ", "I-LST", "I-PRT", "I-NP", "I-SBAR", "I-VP", "I-ADJP", "I-CONJP", "I-PP", ] class POS(TokenClassificationTask): def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]: if isinstance(mode, Split): mode = mode.value file_path = os.path.join(data_dir, f"{mode}.txt") guid_index = 1 examples = [] with open(file_path, encoding="utf-8") as f: for sentence in parse_incr(f): words = [] labels = [] for token in sentence: words.append(token["form"]) labels.append(token["upos"]) assert len(words) == len(labels) if words: examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) guid_index += 1 return examples def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List): example_id = 0 for sentence in parse_incr(test_input_reader): s_p = preds_list[example_id] out = "" for token in sentence: out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0)}) ' out += "\n" writer.write(out) example_id += 1 def get_labels(self, path: str) -> List[str]: if path: with open(path, "r") as f: return f.read().splitlines() else: return [ "ADJ", "ADP", "ADV", "AUX", "CCONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X", ]

import logging import os from typing import List, TextIO, Union from conllu import parse_incr from utils_ner import InputExample, Split, TokenClassificationTask logger = logging.getLogger(__name__) class NER(TokenClassificationTask): def __init__(self, label_idx=-1): # in NER datasets, the last column is usually reserved for NER label self.label_idx = label_idx def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]: if isinstance(mode, Split): mode = mode.value file_path = os.path.join(data_dir, f"{mode}.txt") guid_index = 1 examples = [] with open(file_path, encoding="utf-8") as f: words = [] labels = [] for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": if words: examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) guid_index += 1 words = [] labels = [] else: splits = line.split(" ") words.append(splits[0]) if len(splits) > 1: labels.append(splits[self.label_idx].replace("\n", "")) else: # Examples could have no label for mode = "test" labels.append("O") if words: examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) return examples def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List): example_id = 0 for line in test_input_reader: if line.startswith("-DOCSTART-") or line == "" or line == "\n": writer.write(line) if not preds_list[example_id]: example_id += 1 elif preds_list[example_id]: output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n" writer.write(output_line) else: logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]) def get_labels(self, path: str) -> List[str]: if path: with open(path, "r") as f: labels = f.read().splitlines() if "O" not in labels: labels = ["O"] + labels return labels else: return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"] class Chunk(NER): def __init__(self): # in CONLL2003 dataset chunk column is second-to-last super().__init__(label_idx=-2) def get_labels(self, path: str) -> List[str]: if path: with open(path, "r") as f: labels = f.read().splitlines() if "O" not in labels: labels = ["O"] + labels return labels else: return [ "O", "B-ADVP", "B-INTJ", "B-LST", "B-PRT", "B-NP", "B-SBAR", "B-VP", "B-ADJP", "B-CONJP", "B-PP", "I-ADVP", "I-INTJ", "I-LST", "I-PRT", "I-NP", "I-SBAR", "I-VP", "I-ADJP", "I-CONJP", "I-PP", ] class POS(TokenClassificationTask): def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]: if isinstance(mode, Split): mode = mode.value file_path = os.path.join(data_dir, f"{mode}.txt") guid_index = 1 examples = [] with open(file_path, encoding="utf-8") as f: for sentence in parse_incr(f): words = [] labels = [] for token in sentence: words.append(token["form"]) labels.append(token["upos"]) assert len(words) == len(labels) if words: examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) guid_index += 1 return examples def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List): example_id = 0 for sentence in parse_incr(test_input_reader): s_p = preds_list[example_id] out = "" for token in sentence: out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0)}) ' out += "\n" writer.write(out) example_id += 1 def get_labels(self, path: str) -> List[str]: if path: with open(path, "r") as f: return f.read().splitlines() else: return [ "ADJ", "ADP", "ADV", "AUX", "CCONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X", ]

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/distillation/run_squad_w_distillation.py

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This is the exact same script as `examples/question-answering/run_squad.py` (as of 2020, January 8th) with an additional and optional step of distillation.""" import argparse import glob import logging import os import random import timeit import numpy as np import torch from torch import nn from torch.utils.data import DataLoader, RandomSampler, SequentialSampler from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange import transformers from transformers import ( WEIGHTS_NAME, AdamW, BertConfig, BertForQuestionAnswering, BertTokenizer, DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer, RobertaConfig, RobertaForQuestionAnswering, RobertaTokenizer, XLMConfig, XLMForQuestionAnswering, XLMTokenizer, XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer, get_linear_schedule_with_warmup, squad_convert_examples_to_features, ) from transformers.data.metrics.squad_metrics import ( compute_predictions_log_probs, compute_predictions_logits, squad_evaluate, ) from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor from transformers.trainer_utils import is_main_process try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CLASSES = { "bert": (BertConfig, BertForQuestionAnswering, BertTokenizer), "xlnet": (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer), "xlm": (XLMConfig, XLMForQuestionAnswering, XLMTokenizer), "distilbert": (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer), "roberta": (RobertaConfig, RobertaForQuestionAnswering, RobertaTokenizer), } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def to_list(tensor): return tensor.detach().cpu().tolist() def train(args, train_dataset, model, tokenizer, teacher=None): """Train the model""" if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) # Check if saved optimizer or scheduler states exist if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt") ): # Load in optimizer and scheduler states optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = 1 epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): try: # set global_step to gobal_step of last saved checkpoint from model path checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0] global_step = int(checkpoint_suffix) epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) except ValueError: logger.info(" Starting fine-tuning.") tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0] ) # Added here for reproductibility set_seed(args) for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() if teacher is not None: teacher.eval() batch = tuple(t.to(args.device) for t in batch) inputs = { "input_ids": batch[0], "attention_mask": batch[1], "start_positions": batch[3], "end_positions": batch[4], } if args.model_type != "distilbert": inputs["token_type_ids"] = None if args.model_type == "xlm" else batch[2] if args.model_type in ["xlnet", "xlm"]: inputs.update({"cls_index": batch[5], "p_mask": batch[6]}) if args.version_2_with_negative: inputs.update({"is_impossible": batch[7]}) outputs = model(**inputs) loss, start_logits_stu, end_logits_stu = outputs # Distillation loss if teacher is not None: if "token_type_ids" not in inputs: inputs["token_type_ids"] = None if args.teacher_type == "xlm" else batch[2] with torch.no_grad(): start_logits_tea, end_logits_tea = teacher( input_ids=inputs["input_ids"], token_type_ids=inputs["token_type_ids"], attention_mask=inputs["attention_mask"], ) assert start_logits_tea.size() == start_logits_stu.size() assert end_logits_tea.size() == end_logits_stu.size() loss_fct = nn.KLDivLoss(reduction="batchmean") loss_start = loss_fct( nn.functional.log_softmax(start_logits_stu / args.temperature, dim=-1), nn.functional.softmax(start_logits_tea / args.temperature, dim=-1), ) * (args.temperature**2) loss_end = loss_fct( nn.functional.log_softmax(end_logits_stu / args.temperature, dim=-1), nn.functional.softmax(end_logits_tea / args.temperature, dim=-1), ) * (args.temperature**2) loss_ce = (loss_start + loss_end) / 2.0 loss = args.alpha_ce * loss_ce + args.alpha_squad * loss if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 # Log metrics if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Only evaluate when single GPU otherwise metrics may not average well if args.local_rank == -1 and args.evaluate_during_training: results = evaluate(args, model, tokenizer) for key, value in results.items(): tb_writer.add_scalar("eval_{}".format(key), value, global_step) tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step) tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step) logging_loss = tr_loss if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix=""): dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True) if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(dataset) eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu evaluate if args.n_gpu > 1 and not isinstance(model, nn.DataParallel): model = nn.DataParallel(model) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(dataset)) logger.info(" Batch size = %d", args.eval_batch_size) all_results = [] start_time = timeit.default_timer() for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = {"input_ids": batch[0], "attention_mask": batch[1]} if args.model_type != "distilbert": inputs["token_type_ids"] = None if args.model_type == "xlm" else batch[2] # XLM don't use segment_ids example_indices = batch[3] if args.model_type in ["xlnet", "xlm"]: inputs.update({"cls_index": batch[4], "p_mask": batch[5]}) outputs = model(**inputs) for i, example_index in enumerate(example_indices): eval_feature = features[example_index.item()] unique_id = int(eval_feature.unique_id) output = [to_list(output[i]) for output in outputs] # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler" # models only use two. if len(output) >= 5: start_logits = output[0] start_top_index = output[1] end_logits = output[2] end_top_index = output[3] cls_logits = output[4] result = SquadResult( unique_id, start_logits, end_logits, start_top_index=start_top_index, end_top_index=end_top_index, cls_logits=cls_logits, ) else: start_logits, end_logits = output result = SquadResult(unique_id, start_logits, end_logits) all_results.append(result) evalTime = timeit.default_timer() - start_time logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset)) # Compute predictions output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix)) output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix)) if args.version_2_with_negative: output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix)) else: output_null_log_odds_file = None if args.model_type in ["xlnet", "xlm"]: # XLNet uses a more complex post-processing procedure predictions = compute_predictions_log_probs( examples, features, all_results, args.n_best_size, args.max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file, model.config.start_n_top, model.config.end_n_top, args.version_2_with_negative, tokenizer, args.verbose_logging, ) else: predictions = compute_predictions_logits( examples, features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, args.verbose_logging, args.version_2_with_negative, args.null_score_diff_threshold, tokenizer, ) # Compute the F1 and exact scores. results = squad_evaluate(examples, predictions) return results def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False): if args.local_rank not in [-1, 0] and not evaluate: # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() # Load data features from cache or dataset file input_file = args.predict_file if evaluate else args.train_file cached_features_file = os.path.join( os.path.dirname(input_file), "cached_distillation_{}_{}_{}".format( "dev" if evaluate else "train", list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), ), ) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features_and_dataset = torch.load(cached_features_file) try: features, dataset, examples = ( features_and_dataset["features"], features_and_dataset["dataset"], features_and_dataset["examples"], ) except KeyError: raise DeprecationWarning( "You seem to be loading features from an older version of this script please delete the " "file %s in order for it to be created again" % cached_features_file ) else: logger.info("Creating features from dataset file at %s", input_file) processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor() if evaluate: examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file) else: examples = processor.get_train_examples(args.data_dir, filename=args.train_file) features, dataset = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=not evaluate, return_dataset="pt", threads=args.threads, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save({"features": features, "dataset": dataset, "examples": examples}, cached_features_file) if args.local_rank == 0 and not evaluate: # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() if output_examples: return dataset, examples, features return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pretrained model or model identifier from huggingface.co/models", ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints and predictions will be written.", ) # Distillation parameters (optional) parser.add_argument( "--teacher_type", default=None, type=str, help=( "Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for" " distillation." ), ) parser.add_argument( "--teacher_name_or_path", default=None, type=str, help="Path to the already SQuAD fine-tuned teacher model. Only for distillation.", ) parser.add_argument( "--alpha_ce", default=0.5, type=float, help="Distillation loss linear weight. Only for distillation." ) parser.add_argument( "--alpha_squad", default=0.5, type=float, help="True SQuAD loss linear weight. Only for distillation." ) parser.add_argument( "--temperature", default=2.0, type=float, help="Distillation temperature. Only for distillation." ) # Other parameters parser.add_argument( "--data_dir", default=None, type=str, help="The input data dir. Should contain the .json files for the task." + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--train_file", default=None, type=str, help="The input training file. If a data dir is specified, will look for the file there" + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--predict_file", default=None, type=str, help="The input evaluation file. If a data dir is specified, will look for the file there" + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name" ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from huggingface.co", ) parser.add_argument( "--version_2_with_negative", action="store_true", help="If true, the SQuAD examples contain some that do not have an answer.", ) parser.add_argument( "--null_score_diff_threshold", type=float, default=0.0, help="If null_score - best_non_null is greater than the threshold predict null.", ) parser.add_argument( "--max_seq_length", default=384, type=int, help=( "The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded." ), ) parser.add_argument( "--doc_stride", default=128, type=int, help="When splitting up a long document into chunks, how much stride to take between chunks.", ) parser.add_argument( "--max_query_length", default=64, type=int, help=( "The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length." ), ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step." ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model." ) parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation." ) parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform." ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument( "--n_best_size", default=20, type=int, help="The total number of n-best predictions to generate in the nbest_predictions.json output file.", ) parser.add_argument( "--max_answer_length", default=30, type=int, help=( "The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another." ), ) parser.add_argument( "--verbose_logging", action="store_true", help=( "If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation." ), ) parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.") parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory" ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help=( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html" ), ) parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--threads", type=int, default=1, help="multiple threads for converting example to features") args = parser.parse_args() if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Set seed set_seed(args) # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None, ) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.teacher_type is not None: assert args.teacher_name_or_path is not None assert args.alpha_ce > 0.0 assert args.alpha_ce + args.alpha_squad > 0.0 assert args.teacher_type != "distilbert", "We constraint teachers not to be of type DistilBERT." teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[args.teacher_type] teacher_config = teacher_config_class.from_pretrained( args.teacher_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None ) teacher = teacher_model_class.from_pretrained( args.teacher_name_or_path, config=teacher_config, cache_dir=args.cache_dir if args.cache_dir else None ) teacher.to(args.device) else: teacher = None if args.local_rank == 0: # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set. # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will # remove the need for this code, but it is still valid. if args.fp16: try: import apex apex.amp.register_half_function(torch, "einsum") except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer, teacher=teacher) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Save the trained model and the tokenizer if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(args.device) # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory results = {} if args.do_eval and args.local_rank in [-1, 0]: if args.do_train: logger.info("Loading checkpoints saved during training for evaluation") checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ) logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: # Reload the model global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) # Evaluate result = evaluate(args, model, tokenizer, prefix=global_step) result = dict((k + ("_{}".format(global_step) if global_step else ""), v) for k, v in result.items()) results.update(result) logger.info("Results: {}".format(results)) return results if __name__ == "__main__": main()

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This is the exact same script as `examples/question-answering/run_squad.py` (as of 2020, January 8th) with an additional and optional step of distillation.""" import argparse import glob import logging import os import random import timeit import numpy as np import torch from torch import nn from torch.utils.data import DataLoader, RandomSampler, SequentialSampler from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange import transformers from transformers import ( WEIGHTS_NAME, AdamW, BertConfig, BertForQuestionAnswering, BertTokenizer, DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer, RobertaConfig, RobertaForQuestionAnswering, RobertaTokenizer, XLMConfig, XLMForQuestionAnswering, XLMTokenizer, XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer, get_linear_schedule_with_warmup, squad_convert_examples_to_features, ) from transformers.data.metrics.squad_metrics import ( compute_predictions_log_probs, compute_predictions_logits, squad_evaluate, ) from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor from transformers.trainer_utils import is_main_process try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CLASSES = { "bert": (BertConfig, BertForQuestionAnswering, BertTokenizer), "xlnet": (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer), "xlm": (XLMConfig, XLMForQuestionAnswering, XLMTokenizer), "distilbert": (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer), "roberta": (RobertaConfig, RobertaForQuestionAnswering, RobertaTokenizer), } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def to_list(tensor): return tensor.detach().cpu().tolist() def train(args, train_dataset, model, tokenizer, teacher=None): """Train the model""" if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) # Check if saved optimizer or scheduler states exist if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt") ): # Load in optimizer and scheduler states optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = 1 epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): try: # set global_step to gobal_step of last saved checkpoint from model path checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0] global_step = int(checkpoint_suffix) epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) except ValueError: logger.info(" Starting fine-tuning.") tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0] ) # Added here for reproductibility set_seed(args) for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() if teacher is not None: teacher.eval() batch = tuple(t.to(args.device) for t in batch) inputs = { "input_ids": batch[0], "attention_mask": batch[1], "start_positions": batch[3], "end_positions": batch[4], } if args.model_type != "distilbert": inputs["token_type_ids"] = None if args.model_type == "xlm" else batch[2] if args.model_type in ["xlnet", "xlm"]: inputs.update({"cls_index": batch[5], "p_mask": batch[6]}) if args.version_2_with_negative: inputs.update({"is_impossible": batch[7]}) outputs = model(**inputs) loss, start_logits_stu, end_logits_stu = outputs # Distillation loss if teacher is not None: if "token_type_ids" not in inputs: inputs["token_type_ids"] = None if args.teacher_type == "xlm" else batch[2] with torch.no_grad(): start_logits_tea, end_logits_tea = teacher( input_ids=inputs["input_ids"], token_type_ids=inputs["token_type_ids"], attention_mask=inputs["attention_mask"], ) assert start_logits_tea.size() == start_logits_stu.size() assert end_logits_tea.size() == end_logits_stu.size() loss_fct = nn.KLDivLoss(reduction="batchmean") loss_start = loss_fct( nn.functional.log_softmax(start_logits_stu / args.temperature, dim=-1), nn.functional.softmax(start_logits_tea / args.temperature, dim=-1), ) * (args.temperature**2) loss_end = loss_fct( nn.functional.log_softmax(end_logits_stu / args.temperature, dim=-1), nn.functional.softmax(end_logits_tea / args.temperature, dim=-1), ) * (args.temperature**2) loss_ce = (loss_start + loss_end) / 2.0 loss = args.alpha_ce * loss_ce + args.alpha_squad * loss if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 # Log metrics if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Only evaluate when single GPU otherwise metrics may not average well if args.local_rank == -1 and args.evaluate_during_training: results = evaluate(args, model, tokenizer) for key, value in results.items(): tb_writer.add_scalar("eval_{}".format(key), value, global_step) tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step) tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step) logging_loss = tr_loss if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix=""): dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True) if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(dataset) eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu evaluate if args.n_gpu > 1 and not isinstance(model, nn.DataParallel): model = nn.DataParallel(model) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(dataset)) logger.info(" Batch size = %d", args.eval_batch_size) all_results = [] start_time = timeit.default_timer() for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = {"input_ids": batch[0], "attention_mask": batch[1]} if args.model_type != "distilbert": inputs["token_type_ids"] = None if args.model_type == "xlm" else batch[2] # XLM don't use segment_ids example_indices = batch[3] if args.model_type in ["xlnet", "xlm"]: inputs.update({"cls_index": batch[4], "p_mask": batch[5]}) outputs = model(**inputs) for i, example_index in enumerate(example_indices): eval_feature = features[example_index.item()] unique_id = int(eval_feature.unique_id) output = [to_list(output[i]) for output in outputs] # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler" # models only use two. if len(output) >= 5: start_logits = output[0] start_top_index = output[1] end_logits = output[2] end_top_index = output[3] cls_logits = output[4] result = SquadResult( unique_id, start_logits, end_logits, start_top_index=start_top_index, end_top_index=end_top_index, cls_logits=cls_logits, ) else: start_logits, end_logits = output result = SquadResult(unique_id, start_logits, end_logits) all_results.append(result) evalTime = timeit.default_timer() - start_time logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset)) # Compute predictions output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix)) output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix)) if args.version_2_with_negative: output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix)) else: output_null_log_odds_file = None if args.model_type in ["xlnet", "xlm"]: # XLNet uses a more complex post-processing procedure predictions = compute_predictions_log_probs( examples, features, all_results, args.n_best_size, args.max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file, model.config.start_n_top, model.config.end_n_top, args.version_2_with_negative, tokenizer, args.verbose_logging, ) else: predictions = compute_predictions_logits( examples, features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, args.verbose_logging, args.version_2_with_negative, args.null_score_diff_threshold, tokenizer, ) # Compute the F1 and exact scores. results = squad_evaluate(examples, predictions) return results def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False): if args.local_rank not in [-1, 0] and not evaluate: # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() # Load data features from cache or dataset file input_file = args.predict_file if evaluate else args.train_file cached_features_file = os.path.join( os.path.dirname(input_file), "cached_distillation_{}_{}_{}".format( "dev" if evaluate else "train", list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), ), ) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features_and_dataset = torch.load(cached_features_file) try: features, dataset, examples = ( features_and_dataset["features"], features_and_dataset["dataset"], features_and_dataset["examples"], ) except KeyError: raise DeprecationWarning( "You seem to be loading features from an older version of this script please delete the " "file %s in order for it to be created again" % cached_features_file ) else: logger.info("Creating features from dataset file at %s", input_file) processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor() if evaluate: examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file) else: examples = processor.get_train_examples(args.data_dir, filename=args.train_file) features, dataset = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=not evaluate, return_dataset="pt", threads=args.threads, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save({"features": features, "dataset": dataset, "examples": examples}, cached_features_file) if args.local_rank == 0 and not evaluate: # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() if output_examples: return dataset, examples, features return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pretrained model or model identifier from huggingface.co/models", ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints and predictions will be written.", ) # Distillation parameters (optional) parser.add_argument( "--teacher_type", default=None, type=str, help=( "Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for" " distillation." ), ) parser.add_argument( "--teacher_name_or_path", default=None, type=str, help="Path to the already SQuAD fine-tuned teacher model. Only for distillation.", ) parser.add_argument( "--alpha_ce", default=0.5, type=float, help="Distillation loss linear weight. Only for distillation." ) parser.add_argument( "--alpha_squad", default=0.5, type=float, help="True SQuAD loss linear weight. Only for distillation." ) parser.add_argument( "--temperature", default=2.0, type=float, help="Distillation temperature. Only for distillation." ) # Other parameters parser.add_argument( "--data_dir", default=None, type=str, help="The input data dir. Should contain the .json files for the task." + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--train_file", default=None, type=str, help="The input training file. If a data dir is specified, will look for the file there" + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--predict_file", default=None, type=str, help="The input evaluation file. If a data dir is specified, will look for the file there" + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name" ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from huggingface.co", ) parser.add_argument( "--version_2_with_negative", action="store_true", help="If true, the SQuAD examples contain some that do not have an answer.", ) parser.add_argument( "--null_score_diff_threshold", type=float, default=0.0, help="If null_score - best_non_null is greater than the threshold predict null.", ) parser.add_argument( "--max_seq_length", default=384, type=int, help=( "The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded." ), ) parser.add_argument( "--doc_stride", default=128, type=int, help="When splitting up a long document into chunks, how much stride to take between chunks.", ) parser.add_argument( "--max_query_length", default=64, type=int, help=( "The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length." ), ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step." ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model." ) parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation." ) parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform." ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument( "--n_best_size", default=20, type=int, help="The total number of n-best predictions to generate in the nbest_predictions.json output file.", ) parser.add_argument( "--max_answer_length", default=30, type=int, help=( "The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another." ), ) parser.add_argument( "--verbose_logging", action="store_true", help=( "If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation." ), ) parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.") parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory" ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help=( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html" ), ) parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--threads", type=int, default=1, help="multiple threads for converting example to features") args = parser.parse_args() if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Set seed set_seed(args) # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None, ) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.teacher_type is not None: assert args.teacher_name_or_path is not None assert args.alpha_ce > 0.0 assert args.alpha_ce + args.alpha_squad > 0.0 assert args.teacher_type != "distilbert", "We constraint teachers not to be of type DistilBERT." teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[args.teacher_type] teacher_config = teacher_config_class.from_pretrained( args.teacher_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None ) teacher = teacher_model_class.from_pretrained( args.teacher_name_or_path, config=teacher_config, cache_dir=args.cache_dir if args.cache_dir else None ) teacher.to(args.device) else: teacher = None if args.local_rank == 0: # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set. # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will # remove the need for this code, but it is still valid. if args.fp16: try: import apex apex.amp.register_half_function(torch, "einsum") except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer, teacher=teacher) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Save the trained model and the tokenizer if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(args.device) # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory results = {} if args.do_eval and args.local_rank in [-1, 0]: if args.do_train: logger.info("Loading checkpoints saved during training for evaluation") checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ) logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: # Reload the model global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) # Evaluate result = evaluate(args, model, tokenizer, prefix=global_step) result = dict((k + ("_{}".format(global_step) if global_step else ""), v) for k, v in result.items()) results.update(result) logger.info("Results: {}".format(results)) return results if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/auto/modeling_flax_auto.py

# coding=utf-8 # Copyright 2018 The Google Flax Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Auto Model class.""" from collections import OrderedDict from ...utils import logging from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update from .configuration_auto import CONFIG_MAPPING_NAMES logger = logging.get_logger(__name__) FLAX_MODEL_MAPPING_NAMES = OrderedDict( [ # Base model mapping ("albert", "FlaxAlbertModel"), ("bart", "FlaxBartModel"), ("beit", "FlaxBeitModel"), ("bert", "FlaxBertModel"), ("big_bird", "FlaxBigBirdModel"), ("blenderbot", "FlaxBlenderbotModel"), ("blenderbot-small", "FlaxBlenderbotSmallModel"), ("clip", "FlaxCLIPModel"), ("distilbert", "FlaxDistilBertModel"), ("electra", "FlaxElectraModel"), ("gpt2", "FlaxGPT2Model"), ("gpt_neo", "FlaxGPTNeoModel"), ("gptj", "FlaxGPTJModel"), ("longt5", "FlaxLongT5Model"), ("marian", "FlaxMarianModel"), ("mbart", "FlaxMBartModel"), ("mt5", "FlaxMT5Model"), ("opt", "FlaxOPTModel"), ("pegasus", "FlaxPegasusModel"), ("roberta", "FlaxRobertaModel"), ("roformer", "FlaxRoFormerModel"), ("t5", "FlaxT5Model"), ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"), ("vit", "FlaxViTModel"), ("wav2vec2", "FlaxWav2Vec2Model"), ("xglm", "FlaxXGLMModel"), ("xlm-roberta", "FlaxXLMRobertaModel"), ] ) FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict( [ # Model for pre-training mapping ("albert", "FlaxAlbertForPreTraining"), ("bart", "FlaxBartForConditionalGeneration"), ("bert", "FlaxBertForPreTraining"), ("big_bird", "FlaxBigBirdForPreTraining"), ("electra", "FlaxElectraForPreTraining"), ("longt5", "FlaxLongT5ForConditionalGeneration"), ("mbart", "FlaxMBartForConditionalGeneration"), ("mt5", "FlaxMT5ForConditionalGeneration"), ("roberta", "FlaxRobertaForMaskedLM"), ("roformer", "FlaxRoFormerForMaskedLM"), ("t5", "FlaxT5ForConditionalGeneration"), ("wav2vec2", "FlaxWav2Vec2ForPreTraining"), ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"), ] ) FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict( [ # Model for Masked LM mapping ("albert", "FlaxAlbertForMaskedLM"), ("bart", "FlaxBartForConditionalGeneration"), ("bert", "FlaxBertForMaskedLM"), ("big_bird", "FlaxBigBirdForMaskedLM"), ("distilbert", "FlaxDistilBertForMaskedLM"), ("electra", "FlaxElectraForMaskedLM"), ("mbart", "FlaxMBartForConditionalGeneration"), ("roberta", "FlaxRobertaForMaskedLM"), ("roformer", "FlaxRoFormerForMaskedLM"), ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"), ] ) FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Seq2Seq Causal LM mapping ("bart", "FlaxBartForConditionalGeneration"), ("blenderbot", "FlaxBlenderbotForConditionalGeneration"), ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"), ("encoder-decoder", "FlaxEncoderDecoderModel"), ("longt5", "FlaxLongT5ForConditionalGeneration"), ("marian", "FlaxMarianMTModel"), ("mbart", "FlaxMBartForConditionalGeneration"), ("mt5", "FlaxMT5ForConditionalGeneration"), ("pegasus", "FlaxPegasusForConditionalGeneration"), ("t5", "FlaxT5ForConditionalGeneration"), ] ) FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Image-classsification ("beit", "FlaxBeitForImageClassification"), ("vit", "FlaxViTForImageClassification"), ] ) FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"), ] ) FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Causal LM mapping ("bart", "FlaxBartForCausalLM"), ("bert", "FlaxBertForCausalLM"), ("big_bird", "FlaxBigBirdForCausalLM"), ("electra", "FlaxElectraForCausalLM"), ("gpt2", "FlaxGPT2LMHeadModel"), ("gpt_neo", "FlaxGPTNeoForCausalLM"), ("gptj", "FlaxGPTJForCausalLM"), ("opt", "FlaxOPTForCausalLM"), ("roberta", "FlaxRobertaForCausalLM"), ("xglm", "FlaxXGLMForCausalLM"), ] ) FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Sequence Classification mapping ("albert", "FlaxAlbertForSequenceClassification"), ("bart", "FlaxBartForSequenceClassification"), ("bert", "FlaxBertForSequenceClassification"), ("big_bird", "FlaxBigBirdForSequenceClassification"), ("distilbert", "FlaxDistilBertForSequenceClassification"), ("electra", "FlaxElectraForSequenceClassification"), ("mbart", "FlaxMBartForSequenceClassification"), ("roberta", "FlaxRobertaForSequenceClassification"), ("roformer", "FlaxRoFormerForSequenceClassification"), ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"), ] ) FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ # Model for Question Answering mapping ("albert", "FlaxAlbertForQuestionAnswering"), ("bart", "FlaxBartForQuestionAnswering"), ("bert", "FlaxBertForQuestionAnswering"), ("big_bird", "FlaxBigBirdForQuestionAnswering"), ("distilbert", "FlaxDistilBertForQuestionAnswering"), ("electra", "FlaxElectraForQuestionAnswering"), ("mbart", "FlaxMBartForQuestionAnswering"), ("roberta", "FlaxRobertaForQuestionAnswering"), ("roformer", "FlaxRoFormerForQuestionAnswering"), ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"), ] ) FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Token Classification mapping ("albert", "FlaxAlbertForTokenClassification"), ("bert", "FlaxBertForTokenClassification"), ("big_bird", "FlaxBigBirdForTokenClassification"), ("distilbert", "FlaxDistilBertForTokenClassification"), ("electra", "FlaxElectraForTokenClassification"), ("roberta", "FlaxRobertaForTokenClassification"), ("roformer", "FlaxRoFormerForTokenClassification"), ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"), ] ) FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict( [ # Model for Multiple Choice mapping ("albert", "FlaxAlbertForMultipleChoice"), ("bert", "FlaxBertForMultipleChoice"), ("big_bird", "FlaxBigBirdForMultipleChoice"), ("distilbert", "FlaxDistilBertForMultipleChoice"), ("electra", "FlaxElectraForMultipleChoice"), ("roberta", "FlaxRobertaForMultipleChoice"), ("roformer", "FlaxRoFormerForMultipleChoice"), ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"), ] ) FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict( [ ("bert", "FlaxBertForNextSentencePrediction"), ] ) FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"), ] ) FLAX_MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES) FLAX_MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES) FLAX_MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES) FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES ) FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES) FLAX_MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES) FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES ) FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES ) FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES ) FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES ) class FlaxAutoModel(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_MAPPING FlaxAutoModel = auto_class_update(FlaxAutoModel) class FlaxAutoModelForPreTraining(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_PRETRAINING_MAPPING FlaxAutoModelForPreTraining = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining") class FlaxAutoModelForCausalLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING FlaxAutoModelForCausalLM = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling") class FlaxAutoModelForMaskedLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_MASKED_LM_MAPPING FlaxAutoModelForMaskedLM = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling") class FlaxAutoModelForSeq2SeqLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING FlaxAutoModelForSeq2SeqLM = auto_class_update( FlaxAutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base" ) class FlaxAutoModelForSequenceClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING FlaxAutoModelForSequenceClassification = auto_class_update( FlaxAutoModelForSequenceClassification, head_doc="sequence classification" ) class FlaxAutoModelForQuestionAnswering(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING FlaxAutoModelForQuestionAnswering = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering") class FlaxAutoModelForTokenClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING FlaxAutoModelForTokenClassification = auto_class_update( FlaxAutoModelForTokenClassification, head_doc="token classification" ) class FlaxAutoModelForMultipleChoice(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING FlaxAutoModelForMultipleChoice = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice") class FlaxAutoModelForNextSentencePrediction(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING FlaxAutoModelForNextSentencePrediction = auto_class_update( FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction" ) class FlaxAutoModelForImageClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING FlaxAutoModelForImageClassification = auto_class_update( FlaxAutoModelForImageClassification, head_doc="image classification" ) class FlaxAutoModelForVision2Seq(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING FlaxAutoModelForVision2Seq = auto_class_update(FlaxAutoModelForVision2Seq, head_doc="vision-to-text modeling") class FlaxAutoModelForSpeechSeq2Seq(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING FlaxAutoModelForSpeechSeq2Seq = auto_class_update( FlaxAutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling" )

# coding=utf-8 # Copyright 2018 The Google Flax Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Auto Model class.""" from collections import OrderedDict from ...utils import logging from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update from .configuration_auto import CONFIG_MAPPING_NAMES logger = logging.get_logger(__name__) FLAX_MODEL_MAPPING_NAMES = OrderedDict( [ # Base model mapping ("albert", "FlaxAlbertModel"), ("bart", "FlaxBartModel"), ("beit", "FlaxBeitModel"), ("bert", "FlaxBertModel"), ("big_bird", "FlaxBigBirdModel"), ("blenderbot", "FlaxBlenderbotModel"), ("blenderbot-small", "FlaxBlenderbotSmallModel"), ("clip", "FlaxCLIPModel"), ("distilbert", "FlaxDistilBertModel"), ("electra", "FlaxElectraModel"), ("gpt2", "FlaxGPT2Model"), ("gpt_neo", "FlaxGPTNeoModel"), ("gptj", "FlaxGPTJModel"), ("longt5", "FlaxLongT5Model"), ("marian", "FlaxMarianModel"), ("mbart", "FlaxMBartModel"), ("mt5", "FlaxMT5Model"), ("opt", "FlaxOPTModel"), ("pegasus", "FlaxPegasusModel"), ("roberta", "FlaxRobertaModel"), ("roformer", "FlaxRoFormerModel"), ("t5", "FlaxT5Model"), ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"), ("vit", "FlaxViTModel"), ("wav2vec2", "FlaxWav2Vec2Model"), ("xglm", "FlaxXGLMModel"), ("xlm-roberta", "FlaxXLMRobertaModel"), ] ) FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict( [ # Model for pre-training mapping ("albert", "FlaxAlbertForPreTraining"), ("bart", "FlaxBartForConditionalGeneration"), ("bert", "FlaxBertForPreTraining"), ("big_bird", "FlaxBigBirdForPreTraining"), ("electra", "FlaxElectraForPreTraining"), ("longt5", "FlaxLongT5ForConditionalGeneration"), ("mbart", "FlaxMBartForConditionalGeneration"), ("mt5", "FlaxMT5ForConditionalGeneration"), ("roberta", "FlaxRobertaForMaskedLM"), ("roformer", "FlaxRoFormerForMaskedLM"), ("t5", "FlaxT5ForConditionalGeneration"), ("wav2vec2", "FlaxWav2Vec2ForPreTraining"), ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"), ] ) FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict( [ # Model for Masked LM mapping ("albert", "FlaxAlbertForMaskedLM"), ("bart", "FlaxBartForConditionalGeneration"), ("bert", "FlaxBertForMaskedLM"), ("big_bird", "FlaxBigBirdForMaskedLM"), ("distilbert", "FlaxDistilBertForMaskedLM"), ("electra", "FlaxElectraForMaskedLM"), ("mbart", "FlaxMBartForConditionalGeneration"), ("roberta", "FlaxRobertaForMaskedLM"), ("roformer", "FlaxRoFormerForMaskedLM"), ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"), ] ) FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Seq2Seq Causal LM mapping ("bart", "FlaxBartForConditionalGeneration"), ("blenderbot", "FlaxBlenderbotForConditionalGeneration"), ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"), ("encoder-decoder", "FlaxEncoderDecoderModel"), ("longt5", "FlaxLongT5ForConditionalGeneration"), ("marian", "FlaxMarianMTModel"), ("mbart", "FlaxMBartForConditionalGeneration"), ("mt5", "FlaxMT5ForConditionalGeneration"), ("pegasus", "FlaxPegasusForConditionalGeneration"), ("t5", "FlaxT5ForConditionalGeneration"), ] ) FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Image-classsification ("beit", "FlaxBeitForImageClassification"), ("vit", "FlaxViTForImageClassification"), ] ) FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"), ] ) FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Causal LM mapping ("bart", "FlaxBartForCausalLM"), ("bert", "FlaxBertForCausalLM"), ("big_bird", "FlaxBigBirdForCausalLM"), ("electra", "FlaxElectraForCausalLM"), ("gpt2", "FlaxGPT2LMHeadModel"), ("gpt_neo", "FlaxGPTNeoForCausalLM"), ("gptj", "FlaxGPTJForCausalLM"), ("opt", "FlaxOPTForCausalLM"), ("roberta", "FlaxRobertaForCausalLM"), ("xglm", "FlaxXGLMForCausalLM"), ] ) FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Sequence Classification mapping ("albert", "FlaxAlbertForSequenceClassification"), ("bart", "FlaxBartForSequenceClassification"), ("bert", "FlaxBertForSequenceClassification"), ("big_bird", "FlaxBigBirdForSequenceClassification"), ("distilbert", "FlaxDistilBertForSequenceClassification"), ("electra", "FlaxElectraForSequenceClassification"), ("mbart", "FlaxMBartForSequenceClassification"), ("roberta", "FlaxRobertaForSequenceClassification"), ("roformer", "FlaxRoFormerForSequenceClassification"), ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"), ] ) FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ # Model for Question Answering mapping ("albert", "FlaxAlbertForQuestionAnswering"), ("bart", "FlaxBartForQuestionAnswering"), ("bert", "FlaxBertForQuestionAnswering"), ("big_bird", "FlaxBigBirdForQuestionAnswering"), ("distilbert", "FlaxDistilBertForQuestionAnswering"), ("electra", "FlaxElectraForQuestionAnswering"), ("mbart", "FlaxMBartForQuestionAnswering"), ("roberta", "FlaxRobertaForQuestionAnswering"), ("roformer", "FlaxRoFormerForQuestionAnswering"), ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"), ] ) FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Token Classification mapping ("albert", "FlaxAlbertForTokenClassification"), ("bert", "FlaxBertForTokenClassification"), ("big_bird", "FlaxBigBirdForTokenClassification"), ("distilbert", "FlaxDistilBertForTokenClassification"), ("electra", "FlaxElectraForTokenClassification"), ("roberta", "FlaxRobertaForTokenClassification"), ("roformer", "FlaxRoFormerForTokenClassification"), ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"), ] ) FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict( [ # Model for Multiple Choice mapping ("albert", "FlaxAlbertForMultipleChoice"), ("bert", "FlaxBertForMultipleChoice"), ("big_bird", "FlaxBigBirdForMultipleChoice"), ("distilbert", "FlaxDistilBertForMultipleChoice"), ("electra", "FlaxElectraForMultipleChoice"), ("roberta", "FlaxRobertaForMultipleChoice"), ("roformer", "FlaxRoFormerForMultipleChoice"), ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"), ] ) FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict( [ ("bert", "FlaxBertForNextSentencePrediction"), ] ) FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"), ] ) FLAX_MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES) FLAX_MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES) FLAX_MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES) FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES ) FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES) FLAX_MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES) FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES ) FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES ) FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES ) FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES ) class FlaxAutoModel(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_MAPPING FlaxAutoModel = auto_class_update(FlaxAutoModel) class FlaxAutoModelForPreTraining(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_PRETRAINING_MAPPING FlaxAutoModelForPreTraining = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining") class FlaxAutoModelForCausalLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING FlaxAutoModelForCausalLM = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling") class FlaxAutoModelForMaskedLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_MASKED_LM_MAPPING FlaxAutoModelForMaskedLM = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling") class FlaxAutoModelForSeq2SeqLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING FlaxAutoModelForSeq2SeqLM = auto_class_update( FlaxAutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base" ) class FlaxAutoModelForSequenceClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING FlaxAutoModelForSequenceClassification = auto_class_update( FlaxAutoModelForSequenceClassification, head_doc="sequence classification" ) class FlaxAutoModelForQuestionAnswering(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING FlaxAutoModelForQuestionAnswering = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering") class FlaxAutoModelForTokenClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING FlaxAutoModelForTokenClassification = auto_class_update( FlaxAutoModelForTokenClassification, head_doc="token classification" ) class FlaxAutoModelForMultipleChoice(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING FlaxAutoModelForMultipleChoice = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice") class FlaxAutoModelForNextSentencePrediction(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING FlaxAutoModelForNextSentencePrediction = auto_class_update( FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction" ) class FlaxAutoModelForImageClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING FlaxAutoModelForImageClassification = auto_class_update( FlaxAutoModelForImageClassification, head_doc="image classification" ) class FlaxAutoModelForVision2Seq(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING FlaxAutoModelForVision2Seq = auto_class_update(FlaxAutoModelForVision2Seq, head_doc="vision-to-text modeling") class FlaxAutoModelForSpeechSeq2Seq(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING FlaxAutoModelForSpeechSeq2Seq = auto_class_update( FlaxAutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling" )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/dit/test_modeling_dit.py

# coding=utf-8 # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_vision, slow, torch_device if is_torch_available(): import torch from transformers import AutoModelForImageClassification if is_vision_available(): from transformers import AutoFeatureExtractor @require_torch @require_vision class DiTIntegrationTest(unittest.TestCase): @slow def test_for_image_classification(self): feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip") model = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip") model.to(torch_device) from datasets import load_dataset dataset = load_dataset("nielsr/rvlcdip-demo") image = dataset["train"][0]["image"].convert("RGB") inputs = feature_extractor(image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits expected_shape = torch.Size((1, 16)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor( [-0.4158, -0.4092, -0.4347], device=torch_device, dtype=torch.float, ) self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))

# coding=utf-8 # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_vision, slow, torch_device if is_torch_available(): import torch from transformers import AutoModelForImageClassification if is_vision_available(): from transformers import AutoFeatureExtractor @require_torch @require_vision class DiTIntegrationTest(unittest.TestCase): @slow def test_for_image_classification(self): feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip") model = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip") model.to(torch_device) from datasets import load_dataset dataset = load_dataset("nielsr/rvlcdip-demo") image = dataset["train"][0]["image"].convert("RGB") inputs = feature_extractor(image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits expected_shape = torch.Size((1, 16)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor( [-0.4158, -0.4092, -0.4347], device=torch_device, dtype=torch.float, ) self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/roberta/modeling_flax_roberta.py

# coding=utf-8 # Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable, Optional, Tuple import numpy as np import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen import partitioning as nn_partitioning from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from ...modeling_flax_outputs import ( FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxBaseModelOutputWithPooling, FlaxBaseModelOutputWithPoolingAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions, FlaxMaskedLMOutput, FlaxMultipleChoiceModelOutput, FlaxQuestionAnsweringModelOutput, FlaxSequenceClassifierOutput, FlaxTokenClassifierOutput, ) from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, overwrite_call_docstring from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_roberta import RobertaConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "roberta-base" _CONFIG_FOR_DOC = "RobertaConfig" _TOKENIZER_FOR_DOC = "RobertaTokenizer" remat = nn_partitioning.remat def create_position_ids_from_input_ids(input_ids, padding_idx): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: input_ids: jnp.ndarray padding_idx: int Returns: jnp.ndarray """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = (input_ids != padding_idx).astype("i4") if mask.ndim > 2: mask = mask.reshape((-1, mask.shape[-1])) incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask incremental_indices = incremental_indices.reshape(input_ids.shape) else: incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask return incremental_indices.astype("i4") + padding_idx ROBERTA_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models) This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`RobertaConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. """ ROBERTA_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`numpy.ndarray` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. head_mask (`numpy.ndarray` of shape `({0})`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings with Bert->Roberta class FlaxRobertaEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.word_embeddings = nn.Embed( self.config.vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.position_embeddings = nn.Embed( self.config.max_position_embeddings, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.token_type_embeddings = nn.Embed( self.config.type_vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True): # Embed inputs_embeds = self.word_embeddings(input_ids.astype("i4")) position_embeds = self.position_embeddings(position_ids.astype("i4")) token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4")) # Sum all embeddings hidden_states = inputs_embeds + token_type_embeddings + position_embeds # Layer Norm hidden_states = self.LayerNorm(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->Roberta class FlaxRobertaSelfAttention(nn.Module): config: RobertaConfig causal: bool = False dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.head_dim = self.config.hidden_size // self.config.num_attention_heads if self.config.hidden_size % self.config.num_attention_heads != 0: raise ValueError( "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` " " : {self.config.num_attention_heads}" ) self.query = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.key = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.value = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,)) @nn.compact # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states, attention_mask, layer_head_mask, key_value_states: Optional[jnp.array] = None, init_cache: bool = False, deterministic=True, output_attentions: bool = False, ): # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.query(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.key(key_value_states) value_states = self.value(key_value_states) else: # self_attention key_states = self.key(hidden_states) value_states = self.value(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, -1e10).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.config.attention_probs_dropout_prob > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.config.attention_probs_dropout_prob, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) # Mask heads if we want to if layer_head_mask is not None: attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,)) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->Roberta class FlaxRobertaSelfOutput(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, hidden_states, input_tensor, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->Roberta class FlaxRobertaAttention(nn.Module): config: RobertaConfig causal: bool = False dtype: jnp.dtype = jnp.float32 def setup(self): self.self = FlaxRobertaSelfAttention(self.config, causal=self.causal, dtype=self.dtype) self.output = FlaxRobertaSelfOutput(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, layer_head_mask, key_value_states=None, init_cache=False, deterministic=True, output_attentions: bool = False, ): # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length) # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length) attn_outputs = self.self( hidden_states, attention_mask, layer_head_mask=layer_head_mask, key_value_states=key_value_states, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, ) attn_output = attn_outputs[0] hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attn_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->Roberta class FlaxRobertaIntermediate(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.activation = ACT2FN[self.config.hidden_act] def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->Roberta class FlaxRobertaOutput(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states, attention_output, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + attention_output) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer with Bert->Roberta class FlaxRobertaLayer(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.attention = FlaxRobertaAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype) self.intermediate = FlaxRobertaIntermediate(self.config, dtype=self.dtype) self.output = FlaxRobertaOutput(self.config, dtype=self.dtype) if self.config.add_cross_attention: self.crossattention = FlaxRobertaAttention(self.config, causal=False, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, ): # Self Attention attention_outputs = self.attention( hidden_states, attention_mask, layer_head_mask=layer_head_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = attention_outputs[0] # Cross-Attention Block if encoder_hidden_states is not None: cross_attention_outputs = self.crossattention( attention_output, attention_mask=encoder_attention_mask, layer_head_mask=layer_head_mask, key_value_states=encoder_hidden_states, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = cross_attention_outputs[0] hidden_states = self.intermediate(attention_output) hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attention_outputs[1],) if encoder_hidden_states is not None: outputs += (cross_attention_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection with Bert->Roberta class FlaxRobertaLayerCollection(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): if self.gradient_checkpointing: FlaxRobertaCheckpointLayer = remat(FlaxRobertaLayer, static_argnums=(5, 6, 7)) self.layers = [ FlaxRobertaCheckpointLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] else: self.layers = [ FlaxRobertaLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None # Check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.shape[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for " f" {head_mask.shape[0]}." ) for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, head_mask[i] if head_mask is not None else None, encoder_hidden_states, encoder_attention_mask, init_cache, deterministic, output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->Roberta class FlaxRobertaEncoder(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): self.layer = FlaxRobertaLayerCollection( self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) def __call__( self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.layer( hidden_states, attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPooler with Bert->Roberta class FlaxRobertaPooler(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) def __call__(self, hidden_states): cls_hidden_state = hidden_states[:, 0] cls_hidden_state = self.dense(cls_hidden_state) return nn.tanh(cls_hidden_state) class FlaxRobertaLMHead(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros def setup(self): self.dense = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.decoder = nn.Dense( self.config.vocab_size, dtype=self.dtype, use_bias=False, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,)) def __call__(self, hidden_states, shared_embedding=None): hidden_states = self.dense(hidden_states) hidden_states = ACT2FN["gelu"](hidden_states) hidden_states = self.layer_norm(hidden_states) if shared_embedding is not None: hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: hidden_states = self.decoder(hidden_states) bias = jnp.asarray(self.bias, self.dtype) hidden_states += bias return hidden_states class FlaxRobertaClassificationHead(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) classifier_dropout = ( self.config.classifier_dropout if self.config.classifier_dropout is not None else self.config.hidden_dropout_prob ) self.dropout = nn.Dropout(rate=classifier_dropout) self.out_proj = nn.Dense( self.config.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) def __call__(self, hidden_states, deterministic=True): hidden_states = hidden_states[:, 0, :] # take <s> token (equiv. to [CLS]) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.dense(hidden_states) hidden_states = nn.tanh(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.out_proj(hidden_states) return hidden_states class FlaxRobertaPreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RobertaConfig base_model_prefix = "roberta" module_class: nn.Module = None def __init__( self, config: RobertaConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, gradient_checkpointing: bool = False, **kwargs ): module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing def enable_gradient_checkpointing(self): self._module = self.module_class( config=self.config, dtype=self.dtype, gradient_checkpointing=True, ) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") token_type_ids = jnp.ones_like(input_ids) position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id) attention_mask = jnp.ones_like(input_ids) head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} if self.config.add_cross_attention: encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,)) encoder_attention_mask = attention_mask module_init_outputs = self.module.init( rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states, encoder_attention_mask, return_dict=False, ) else: module_init_outputs = self.module.init( rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False ) random_params = module_init_outputs["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length), dtype="i4") attention_mask = jnp.ones_like(input_ids, dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, past_key_values: dict = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # init input tensors if not passed if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if position_ids is None: position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if head_mask is None: head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} if self.config.add_cross_attention: # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be # changed by FlaxRobertaAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] else: outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs, ) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->Roberta class FlaxRobertaModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation add_pooling_layer: bool = True gradient_checkpointing: bool = False def setup(self): self.embeddings = FlaxRobertaEmbeddings(self.config, dtype=self.dtype) self.encoder = FlaxRobertaEncoder( self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) self.pooler = FlaxRobertaPooler(self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # make sure `token_type_ids` is correctly initialized when not passed if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) # make sure `position_ids` is correctly initialized when not passed if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) hidden_states = self.embeddings( input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic ) outputs = self.encoder( hidden_states, attention_mask, head_mask=head_mask, deterministic=deterministic, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] pooled = self.pooler(hidden_states) if self.add_pooling_layer else None if not return_dict: # if pooled is None, don't return it if pooled is None: return (hidden_states,) + outputs[1:] return (hidden_states, pooled) + outputs[1:] return FlaxBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=hidden_states, pooler_output=pooled, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) @add_start_docstrings( "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.", ROBERTA_START_DOCSTRING, ) class FlaxRobertaModel(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaModule append_call_sample_docstring( FlaxRobertaModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC ) class FlaxRobertaForMaskedLMModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) self.lm_head = FlaxRobertaLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.roberta.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.lm_head(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxMaskedLMOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""RoBERTa Model with a `language modeling` head on top.""", ROBERTA_START_DOCSTRING) class FlaxRobertaForMaskedLM(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForMaskedLMModule append_call_sample_docstring( FlaxRobertaForMaskedLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC, mask="<mask>", ) class FlaxRobertaForSequenceClassificationModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing, ) self.classifier = FlaxRobertaClassificationHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output, deterministic=deterministic) if not return_dict: return (logits,) + outputs[1:] return FlaxSequenceClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Roberta Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ROBERTA_START_DOCSTRING, ) class FlaxRobertaForSequenceClassification(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForSequenceClassificationModule append_call_sample_docstring( FlaxRobertaForSequenceClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSequenceClassifierOutput, _CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMultipleChoiceModule with Bert->Roberta, with self.bert->self.roberta class FlaxRobertaForMultipleChoiceModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.classifier = nn.Dense(1, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): num_choices = input_ids.shape[1] input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output, deterministic=deterministic) logits = self.classifier(pooled_output) reshaped_logits = logits.reshape(-1, num_choices) if not return_dict: return (reshaped_logits,) + outputs[2:] return FlaxMultipleChoiceModelOutput( logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ROBERTA_START_DOCSTRING, ) class FlaxRobertaForMultipleChoice(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForMultipleChoiceModule overwrite_call_docstring( FlaxRobertaForMultipleChoice, ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) append_call_sample_docstring( FlaxRobertaForMultipleChoice, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForTokenClassificationModule with Bert->Roberta, with self.bert->self.roberta class FlaxRobertaForTokenClassificationModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing, ) classifier_dropout = ( self.config.classifier_dropout if self.config.classifier_dropout is not None else self.config.hidden_dropout_prob ) self.dropout = nn.Dropout(rate=classifier_dropout) self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states, deterministic=deterministic) logits = self.classifier(hidden_states) if not return_dict: return (logits,) + outputs[1:] return FlaxTokenClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Roberta Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, ROBERTA_START_DOCSTRING, ) class FlaxRobertaForTokenClassification(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForTokenClassificationModule append_call_sample_docstring( FlaxRobertaForTokenClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForQuestionAnsweringModule with Bert->Roberta, with self.bert->self.roberta class FlaxRobertaForQuestionAnsweringModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing, ) self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.qa_outputs(hidden_states) start_logits, end_logits = logits.split(self.config.num_labels, axis=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if not return_dict: return (start_logits, end_logits) + outputs[1:] return FlaxQuestionAnsweringModelOutput( start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ROBERTA_START_DOCSTRING, ) class FlaxRobertaForQuestionAnswering(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForQuestionAnsweringModule append_call_sample_docstring( FlaxRobertaForQuestionAnswering, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxQuestionAnsweringModelOutput, _CONFIG_FOR_DOC, ) class FlaxRobertaForCausalLMModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) self.lm_head = FlaxRobertaLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, token_type_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.roberta.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.lm_head(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxCausalLMOutputWithCrossAttentions( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) @add_start_docstrings( """ Roberta Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for autoregressive tasks. """, ROBERTA_START_DOCSTRING, ) class FlaxRobertaForCausalLM(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyway. # Thus, we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( FlaxRobertaForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutputWithCrossAttentions, _CONFIG_FOR_DOC, )

# coding=utf-8 # Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable, Optional, Tuple import numpy as np import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen import partitioning as nn_partitioning from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from ...modeling_flax_outputs import ( FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxBaseModelOutputWithPooling, FlaxBaseModelOutputWithPoolingAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions, FlaxMaskedLMOutput, FlaxMultipleChoiceModelOutput, FlaxQuestionAnsweringModelOutput, FlaxSequenceClassifierOutput, FlaxTokenClassifierOutput, ) from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, overwrite_call_docstring from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_roberta import RobertaConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "roberta-base" _CONFIG_FOR_DOC = "RobertaConfig" _TOKENIZER_FOR_DOC = "RobertaTokenizer" remat = nn_partitioning.remat def create_position_ids_from_input_ids(input_ids, padding_idx): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: input_ids: jnp.ndarray padding_idx: int Returns: jnp.ndarray """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = (input_ids != padding_idx).astype("i4") if mask.ndim > 2: mask = mask.reshape((-1, mask.shape[-1])) incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask incremental_indices = incremental_indices.reshape(input_ids.shape) else: incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask return incremental_indices.astype("i4") + padding_idx ROBERTA_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models) This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`RobertaConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. """ ROBERTA_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`numpy.ndarray` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. head_mask (`numpy.ndarray` of shape `({0})`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings with Bert->Roberta class FlaxRobertaEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.word_embeddings = nn.Embed( self.config.vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.position_embeddings = nn.Embed( self.config.max_position_embeddings, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.token_type_embeddings = nn.Embed( self.config.type_vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True): # Embed inputs_embeds = self.word_embeddings(input_ids.astype("i4")) position_embeds = self.position_embeddings(position_ids.astype("i4")) token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4")) # Sum all embeddings hidden_states = inputs_embeds + token_type_embeddings + position_embeds # Layer Norm hidden_states = self.LayerNorm(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->Roberta class FlaxRobertaSelfAttention(nn.Module): config: RobertaConfig causal: bool = False dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.head_dim = self.config.hidden_size // self.config.num_attention_heads if self.config.hidden_size % self.config.num_attention_heads != 0: raise ValueError( "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` " " : {self.config.num_attention_heads}" ) self.query = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.key = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.value = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,)) @nn.compact # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states, attention_mask, layer_head_mask, key_value_states: Optional[jnp.array] = None, init_cache: bool = False, deterministic=True, output_attentions: bool = False, ): # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.query(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.key(key_value_states) value_states = self.value(key_value_states) else: # self_attention key_states = self.key(hidden_states) value_states = self.value(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, -1e10).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.config.attention_probs_dropout_prob > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.config.attention_probs_dropout_prob, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) # Mask heads if we want to if layer_head_mask is not None: attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,)) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->Roberta class FlaxRobertaSelfOutput(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, hidden_states, input_tensor, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->Roberta class FlaxRobertaAttention(nn.Module): config: RobertaConfig causal: bool = False dtype: jnp.dtype = jnp.float32 def setup(self): self.self = FlaxRobertaSelfAttention(self.config, causal=self.causal, dtype=self.dtype) self.output = FlaxRobertaSelfOutput(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, layer_head_mask, key_value_states=None, init_cache=False, deterministic=True, output_attentions: bool = False, ): # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length) # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length) attn_outputs = self.self( hidden_states, attention_mask, layer_head_mask=layer_head_mask, key_value_states=key_value_states, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, ) attn_output = attn_outputs[0] hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attn_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->Roberta class FlaxRobertaIntermediate(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.activation = ACT2FN[self.config.hidden_act] def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->Roberta class FlaxRobertaOutput(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states, attention_output, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + attention_output) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer with Bert->Roberta class FlaxRobertaLayer(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.attention = FlaxRobertaAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype) self.intermediate = FlaxRobertaIntermediate(self.config, dtype=self.dtype) self.output = FlaxRobertaOutput(self.config, dtype=self.dtype) if self.config.add_cross_attention: self.crossattention = FlaxRobertaAttention(self.config, causal=False, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, ): # Self Attention attention_outputs = self.attention( hidden_states, attention_mask, layer_head_mask=layer_head_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = attention_outputs[0] # Cross-Attention Block if encoder_hidden_states is not None: cross_attention_outputs = self.crossattention( attention_output, attention_mask=encoder_attention_mask, layer_head_mask=layer_head_mask, key_value_states=encoder_hidden_states, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = cross_attention_outputs[0] hidden_states = self.intermediate(attention_output) hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attention_outputs[1],) if encoder_hidden_states is not None: outputs += (cross_attention_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection with Bert->Roberta class FlaxRobertaLayerCollection(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): if self.gradient_checkpointing: FlaxRobertaCheckpointLayer = remat(FlaxRobertaLayer, static_argnums=(5, 6, 7)) self.layers = [ FlaxRobertaCheckpointLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] else: self.layers = [ FlaxRobertaLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None # Check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.shape[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for " f" {head_mask.shape[0]}." ) for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, head_mask[i] if head_mask is not None else None, encoder_hidden_states, encoder_attention_mask, init_cache, deterministic, output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->Roberta class FlaxRobertaEncoder(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): self.layer = FlaxRobertaLayerCollection( self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) def __call__( self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.layer( hidden_states, attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPooler with Bert->Roberta class FlaxRobertaPooler(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) def __call__(self, hidden_states): cls_hidden_state = hidden_states[:, 0] cls_hidden_state = self.dense(cls_hidden_state) return nn.tanh(cls_hidden_state) class FlaxRobertaLMHead(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros def setup(self): self.dense = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.decoder = nn.Dense( self.config.vocab_size, dtype=self.dtype, use_bias=False, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,)) def __call__(self, hidden_states, shared_embedding=None): hidden_states = self.dense(hidden_states) hidden_states = ACT2FN["gelu"](hidden_states) hidden_states = self.layer_norm(hidden_states) if shared_embedding is not None: hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: hidden_states = self.decoder(hidden_states) bias = jnp.asarray(self.bias, self.dtype) hidden_states += bias return hidden_states class FlaxRobertaClassificationHead(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) classifier_dropout = ( self.config.classifier_dropout if self.config.classifier_dropout is not None else self.config.hidden_dropout_prob ) self.dropout = nn.Dropout(rate=classifier_dropout) self.out_proj = nn.Dense( self.config.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) def __call__(self, hidden_states, deterministic=True): hidden_states = hidden_states[:, 0, :] # take <s> token (equiv. to [CLS]) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.dense(hidden_states) hidden_states = nn.tanh(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.out_proj(hidden_states) return hidden_states class FlaxRobertaPreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RobertaConfig base_model_prefix = "roberta" module_class: nn.Module = None def __init__( self, config: RobertaConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, gradient_checkpointing: bool = False, **kwargs ): module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing def enable_gradient_checkpointing(self): self._module = self.module_class( config=self.config, dtype=self.dtype, gradient_checkpointing=True, ) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") token_type_ids = jnp.ones_like(input_ids) position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id) attention_mask = jnp.ones_like(input_ids) head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} if self.config.add_cross_attention: encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,)) encoder_attention_mask = attention_mask module_init_outputs = self.module.init( rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states, encoder_attention_mask, return_dict=False, ) else: module_init_outputs = self.module.init( rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False ) random_params = module_init_outputs["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length), dtype="i4") attention_mask = jnp.ones_like(input_ids, dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, past_key_values: dict = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # init input tensors if not passed if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if position_ids is None: position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if head_mask is None: head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} if self.config.add_cross_attention: # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be # changed by FlaxRobertaAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] else: outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs, ) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->Roberta class FlaxRobertaModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation add_pooling_layer: bool = True gradient_checkpointing: bool = False def setup(self): self.embeddings = FlaxRobertaEmbeddings(self.config, dtype=self.dtype) self.encoder = FlaxRobertaEncoder( self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) self.pooler = FlaxRobertaPooler(self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # make sure `token_type_ids` is correctly initialized when not passed if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) # make sure `position_ids` is correctly initialized when not passed if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) hidden_states = self.embeddings( input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic ) outputs = self.encoder( hidden_states, attention_mask, head_mask=head_mask, deterministic=deterministic, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] pooled = self.pooler(hidden_states) if self.add_pooling_layer else None if not return_dict: # if pooled is None, don't return it if pooled is None: return (hidden_states,) + outputs[1:] return (hidden_states, pooled) + outputs[1:] return FlaxBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=hidden_states, pooler_output=pooled, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) @add_start_docstrings( "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.", ROBERTA_START_DOCSTRING, ) class FlaxRobertaModel(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaModule append_call_sample_docstring( FlaxRobertaModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC ) class FlaxRobertaForMaskedLMModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) self.lm_head = FlaxRobertaLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.roberta.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.lm_head(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxMaskedLMOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""RoBERTa Model with a `language modeling` head on top.""", ROBERTA_START_DOCSTRING) class FlaxRobertaForMaskedLM(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForMaskedLMModule append_call_sample_docstring( FlaxRobertaForMaskedLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC, mask="<mask>", ) class FlaxRobertaForSequenceClassificationModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing, ) self.classifier = FlaxRobertaClassificationHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output, deterministic=deterministic) if not return_dict: return (logits,) + outputs[1:] return FlaxSequenceClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Roberta Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ROBERTA_START_DOCSTRING, ) class FlaxRobertaForSequenceClassification(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForSequenceClassificationModule append_call_sample_docstring( FlaxRobertaForSequenceClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSequenceClassifierOutput, _CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMultipleChoiceModule with Bert->Roberta, with self.bert->self.roberta class FlaxRobertaForMultipleChoiceModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.classifier = nn.Dense(1, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): num_choices = input_ids.shape[1] input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output, deterministic=deterministic) logits = self.classifier(pooled_output) reshaped_logits = logits.reshape(-1, num_choices) if not return_dict: return (reshaped_logits,) + outputs[2:] return FlaxMultipleChoiceModelOutput( logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ROBERTA_START_DOCSTRING, ) class FlaxRobertaForMultipleChoice(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForMultipleChoiceModule overwrite_call_docstring( FlaxRobertaForMultipleChoice, ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) append_call_sample_docstring( FlaxRobertaForMultipleChoice, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForTokenClassificationModule with Bert->Roberta, with self.bert->self.roberta class FlaxRobertaForTokenClassificationModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing, ) classifier_dropout = ( self.config.classifier_dropout if self.config.classifier_dropout is not None else self.config.hidden_dropout_prob ) self.dropout = nn.Dropout(rate=classifier_dropout) self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states, deterministic=deterministic) logits = self.classifier(hidden_states) if not return_dict: return (logits,) + outputs[1:] return FlaxTokenClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Roberta Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, ROBERTA_START_DOCSTRING, ) class FlaxRobertaForTokenClassification(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForTokenClassificationModule append_call_sample_docstring( FlaxRobertaForTokenClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForQuestionAnsweringModule with Bert->Roberta, with self.bert->self.roberta class FlaxRobertaForQuestionAnsweringModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing, ) self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.qa_outputs(hidden_states) start_logits, end_logits = logits.split(self.config.num_labels, axis=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if not return_dict: return (start_logits, end_logits) + outputs[1:] return FlaxQuestionAnsweringModelOutput( start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ROBERTA_START_DOCSTRING, ) class FlaxRobertaForQuestionAnswering(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForQuestionAnsweringModule append_call_sample_docstring( FlaxRobertaForQuestionAnswering, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxQuestionAnsweringModelOutput, _CONFIG_FOR_DOC, ) class FlaxRobertaForCausalLMModule(nn.Module): config: RobertaConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.roberta = FlaxRobertaModule( config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) self.lm_head = FlaxRobertaLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, token_type_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roberta( input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.roberta.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.lm_head(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxCausalLMOutputWithCrossAttentions( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) @add_start_docstrings( """ Roberta Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for autoregressive tasks. """, ROBERTA_START_DOCSTRING, ) class FlaxRobertaForCausalLM(FlaxRobertaPreTrainedModel): module_class = FlaxRobertaForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyway. # Thus, we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( FlaxRobertaForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutputWithCrossAttentions, _CONFIG_FOR_DOC, )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/fnet/configuration_fnet.py

# coding=utf-8 # Copyright 2021 Google AI and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ FNet model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) FNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json", "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json" # See all FNet models at https://huggingface.co/models?filter=fnet } class FNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`FNetModel`]. It is used to instantiate an FNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the FNet [google/fnet-base](https://huggingface.co/google/fnet-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the FNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimension of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 4): The vocabulary size of the `token_type_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. use_tpu_fourier_optimizations (`bool`, *optional*, defaults to `False`): Determines whether to use TPU optimized FFTs. If `True`, the model will favor axis-wise FFTs transforms. Set to `False` for GPU/CPU hardware, in which case n-dimensional FFTs are used. tpu_short_seq_length (`int`, *optional*, defaults to 512): The sequence length that is expected by the model when using TPUs. This will be used to initialize the DFT matrix only when *use_tpu_fourier_optimizations* is set to `True` and the input sequence is shorter than or equal to 4096 tokens. Example: ```python >>> from transformers import FNetConfig, FNetModel >>> # Initializing a FNet fnet-base style configuration >>> configuration = FNetConfig() >>> # Initializing a model (with random weights) from the fnet-base style configuration >>> model = FNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "fnet" def __init__( self, vocab_size=32000, hidden_size=768, num_hidden_layers=12, intermediate_size=3072, hidden_act="gelu_new", hidden_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=4, initializer_range=0.02, layer_norm_eps=1e-12, use_tpu_fourier_optimizations=False, tpu_short_seq_length=512, pad_token_id=3, bos_token_id=1, eos_token_id=2, **kwargs ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.initializer_range = initializer_range self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.use_tpu_fourier_optimizations = use_tpu_fourier_optimizations self.tpu_short_seq_length = tpu_short_seq_length

# coding=utf-8 # Copyright 2021 Google AI and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ FNet model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) FNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json", "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json" # See all FNet models at https://huggingface.co/models?filter=fnet } class FNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`FNetModel`]. It is used to instantiate an FNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the FNet [google/fnet-base](https://huggingface.co/google/fnet-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the FNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimension of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 4): The vocabulary size of the `token_type_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. use_tpu_fourier_optimizations (`bool`, *optional*, defaults to `False`): Determines whether to use TPU optimized FFTs. If `True`, the model will favor axis-wise FFTs transforms. Set to `False` for GPU/CPU hardware, in which case n-dimensional FFTs are used. tpu_short_seq_length (`int`, *optional*, defaults to 512): The sequence length that is expected by the model when using TPUs. This will be used to initialize the DFT matrix only when *use_tpu_fourier_optimizations* is set to `True` and the input sequence is shorter than or equal to 4096 tokens. Example: ```python >>> from transformers import FNetConfig, FNetModel >>> # Initializing a FNet fnet-base style configuration >>> configuration = FNetConfig() >>> # Initializing a model (with random weights) from the fnet-base style configuration >>> model = FNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "fnet" def __init__( self, vocab_size=32000, hidden_size=768, num_hidden_layers=12, intermediate_size=3072, hidden_act="gelu_new", hidden_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=4, initializer_range=0.02, layer_norm_eps=1e-12, use_tpu_fourier_optimizations=False, tpu_short_seq_length=512, pad_token_id=3, bos_token_id=1, eos_token_id=2, **kwargs ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.initializer_range = initializer_range self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.use_tpu_fourier_optimizations = use_tpu_fourier_optimizations self.tpu_short_seq_length = tpu_short_seq_length

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/auto/test_modeling_tf_auto.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import tempfile import unittest from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPT2Config, T5Config, TapasConfig, is_tf_available from transformers.testing_utils import ( DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, RequestCounter, require_tensorflow_probability, require_tf, slow, ) from ..bert.test_modeling_bert import BertModelTester if is_tf_available(): from transformers import ( TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelWithLMHead, TFBertForMaskedLM, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFBertModel, TFFunnelBaseModel, TFFunnelModel, TFGPT2LMHeadModel, TFRobertaForMaskedLM, TFT5ForConditionalGeneration, TFTapasForQuestionAnswering, ) from transformers.models.auto.modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_MAPPING, ) from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST class NewModelConfig(BertConfig): model_type = "new-model" if is_tf_available(): class TFNewModel(TFBertModel): config_class = NewModelConfig @require_tf class TFAutoModelTest(unittest.TestCase): @slow def test_model_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModel.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertModel) @slow def test_model_for_pretraining_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForPreTraining.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForPreTraining) @slow def test_model_for_causal_lm(self): for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, GPT2Config) model = TFAutoModelForCausalLM.from_pretrained(model_name) model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFGPT2LMHeadModel) @slow def test_lmhead_model_from_pretrained(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelWithLMHead.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_masked_lm(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForMaskedLM.from_pretrained(model_name) model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_encoder_decoder_lm(self): for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, T5Config) model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name) model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFT5ForConditionalGeneration) @slow def test_sequence_classification_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForSequenceClassification.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForSequenceClassification) @slow def test_question_answering_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForQuestionAnswering.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForQuestionAnswering) @slow @require_tensorflow_probability def test_table_question_answering_model_from_pretrained(self): for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, TapasConfig) model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name) model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained( model_name, output_loading_info=True ) self.assertIsNotNone(model) self.assertIsInstance(model, TFTapasForQuestionAnswering) def test_from_pretrained_identifier(self): model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(model, TFBertForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_identifier_from_model_type(self): model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER) self.assertIsInstance(model, TFRobertaForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_pretrained_with_tuple_values(self): # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel model = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny") self.assertIsInstance(model, TFFunnelModel) config = copy.deepcopy(model.config) config.architectures = ["FunnelBaseModel"] model = TFAutoModel.from_config(config) self.assertIsInstance(model, TFFunnelBaseModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) model = TFAutoModel.from_pretrained(tmp_dir) self.assertIsInstance(model, TFFunnelBaseModel) def test_new_model_registration(self): try: AutoConfig.register("new-model", NewModelConfig) auto_classes = [ TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSequenceClassification, TFAutoModelForTokenClassification, ] for auto_class in auto_classes: with self.subTest(auto_class.__name__): # Wrong config class will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFNewModel) auto_class.register(NewModelConfig, TFNewModel) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFBertModel) # Now that the config is registered, it can be used as any other config with the auto-API tiny_config = BertModelTester(self).get_config() config = NewModelConfig(**tiny_config.to_dict()) model = auto_class.from_config(config) self.assertIsInstance(model, TFNewModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = auto_class.from_pretrained(tmp_dir) self.assertIsInstance(new_model, TFNewModel) finally: if "new-model" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["new-model"] for mapping in ( TF_MODEL_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, ): if NewModelConfig in mapping._extra_content: del mapping._extra_content[NewModelConfig] def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "bert-base is not a local folder and is not a valid model identifier" ): _ = TFAutoModel.from_pretrained("bert-base") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = TFAutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") def test_model_file_not_found(self): with self.assertRaisesRegex( EnvironmentError, "hf-internal-testing/config-no-model does not appear to have a file named tf_model.h5", ): _ = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model") def test_model_from_pt_suggestion(self): with self.assertRaisesRegex(EnvironmentError, "Use `from_pt=True` to load this model"): _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") def test_cached_model_has_minimum_calls_to_head(self): # Make sure we have cached the model. _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0) # With a sharded checkpoint _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0)

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import tempfile import unittest from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPT2Config, T5Config, TapasConfig, is_tf_available from transformers.testing_utils import ( DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, RequestCounter, require_tensorflow_probability, require_tf, slow, ) from ..bert.test_modeling_bert import BertModelTester if is_tf_available(): from transformers import ( TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelWithLMHead, TFBertForMaskedLM, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFBertModel, TFFunnelBaseModel, TFFunnelModel, TFGPT2LMHeadModel, TFRobertaForMaskedLM, TFT5ForConditionalGeneration, TFTapasForQuestionAnswering, ) from transformers.models.auto.modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_MAPPING, ) from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST class NewModelConfig(BertConfig): model_type = "new-model" if is_tf_available(): class TFNewModel(TFBertModel): config_class = NewModelConfig @require_tf class TFAutoModelTest(unittest.TestCase): @slow def test_model_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModel.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertModel) @slow def test_model_for_pretraining_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForPreTraining.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForPreTraining) @slow def test_model_for_causal_lm(self): for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, GPT2Config) model = TFAutoModelForCausalLM.from_pretrained(model_name) model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFGPT2LMHeadModel) @slow def test_lmhead_model_from_pretrained(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelWithLMHead.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_masked_lm(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForMaskedLM.from_pretrained(model_name) model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_encoder_decoder_lm(self): for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, T5Config) model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name) model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFT5ForConditionalGeneration) @slow def test_sequence_classification_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForSequenceClassification.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForSequenceClassification) @slow def test_question_answering_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForQuestionAnswering.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForQuestionAnswering) @slow @require_tensorflow_probability def test_table_question_answering_model_from_pretrained(self): for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, TapasConfig) model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name) model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained( model_name, output_loading_info=True ) self.assertIsNotNone(model) self.assertIsInstance(model, TFTapasForQuestionAnswering) def test_from_pretrained_identifier(self): model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(model, TFBertForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_identifier_from_model_type(self): model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER) self.assertIsInstance(model, TFRobertaForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_pretrained_with_tuple_values(self): # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel model = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny") self.assertIsInstance(model, TFFunnelModel) config = copy.deepcopy(model.config) config.architectures = ["FunnelBaseModel"] model = TFAutoModel.from_config(config) self.assertIsInstance(model, TFFunnelBaseModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) model = TFAutoModel.from_pretrained(tmp_dir) self.assertIsInstance(model, TFFunnelBaseModel) def test_new_model_registration(self): try: AutoConfig.register("new-model", NewModelConfig) auto_classes = [ TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSequenceClassification, TFAutoModelForTokenClassification, ] for auto_class in auto_classes: with self.subTest(auto_class.__name__): # Wrong config class will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFNewModel) auto_class.register(NewModelConfig, TFNewModel) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFBertModel) # Now that the config is registered, it can be used as any other config with the auto-API tiny_config = BertModelTester(self).get_config() config = NewModelConfig(**tiny_config.to_dict()) model = auto_class.from_config(config) self.assertIsInstance(model, TFNewModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = auto_class.from_pretrained(tmp_dir) self.assertIsInstance(new_model, TFNewModel) finally: if "new-model" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["new-model"] for mapping in ( TF_MODEL_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, ): if NewModelConfig in mapping._extra_content: del mapping._extra_content[NewModelConfig] def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "bert-base is not a local folder and is not a valid model identifier" ): _ = TFAutoModel.from_pretrained("bert-base") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = TFAutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") def test_model_file_not_found(self): with self.assertRaisesRegex( EnvironmentError, "hf-internal-testing/config-no-model does not appear to have a file named tf_model.h5", ): _ = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model") def test_model_from_pt_suggestion(self): with self.assertRaisesRegex(EnvironmentError, "Use `from_pt=True` to load this model"): _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") def test_cached_model_has_minimum_calls_to_head(self): # Make sure we have cached the model. _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0) # With a sharded checkpoint _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/gpt_neox_japanese/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...file_utils import _LazyModule, is_torch_available from ...utils import OptionalDependencyNotAvailable _import_structure = { "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"], "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_gpt_neox_japanese"] = [ "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoXJapaneseForCausalLM", "GPTNeoXJapaneseLayer", "GPTNeoXJapaneseModel", "GPTNeoXJapanesePreTrainedModel", ] if TYPE_CHECKING: from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_gpt_neox_japanese import ( GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseLayer, GPTNeoXJapaneseModel, GPTNeoXJapanesePreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...file_utils import _LazyModule, is_torch_available from ...utils import OptionalDependencyNotAvailable _import_structure = { "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"], "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_gpt_neox_japanese"] = [ "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoXJapaneseForCausalLM", "GPTNeoXJapaneseLayer", "GPTNeoXJapaneseModel", "GPTNeoXJapanesePreTrainedModel", ] if TYPE_CHECKING: from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_gpt_neox_japanese import ( GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseLayer, GPTNeoXJapaneseModel, GPTNeoXJapanesePreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/sew/configuration_sew.py

# coding=utf-8 # Copyright 2021 ASAPP Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ SEW model configuration""" import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) SEW_PRETRAINED_CONFIG_ARCHIVE_MAP = { "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json", # See all SEW models at https://huggingface.co/models?filter=sew } class SEWConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`SEWModel`]. It is used to instantiate a SEW model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SEW [asapp/sew-tiny-100k](https://huggingface.co/asapp/sew-tiny-100k) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32): Vocabulary size of the SEW model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SEW`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. squeeze_factor (`int`, *optional*, defaults to 2): Sequence length downsampling factor after the encoder and upsampling factor after the transformer. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. final_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the final projection layer of [`SEWForCTC`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. feat_extract_norm (`str`, *optional*, defaults to `"group"`): The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D convolutional layers. feat_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for output of the feature encoder. feat_extract_activation (`str, `optional`, defaults to `"gelu"`): The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)`): A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers. conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)`): A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The length of *conv_kernel* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_bias (`bool`, *optional*, defaults to `False`): Whether the 1D convolutional layers have a bias. num_conv_pos_embeddings (`int`, *optional*, defaults to 128): Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional embeddings layer. num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): Number of groups of 1D convolutional positional embeddings layer. apply_spec_augment (`bool`, *optional*, defaults to `True`): Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://arxiv.org/abs/1904.08779). mask_time_prob (`float`, *optional*, defaults to 0.05): Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector span to be masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_time_length (`int`, *optional*, defaults to 10): Length of vector span along the time axis. mask_time_min_masks (`int`, *optional*, defaults to 2),: The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < mask_time_min_masks'' mask_feature_prob (`float`, *optional*, defaults to 0.0): Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_feature_length (`int`, *optional*, defaults to 10): Length of vector span along the feature axis. mask_feature_min_masks (`int`, *optional*, defaults to 0),: The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`): Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an instance of [`SEWForCTC`]. ctc_zero_infinity (`bool`, *optional*, defaults to `False`): Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance of [`SEWForCTC`]. use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an instance of [`Wav2Vec2ForSequenceClassification`]. classifier_proj_size (`int`, *optional*, defaults to 256): Dimensionality of the projection before token mean-pooling for classification. Example: ```python >>> from transformers import SEWConfig, SEWModel >>> # Initializing a SEW asapp/sew-tiny-100k style configuration >>> configuration = SEWConfig() >>> # Initializing a model (with random weights) from the asapp/sew-tiny-100k style configuration >>> model = SEWModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "sew" def __init__( self, vocab_size=32, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, squeeze_factor=2, hidden_act="gelu", hidden_dropout=0.1, activation_dropout=0.1, attention_dropout=0.1, feat_proj_dropout=0.0, final_dropout=0.1, layerdrop=0.1, initializer_range=0.02, layer_norm_eps=1e-5, feat_extract_norm="group", feat_extract_activation="gelu", conv_dim=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512), conv_stride=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1), conv_kernel=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1), conv_bias=False, num_conv_pos_embeddings=128, num_conv_pos_embedding_groups=16, apply_spec_augment=True, mask_time_prob=0.05, mask_time_length=10, mask_time_min_masks=2, mask_feature_prob=0.0, mask_feature_length=10, mask_feature_min_masks=0, ctc_loss_reduction="mean", ctc_zero_infinity=False, use_weighted_layer_sum=False, classifier_proj_size=256, pad_token_id=0, bos_token_id=1, eos_token_id=2, **kwargs ): super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id) self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_activation = feat_extract_activation self.conv_dim = list(conv_dim) self.conv_stride = list(conv_stride) self.conv_kernel = list(conv_kernel) self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_feat_extract_layers = len(self.conv_dim) self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.squeeze_factor = squeeze_factor self.hidden_act = hidden_act self.num_attention_heads = num_attention_heads self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.feat_proj_dropout = feat_proj_dropout self.final_dropout = final_dropout self.layerdrop = layerdrop self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.vocab_size = vocab_size if ( (len(self.conv_stride) != self.num_feat_extract_layers) or (len(self.conv_kernel) != self.num_feat_extract_layers) or (len(self.conv_dim) != self.num_feat_extract_layers) ): raise ValueError( "Configuration for convolutional layers is incorrect." "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`," f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)" f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`." ) # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 self.apply_spec_augment = apply_spec_augment self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.mask_time_min_masks = mask_time_min_masks self.mask_feature_prob = mask_feature_prob self.mask_feature_length = mask_feature_length self.mask_feature_min_masks = mask_feature_min_masks # ctc loss self.ctc_loss_reduction = ctc_loss_reduction self.ctc_zero_infinity = ctc_zero_infinity # sequence classification self.use_weighted_layer_sum = use_weighted_layer_sum self.classifier_proj_size = classifier_proj_size @property def inputs_to_logits_ratio(self): return functools.reduce(operator.mul, self.conv_stride, 1)

# coding=utf-8 # Copyright 2021 ASAPP Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ SEW model configuration""" import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) SEW_PRETRAINED_CONFIG_ARCHIVE_MAP = { "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json", # See all SEW models at https://huggingface.co/models?filter=sew } class SEWConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`SEWModel`]. It is used to instantiate a SEW model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SEW [asapp/sew-tiny-100k](https://huggingface.co/asapp/sew-tiny-100k) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32): Vocabulary size of the SEW model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SEW`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. squeeze_factor (`int`, *optional*, defaults to 2): Sequence length downsampling factor after the encoder and upsampling factor after the transformer. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. final_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the final projection layer of [`SEWForCTC`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. feat_extract_norm (`str`, *optional*, defaults to `"group"`): The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D convolutional layers. feat_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for output of the feature encoder. feat_extract_activation (`str, `optional`, defaults to `"gelu"`): The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)`): A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers. conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)`): A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The length of *conv_kernel* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_bias (`bool`, *optional*, defaults to `False`): Whether the 1D convolutional layers have a bias. num_conv_pos_embeddings (`int`, *optional*, defaults to 128): Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional embeddings layer. num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): Number of groups of 1D convolutional positional embeddings layer. apply_spec_augment (`bool`, *optional*, defaults to `True`): Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://arxiv.org/abs/1904.08779). mask_time_prob (`float`, *optional*, defaults to 0.05): Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector span to be masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_time_length (`int`, *optional*, defaults to 10): Length of vector span along the time axis. mask_time_min_masks (`int`, *optional*, defaults to 2),: The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < mask_time_min_masks'' mask_feature_prob (`float`, *optional*, defaults to 0.0): Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_feature_length (`int`, *optional*, defaults to 10): Length of vector span along the feature axis. mask_feature_min_masks (`int`, *optional*, defaults to 0),: The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`): Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an instance of [`SEWForCTC`]. ctc_zero_infinity (`bool`, *optional*, defaults to `False`): Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance of [`SEWForCTC`]. use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an instance of [`Wav2Vec2ForSequenceClassification`]. classifier_proj_size (`int`, *optional*, defaults to 256): Dimensionality of the projection before token mean-pooling for classification. Example: ```python >>> from transformers import SEWConfig, SEWModel >>> # Initializing a SEW asapp/sew-tiny-100k style configuration >>> configuration = SEWConfig() >>> # Initializing a model (with random weights) from the asapp/sew-tiny-100k style configuration >>> model = SEWModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "sew" def __init__( self, vocab_size=32, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, squeeze_factor=2, hidden_act="gelu", hidden_dropout=0.1, activation_dropout=0.1, attention_dropout=0.1, feat_proj_dropout=0.0, final_dropout=0.1, layerdrop=0.1, initializer_range=0.02, layer_norm_eps=1e-5, feat_extract_norm="group", feat_extract_activation="gelu", conv_dim=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512), conv_stride=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1), conv_kernel=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1), conv_bias=False, num_conv_pos_embeddings=128, num_conv_pos_embedding_groups=16, apply_spec_augment=True, mask_time_prob=0.05, mask_time_length=10, mask_time_min_masks=2, mask_feature_prob=0.0, mask_feature_length=10, mask_feature_min_masks=0, ctc_loss_reduction="mean", ctc_zero_infinity=False, use_weighted_layer_sum=False, classifier_proj_size=256, pad_token_id=0, bos_token_id=1, eos_token_id=2, **kwargs ): super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id) self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_activation = feat_extract_activation self.conv_dim = list(conv_dim) self.conv_stride = list(conv_stride) self.conv_kernel = list(conv_kernel) self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_feat_extract_layers = len(self.conv_dim) self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.squeeze_factor = squeeze_factor self.hidden_act = hidden_act self.num_attention_heads = num_attention_heads self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.feat_proj_dropout = feat_proj_dropout self.final_dropout = final_dropout self.layerdrop = layerdrop self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.vocab_size = vocab_size if ( (len(self.conv_stride) != self.num_feat_extract_layers) or (len(self.conv_kernel) != self.num_feat_extract_layers) or (len(self.conv_dim) != self.num_feat_extract_layers) ): raise ValueError( "Configuration for convolutional layers is incorrect." "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`," f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)" f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`." ) # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 self.apply_spec_augment = apply_spec_augment self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.mask_time_min_masks = mask_time_min_masks self.mask_feature_prob = mask_feature_prob self.mask_feature_length = mask_feature_length self.mask_feature_min_masks = mask_feature_min_masks # ctc loss self.ctc_loss_reduction = ctc_loss_reduction self.ctc_zero_infinity = ctc_zero_infinity # sequence classification self.use_weighted_layer_sum = use_weighted_layer_sum self.classifier_proj_size = classifier_proj_size @property def inputs_to_logits_ratio(self): return functools.reduce(operator.mul, self.conv_stride, 1)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./utils/test_module/custom_tokenization_fast.py

from transformers import BertTokenizerFast from .custom_tokenization import CustomTokenizer class CustomTokenizerFast(BertTokenizerFast): slow_tokenizer_class = CustomTokenizer pass

from transformers import BertTokenizerFast from .custom_tokenization import CustomTokenizer class CustomTokenizerFast(BertTokenizerFast): slow_tokenizer_class = CustomTokenizer pass

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/dpr/convert_dpr_original_checkpoint_to_pytorch.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import collections from pathlib import Path import torch from torch.serialization import default_restore_location from .transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader CheckpointState = collections.namedtuple( "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"] ) def load_states_from_checkpoint(model_file: str) -> CheckpointState: print(f"Reading saved model from {model_file}") state_dict = torch.load(model_file, map_location=lambda s, l: default_restore_location(s, "cpu")) return CheckpointState(**state_dict) class DPRState: def __init__(self, src_file: Path): self.src_file = src_file def load_dpr_model(self): raise NotImplementedError @staticmethod def from_type(comp_type: str, *args, **kwargs) -> "DPRState": if comp_type.startswith("c"): return DPRContextEncoderState(*args, **kwargs) if comp_type.startswith("q"): return DPRQuestionEncoderState(*args, **kwargs) if comp_type.startswith("r"): return DPRReaderState(*args, **kwargs) else: raise ValueError("Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'.") class DPRContextEncoderState(DPRState): def load_dpr_model(self): model = DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0])) print(f"Loading DPR biencoder from {self.src_file}") saved_state = load_states_from_checkpoint(self.src_file) encoder, prefix = model.ctx_encoder, "ctx_model." # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3 state_dict = {"bert_model.embeddings.position_ids": model.ctx_encoder.bert_model.embeddings.position_ids} for key, value in saved_state.model_dict.items(): if key.startswith(prefix): key = key[len(prefix) :] if not key.startswith("encode_proj."): key = "bert_model." + key state_dict[key] = value encoder.load_state_dict(state_dict) return model class DPRQuestionEncoderState(DPRState): def load_dpr_model(self): model = DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0])) print(f"Loading DPR biencoder from {self.src_file}") saved_state = load_states_from_checkpoint(self.src_file) encoder, prefix = model.question_encoder, "question_model." # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3 state_dict = {"bert_model.embeddings.position_ids": model.question_encoder.bert_model.embeddings.position_ids} for key, value in saved_state.model_dict.items(): if key.startswith(prefix): key = key[len(prefix) :] if not key.startswith("encode_proj."): key = "bert_model." + key state_dict[key] = value encoder.load_state_dict(state_dict) return model class DPRReaderState(DPRState): def load_dpr_model(self): model = DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0])) print(f"Loading DPR reader from {self.src_file}") saved_state = load_states_from_checkpoint(self.src_file) # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3 state_dict = { "encoder.bert_model.embeddings.position_ids": model.span_predictor.encoder.bert_model.embeddings.position_ids } for key, value in saved_state.model_dict.items(): if key.startswith("encoder.") and not key.startswith("encoder.encode_proj"): key = "encoder.bert_model." + key[len("encoder.") :] state_dict[key] = value model.span_predictor.load_state_dict(state_dict) return model def convert(comp_type: str, src_file: Path, dest_dir: Path): dest_dir = Path(dest_dir) dest_dir.mkdir(exist_ok=True) dpr_state = DPRState.from_type(comp_type, src_file=src_file) model = dpr_state.load_dpr_model() model.save_pretrained(dest_dir) model.from_pretrained(dest_dir) # sanity check if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--type", type=str, help="Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'." ) parser.add_argument( "--src", type=str, help=( "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo" " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the" " 'retriever' checkpoints." ), ) parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.") args = parser.parse_args() src_file = Path(args.src) dest_dir = f"converted-{src_file.name}" if args.dest is None else args.dest dest_dir = Path(dest_dir) assert src_file.exists() assert ( args.type is not None ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'." convert(args.type, src_file, dest_dir)

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import collections from pathlib import Path import torch from torch.serialization import default_restore_location from .transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader CheckpointState = collections.namedtuple( "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"] ) def load_states_from_checkpoint(model_file: str) -> CheckpointState: print(f"Reading saved model from {model_file}") state_dict = torch.load(model_file, map_location=lambda s, l: default_restore_location(s, "cpu")) return CheckpointState(**state_dict) class DPRState: def __init__(self, src_file: Path): self.src_file = src_file def load_dpr_model(self): raise NotImplementedError @staticmethod def from_type(comp_type: str, *args, **kwargs) -> "DPRState": if comp_type.startswith("c"): return DPRContextEncoderState(*args, **kwargs) if comp_type.startswith("q"): return DPRQuestionEncoderState(*args, **kwargs) if comp_type.startswith("r"): return DPRReaderState(*args, **kwargs) else: raise ValueError("Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'.") class DPRContextEncoderState(DPRState): def load_dpr_model(self): model = DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0])) print(f"Loading DPR biencoder from {self.src_file}") saved_state = load_states_from_checkpoint(self.src_file) encoder, prefix = model.ctx_encoder, "ctx_model." # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3 state_dict = {"bert_model.embeddings.position_ids": model.ctx_encoder.bert_model.embeddings.position_ids} for key, value in saved_state.model_dict.items(): if key.startswith(prefix): key = key[len(prefix) :] if not key.startswith("encode_proj."): key = "bert_model." + key state_dict[key] = value encoder.load_state_dict(state_dict) return model class DPRQuestionEncoderState(DPRState): def load_dpr_model(self): model = DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0])) print(f"Loading DPR biencoder from {self.src_file}") saved_state = load_states_from_checkpoint(self.src_file) encoder, prefix = model.question_encoder, "question_model." # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3 state_dict = {"bert_model.embeddings.position_ids": model.question_encoder.bert_model.embeddings.position_ids} for key, value in saved_state.model_dict.items(): if key.startswith(prefix): key = key[len(prefix) :] if not key.startswith("encode_proj."): key = "bert_model." + key state_dict[key] = value encoder.load_state_dict(state_dict) return model class DPRReaderState(DPRState): def load_dpr_model(self): model = DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0])) print(f"Loading DPR reader from {self.src_file}") saved_state = load_states_from_checkpoint(self.src_file) # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3 state_dict = { "encoder.bert_model.embeddings.position_ids": model.span_predictor.encoder.bert_model.embeddings.position_ids } for key, value in saved_state.model_dict.items(): if key.startswith("encoder.") and not key.startswith("encoder.encode_proj"): key = "encoder.bert_model." + key[len("encoder.") :] state_dict[key] = value model.span_predictor.load_state_dict(state_dict) return model def convert(comp_type: str, src_file: Path, dest_dir: Path): dest_dir = Path(dest_dir) dest_dir.mkdir(exist_ok=True) dpr_state = DPRState.from_type(comp_type, src_file=src_file) model = dpr_state.load_dpr_model() model.save_pretrained(dest_dir) model.from_pretrained(dest_dir) # sanity check if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--type", type=str, help="Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'." ) parser.add_argument( "--src", type=str, help=( "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo" " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the" " 'retriever' checkpoints." ), ) parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.") args = parser.parse_args() src_file = Path(args.src) dest_dir = f"converted-{src_file.name}" if args.dest is None else args.dest dest_dir = Path(dest_dir) assert src_file.exists() assert ( args.type is not None ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'." convert(args.type, src_file, dest_dir)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Wav2Vec2 checkpoint.""" import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( Wav2Vec2Config, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC, Wav2Vec2ForPreTraining, Wav2Vec2Processor, logging, ) logging.set_verbosity_info() logger = logging.get_logger(__name__) MAPPING = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "w2v_model.layer_norm": "feature_projection.layer_norm", "quantizer.weight_proj": "quantizer.weight_proj", "quantizer.vars": "quantizer.codevectors", "project_q": "project_q", "final_proj": "project_hid", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", } TOP_LEVEL_KEYS = [ "lm_head", "quantizer.weight_proj", "quantizer.codevectors", "project_q", "project_hid", ] def set_recursively(hf_pointer, key, value, full_name, weight_type): for attribute in key.split("."): hf_pointer = getattr(hf_pointer, attribute) if weight_type is not None: hf_shape = getattr(hf_pointer, weight_type).shape else: hf_shape = hf_pointer.shape if hf_shape != value.shape: raise ValueError( f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be" f" {value.shape} for {full_name}" ) if weight_type == "weight": hf_pointer.weight.data = value elif weight_type == "weight_g": hf_pointer.weight_g.data = value elif weight_type == "weight_v": hf_pointer.weight_v.data = value elif weight_type == "bias": hf_pointer.bias.data = value else: hf_pointer.data = value logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.") def recursively_load_weights(fairseq_model, hf_model, is_headless): unused_weights = [] fairseq_dict = fairseq_model.state_dict() feature_extractor = hf_model.wav2vec2.feature_extractor for name, value in fairseq_dict.items(): is_used = False if "conv_layers" in name: load_conv_layer( name, value, feature_extractor, unused_weights, hf_model.config.feat_extract_norm == "group", ) is_used = True else: for key, mapped_key in MAPPING.items(): mapped_key = "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]: is_used = True if "*" in mapped_key: layer_index = name.split(key)[0].split(".")[-2] mapped_key = mapped_key.replace("*", layer_index) if "weight_g" in name: weight_type = "weight_g" elif "weight_v" in name: weight_type = "weight_v" elif "bias" in name: weight_type = "bias" elif "weight" in name: # TODO: don't match quantizer.weight_proj weight_type = "weight" else: weight_type = None set_recursively(hf_model, mapped_key, value, name, weight_type) continue if not is_used: unused_weights.append(name) logger.warning(f"Unused weights: {unused_weights}") def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm): name = full_name.split("conv_layers.")[-1] items = name.split(".") layer_id = int(items[0]) type_id = int(items[1]) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." ) feature_extractor.conv_layers[layer_id].conv.bias.data = value logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." ) feature_extractor.conv_layers[layer_id].conv.weight.data = value logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found." ) feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found." ) feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") else: unused_weights.append(full_name) @torch.no_grad() def convert_wav2vec2_checkpoint( checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True ): """ Copy/paste/tweak model's weights to transformers design. """ if config_path is not None: config = Wav2Vec2Config.from_pretrained(config_path) else: config = Wav2Vec2Config() if is_finetuned: if dict_path: target_dict = Dictionary.load(dict_path) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq config.bos_token_id = target_dict.pad_index config.pad_token_id = target_dict.bos_index config.eos_token_id = target_dict.eos_index config.vocab_size = len(target_dict.symbols) vocab_path = os.path.join(pytorch_dump_folder_path, "vocab.json") if not os.path.isdir(pytorch_dump_folder_path): logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(pytorch_dump_folder_path)) return os.makedirs(pytorch_dump_folder_path, exist_ok=True) vocab_dict = target_dict.indices # fairseq has the <pad> and <s> switched vocab_dict["<pad>"] = 0 vocab_dict["<s>"] = 1 with open(vocab_path, "w", encoding="utf-8") as vocab_handle: json.dump(vocab_dict, vocab_handle) tokenizer = Wav2Vec2CTCTokenizer( vocab_path, unk_token=target_dict.unk_word, pad_token=target_dict.pad_word, bos_token=target_dict.bos_word, eos_token=target_dict.eos_word, word_delimiter_token="|", do_lower_case=False, ) return_attention_mask = True if config.feat_extract_norm == "layer" else False feature_extractor = Wav2Vec2FeatureExtractor( feature_size=1, sampling_rate=16000, padding_value=0, do_normalize=True, return_attention_mask=return_attention_mask, ) processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.save_pretrained(pytorch_dump_folder_path) hf_wav2vec = Wav2Vec2ForCTC(config) else: hf_wav2vec = Wav2Vec2ForPreTraining(config) if is_finetuned: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])} ) else: task_arg = argparse.Namespace(task="audio_pretraining") task = fairseq.tasks.setup_task(task_arg) model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], task=task) model = model[0].eval() recursively_load_weights(model, hf_wav2vec, not is_finetuned) hf_wav2vec.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" ) args = parser.parse_args() convert_wav2vec2_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned )

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Wav2Vec2 checkpoint.""" import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( Wav2Vec2Config, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC, Wav2Vec2ForPreTraining, Wav2Vec2Processor, logging, ) logging.set_verbosity_info() logger = logging.get_logger(__name__) MAPPING = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "w2v_model.layer_norm": "feature_projection.layer_norm", "quantizer.weight_proj": "quantizer.weight_proj", "quantizer.vars": "quantizer.codevectors", "project_q": "project_q", "final_proj": "project_hid", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", } TOP_LEVEL_KEYS = [ "lm_head", "quantizer.weight_proj", "quantizer.codevectors", "project_q", "project_hid", ] def set_recursively(hf_pointer, key, value, full_name, weight_type): for attribute in key.split("."): hf_pointer = getattr(hf_pointer, attribute) if weight_type is not None: hf_shape = getattr(hf_pointer, weight_type).shape else: hf_shape = hf_pointer.shape if hf_shape != value.shape: raise ValueError( f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be" f" {value.shape} for {full_name}" ) if weight_type == "weight": hf_pointer.weight.data = value elif weight_type == "weight_g": hf_pointer.weight_g.data = value elif weight_type == "weight_v": hf_pointer.weight_v.data = value elif weight_type == "bias": hf_pointer.bias.data = value else: hf_pointer.data = value logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.") def recursively_load_weights(fairseq_model, hf_model, is_headless): unused_weights = [] fairseq_dict = fairseq_model.state_dict() feature_extractor = hf_model.wav2vec2.feature_extractor for name, value in fairseq_dict.items(): is_used = False if "conv_layers" in name: load_conv_layer( name, value, feature_extractor, unused_weights, hf_model.config.feat_extract_norm == "group", ) is_used = True else: for key, mapped_key in MAPPING.items(): mapped_key = "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]: is_used = True if "*" in mapped_key: layer_index = name.split(key)[0].split(".")[-2] mapped_key = mapped_key.replace("*", layer_index) if "weight_g" in name: weight_type = "weight_g" elif "weight_v" in name: weight_type = "weight_v" elif "bias" in name: weight_type = "bias" elif "weight" in name: # TODO: don't match quantizer.weight_proj weight_type = "weight" else: weight_type = None set_recursively(hf_model, mapped_key, value, name, weight_type) continue if not is_used: unused_weights.append(name) logger.warning(f"Unused weights: {unused_weights}") def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm): name = full_name.split("conv_layers.")[-1] items = name.split(".") layer_id = int(items[0]) type_id = int(items[1]) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." ) feature_extractor.conv_layers[layer_id].conv.bias.data = value logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." ) feature_extractor.conv_layers[layer_id].conv.weight.data = value logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found." ) feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found." ) feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") else: unused_weights.append(full_name) @torch.no_grad() def convert_wav2vec2_checkpoint( checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True ): """ Copy/paste/tweak model's weights to transformers design. """ if config_path is not None: config = Wav2Vec2Config.from_pretrained(config_path) else: config = Wav2Vec2Config() if is_finetuned: if dict_path: target_dict = Dictionary.load(dict_path) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq config.bos_token_id = target_dict.pad_index config.pad_token_id = target_dict.bos_index config.eos_token_id = target_dict.eos_index config.vocab_size = len(target_dict.symbols) vocab_path = os.path.join(pytorch_dump_folder_path, "vocab.json") if not os.path.isdir(pytorch_dump_folder_path): logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(pytorch_dump_folder_path)) return os.makedirs(pytorch_dump_folder_path, exist_ok=True) vocab_dict = target_dict.indices # fairseq has the <pad> and <s> switched vocab_dict["<pad>"] = 0 vocab_dict["<s>"] = 1 with open(vocab_path, "w", encoding="utf-8") as vocab_handle: json.dump(vocab_dict, vocab_handle) tokenizer = Wav2Vec2CTCTokenizer( vocab_path, unk_token=target_dict.unk_word, pad_token=target_dict.pad_word, bos_token=target_dict.bos_word, eos_token=target_dict.eos_word, word_delimiter_token="|", do_lower_case=False, ) return_attention_mask = True if config.feat_extract_norm == "layer" else False feature_extractor = Wav2Vec2FeatureExtractor( feature_size=1, sampling_rate=16000, padding_value=0, do_normalize=True, return_attention_mask=return_attention_mask, ) processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.save_pretrained(pytorch_dump_folder_path) hf_wav2vec = Wav2Vec2ForCTC(config) else: hf_wav2vec = Wav2Vec2ForPreTraining(config) if is_finetuned: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])} ) else: task_arg = argparse.Namespace(task="audio_pretraining") task = fairseq.tasks.setup_task(task_arg) model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], task=task) model = model[0].eval() recursively_load_weights(model, hf_wav2vec, not is_finetuned) hf_wav2vec.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" ) args = parser.parse_args() convert_wav2vec2_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/videomae/__init__.py

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/utils/dummy_detectron2_objects.py

# This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import requires_backends LAYOUTLM_V2_PRETRAINED_MODEL_ARCHIVE_LIST = None class LayoutLMv2Model: def __init__(self, *args, **kwargs): requires_backends(self, ["detectron2"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["detectron2"])

# This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import requires_backends LAYOUTLM_V2_PRETRAINED_MODEL_ARCHIVE_LIST = None class LayoutLMv2Model: def __init__(self, *args, **kwargs): requires_backends(self, ["detectron2"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["detectron2"])

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/poolformer/test_modeling_poolformer.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch PoolFormer model. """ import inspect import unittest from transformers import is_torch_available, is_vision_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import PoolFormerFeatureExtractor class PoolFormerConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "hidden_sizes")) self.parent.assertTrue(hasattr(config, "num_encoder_blocks")) class PoolFormerModelTester: def __init__( self, parent, batch_size=13, image_size=64, num_channels=3, num_encoder_blocks=4, depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], hidden_sizes=[16, 32, 64, 128], downsampling_rates=[1, 4, 8, 16], is_training=False, use_labels=True, hidden_act="gelu", hidden_dropout_prob=0.1, initializer_range=0.02, num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.num_encoder_blocks = num_encoder_blocks self.sr_ratios = sr_ratios self.depths = depths self.hidden_sizes = hidden_sizes self.downsampling_rates = downsampling_rates self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels) config = PoolFormerConfig( image_size=self.image_size, num_channels=self.num_channels, num_encoder_blocks=self.num_encoder_blocks, depths=self.depths, hidden_sizes=self.hidden_sizes, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, initializer_range=self.initializer_range, ) return config, pixel_values, labels def create_and_check_model(self, config, pixel_values, labels): model = PoolFormerModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) expected_height = expected_width = self.image_size // 32.0 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class PoolFormerModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else () test_head_masking = False test_pruning = False test_resize_embeddings = False test_torchscript = False has_attentions = False def setUp(self): self.model_tester = PoolFormerModelTester(self) self.config_tester = PoolFormerConfigTester(self, config_class=PoolFormerConfig) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="PoolFormer does not output attentions") def test_attention_outputs(self): pass @unittest.skip("PoolFormer does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip("PoolFormer does not have get_input_embeddings method and get_output_embeddings methods") def test_model_common_attributes(self): pass def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = self.model_tester.num_encoder_blocks self.assertEqual(len(hidden_states), expected_num_layers) # verify the first hidden states (first block) self.assertListEqual( list(hidden_states[0].shape[-3:]), [ self.model_tester.hidden_sizes[0], self.model_tester.image_size // 4, self.model_tester.image_size // 4, ], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_training(self): if not self.model_tester.is_training: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: if model_class in get_values(MODEL_MAPPING): continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) @slow def test_model_from_pretrained(self): for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = PoolFormerModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch class PoolFormerModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_classification_head(self): feature_extractor = PoolFormerFeatureExtractor() model = PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12").to(torch_device) inputs = feature_extractor(images=prepare_img(), return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-0.6113, 0.1685, -0.0492]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch PoolFormer model. """ import inspect import unittest from transformers import is_torch_available, is_vision_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import PoolFormerFeatureExtractor class PoolFormerConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "hidden_sizes")) self.parent.assertTrue(hasattr(config, "num_encoder_blocks")) class PoolFormerModelTester: def __init__( self, parent, batch_size=13, image_size=64, num_channels=3, num_encoder_blocks=4, depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], hidden_sizes=[16, 32, 64, 128], downsampling_rates=[1, 4, 8, 16], is_training=False, use_labels=True, hidden_act="gelu", hidden_dropout_prob=0.1, initializer_range=0.02, num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.num_encoder_blocks = num_encoder_blocks self.sr_ratios = sr_ratios self.depths = depths self.hidden_sizes = hidden_sizes self.downsampling_rates = downsampling_rates self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels) config = PoolFormerConfig( image_size=self.image_size, num_channels=self.num_channels, num_encoder_blocks=self.num_encoder_blocks, depths=self.depths, hidden_sizes=self.hidden_sizes, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, initializer_range=self.initializer_range, ) return config, pixel_values, labels def create_and_check_model(self, config, pixel_values, labels): model = PoolFormerModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) expected_height = expected_width = self.image_size // 32.0 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class PoolFormerModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else () test_head_masking = False test_pruning = False test_resize_embeddings = False test_torchscript = False has_attentions = False def setUp(self): self.model_tester = PoolFormerModelTester(self) self.config_tester = PoolFormerConfigTester(self, config_class=PoolFormerConfig) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="PoolFormer does not output attentions") def test_attention_outputs(self): pass @unittest.skip("PoolFormer does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip("PoolFormer does not have get_input_embeddings method and get_output_embeddings methods") def test_model_common_attributes(self): pass def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = self.model_tester.num_encoder_blocks self.assertEqual(len(hidden_states), expected_num_layers) # verify the first hidden states (first block) self.assertListEqual( list(hidden_states[0].shape[-3:]), [ self.model_tester.hidden_sizes[0], self.model_tester.image_size // 4, self.model_tester.image_size // 4, ], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_training(self): if not self.model_tester.is_training: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: if model_class in get_values(MODEL_MAPPING): continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) @slow def test_model_from_pretrained(self): for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = PoolFormerModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch class PoolFormerModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_classification_head(self): feature_extractor = PoolFormerFeatureExtractor() model = PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12").to(torch_device) inputs = feature_extractor(images=prepare_img(), return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-0.6113, 0.1685, -0.0492]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/m2m_100/tokenization_m2m_100.py

# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for M2M100.""" import json import os from pathlib import Path from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import sentencepiece from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "spm_file": "sentencepiece.bpe.model", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json", "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json", }, "spm_file": { "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model", "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model", }, "tokenizer_config_file": { "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json", "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/m2m100_418M": 1024, } # fmt: off FAIRSEQ_LANGUAGE_CODES = { "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"], "wmt21": ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de'] } # fmt: on class M2M100Tokenizer(PreTrainedTokenizer): """ Construct an M2M100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. spm_file (`str`): Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that contains the vocabulary. src_lang (`str`, *optional*): A string representing the source language. tgt_lang (`str`, *optional*): A string representing the target language. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. language_codes (`str`, *optional*, defaults to `"m2m100"`): What language codes to use. Should be one of `"m2m100"` or `"wmt21"`. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Examples: ```python >>> from transformers import M2M100Tokenizer >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro") >>> src_text = " UN Chief Says There Is No Military Solution in Syria" >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria" >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt") >>> model(**model_inputs) # should work ```""" vocab_files_names = VOCAB_FILES_NAMES max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] suffix_tokens: List[int] = [] def __init__( self, vocab_file, spm_file, src_lang=None, tgt_lang=None, bos_token="<s>", eos_token="</s>", sep_token="</s>", pad_token="<pad>", unk_token="<unk>", language_codes="m2m100", sp_model_kwargs: Optional[Dict[str, Any]] = None, num_madeup_words=8, **kwargs, ) -> None: self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.language_codes = language_codes fairseq_language_code = FAIRSEQ_LANGUAGE_CODES[language_codes] self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code} kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) kwargs["additional_special_tokens"] += [ self.get_lang_token(lang_code) for lang_code in fairseq_language_code if self.get_lang_token(lang_code) not in kwargs["additional_special_tokens"] ] super().__init__( src_lang=src_lang, tgt_lang=tgt_lang, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, unk_token=unk_token, pad_token=pad_token, language_codes=language_codes, sp_model_kwargs=self.sp_model_kwargs, num_madeup_words=num_madeup_words, **kwargs, ) self.vocab_file = vocab_file self.encoder = load_json(vocab_file) self.decoder = {v: k for k, v in self.encoder.items()} self.spm_file = spm_file self.sp_model = load_spm(spm_file, self.sp_model_kwargs) self.encoder_size = len(self.encoder) self.lang_token_to_id = { self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code) } self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)} self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()} self._src_lang = src_lang if src_lang is not None else "en" self.tgt_lang = tgt_lang self.cur_lang_id = self.get_lang_id(self._src_lang) self.set_src_lang_special_tokens(self._src_lang) self.num_madeup_words = num_madeup_words @property def vocab_size(self) -> int: return len(self.encoder) + len(self.lang_token_to_id) + self.num_madeup_words @property def src_lang(self) -> str: return self._src_lang @src_lang.setter def src_lang(self, new_src_lang: str) -> None: self._src_lang = new_src_lang self.set_src_lang_special_tokens(self._src_lang) def _tokenize(self, text: str) -> List[str]: return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): if token in self.lang_token_to_id: return self.lang_token_to_id[token] return self.encoder.get(token, self.encoder[self.unk_token]) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the decoder.""" if index in self.id_to_lang_token: return self.id_to_lang_token[index] return self.decoder.get(index, self.unk_token) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += self.sp_model.decode(current_sub_tokens) + token current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += self.sp_model.decode(current_sub_tokens) return out_string.strip() def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) prefix_ones = [1] * len(self.prefix_tokens) suffix_ones = [1] * len(self.suffix_tokens) if token_ids_1 is None: return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An MBART sequence has the following format, where `X` represents the sequence: - `input_ids` (for encoder) `X [eos, src_lang_code]` - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]` BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator. Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return self.prefix_tokens + token_ids_0 + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens def get_vocab(self) -> Dict: vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def __getstate__(self) -> Dict: state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d: Dict) -> None: self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: save_dir = Path(save_directory) if not save_dir.is_dir(): raise OSError(f"{save_directory} should be a directory") vocab_save_path = save_dir / ( (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"] ) spm_save_path = save_dir / ( (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"] ) save_json(self.encoder, vocab_save_path) if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file): copyfile(self.spm_file, spm_save_path) elif not os.path.isfile(self.spm_file): with open(spm_save_path, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (str(vocab_save_path), str(spm_save_path)) def prepare_seq2seq_batch( self, src_texts: List[str], src_lang: str = "en", tgt_texts: Optional[List[str]] = None, tgt_lang: str = "ro", **kwargs, ) -> BatchEncoding: self.src_lang = src_lang self.tgt_lang = tgt_lang self.set_src_lang_special_tokens(self.src_lang) return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs) def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs): """Used by translation pipeline, to prepare inputs for the generate function""" if src_lang is None or tgt_lang is None: raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") self.src_lang = src_lang inputs = self(raw_inputs, add_special_tokens=True, **extra_kwargs) tgt_lang_id = self.get_lang_id(tgt_lang) inputs["forced_bos_token_id"] = tgt_lang_id return inputs def _switch_to_input_mode(self): self.set_src_lang_special_tokens(self.src_lang) def _switch_to_target_mode(self): self.set_tgt_lang_special_tokens(self.tgt_lang) def set_src_lang_special_tokens(self, src_lang: str) -> None: """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code].""" lang_token = self.get_lang_token(src_lang) self.cur_lang_id = self.lang_token_to_id[lang_token] self.prefix_tokens = [self.cur_lang_id] self.suffix_tokens = [self.eos_token_id] def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None: """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code].""" lang_token = self.get_lang_token(tgt_lang) self.cur_lang_id = self.lang_token_to_id[lang_token] self.prefix_tokens = [self.cur_lang_id] self.suffix_tokens = [self.eos_token_id] def get_lang_token(self, lang: str) -> str: return self.lang_code_to_token[lang] def get_lang_id(self, lang: str) -> int: lang_token = self.get_lang_token(lang) return self.lang_token_to_id[lang_token] def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor: spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs) spm.Load(str(path)) return spm def load_json(path: str) -> Union[Dict, List]: with open(path, "r") as f: return json.load(f) def save_json(data, path: str) -> None: with open(path, "w") as f: json.dump(data, f, indent=2)

# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for M2M100.""" import json import os from pathlib import Path from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import sentencepiece from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "spm_file": "sentencepiece.bpe.model", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json", "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json", }, "spm_file": { "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model", "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model", }, "tokenizer_config_file": { "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json", "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/m2m100_418M": 1024, } # fmt: off FAIRSEQ_LANGUAGE_CODES = { "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"], "wmt21": ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de'] } # fmt: on class M2M100Tokenizer(PreTrainedTokenizer): """ Construct an M2M100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. spm_file (`str`): Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that contains the vocabulary. src_lang (`str`, *optional*): A string representing the source language. tgt_lang (`str`, *optional*): A string representing the target language. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. language_codes (`str`, *optional*, defaults to `"m2m100"`): What language codes to use. Should be one of `"m2m100"` or `"wmt21"`. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Examples: ```python >>> from transformers import M2M100Tokenizer >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro") >>> src_text = " UN Chief Says There Is No Military Solution in Syria" >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria" >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt") >>> model(**model_inputs) # should work ```""" vocab_files_names = VOCAB_FILES_NAMES max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] suffix_tokens: List[int] = [] def __init__( self, vocab_file, spm_file, src_lang=None, tgt_lang=None, bos_token="<s>", eos_token="</s>", sep_token="</s>", pad_token="<pad>", unk_token="<unk>", language_codes="m2m100", sp_model_kwargs: Optional[Dict[str, Any]] = None, num_madeup_words=8, **kwargs, ) -> None: self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.language_codes = language_codes fairseq_language_code = FAIRSEQ_LANGUAGE_CODES[language_codes] self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code} kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) kwargs["additional_special_tokens"] += [ self.get_lang_token(lang_code) for lang_code in fairseq_language_code if self.get_lang_token(lang_code) not in kwargs["additional_special_tokens"] ] super().__init__( src_lang=src_lang, tgt_lang=tgt_lang, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, unk_token=unk_token, pad_token=pad_token, language_codes=language_codes, sp_model_kwargs=self.sp_model_kwargs, num_madeup_words=num_madeup_words, **kwargs, ) self.vocab_file = vocab_file self.encoder = load_json(vocab_file) self.decoder = {v: k for k, v in self.encoder.items()} self.spm_file = spm_file self.sp_model = load_spm(spm_file, self.sp_model_kwargs) self.encoder_size = len(self.encoder) self.lang_token_to_id = { self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code) } self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)} self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()} self._src_lang = src_lang if src_lang is not None else "en" self.tgt_lang = tgt_lang self.cur_lang_id = self.get_lang_id(self._src_lang) self.set_src_lang_special_tokens(self._src_lang) self.num_madeup_words = num_madeup_words @property def vocab_size(self) -> int: return len(self.encoder) + len(self.lang_token_to_id) + self.num_madeup_words @property def src_lang(self) -> str: return self._src_lang @src_lang.setter def src_lang(self, new_src_lang: str) -> None: self._src_lang = new_src_lang self.set_src_lang_special_tokens(self._src_lang) def _tokenize(self, text: str) -> List[str]: return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): if token in self.lang_token_to_id: return self.lang_token_to_id[token] return self.encoder.get(token, self.encoder[self.unk_token]) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the decoder.""" if index in self.id_to_lang_token: return self.id_to_lang_token[index] return self.decoder.get(index, self.unk_token) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += self.sp_model.decode(current_sub_tokens) + token current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += self.sp_model.decode(current_sub_tokens) return out_string.strip() def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) prefix_ones = [1] * len(self.prefix_tokens) suffix_ones = [1] * len(self.suffix_tokens) if token_ids_1 is None: return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An MBART sequence has the following format, where `X` represents the sequence: - `input_ids` (for encoder) `X [eos, src_lang_code]` - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]` BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator. Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return self.prefix_tokens + token_ids_0 + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens def get_vocab(self) -> Dict: vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def __getstate__(self) -> Dict: state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d: Dict) -> None: self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: save_dir = Path(save_directory) if not save_dir.is_dir(): raise OSError(f"{save_directory} should be a directory") vocab_save_path = save_dir / ( (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"] ) spm_save_path = save_dir / ( (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"] ) save_json(self.encoder, vocab_save_path) if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file): copyfile(self.spm_file, spm_save_path) elif not os.path.isfile(self.spm_file): with open(spm_save_path, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (str(vocab_save_path), str(spm_save_path)) def prepare_seq2seq_batch( self, src_texts: List[str], src_lang: str = "en", tgt_texts: Optional[List[str]] = None, tgt_lang: str = "ro", **kwargs, ) -> BatchEncoding: self.src_lang = src_lang self.tgt_lang = tgt_lang self.set_src_lang_special_tokens(self.src_lang) return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs) def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs): """Used by translation pipeline, to prepare inputs for the generate function""" if src_lang is None or tgt_lang is None: raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") self.src_lang = src_lang inputs = self(raw_inputs, add_special_tokens=True, **extra_kwargs) tgt_lang_id = self.get_lang_id(tgt_lang) inputs["forced_bos_token_id"] = tgt_lang_id return inputs def _switch_to_input_mode(self): self.set_src_lang_special_tokens(self.src_lang) def _switch_to_target_mode(self): self.set_tgt_lang_special_tokens(self.tgt_lang) def set_src_lang_special_tokens(self, src_lang: str) -> None: """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code].""" lang_token = self.get_lang_token(src_lang) self.cur_lang_id = self.lang_token_to_id[lang_token] self.prefix_tokens = [self.cur_lang_id] self.suffix_tokens = [self.eos_token_id] def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None: """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code].""" lang_token = self.get_lang_token(tgt_lang) self.cur_lang_id = self.lang_token_to_id[lang_token] self.prefix_tokens = [self.cur_lang_id] self.suffix_tokens = [self.eos_token_id] def get_lang_token(self, lang: str) -> str: return self.lang_code_to_token[lang] def get_lang_id(self, lang: str) -> int: lang_token = self.get_lang_token(lang) return self.lang_token_to_id[lang_token] def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor: spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs) spm.Load(str(path)) return spm def load_json(path: str) -> Union[Dict, List]: with open(path, "r") as f: return json.load(f) def save_json(data, path: str) -> None: with open(path, "w") as f: json.dump(data, f, indent=2)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/bart/test_modeling_tf_bart.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers import BartConfig, BartTokenizer, is_tf_available from transformers.testing_utils import require_tf, slow, tooslow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFBartForConditionalGeneration, TFBartModel @require_tf class TFBartModelTester: config_cls = BartConfig config_updates = {} hidden_act = "gelu" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id def prepare_config_and_inputs_for_common(self): input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size) eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1) input_ids = tf.concat([input_ids, eos_tensor], axis=1) decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.config_cls( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_ids=[2], bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.pad_token_id, **self.config_updates, ) inputs_dict = prepare_bart_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFBartModel(config=config).get_decoder() input_ids = inputs_dict["input_ids"] input_ids = input_ids[:1, :] attention_mask = inputs_dict["attention_mask"][:1, :] head_mask = inputs_dict["head_mask"] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask) output_from_no_past = output_from_no_past[0] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values) output_from_past = output_from_past[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def prepare_bart_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8) if decoder_attention_mask is None: decoder_attention_mask = tf.concat( [ tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8), ], axis=-1, ) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class TFBartModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestCase): all_model_classes = (TFBartForConditionalGeneration, TFBartModel) if is_tf_available() else () all_generative_model_classes = (TFBartForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_onnx = True onnx_min_opset = 10 def setUp(self): self.model_tester = TFBartModelTester(self) self.config_tester = ConfigTester(self, config_class=BartConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in self.all_generative_model_classes: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None @tooslow def test_saved_model_creation(self): pass # TODO (Joao): fix me @unittest.skip("Onnx compliancy broke with TF 2.10") def test_onnx_compliancy(self): pass def _long_tensor(tok_lst): return tf.constant(tok_lst, dtype=tf.int32) @require_tf class TFBartHeadTests(unittest.TestCase): vocab_size = 99 def _get_config_and_data(self): eos_column_vector = tf.ones((4, 1), dtype=tf.int32) * 2 input_ids = tf.concat([ids_tensor((4, 6), self.vocab_size - 3) + 3, eos_column_vector], axis=1) batch_size = input_ids.shape[0] config = BartConfig( vocab_size=self.vocab_size, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, eos_token_id=2, pad_token_id=1, bos_token_id=0, decoder_start_token_id=2, ) return config, input_ids, batch_size def test_lm_forward(self): config, input_ids, batch_size = self._get_config_and_data() decoder_lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size) lm_model = TFBartForConditionalGeneration(config) outputs = lm_model(input_ids=input_ids, labels=decoder_lm_labels, decoder_input_ids=input_ids, use_cache=False) expected_shape = (batch_size, input_ids.shape[1], config.vocab_size) self.assertEqual(outputs.logits.shape, expected_shape) def test_lm_uneven_forward(self): config = BartConfig( vocab_size=10, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, ) lm_model = TFBartForConditionalGeneration(config) context = tf.fill((7, 2), 4) summary = tf.fill((7, 7), 6) outputs = lm_model(input_ids=context, decoder_input_ids=summary, use_cache=False) expected_shape = (*summary.shape, config.vocab_size) self.assertEqual(outputs.logits.shape, expected_shape) @slow @require_tf class TFBartModelIntegrationTest(unittest.TestCase): def test_inference_no_head(self): model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large").model input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) attention_mask = tf.cast(tf.math.not_equal(input_ids, model.config.pad_token_id), tf.int8) output = model(input_ids=input_ids, attention_mask=attention_mask)[0] expected_shape = (1, 11, 1024) self.assertEqual(output.shape, expected_shape) expected_slice = tf.convert_to_tensor( [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-3) def test_cnn_summarization_same_as_fairseq_hard(self): hf = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") tok = self.tok FRANCE_ARTICLE = ( # @noqa " Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings" " Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane." ' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."' ' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s' " comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French" " Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a" " phone at the wreckage site. The two publications described the supposed video, but did not post it on" " their websites. The publications said that they watched the video, which was found by a source close to" " the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported." ' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the' " cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the" ' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,' " editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said" " the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman" " in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the" ' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,' ' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be' " sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by" " specialized technicians working hand-in-hand with investigators. But none of the cell phones found so" " far have been sent to the institute, Menichini said. Asked whether staff involved in the search could" ' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin' ' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match' ' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is something' " we did not know before. ... Overall we can say many things of the investigation weren't revealed by the" ' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline' " Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the" " controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the" ' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of' ' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school' " discovered in an internal investigation, Lufthansa said, included medical documents he submitted in" " connection with resuming his flight training. The announcement indicates that Lufthansa, the parent" " company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and" " ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%" ' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was' " sharing the information and documents -- including training and medical records -- with public" " prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the" " past week to recover human remains and plane debris scattered across a steep mountainside. He saw the" " crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash" " site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late" " Tuesday that no visible human remains were left at the site but recovery teams would keep searching." " French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all" " the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said." " Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew" " on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with" " the flight school during his training were among several developments as investigators continued to" " delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa" " spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his" ' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in' " Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at" " some point before his aviation career and underwent psychotherapy before he got his pilot's license." " Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the" " crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to" " lose his pilot's license, a European government official briefed on the investigation told CNN on" ' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being' " considered. Another source, a law enforcement official briefed on the investigation, also told CNN that" " authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would" " not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had" " seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded" " he had psychological issues, the European government official said. But no matter what details emerge" " about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact' " that maybe they weren't going to keep doing their job and they're upset about that and so they're" ' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to' " also take that rage and turn it outward on 149 other people who had nothing to do with the person's" ' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight' " 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura" " Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine" " Amiel and Anna-Maja Rappard contributed to this report." ) EXPECTED_SUMMARY_FRANCE = ( "French prosecutor says he's not aware of any video footage from on board the plane. German daily Bild" " and French Paris Match claim to have found a cell phone video of the crash. A French Gendarmerie" ' spokesman calls the reports "completely wrong" and "unwarranted" German airline Lufthansa confirms' " co-pilot Andreas Lubitz had battled depression." ) SHORTER_ARTICLE = ( " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder" " and Faith Karimi contributed to this report." ) EXPECTED_SUMMARY_SHORTER = ( "The Palestinian Authority becomes the 123rd member of the International Criminal Court. The move gives" " the court jurisdiction over alleged crimes in Palestinian territories. Israel and the United States" " opposed the Palestinians' efforts to join the body. But Palestinian Foreign Minister Riad al-Malki said" " it was a move toward greater justice." ) # The below article tests that we don't add any hypotheses outside of the top n_beams IRAN_ARTICLE = ( " (CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran" " in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively" " block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger." " Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli" " Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a" " letter to the Iranian leadership warning them away from a deal. The debate that has already begun since" " the announcement of the new framework will likely result in more heat than light. It will not be helped" " by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ." " The most misleading assertion, despite universal rejection by experts, is that the negotiations'" " objective at the outset was the total elimination of any nuclear program in Iran. That is the position" " of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it" " had been, there would have been no Iranian team at the negotiating table. Rather, the objective has" " always been to structure an agreement or series of agreements so that Iran could not covertly develop a" " nuclear arsenal before the United States and its allies could respond. The new framework has exceeded" " expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by" " two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another" " dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite" " sharp accusations by some in the United States and its allies, Iran denies having such a program, and" " U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's" " continued cooperation with International Atomic Energy Agency inspections is further evidence on this" " point, and we'll know even more about Iran's program in the coming months and years because of the deal." " In fact, the inspections provisions that are part of this agreement are designed to protect against any" " covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that" " the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter" " warning that a deal might be killed by Congress or a future president). This of course is not the case." " The talks were between Iran and the five permanent members of the U.N. Security Council (United States," " United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has" " played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement" " reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran" " and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement" " contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the" " case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased" " or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes" " Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear" " sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going" " forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such" " a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the" ' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not' " suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New" " START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement" " with Iran will not be so balanced. The restrictions and obligations in the final framework agreement" " will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove" " most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally" " some insist that any agreement must address Iranian missile programs, human rights violations or support" " for Hamas or Hezbollah. As important as these issues are, and they must indeed be addressed, they are" " unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran. To include them in" " the negotiations would be a poison pill. This agreement should be judged on its merits and on how it" " affects the security of our negotiating partners and allies, including Israel. Those judgments should be" " fact-based, not based on questionable assertions or dubious assumptions." ) EXPECTED_SUMMARY_IRAN = ( "The U.S. and its negotiating partners reached a very strong framework agreement with Iran. Peter Bergen:" " The debate that has already begun will likely result in more heat than light. He says the agreement" " limits Iran's nuclear program in such a way as to effectively block it from building a nuclear weapon." " Bergen says the most important aim of a nuclear deal is preventing a nuclear Iran." ) ARTICLE_SUBWAY = ( " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) EXPECTED_SUMMARY_SUBWAY = ( "Liana Barrientos has been married 10 times, sometimes within two weeks of each other. Prosecutors say the" " marriages were part of an immigration scam. On Friday, she pleaded not guilty at State Supreme Court in" " the Bronx. She was arrested and charged with theft of service and criminal trespass for allegedly" " sneaking into the subway." ) dct = tok( [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY], max_length=1024, truncation_strategy="only_first", padding="longest", truncation=True, return_tensors="tf", ) self.assertEqual(1024, dct["input_ids"].shape[1]) hypotheses_batch = hf.generate( input_ids=dct["input_ids"], attention_mask=dct["attention_mask"], ) assert hypotheses_batch[:, 1].numpy().tolist() == [0, 0, 0, 0] # test force_bos_token_to_be_generated decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True, clean_up_tokenization_spaces=False) expected_batch = [ EXPECTED_SUMMARY_FRANCE, EXPECTED_SUMMARY_SHORTER, EXPECTED_SUMMARY_IRAN, EXPECTED_SUMMARY_SUBWAY, ] assert decoded == expected_batch @cached_property def tok(self): return BartTokenizer.from_pretrained("facebook/bart-large") @slow def test_contrastive_search_bart(self): article = ( " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn") bart_model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") input_ids = bart_tokenizer( article, add_special_tokens=False, truncation=True, max_length=512, return_tensors="tf" ).input_ids outputs = bart_model.generate(input_ids, penalty_alpha=0.5, top_k=5, max_length=64) generated_text = bart_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "Liana Barrientos, 39, pleaded not guilty to charges related to false marriage statements. " "Prosecutors say she married at least 10 times, sometimes within two weeks of each other. She is " "accused of being part of an immigration scam to get permanent residency. If convicted, she faces up " "to four years in" ], ) @slow def test_contrastive_search_bart_xla(self): article = ( " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn") bart_model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") input_ids = bart_tokenizer( article, add_special_tokens=False, truncation=True, max_length=512, return_tensors="tf" ).input_ids xla_generate = tf.function(bart_model.generate, jit_compile=True) # no_repeat_ngram_size set to 0 because it isn't compatible with XLA, but doesn't change the original output outputs = xla_generate(input_ids, penalty_alpha=0.5, top_k=5, max_length=64, no_repeat_ngram_size=0) generated_text = bart_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "Liana Barrientos, 39, pleaded not guilty to charges related to false marriage statements. " "Prosecutors say she married at least 10 times, sometimes within two weeks of each other. She is " "accused of being part of an immigration scam to get permanent residency. If convicted, she faces up " "to four years in" ], ) @slow @require_tf class FasterTFBartModelIntegrationTests(unittest.TestCase): """These tests are useful for debugging since they operate on a model with 1 encoder layer and 1 decoder layer.""" @cached_property def tok(self): return BartTokenizer.from_pretrained("facebook/bart-large") @cached_property def xsum_1_1_model(self): return TFBartForConditionalGeneration.from_pretrained("sshleifer/distilbart-xsum-1-1") def test_xsum_1_1_generation(self): model = self.xsum_1_1_model assert model.model.decoder.embed_tokens == model.model.shared ARTICLE = ( "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes." ) EXPECTED = ( " The International Criminal Court (ICC) has announced that it has been announced by the International" " Criminal court." ) dct = self.tok(ARTICLE, return_tensors="tf") generated_ids = model.generate(**dct, num_beams=4) result = self.tok.batch_decode(generated_ids, skip_special_tokens=True)[0] assert result == EXPECTED def test_xsum_1_1_xla_generation(self): # same test as above, but with `no_repeat_ngram_size=0` (not compatible with XLA) and XLA comparison enabled model = self.xsum_1_1_model assert model.model.decoder.embed_tokens == model.model.shared ARTICLE = ( "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes." ) EXPECTED = ( " The International Criminal Court (ICC) has announced that it is to be investigated by the International" " Criminal Court (ICC) over allegations of war crimes." ) dct = self.tok(ARTICLE, return_tensors="tf") generated_ids = model.generate(**dct, num_beams=4, no_repeat_ngram_size=0) result = self.tok.batch_decode(generated_ids, skip_special_tokens=True)[0] assert result == EXPECTED xla_generate = tf.function(model.generate, jit_compile=True) generated_ids = xla_generate(**dct, num_beams=4, no_repeat_ngram_size=0) result = self.tok.batch_decode(generated_ids, skip_special_tokens=True)[0] assert result == EXPECTED def test_xsum_1_1_batch_generation(self): batch = self.tok( [ "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories." " The formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is" " based. The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted" ' its jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including' ' East Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination' " into the situation in Palestinian territories, paving the way for possible war crimes investigations" " against Israelis. As members of the court, Palestinians may be subject to counter-charges as well." " Israel and the United States, neither of which is an ICC member, opposed the Palestinians' efforts" " to join the body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony," ' said it was a move toward greater justice. "As Palestine formally becomes a State Party to the Rome' ' Statute today, the world is also a step closer to ending a long era of impunity and injustice," he' ' said, according to an ICC news release. "Indeed, today brings us closer to our shared goals of' ' justice and peace." Judge Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was' ' just the first step for the Palestinians. "As the Rome Statute today enters into force for the State' " of Palestine, Palestine acquires all the rights as well as responsibilities that come with being a" ' State Party to the Statute. These are substantive commitments, which cannot be taken lightly," she' ' said. Rights group Human Rights Watch welcomed the development. "Governments seeking to penalize' " Palestine for joining the ICC should immediately end their pressure, and countries that support" " universal acceptance of the court's treaty should speak out to welcome its membership,\" said" " Balkees Jarrah, international justice counsel for the group. \"What's objectionable is the attempts" " to undermine international justice, not Palestine's decision to join a treaty to which over 100" ' countries around the world are members." In January, when the preliminary ICC examination was' " opened, Israeli Prime Minister Benjamin Netanyahu described it as an outrage, saying the court was" ' overstepping its boundaries. The United States also said it "strongly" disagreed with the court\'s' ' decision. "As we have said repeatedly, we do not believe that Palestine is a state and therefore we' ' do not believe that it is eligible to join the ICC," the State Department said in a statement. It' ' urged the warring sides to resolve their differences through direct negotiations. "We will continue' ' to oppose actions against Israel at the ICC as counterproductive to the cause of peace," it said.' " But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows' " the court to review evidence and determine whether to investigate suspects on both sides. Prosecutor" ' Fatou Bensouda said her office would "conduct its analysis in full independence and impartiality."' " The war between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The" " inquiry will include alleged war crimes committed since June. The International Criminal Court was" " set up in 2002 to prosecute genocide, crimes against humanity and war crimes.", "The French prosecutor leading an investigation into the crash of Germanwings Flight 9525 insisted" " Wednesday that he was not aware of any video footage from on board the plane. Marseille prosecutor" ' Brice Robin told CNN that "so far no videos were used in the crash investigation." He added, "A' " person who has such a video needs to immediately give it to the investigators.\" Robin's comments" " follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the" " French Alps. All 150 on board were killed. Paris Match and Bild reported that the video was" " recovered from a phone at the wreckage site. The two publications described the supposed video, but" " did not post it on their websites. The publications said that they watched the video, which was" " found by a source close to the investigation. \"One can hear cries of 'My God' in several" ' languages," Paris Match reported. "Metallic banging can also be heard more than three times, perhaps' " of the pilot trying to open the cockpit door with a heavy object. Towards the end, after a heavy" ' shake, stronger than the others, the screaming intensifies. Then nothing." "It is a very disturbing' " scene,\" said Julian Reichelt, editor-in-chief of Bild online. An official with France's accident" " investigation agency, the BEA, said the agency is not aware of any such video. Lt. Col. Jean-Marc" " Menichini, a French Gendarmerie spokesman in charge of communications on rescue efforts around the" ' Germanwings crash site, told CNN that the reports were "completely wrong" and "unwarranted." Cell' ' phones have been collected at the site, he said, but that they "hadn\'t been exploited yet."' " Menichini said he believed the cell phones would need to be sent to the Criminal Research Institute" " in Rosny sous-Bois, near Paris, in order to be analyzed by specialized technicians working" " hand-in-hand with investigators. But none of the cell phones found so far have been sent to the" " institute, Menichini said. Asked whether staff involved in the search could have leaked a memory" ' card to the media, Menichini answered with a categorical "no." Reichelt told "Erin Burnett:' ' Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match are' ' "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is' " something we did not know before. ... Overall we can say many things of the investigation weren't" ' revealed by the investigation at the beginning," he said. What was mental state of Germanwings' " co-pilot? German airline Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled" " depression years before he took the controls of Germanwings Flight 9525, which he's accused of" " deliberately crashing last week in the French Alps. Lubitz told his Lufthansa flight training school" ' in 2009 that he had a "previous episode of severe depression," the airline said Tuesday. Email' " correspondence between Lubitz and the school discovered in an internal investigation, Lufthansa" " said, included medical documents he submitted in connection with resuming his flight training. The" " announcement indicates that Lufthansa, the parent company of Germanwings, knew of Lubitz's battle" " with depression, allowed him to continue training and ultimately put him in the cockpit. Lufthansa," " whose CEO Carsten Spohr previously said Lubitz was 100% fit to fly, described its statement Tuesday" ' as a "swift and seamless clarification" and said it was sharing the information and documents --' " including training and medical records -- with public prosecutors. Spohr traveled to the crash site" " Wednesday, where recovery teams have been working for the past week to recover human remains and" " plane debris scattered across a steep mountainside. He saw the crisis center set up in" " Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash site, where grieving" " families have left flowers at a simple stone memorial. Menichini told CNN late Tuesday that no" " visible human remains were left at the site but recovery teams would keep searching. French" " President Francois Hollande, speaking Tuesday, said that it should be possible to identify all the" " victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini" " said. Among those personal belongings could be more cell phones belonging to the 144 passengers and" " six crew on board. Check out the latest from our correspondents . The details about Lubitz's" " correspondence with the flight school during his training were among several developments as" " investigators continued to delve into what caused the crash and Lubitz's possible motive for" " downing the jet. A Lufthansa spokesperson told CNN on Tuesday that Lubitz had a valid medical" ' certificate, had passed all his examinations and "held all the licenses required." Earlier, a' " spokesman for the prosecutor's office in Dusseldorf, Christoph Kumpa, said medical records reveal" " Lubitz suffered from suicidal tendencies at some point before his aviation career and underwent" " psychotherapy before he got his pilot's license. Kumpa emphasized there's no evidence suggesting" " Lubitz was suicidal or acting aggressively before the crash. Investigators are looking into whether" " Lubitz feared his medical condition would cause him to lose his pilot's license, a European" ' government official briefed on the investigation told CNN on Tuesday. While flying was "a big part' " of his life,\" the source said, it's only one theory being considered. Another source, a law" " enforcement official briefed on the investigation, also told CNN that authorities believe the" " primary motive for Lubitz to bring down the plane was that he feared he would not be allowed to fly" " because of his medical problems. Lubitz's girlfriend told investigators he had seen an eye doctor" " and a neuropsychologist, both of whom deemed him unfit to work recently and concluded he had" " psychological issues, the European government official said. But no matter what details emerge about" " his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the' " fact that maybe they weren't going to keep doing their job and they're upset about that and so" ' they\'re suicidal," he said. "But there is no mental illness that explains why somebody then feels' " entitled to also take that rage and turn it outward on 149 other people who had nothing to do with" " the person's problems.\" Germanwings crash compensation: What we know . Who was the captain of" " Germanwings Flight 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from" " Dusseldorf, while Laura Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff," " Antonia Mortensen, Sandrine Amiel and Anna-Maja Rappard contributed to this report.", ], return_tensors="tf", padding="longest", truncation=True, ) generated_ids = self.xsum_1_1_model.generate(**batch, num_beams=4) result = self.tok.batch_decode(generated_ids, skip_special_tokens=True) assert ( result[0] == " The International Criminal Court (ICC) has announced that it has been announced by the International" " Criminal court." ) assert ( result[1] == " An investigation into the crash that killed at least 10 people in the French capital has been" " released by the French police investigating the crash." ) def test_encoder_equiv(self): batch = self.tok( [ "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories." " The formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is" " based. The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted" ' its jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including' ' East Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination' " into the situation in Palestinian territories, paving the way for possible war crimes investigations" " against Israelis. As members of the court, Palestinians may be subject to counter-charges as well." " Israel and the United States, neither of which is an ICC member, opposed the Palestinians' efforts" " to join the body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony," ' said it was a move toward greater justice. "As Palestine formally becomes a State Party to the Rome' ' Statute today, the world is also a step closer to ending a long era of impunity and injustice," he' ' said, according to an ICC news release. "Indeed, today brings us closer to our shared goals of' ' justice and peace." Judge Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was' ' just the first step for the Palestinians. "As the Rome Statute today enters into force for the State' " of Palestine, Palestine acquires all the rights as well as responsibilities that come with being a" ' State Party to the Statute. These are substantive commitments, which cannot be taken lightly," she' ' said. Rights group Human Rights Watch welcomed the development. "Governments seeking to penalize' " Palestine for joining the ICC should immediately end their pressure, and countries that support" " universal acceptance of the court's treaty should speak out to welcome its membership,\" said" " Balkees Jarrah, international justice counsel for the group. \"What's objectionable is the attempts" " to undermine international justice, not Palestine's decision to join a treaty to which over 100" ' countries around the world are members." In January, when the preliminary ICC examination was' " opened, Israeli Prime Minister Benjamin Netanyahu described it as an outrage, saying the court was" ' overstepping its boundaries. The United States also said it "strongly" disagreed with the court\'s' ' decision. "As we have said repeatedly, we do not believe that Palestine is a state and therefore we' ' do not believe that it is eligible to join the ICC," the State Department said in a statement. It' ' urged the warring sides to resolve their differences through direct negotiations. "We will continue' ' to oppose actions against Israel at the ICC as counterproductive to the cause of peace," it said.' " But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows' " the court to review evidence and determine whether to investigate suspects on both sides. Prosecutor" ' Fatou Bensouda said her office would "conduct its analysis in full independence and impartiality."' " The war between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The" " inquiry will include alleged war crimes committed since June. The International Criminal Court was" " set up in 2002 to prosecute genocide, crimes against humanity and war crimes.", "The French prosecutor leading an investigation into the crash of Germanwings Flight 9525 insisted" " Wednesday that he was not aware of any video footage from on board the plane. Marseille prosecutor" ' Brice Robin told CNN that "so far no videos were used in the crash investigation." He added, "A' " person who has such a video needs to immediately give it to the investigators.\" Robin's comments" " follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the" " French Alps. All 150 on board were killed. Paris Match and Bild reported that the video was" " recovered from a phone at the wreckage site. The two publications described the supposed video, but" " did not post it on their websites. The publications said that they watched the video, which was" " found by a source close to the investigation. \"One can hear cries of 'My God' in several" ' languages," Paris Match reported. "Metallic banging can also be heard more than three times, perhaps' " of the pilot trying to open the cockpit door with a heavy object. Towards the end, after a heavy" ' shake, stronger than the others, the screaming intensifies. Then nothing." "It is a very disturbing' " scene,\" said Julian Reichelt, editor-in-chief of Bild online. An official with France's accident" " investigation agency, the BEA, said the agency is not aware of any such video. Lt. Col. Jean-Marc" " Menichini, a French Gendarmerie spokesman in charge of communications on rescue efforts around the" ' Germanwings crash site, told CNN that the reports were "completely wrong" and "unwarranted." Cell' ' phones have been collected at the site, he said, but that they "hadn\'t been exploited yet."' " Menichini said he believed the cell phones would need to be sent to the Criminal Research Institute" " in Rosny sous-Bois, near Paris, in order to be analyzed by specialized technicians working" " hand-in-hand with investigators. But none of the cell phones found so far have been sent to the" " institute, Menichini said. Asked whether staff involved in the search could have leaked a memory" ' card to the media, Menichini answered with a categorical "no." Reichelt told "Erin Burnett:' ' Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match are' ' "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is' " something we did not know before. ... Overall we can say many things of the investigation weren't" ' revealed by the investigation at the beginning," he said. What was mental state of Germanwings' " co-pilot? German airline Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled" " depression years before he took the controls of Germanwings Flight 9525, which he's accused of" " deliberately crashing last week in the French Alps. Lubitz told his Lufthansa flight training school" ' in 2009 that he had a "previous episode of severe depression," the airline said Tuesday. Email' " correspondence between Lubitz and the school discovered in an internal investigation, Lufthansa" " said, included medical documents he submitted in connection with resuming his flight training. The" " announcement indicates that Lufthansa, the parent company of Germanwings, knew of Lubitz's battle" " with depression, allowed him to continue training and ultimately put him in the cockpit. Lufthansa," " whose CEO Carsten Spohr previously said Lubitz was 100% fit to fly, described its statement Tuesday" ' as a "swift and seamless clarification" and said it was sharing the information and documents --' " including training and medical records -- with public prosecutors. Spohr traveled to the crash site" " Wednesday, where recovery teams have been working for the past week to recover human remains and" " plane debris scattered across a steep mountainside. He saw the crisis center set up in" " Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash site, where grieving" " families have left flowers at a simple stone memorial. Menichini told CNN late Tuesday that no" " visible human remains were left at the site but recovery teams would keep searching. French" " President Francois Hollande, speaking Tuesday, said that it should be possible to identify all the" " victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini" " said. Among those personal belongings could be more cell phones belonging to the 144 passengers and" " six crew on board. Check out the latest from our correspondents . The details about Lubitz's" " correspondence with the flight school during his training were among several developments as" " investigators continued to delve into what caused the crash and Lubitz's possible motive for" " downing the jet. A Lufthansa spokesperson told CNN on Tuesday that Lubitz had a valid medical" ' certificate, had passed all his examinations and "held all the licenses required." Earlier, a' " spokesman for the prosecutor's office in Dusseldorf, Christoph Kumpa, said medical records reveal" " Lubitz suffered from suicidal tendencies at some point before his aviation career and underwent" " psychotherapy before he got his pilot's license. Kumpa emphasized there's no evidence suggesting" " Lubitz was suicidal or acting aggressively before the crash. Investigators are looking into whether" " Lubitz feared his medical condition would cause him to lose his pilot's license, a European" ' government official briefed on the investigation told CNN on Tuesday. While flying was "a big part' " of his life,\" the source said, it's only one theory being considered. Another source, a law" " enforcement official briefed on the investigation, also told CNN that authorities believe the" " primary motive for Lubitz to bring down the plane was that he feared he would not be allowed to fly" " because of his medical problems. Lubitz's girlfriend told investigators he had seen an eye doctor" " and a neuropsychologist, both of whom deemed him unfit to work recently and concluded he had" " psychological issues, the European government official said. But no matter what details emerge about" " his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the' " fact that maybe they weren't going to keep doing their job and they're upset about that and so" ' they\'re suicidal," he said. "But there is no mental illness that explains why somebody then feels' " entitled to also take that rage and turn it outward on 149 other people who had nothing to do with" " the person's problems.\" Germanwings crash compensation: What we know . Who was the captain of" " Germanwings Flight 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from" " Dusseldorf, while Laura Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff," " Antonia Mortensen, Sandrine Amiel and Anna-Maja Rappard contributed to this report.", ], return_tensors="tf", padding="longest", truncation=True, ) features = self.xsum_1_1_model.get_encoder()(**batch).last_hidden_state expected = np.array([[-0.0828, -0.0251, -0.0674], [0.1277, 0.3311, -0.0255], [0.2613, -0.0840, -0.2763]]) assert np.allclose(features[0, :3, :3].numpy(), expected, atol=1e-3)

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers import BartConfig, BartTokenizer, is_tf_available from transformers.testing_utils import require_tf, slow, tooslow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFBartForConditionalGeneration, TFBartModel @require_tf class TFBartModelTester: config_cls = BartConfig config_updates = {} hidden_act = "gelu" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id def prepare_config_and_inputs_for_common(self): input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size) eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1) input_ids = tf.concat([input_ids, eos_tensor], axis=1) decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.config_cls( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_ids=[2], bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.pad_token_id, **self.config_updates, ) inputs_dict = prepare_bart_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFBartModel(config=config).get_decoder() input_ids = inputs_dict["input_ids"] input_ids = input_ids[:1, :] attention_mask = inputs_dict["attention_mask"][:1, :] head_mask = inputs_dict["head_mask"] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask) output_from_no_past = output_from_no_past[0] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values) output_from_past = output_from_past[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def prepare_bart_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8) if decoder_attention_mask is None: decoder_attention_mask = tf.concat( [ tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8), ], axis=-1, ) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class TFBartModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestCase): all_model_classes = (TFBartForConditionalGeneration, TFBartModel) if is_tf_available() else () all_generative_model_classes = (TFBartForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_onnx = True onnx_min_opset = 10 def setUp(self): self.model_tester = TFBartModelTester(self) self.config_tester = ConfigTester(self, config_class=BartConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in self.all_generative_model_classes: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None @tooslow def test_saved_model_creation(self): pass # TODO (Joao): fix me @unittest.skip("Onnx compliancy broke with TF 2.10") def test_onnx_compliancy(self): pass def _long_tensor(tok_lst): return tf.constant(tok_lst, dtype=tf.int32) @require_tf class TFBartHeadTests(unittest.TestCase): vocab_size = 99 def _get_config_and_data(self): eos_column_vector = tf.ones((4, 1), dtype=tf.int32) * 2 input_ids = tf.concat([ids_tensor((4, 6), self.vocab_size - 3) + 3, eos_column_vector], axis=1) batch_size = input_ids.shape[0] config = BartConfig( vocab_size=self.vocab_size, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, eos_token_id=2, pad_token_id=1, bos_token_id=0, decoder_start_token_id=2, ) return config, input_ids, batch_size def test_lm_forward(self): config, input_ids, batch_size = self._get_config_and_data() decoder_lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size) lm_model = TFBartForConditionalGeneration(config) outputs = lm_model(input_ids=input_ids, labels=decoder_lm_labels, decoder_input_ids=input_ids, use_cache=False) expected_shape = (batch_size, input_ids.shape[1], config.vocab_size) self.assertEqual(outputs.logits.shape, expected_shape) def test_lm_uneven_forward(self): config = BartConfig( vocab_size=10, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, ) lm_model = TFBartForConditionalGeneration(config) context = tf.fill((7, 2), 4) summary = tf.fill((7, 7), 6) outputs = lm_model(input_ids=context, decoder_input_ids=summary, use_cache=False) expected_shape = (*summary.shape, config.vocab_size) self.assertEqual(outputs.logits.shape, expected_shape) @slow @require_tf class TFBartModelIntegrationTest(unittest.TestCase): def test_inference_no_head(self): model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large").model input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) attention_mask = tf.cast(tf.math.not_equal(input_ids, model.config.pad_token_id), tf.int8) output = model(input_ids=input_ids, attention_mask=attention_mask)[0] expected_shape = (1, 11, 1024) self.assertEqual(output.shape, expected_shape) expected_slice = tf.convert_to_tensor( [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-3) def test_cnn_summarization_same_as_fairseq_hard(self): hf = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") tok = self.tok FRANCE_ARTICLE = ( # @noqa " Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings" " Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane." ' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."' ' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s' " comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French" " Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a" " phone at the wreckage site. The two publications described the supposed video, but did not post it on" " their websites. The publications said that they watched the video, which was found by a source close to" " the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported." ' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the' " cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the" ' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,' " editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said" " the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman" " in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the" ' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,' ' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be' " sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by" " specialized technicians working hand-in-hand with investigators. But none of the cell phones found so" " far have been sent to the institute, Menichini said. Asked whether staff involved in the search could" ' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin' ' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match' ' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is something' " we did not know before. ... Overall we can say many things of the investigation weren't revealed by the" ' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline' " Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the" " controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the" ' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of' ' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school' " discovered in an internal investigation, Lufthansa said, included medical documents he submitted in" " connection with resuming his flight training. The announcement indicates that Lufthansa, the parent" " company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and" " ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%" ' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was' " sharing the information and documents -- including training and medical records -- with public" " prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the" " past week to recover human remains and plane debris scattered across a steep mountainside. He saw the" " crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash" " site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late" " Tuesday that no visible human remains were left at the site but recovery teams would keep searching." " French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all" " the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said." " Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew" " on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with" " the flight school during his training were among several developments as investigators continued to" " delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa" " spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his" ' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in' " Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at" " some point before his aviation career and underwent psychotherapy before he got his pilot's license." " Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the" " crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to" " lose his pilot's license, a European government official briefed on the investigation told CNN on" ' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being' " considered. Another source, a law enforcement official briefed on the investigation, also told CNN that" " authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would" " not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had" " seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded" " he had psychological issues, the European government official said. But no matter what details emerge" " about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact' " that maybe they weren't going to keep doing their job and they're upset about that and so they're" ' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to' " also take that rage and turn it outward on 149 other people who had nothing to do with the person's" ' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight' " 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura" " Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine" " Amiel and Anna-Maja Rappard contributed to this report." ) EXPECTED_SUMMARY_FRANCE = ( "French prosecutor says he's not aware of any video footage from on board the plane. German daily Bild" " and French Paris Match claim to have found a cell phone video of the crash. A French Gendarmerie" ' spokesman calls the reports "completely wrong" and "unwarranted" German airline Lufthansa confirms' " co-pilot Andreas Lubitz had battled depression." ) SHORTER_ARTICLE = ( " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder" " and Faith Karimi contributed to this report." ) EXPECTED_SUMMARY_SHORTER = ( "The Palestinian Authority becomes the 123rd member of the International Criminal Court. The move gives" " the court jurisdiction over alleged crimes in Palestinian territories. Israel and the United States" " opposed the Palestinians' efforts to join the body. But Palestinian Foreign Minister Riad al-Malki said" " it was a move toward greater justice." ) # The below article tests that we don't add any hypotheses outside of the top n_beams IRAN_ARTICLE = ( " (CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran" " in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively" " block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger." " Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli" " Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a" " letter to the Iranian leadership warning them away from a deal. The debate that has already begun since" " the announcement of the new framework will likely result in more heat than light. It will not be helped" " by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ." " The most misleading assertion, despite universal rejection by experts, is that the negotiations'" " objective at the outset was the total elimination of any nuclear program in Iran. That is the position" " of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it" " had been, there would have been no Iranian team at the negotiating table. Rather, the objective has" " always been to structure an agreement or series of agreements so that Iran could not covertly develop a" " nuclear arsenal before the United States and its allies could respond. The new framework has exceeded" " expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by" " two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another" " dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite" " sharp accusations by some in the United States and its allies, Iran denies having such a program, and" " U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's" " continued cooperation with International Atomic Energy Agency inspections is further evidence on this" " point, and we'll know even more about Iran's program in the coming months and years because of the deal." " In fact, the inspections provisions that are part of this agreement are designed to protect against any" " covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that" " the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter" " warning that a deal might be killed by Congress or a future president). This of course is not the case." " The talks were between Iran and the five permanent members of the U.N. Security Council (United States," " United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has" " played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement" " reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran" " and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement" " contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the" " case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased" " or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes" " Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear" " sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going" " forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such" " a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the" ' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not' " suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New" " START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement" " with Iran will not be so balanced. The restrictions and obligations in the final framework agreement" " will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove" " most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally" " some insist that any agreement must address Iranian missile programs, human rights violations or support" " for Hamas or Hezbollah. As important as these issues are, and they must indeed be addressed, they are" " unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran. To include them in" " the negotiations would be a poison pill. This agreement should be judged on its merits and on how it" " affects the security of our negotiating partners and allies, including Israel. Those judgments should be" " fact-based, not based on questionable assertions or dubious assumptions." ) EXPECTED_SUMMARY_IRAN = ( "The U.S. and its negotiating partners reached a very strong framework agreement with Iran. Peter Bergen:" " The debate that has already begun will likely result in more heat than light. He says the agreement" " limits Iran's nuclear program in such a way as to effectively block it from building a nuclear weapon." " Bergen says the most important aim of a nuclear deal is preventing a nuclear Iran." ) ARTICLE_SUBWAY = ( " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) EXPECTED_SUMMARY_SUBWAY = ( "Liana Barrientos has been married 10 times, sometimes within two weeks of each other. Prosecutors say the" " marriages were part of an immigration scam. On Friday, she pleaded not guilty at State Supreme Court in" " the Bronx. She was arrested and charged with theft of service and criminal trespass for allegedly" " sneaking into the subway." ) dct = tok( [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY], max_length=1024, truncation_strategy="only_first", padding="longest", truncation=True, return_tensors="tf", ) self.assertEqual(1024, dct["input_ids"].shape[1]) hypotheses_batch = hf.generate( input_ids=dct["input_ids"], attention_mask=dct["attention_mask"], ) assert hypotheses_batch[:, 1].numpy().tolist() == [0, 0, 0, 0] # test force_bos_token_to_be_generated decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True, clean_up_tokenization_spaces=False) expected_batch = [ EXPECTED_SUMMARY_FRANCE, EXPECTED_SUMMARY_SHORTER, EXPECTED_SUMMARY_IRAN, EXPECTED_SUMMARY_SUBWAY, ] assert decoded == expected_batch @cached_property def tok(self): return BartTokenizer.from_pretrained("facebook/bart-large") @slow def test_contrastive_search_bart(self): article = ( " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn") bart_model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") input_ids = bart_tokenizer( article, add_special_tokens=False, truncation=True, max_length=512, return_tensors="tf" ).input_ids outputs = bart_model.generate(input_ids, penalty_alpha=0.5, top_k=5, max_length=64) generated_text = bart_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "Liana Barrientos, 39, pleaded not guilty to charges related to false marriage statements. " "Prosecutors say she married at least 10 times, sometimes within two weeks of each other. She is " "accused of being part of an immigration scam to get permanent residency. If convicted, she faces up " "to four years in" ], ) @slow def test_contrastive_search_bart_xla(self): article = ( " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn") bart_model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") input_ids = bart_tokenizer( article, add_special_tokens=False, truncation=True, max_length=512, return_tensors="tf" ).input_ids xla_generate = tf.function(bart_model.generate, jit_compile=True) # no_repeat_ngram_size set to 0 because it isn't compatible with XLA, but doesn't change the original output outputs = xla_generate(input_ids, penalty_alpha=0.5, top_k=5, max_length=64, no_repeat_ngram_size=0) generated_text = bart_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "Liana Barrientos, 39, pleaded not guilty to charges related to false marriage statements. " "Prosecutors say she married at least 10 times, sometimes within two weeks of each other. She is " "accused of being part of an immigration scam to get permanent residency. If convicted, she faces up " "to four years in" ], ) @slow @require_tf class FasterTFBartModelIntegrationTests(unittest.TestCase): """These tests are useful for debugging since they operate on a model with 1 encoder layer and 1 decoder layer.""" @cached_property def tok(self): return BartTokenizer.from_pretrained("facebook/bart-large") @cached_property def xsum_1_1_model(self): return TFBartForConditionalGeneration.from_pretrained("sshleifer/distilbart-xsum-1-1") def test_xsum_1_1_generation(self): model = self.xsum_1_1_model assert model.model.decoder.embed_tokens == model.model.shared ARTICLE = ( "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes." ) EXPECTED = ( " The International Criminal Court (ICC) has announced that it has been announced by the International" " Criminal court." ) dct = self.tok(ARTICLE, return_tensors="tf") generated_ids = model.generate(**dct, num_beams=4) result = self.tok.batch_decode(generated_ids, skip_special_tokens=True)[0] assert result == EXPECTED def test_xsum_1_1_xla_generation(self): # same test as above, but with `no_repeat_ngram_size=0` (not compatible with XLA) and XLA comparison enabled model = self.xsum_1_1_model assert model.model.decoder.embed_tokens == model.model.shared ARTICLE = ( "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes." ) EXPECTED = ( " The International Criminal Court (ICC) has announced that it is to be investigated by the International" " Criminal Court (ICC) over allegations of war crimes." ) dct = self.tok(ARTICLE, return_tensors="tf") generated_ids = model.generate(**dct, num_beams=4, no_repeat_ngram_size=0) result = self.tok.batch_decode(generated_ids, skip_special_tokens=True)[0] assert result == EXPECTED xla_generate = tf.function(model.generate, jit_compile=True) generated_ids = xla_generate(**dct, num_beams=4, no_repeat_ngram_size=0) result = self.tok.batch_decode(generated_ids, skip_special_tokens=True)[0] assert result == EXPECTED def test_xsum_1_1_batch_generation(self): batch = self.tok( [ "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories." " The formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is" " based. The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted" ' its jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including' ' East Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination' " into the situation in Palestinian territories, paving the way for possible war crimes investigations" " against Israelis. As members of the court, Palestinians may be subject to counter-charges as well." " Israel and the United States, neither of which is an ICC member, opposed the Palestinians' efforts" " to join the body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony," ' said it was a move toward greater justice. "As Palestine formally becomes a State Party to the Rome' ' Statute today, the world is also a step closer to ending a long era of impunity and injustice," he' ' said, according to an ICC news release. "Indeed, today brings us closer to our shared goals of' ' justice and peace." Judge Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was' ' just the first step for the Palestinians. "As the Rome Statute today enters into force for the State' " of Palestine, Palestine acquires all the rights as well as responsibilities that come with being a" ' State Party to the Statute. These are substantive commitments, which cannot be taken lightly," she' ' said. Rights group Human Rights Watch welcomed the development. "Governments seeking to penalize' " Palestine for joining the ICC should immediately end their pressure, and countries that support" " universal acceptance of the court's treaty should speak out to welcome its membership,\" said" " Balkees Jarrah, international justice counsel for the group. \"What's objectionable is the attempts" " to undermine international justice, not Palestine's decision to join a treaty to which over 100" ' countries around the world are members." In January, when the preliminary ICC examination was' " opened, Israeli Prime Minister Benjamin Netanyahu described it as an outrage, saying the court was" ' overstepping its boundaries. The United States also said it "strongly" disagreed with the court\'s' ' decision. "As we have said repeatedly, we do not believe that Palestine is a state and therefore we' ' do not believe that it is eligible to join the ICC," the State Department said in a statement. It' ' urged the warring sides to resolve their differences through direct negotiations. "We will continue' ' to oppose actions against Israel at the ICC as counterproductive to the cause of peace," it said.' " But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows' " the court to review evidence and determine whether to investigate suspects on both sides. Prosecutor" ' Fatou Bensouda said her office would "conduct its analysis in full independence and impartiality."' " The war between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The" " inquiry will include alleged war crimes committed since June. The International Criminal Court was" " set up in 2002 to prosecute genocide, crimes against humanity and war crimes.", "The French prosecutor leading an investigation into the crash of Germanwings Flight 9525 insisted" " Wednesday that he was not aware of any video footage from on board the plane. Marseille prosecutor" ' Brice Robin told CNN that "so far no videos were used in the crash investigation." He added, "A' " person who has such a video needs to immediately give it to the investigators.\" Robin's comments" " follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the" " French Alps. All 150 on board were killed. Paris Match and Bild reported that the video was" " recovered from a phone at the wreckage site. The two publications described the supposed video, but" " did not post it on their websites. The publications said that they watched the video, which was" " found by a source close to the investigation. \"One can hear cries of 'My God' in several" ' languages," Paris Match reported. "Metallic banging can also be heard more than three times, perhaps' " of the pilot trying to open the cockpit door with a heavy object. Towards the end, after a heavy" ' shake, stronger than the others, the screaming intensifies. Then nothing." "It is a very disturbing' " scene,\" said Julian Reichelt, editor-in-chief of Bild online. An official with France's accident" " investigation agency, the BEA, said the agency is not aware of any such video. Lt. Col. Jean-Marc" " Menichini, a French Gendarmerie spokesman in charge of communications on rescue efforts around the" ' Germanwings crash site, told CNN that the reports were "completely wrong" and "unwarranted." Cell' ' phones have been collected at the site, he said, but that they "hadn\'t been exploited yet."' " Menichini said he believed the cell phones would need to be sent to the Criminal Research Institute" " in Rosny sous-Bois, near Paris, in order to be analyzed by specialized technicians working" " hand-in-hand with investigators. But none of the cell phones found so far have been sent to the" " institute, Menichini said. Asked whether staff involved in the search could have leaked a memory" ' card to the media, Menichini answered with a categorical "no." Reichelt told "Erin Burnett:' ' Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match are' ' "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is' " something we did not know before. ... Overall we can say many things of the investigation weren't" ' revealed by the investigation at the beginning," he said. What was mental state of Germanwings' " co-pilot? German airline Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled" " depression years before he took the controls of Germanwings Flight 9525, which he's accused of" " deliberately crashing last week in the French Alps. Lubitz told his Lufthansa flight training school" ' in 2009 that he had a "previous episode of severe depression," the airline said Tuesday. Email' " correspondence between Lubitz and the school discovered in an internal investigation, Lufthansa" " said, included medical documents he submitted in connection with resuming his flight training. The" " announcement indicates that Lufthansa, the parent company of Germanwings, knew of Lubitz's battle" " with depression, allowed him to continue training and ultimately put him in the cockpit. Lufthansa," " whose CEO Carsten Spohr previously said Lubitz was 100% fit to fly, described its statement Tuesday" ' as a "swift and seamless clarification" and said it was sharing the information and documents --' " including training and medical records -- with public prosecutors. Spohr traveled to the crash site" " Wednesday, where recovery teams have been working for the past week to recover human remains and" " plane debris scattered across a steep mountainside. He saw the crisis center set up in" " Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash site, where grieving" " families have left flowers at a simple stone memorial. Menichini told CNN late Tuesday that no" " visible human remains were left at the site but recovery teams would keep searching. French" " President Francois Hollande, speaking Tuesday, said that it should be possible to identify all the" " victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini" " said. Among those personal belongings could be more cell phones belonging to the 144 passengers and" " six crew on board. Check out the latest from our correspondents . The details about Lubitz's" " correspondence with the flight school during his training were among several developments as" " investigators continued to delve into what caused the crash and Lubitz's possible motive for" " downing the jet. A Lufthansa spokesperson told CNN on Tuesday that Lubitz had a valid medical" ' certificate, had passed all his examinations and "held all the licenses required." Earlier, a' " spokesman for the prosecutor's office in Dusseldorf, Christoph Kumpa, said medical records reveal" " Lubitz suffered from suicidal tendencies at some point before his aviation career and underwent" " psychotherapy before he got his pilot's license. Kumpa emphasized there's no evidence suggesting" " Lubitz was suicidal or acting aggressively before the crash. Investigators are looking into whether" " Lubitz feared his medical condition would cause him to lose his pilot's license, a European" ' government official briefed on the investigation told CNN on Tuesday. While flying was "a big part' " of his life,\" the source said, it's only one theory being considered. Another source, a law" " enforcement official briefed on the investigation, also told CNN that authorities believe the" " primary motive for Lubitz to bring down the plane was that he feared he would not be allowed to fly" " because of his medical problems. Lubitz's girlfriend told investigators he had seen an eye doctor" " and a neuropsychologist, both of whom deemed him unfit to work recently and concluded he had" " psychological issues, the European government official said. But no matter what details emerge about" " his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the' " fact that maybe they weren't going to keep doing their job and they're upset about that and so" ' they\'re suicidal," he said. "But there is no mental illness that explains why somebody then feels' " entitled to also take that rage and turn it outward on 149 other people who had nothing to do with" " the person's problems.\" Germanwings crash compensation: What we know . Who was the captain of" " Germanwings Flight 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from" " Dusseldorf, while Laura Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff," " Antonia Mortensen, Sandrine Amiel and Anna-Maja Rappard contributed to this report.", ], return_tensors="tf", padding="longest", truncation=True, ) generated_ids = self.xsum_1_1_model.generate(**batch, num_beams=4) result = self.tok.batch_decode(generated_ids, skip_special_tokens=True) assert ( result[0] == " The International Criminal Court (ICC) has announced that it has been announced by the International" " Criminal court." ) assert ( result[1] == " An investigation into the crash that killed at least 10 people in the French capital has been" " released by the French police investigating the crash." ) def test_encoder_equiv(self): batch = self.tok( [ "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories." " The formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is" " based. The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted" ' its jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including' ' East Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination' " into the situation in Palestinian territories, paving the way for possible war crimes investigations" " against Israelis. As members of the court, Palestinians may be subject to counter-charges as well." " Israel and the United States, neither of which is an ICC member, opposed the Palestinians' efforts" " to join the body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony," ' said it was a move toward greater justice. "As Palestine formally becomes a State Party to the Rome' ' Statute today, the world is also a step closer to ending a long era of impunity and injustice," he' ' said, according to an ICC news release. "Indeed, today brings us closer to our shared goals of' ' justice and peace." Judge Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was' ' just the first step for the Palestinians. "As the Rome Statute today enters into force for the State' " of Palestine, Palestine acquires all the rights as well as responsibilities that come with being a" ' State Party to the Statute. These are substantive commitments, which cannot be taken lightly," she' ' said. Rights group Human Rights Watch welcomed the development. "Governments seeking to penalize' " Palestine for joining the ICC should immediately end their pressure, and countries that support" " universal acceptance of the court's treaty should speak out to welcome its membership,\" said" " Balkees Jarrah, international justice counsel for the group. \"What's objectionable is the attempts" " to undermine international justice, not Palestine's decision to join a treaty to which over 100" ' countries around the world are members." In January, when the preliminary ICC examination was' " opened, Israeli Prime Minister Benjamin Netanyahu described it as an outrage, saying the court was" ' overstepping its boundaries. The United States also said it "strongly" disagreed with the court\'s' ' decision. "As we have said repeatedly, we do not believe that Palestine is a state and therefore we' ' do not believe that it is eligible to join the ICC," the State Department said in a statement. It' ' urged the warring sides to resolve their differences through direct negotiations. "We will continue' ' to oppose actions against Israel at the ICC as counterproductive to the cause of peace," it said.' " But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows' " the court to review evidence and determine whether to investigate suspects on both sides. Prosecutor" ' Fatou Bensouda said her office would "conduct its analysis in full independence and impartiality."' " The war between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The" " inquiry will include alleged war crimes committed since June. The International Criminal Court was" " set up in 2002 to prosecute genocide, crimes against humanity and war crimes.", "The French prosecutor leading an investigation into the crash of Germanwings Flight 9525 insisted" " Wednesday that he was not aware of any video footage from on board the plane. Marseille prosecutor" ' Brice Robin told CNN that "so far no videos were used in the crash investigation." He added, "A' " person who has such a video needs to immediately give it to the investigators.\" Robin's comments" " follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the" " French Alps. All 150 on board were killed. Paris Match and Bild reported that the video was" " recovered from a phone at the wreckage site. The two publications described the supposed video, but" " did not post it on their websites. The publications said that they watched the video, which was" " found by a source close to the investigation. \"One can hear cries of 'My God' in several" ' languages," Paris Match reported. "Metallic banging can also be heard more than three times, perhaps' " of the pilot trying to open the cockpit door with a heavy object. Towards the end, after a heavy" ' shake, stronger than the others, the screaming intensifies. Then nothing." "It is a very disturbing' " scene,\" said Julian Reichelt, editor-in-chief of Bild online. An official with France's accident" " investigation agency, the BEA, said the agency is not aware of any such video. Lt. Col. Jean-Marc" " Menichini, a French Gendarmerie spokesman in charge of communications on rescue efforts around the" ' Germanwings crash site, told CNN that the reports were "completely wrong" and "unwarranted." Cell' ' phones have been collected at the site, he said, but that they "hadn\'t been exploited yet."' " Menichini said he believed the cell phones would need to be sent to the Criminal Research Institute" " in Rosny sous-Bois, near Paris, in order to be analyzed by specialized technicians working" " hand-in-hand with investigators. But none of the cell phones found so far have been sent to the" " institute, Menichini said. Asked whether staff involved in the search could have leaked a memory" ' card to the media, Menichini answered with a categorical "no." Reichelt told "Erin Burnett:' ' Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match are' ' "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is' " something we did not know before. ... Overall we can say many things of the investigation weren't" ' revealed by the investigation at the beginning," he said. What was mental state of Germanwings' " co-pilot? German airline Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled" " depression years before he took the controls of Germanwings Flight 9525, which he's accused of" " deliberately crashing last week in the French Alps. Lubitz told his Lufthansa flight training school" ' in 2009 that he had a "previous episode of severe depression," the airline said Tuesday. Email' " correspondence between Lubitz and the school discovered in an internal investigation, Lufthansa" " said, included medical documents he submitted in connection with resuming his flight training. The" " announcement indicates that Lufthansa, the parent company of Germanwings, knew of Lubitz's battle" " with depression, allowed him to continue training and ultimately put him in the cockpit. Lufthansa," " whose CEO Carsten Spohr previously said Lubitz was 100% fit to fly, described its statement Tuesday" ' as a "swift and seamless clarification" and said it was sharing the information and documents --' " including training and medical records -- with public prosecutors. Spohr traveled to the crash site" " Wednesday, where recovery teams have been working for the past week to recover human remains and" " plane debris scattered across a steep mountainside. He saw the crisis center set up in" " Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash site, where grieving" " families have left flowers at a simple stone memorial. Menichini told CNN late Tuesday that no" " visible human remains were left at the site but recovery teams would keep searching. French" " President Francois Hollande, speaking Tuesday, said that it should be possible to identify all the" " victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini" " said. Among those personal belongings could be more cell phones belonging to the 144 passengers and" " six crew on board. Check out the latest from our correspondents . The details about Lubitz's" " correspondence with the flight school during his training were among several developments as" " investigators continued to delve into what caused the crash and Lubitz's possible motive for" " downing the jet. A Lufthansa spokesperson told CNN on Tuesday that Lubitz had a valid medical" ' certificate, had passed all his examinations and "held all the licenses required." Earlier, a' " spokesman for the prosecutor's office in Dusseldorf, Christoph Kumpa, said medical records reveal" " Lubitz suffered from suicidal tendencies at some point before his aviation career and underwent" " psychotherapy before he got his pilot's license. Kumpa emphasized there's no evidence suggesting" " Lubitz was suicidal or acting aggressively before the crash. Investigators are looking into whether" " Lubitz feared his medical condition would cause him to lose his pilot's license, a European" ' government official briefed on the investigation told CNN on Tuesday. While flying was "a big part' " of his life,\" the source said, it's only one theory being considered. Another source, a law" " enforcement official briefed on the investigation, also told CNN that authorities believe the" " primary motive for Lubitz to bring down the plane was that he feared he would not be allowed to fly" " because of his medical problems. Lubitz's girlfriend told investigators he had seen an eye doctor" " and a neuropsychologist, both of whom deemed him unfit to work recently and concluded he had" " psychological issues, the European government official said. But no matter what details emerge about" " his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the' " fact that maybe they weren't going to keep doing their job and they're upset about that and so" ' they\'re suicidal," he said. "But there is no mental illness that explains why somebody then feels' " entitled to also take that rage and turn it outward on 149 other people who had nothing to do with" " the person's problems.\" Germanwings crash compensation: What we know . Who was the captain of" " Germanwings Flight 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from" " Dusseldorf, while Laura Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff," " Antonia Mortensen, Sandrine Amiel and Anna-Maja Rappard contributed to this report.", ], return_tensors="tf", padding="longest", truncation=True, ) features = self.xsum_1_1_model.get_encoder()(**batch).last_hidden_state expected = np.array([[-0.0828, -0.0251, -0.0674], [0.1277, 0.3311, -0.0255], [0.2613, -0.0840, -0.2763]]) assert np.allclose(features[0, :3, :3].numpy(), expected, atol=1e-3)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/roberta/modeling_tf_roberta.py

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 RoBERTa model.""" import math import warnings from typing import Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutputWithPastAndCrossAttentions, TFBaseModelOutputWithPoolingAndCrossAttentions, TFCausalLMOutputWithCrossAttentions, TFMaskedLMOutput, TFMultipleChoiceModelOutput, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutput, TFTokenClassifierOutput, ) from ...modeling_tf_utils import ( TFCausalLanguageModelingLoss, TFMaskedLanguageModelingLoss, TFModelInputType, TFMultipleChoiceLoss, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFTokenClassificationLoss, get_initializer, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import ( DUMMY_INPUTS, MULTIPLE_CHOICE_DUMMY_INPUTS, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from .configuration_roberta import RobertaConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "roberta-base" _CONFIG_FOR_DOC = "RobertaConfig" _TOKENIZER_FOR_DOC = "RobertaTokenizer" TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "roberta-base", "roberta-large", "roberta-large-mnli", "distilroberta-base", # See all RoBERTa models at https://huggingface.co/models?filter=roberta ] class TFRobertaEmbeddings(tf.keras.layers.Layer): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ def __init__(self, config, **kwargs): super().__init__(**kwargs) self.padding_idx = 1 self.vocab_size = config.vocab_size self.type_vocab_size = config.type_vocab_size self.hidden_size = config.hidden_size self.max_position_embeddings = config.max_position_embeddings self.initializer_range = config.initializer_range self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) def build(self, input_shape: tf.TensorShape): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("token_type_embeddings"): self.token_type_embeddings = self.add_weight( name="embeddings", shape=[self.type_vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight( name="embeddings", shape=[self.max_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range), ) super().build(input_shape) def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: input_ids: tf.Tensor Returns: tf.Tensor """ mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=input_ids.dtype) incremental_indices = (tf.math.cumsum(mask, axis=1) + past_key_values_length) * mask return incremental_indices + self.padding_idx def call( self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, past_key_values_length=0, training=False, ): """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ assert not (input_ids is None and inputs_embeds is None) if input_ids is not None: # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound # indices on GPU, returning zeros instead. This is a dangerous silent behavior. tf.debugging.assert_less( input_ids, tf.cast(self.vocab_size, dtype=input_ids.dtype), message=( "input_ids must be smaller than the embedding layer's input dimension (got" f" {tf.math.reduce_max(input_ids)} >= {self.vocab_size})" ), ) inputs_embeds = tf.gather(params=self.weight, indices=input_ids) input_shape = shape_list(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = self.create_position_ids_from_input_ids( input_ids=input_ids, past_key_values_length=past_key_values_length ) else: position_ids = tf.expand_dims( tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1), axis=0 ) position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) final_embeddings = inputs_embeds + position_embeds + token_type_embeds final_embeddings = self.LayerNorm(inputs=final_embeddings) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Roberta class TFRobertaPooler(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) def call(self, hidden_states: tf.Tensor) -> tf.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(inputs=first_token_tensor) return pooled_output # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Roberta class TFRobertaSelfAttention(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number " f"of attention heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.sqrt_att_head_size = math.sqrt(self.attention_head_size) self.query = tf.keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = tf.keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" ) self.value = tf.keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob) self.is_decoder = config.is_decoder def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] return tf.transpose(tensor, perm=[0, 2, 1, 3]) def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(inputs=hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) key_layer = tf.concat([past_key_value[0], key_layer], axis=2) value_layer = tf.concat([past_key_value[1], value_layer], axis=2) else: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) if self.is_decoder: # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) attention_scores = tf.divide(attention_scores, dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFRobertaModel call() function) attention_scores = tf.add(attention_scores, attention_mask) # Normalize the attention scores to probabilities. attention_probs = stable_softmax(logits=attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(inputs=attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = tf.multiply(attention_probs, head_mask) attention_output = tf.matmul(attention_probs, value_layer) attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Roberta class TFRobertaSelfOutput(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Roberta class TFRobertaAttention(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.self_attention = TFRobertaSelfAttention(config, name="self") self.dense_output = TFRobertaSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call( self, input_tensor: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: self_outputs = self.self_attention( hidden_states=input_tensor, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self.dense_output( hidden_states=self_outputs[0], input_tensor=input_tensor, training=training ) # add attentions (possibly with past_key_value) if we output them outputs = (attention_output,) + self_outputs[1:] return outputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Roberta class TFRobertaIntermediate(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Roberta class TFRobertaOutput(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Roberta class TFRobertaLayer(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.attention = TFRobertaAttention(config, name="attention") self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = TFRobertaAttention(config, name="crossattention") self.intermediate = TFRobertaIntermediate(config, name="intermediate") self.bert_output = TFRobertaOutput(config, name="output") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: Optional[tf.Tensor], encoder_attention_mask: Optional[tf.Tensor], past_key_value: Optional[Tuple[tf.Tensor]], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( input_tensor=hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=self_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( input_tensor=attention_output, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value intermediate_output = self.intermediate(hidden_states=attention_output) layer_output = self.bert_output( hidden_states=intermediate_output, input_tensor=attention_output, training=training ) outputs = (layer_output,) + outputs # add attentions if we output them # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Roberta class TFRobertaEncoder(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.config = config self.layer = [TFRobertaLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: Optional[tf.Tensor], encoder_attention_mask: Optional[tf.Tensor], past_key_values: Optional[Tuple[Tuple[tf.Tensor]]], use_cache: Optional[bool], output_attentions: bool, output_hidden_states: bool, return_dict: bool, training: bool = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) past_key_value = past_key_values[i] if past_key_values is not None else None layer_outputs = layer_module( hidden_states=hidden_states, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if self.config.add_cross_attention and encoder_hidden_states is not None: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) @keras_serializable class TFRobertaMainLayer(tf.keras.layers.Layer): config_class = RobertaConfig def __init__(self, config, add_pooling_layer=True, **kwargs): super().__init__(**kwargs) self.config = config self.is_decoder = config.is_decoder self.num_hidden_layers = config.num_hidden_layers self.initializer_range = config.initializer_range self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.return_dict = config.use_return_dict self.encoder = TFRobertaEncoder(config, name="encoder") self.pooler = TFRobertaPooler(config, name="pooler") if add_pooling_layer else None # The embeddings must be the last declaration in order to follow the weights order self.embeddings = TFRobertaEmbeddings(config, name="embeddings") # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings def get_input_embeddings(self) -> tf.keras.layers.Layer: return self.embeddings # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings def set_input_embeddings(self, value: tf.Variable): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError @unpack_inputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.call def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]: if not self.config.is_decoder: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape if past_key_values is None: past_key_values_length = 0 past_key_values = [None] * len(self.encoder.layer) else: past_key_values_length = shape_list(past_key_values[0][0])[-2] if attention_mask is None: attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1) if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, training=training, ) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask_shape = shape_list(attention_mask) mask_seq_length = seq_length + past_key_values_length # Copied from `modeling_tf_t5.py` # Provided a padding mask of dimensions [batch_size, mask_seq_length] # - if the model is a decoder, apply a causal mask in addition to the padding mask # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] if self.is_decoder: seq_ids = tf.range(mask_seq_length) causal_mask = tf.less_equal( tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)), seq_ids[None, :, None], ) causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype) extended_attention_mask = causal_mask * attention_mask[:, None, :] attention_mask_shape = shape_list(extended_attention_mask) extended_attention_mask = tf.reshape( extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2]) ) if past_key_values[0] is not None: # attention_mask needs to be sliced to the shape `[batch_size, 1, from_seq_length - cached_seq_length, to_seq_length] extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :] else: extended_attention_mask = tf.reshape( attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1]) ) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype) one_cst = tf.constant(1.0, dtype=embedding_output.dtype) ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype) extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) # Copied from `modeling_tf_t5.py` with -1e9 -> -10000 if self.is_decoder and encoder_attention_mask is not None: # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=extended_attention_mask.dtype) num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask)) if num_dims_encoder_attention_mask == 3: encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] if num_dims_encoder_attention_mask == 2: encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask, # tf.transpose(encoder_extended_attention_mask, perm=(-1, -2))) encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.config.num_hidden_layers encoder_outputs = self.encoder( hidden_states=embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None if not return_dict: return ( sequence_output, pooled_output, ) + encoder_outputs[1:] return TFBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) class TFRobertaPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RobertaConfig base_model_prefix = "roberta" @property # Copied from transformers.models.bert.modeling_tf_bert.TFBertPreTrainedModel.dummy_inputs def dummy_inputs(self): """ Dummy inputs to build the network. Returns: `Dict[str, tf.Tensor]`: The dummy inputs. """ dummy = {"input_ids": tf.constant(DUMMY_INPUTS)} # Add `encoder_hidden_states` to make the cross-attention layers' weights initialized if self.config.add_cross_attention: batch_size, seq_len = tf.constant(DUMMY_INPUTS).shape shape = (batch_size, seq_len) + (self.config.hidden_size,) h = tf.random.uniform(shape=shape) dummy["encoder_hidden_states"] = h return dummy @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None), tf.int64, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.int64, name="attention_mask"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) ROBERTA_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`RobertaConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ ROBERTA_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.", ROBERTA_START_DOCSTRING, ) class TFRobertaModel(TFRobertaPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta = TFRobertaMainLayer(config, name="roberta") @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[Tuple, TFBaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation """ outputs = self.roberta( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output def serving_output( self, output: TFBaseModelOutputWithPoolingAndCrossAttentions ) -> TFBaseModelOutputWithPoolingAndCrossAttentions: output_cache = self.config.use_cache and self.config.is_decoder pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None if not (self.config.output_attentions and self.config.add_cross_attention): cross_attns = None return TFBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=output.last_hidden_state, pooler_output=output.pooler_output, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns, ) class TFRobertaLMHead(tf.keras.layers.Layer): """Roberta Head for masked language modeling.""" def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.vocab_size = config.vocab_size self.hidden_size = config.hidden_size self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") self.act = get_tf_activation("gelu") # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = input_embeddings def build(self, input_shape): self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias") super().build(input_shape) def get_output_embeddings(self): return self.decoder def set_output_embeddings(self, value): self.decoder.weight = value self.decoder.vocab_size = shape_list(value)[0] def get_bias(self): return {"bias": self.bias} def set_bias(self, value): self.bias = value["bias"] self.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.layer_norm(hidden_states) # project back to size of vocabulary with bias seq_length = shape_list(tensor=hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states @add_start_docstrings("""RoBERTa Model with a `language modeling` head on top.""", ROBERTA_START_DOCSTRING) class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head") def get_lm_head(self): return self.lm_head def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.lm_head.name @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", expected_output="' Paris'", expected_loss=0.1, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFMaskedLMOutput( loss=loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) class TFRobertaForCausalLM(TFRobertaPreTrainedModel, TFCausalLanguageModelingLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"] def __init__(self, config: RobertaConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) if not config.is_decoder: logger.warning("If you want to use `TFRobertaLMHeadModel` as a standalone, add `is_decoder=True.`") self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.lm_head = TFRobertaLMHead(config, input_embeddings=self.roberta.embeddings, name="lm_head") def get_lm_head(self): return self.lm_head def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.lm_head.name # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.prepare_inputs_for_generation def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = tf.ones(input_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past} @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`. """ outputs = self.roberta( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.lm_head(hidden_states=sequence_output, training=training) loss = None if labels is not None: # shift labels to the left and cut last logit token shifted_logits = logits[:, :-1] labels = labels[:, 1:] loss = self.hf_compute_loss(labels=labels, logits=shifted_logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFCausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output def serving_output(self, output: TFCausalLMOutputWithCrossAttentions) -> TFCausalLMOutputWithCrossAttentions: output_cache = self.config.use_cache and self.config.is_decoder pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None if not (self.config.output_attentions and self.config.add_cross_attention): cross_attns = None return TFCausalLMOutputWithCrossAttentions( logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns ) @staticmethod # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel._reorder_cache def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(tf.gather(past_state, beam_idx, axis=0) for past_state in layer_past),) return reordered_past class TFRobertaClassificationHead(tf.keras.layers.Layer): """Head for sentence-level classification tasks.""" def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = tf.keras.layers.Dropout(classifier_dropout) self.out_proj = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj" ) def call(self, features, training=False): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x, training=training) x = self.dense(x) x = self.dropout(x, training=training) x = self.out_proj(x) return x @add_start_docstrings( """ RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ROBERTA_START_DOCSTRING, ) class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.classifier = TFRobertaClassificationHead(config, name="classifier") @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="cardiffnlp/twitter-roberta-base-emotion", output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="'optimism'", expected_loss=0.08, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.classifier(sequence_output, training=training) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( """ Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ROBERTA_START_DOCSTRING, ) class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"lm_head"] _keys_to_ignore_on_load_missing = [r"dropout"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta = TFRobertaMainLayer(config, name="roberta") self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) self.classifier = tf.keras.layers.Dense( 1, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: tf.Tensor with dummy inputs """ return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)} @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ if input_ids is not None: num_choices = shape_list(input_ids)[1] seq_length = shape_list(input_ids)[2] else: num_choices = shape_list(inputs_embeds)[1] seq_length = shape_list(inputs_embeds)[2] flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None outputs = self.roberta( flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict=return_dict, training=training, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output, training=training) logits = self.classifier(pooled_output) reshaped_logits = tf.reshape(logits, (-1, num_choices)) loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFMultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( """ RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, ROBERTA_START_DOCSTRING, ) class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] _keys_to_ignore_on_load_missing = [r"dropout"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = tf.keras.layers.Dropout(classifier_dropout) self.classifier = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="ydshieh/roberta-large-ner-english", output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']", expected_loss=0.01, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output, training=training) logits = self.classifier(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFTokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( """ RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ROBERTA_START_DOCSTRING, ) class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnsweringLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.qa_outputs = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs" ) @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="ydshieh/roberta-base-squad2", output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, expected_output="' puppet'", expected_loss=0.86, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None, end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]: r""" start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = tf.split(logits, 2, axis=-1) start_logits = tf.squeeze(start_logits, axis=-1) end_logits = tf.squeeze(end_logits, axis=-1) loss = None if start_positions is not None and end_positions is not None: labels = {"start_position": start_positions} labels["end_position"] = end_positions loss = self.hf_compute_loss(labels, (start_logits, end_logits)) if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFQuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns )

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 RoBERTa model.""" import math import warnings from typing import Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutputWithPastAndCrossAttentions, TFBaseModelOutputWithPoolingAndCrossAttentions, TFCausalLMOutputWithCrossAttentions, TFMaskedLMOutput, TFMultipleChoiceModelOutput, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutput, TFTokenClassifierOutput, ) from ...modeling_tf_utils import ( TFCausalLanguageModelingLoss, TFMaskedLanguageModelingLoss, TFModelInputType, TFMultipleChoiceLoss, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFTokenClassificationLoss, get_initializer, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import ( DUMMY_INPUTS, MULTIPLE_CHOICE_DUMMY_INPUTS, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from .configuration_roberta import RobertaConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "roberta-base" _CONFIG_FOR_DOC = "RobertaConfig" _TOKENIZER_FOR_DOC = "RobertaTokenizer" TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "roberta-base", "roberta-large", "roberta-large-mnli", "distilroberta-base", # See all RoBERTa models at https://huggingface.co/models?filter=roberta ] class TFRobertaEmbeddings(tf.keras.layers.Layer): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ def __init__(self, config, **kwargs): super().__init__(**kwargs) self.padding_idx = 1 self.vocab_size = config.vocab_size self.type_vocab_size = config.type_vocab_size self.hidden_size = config.hidden_size self.max_position_embeddings = config.max_position_embeddings self.initializer_range = config.initializer_range self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) def build(self, input_shape: tf.TensorShape): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("token_type_embeddings"): self.token_type_embeddings = self.add_weight( name="embeddings", shape=[self.type_vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight( name="embeddings", shape=[self.max_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range), ) super().build(input_shape) def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: input_ids: tf.Tensor Returns: tf.Tensor """ mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=input_ids.dtype) incremental_indices = (tf.math.cumsum(mask, axis=1) + past_key_values_length) * mask return incremental_indices + self.padding_idx def call( self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, past_key_values_length=0, training=False, ): """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ assert not (input_ids is None and inputs_embeds is None) if input_ids is not None: # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound # indices on GPU, returning zeros instead. This is a dangerous silent behavior. tf.debugging.assert_less( input_ids, tf.cast(self.vocab_size, dtype=input_ids.dtype), message=( "input_ids must be smaller than the embedding layer's input dimension (got" f" {tf.math.reduce_max(input_ids)} >= {self.vocab_size})" ), ) inputs_embeds = tf.gather(params=self.weight, indices=input_ids) input_shape = shape_list(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = self.create_position_ids_from_input_ids( input_ids=input_ids, past_key_values_length=past_key_values_length ) else: position_ids = tf.expand_dims( tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1), axis=0 ) position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) final_embeddings = inputs_embeds + position_embeds + token_type_embeds final_embeddings = self.LayerNorm(inputs=final_embeddings) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Roberta class TFRobertaPooler(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) def call(self, hidden_states: tf.Tensor) -> tf.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(inputs=first_token_tensor) return pooled_output # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Roberta class TFRobertaSelfAttention(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number " f"of attention heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.sqrt_att_head_size = math.sqrt(self.attention_head_size) self.query = tf.keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = tf.keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" ) self.value = tf.keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob) self.is_decoder = config.is_decoder def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] return tf.transpose(tensor, perm=[0, 2, 1, 3]) def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(inputs=hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) key_layer = tf.concat([past_key_value[0], key_layer], axis=2) value_layer = tf.concat([past_key_value[1], value_layer], axis=2) else: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) if self.is_decoder: # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) attention_scores = tf.divide(attention_scores, dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFRobertaModel call() function) attention_scores = tf.add(attention_scores, attention_mask) # Normalize the attention scores to probabilities. attention_probs = stable_softmax(logits=attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(inputs=attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = tf.multiply(attention_probs, head_mask) attention_output = tf.matmul(attention_probs, value_layer) attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Roberta class TFRobertaSelfOutput(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Roberta class TFRobertaAttention(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.self_attention = TFRobertaSelfAttention(config, name="self") self.dense_output = TFRobertaSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call( self, input_tensor: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: self_outputs = self.self_attention( hidden_states=input_tensor, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self.dense_output( hidden_states=self_outputs[0], input_tensor=input_tensor, training=training ) # add attentions (possibly with past_key_value) if we output them outputs = (attention_output,) + self_outputs[1:] return outputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Roberta class TFRobertaIntermediate(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Roberta class TFRobertaOutput(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Roberta class TFRobertaLayer(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.attention = TFRobertaAttention(config, name="attention") self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = TFRobertaAttention(config, name="crossattention") self.intermediate = TFRobertaIntermediate(config, name="intermediate") self.bert_output = TFRobertaOutput(config, name="output") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: Optional[tf.Tensor], encoder_attention_mask: Optional[tf.Tensor], past_key_value: Optional[Tuple[tf.Tensor]], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( input_tensor=hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=self_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( input_tensor=attention_output, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value intermediate_output = self.intermediate(hidden_states=attention_output) layer_output = self.bert_output( hidden_states=intermediate_output, input_tensor=attention_output, training=training ) outputs = (layer_output,) + outputs # add attentions if we output them # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Roberta class TFRobertaEncoder(tf.keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.config = config self.layer = [TFRobertaLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: Optional[tf.Tensor], encoder_attention_mask: Optional[tf.Tensor], past_key_values: Optional[Tuple[Tuple[tf.Tensor]]], use_cache: Optional[bool], output_attentions: bool, output_hidden_states: bool, return_dict: bool, training: bool = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) past_key_value = past_key_values[i] if past_key_values is not None else None layer_outputs = layer_module( hidden_states=hidden_states, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if self.config.add_cross_attention and encoder_hidden_states is not None: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) @keras_serializable class TFRobertaMainLayer(tf.keras.layers.Layer): config_class = RobertaConfig def __init__(self, config, add_pooling_layer=True, **kwargs): super().__init__(**kwargs) self.config = config self.is_decoder = config.is_decoder self.num_hidden_layers = config.num_hidden_layers self.initializer_range = config.initializer_range self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.return_dict = config.use_return_dict self.encoder = TFRobertaEncoder(config, name="encoder") self.pooler = TFRobertaPooler(config, name="pooler") if add_pooling_layer else None # The embeddings must be the last declaration in order to follow the weights order self.embeddings = TFRobertaEmbeddings(config, name="embeddings") # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings def get_input_embeddings(self) -> tf.keras.layers.Layer: return self.embeddings # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings def set_input_embeddings(self, value: tf.Variable): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError @unpack_inputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.call def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]: if not self.config.is_decoder: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape if past_key_values is None: past_key_values_length = 0 past_key_values = [None] * len(self.encoder.layer) else: past_key_values_length = shape_list(past_key_values[0][0])[-2] if attention_mask is None: attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1) if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, training=training, ) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask_shape = shape_list(attention_mask) mask_seq_length = seq_length + past_key_values_length # Copied from `modeling_tf_t5.py` # Provided a padding mask of dimensions [batch_size, mask_seq_length] # - if the model is a decoder, apply a causal mask in addition to the padding mask # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] if self.is_decoder: seq_ids = tf.range(mask_seq_length) causal_mask = tf.less_equal( tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)), seq_ids[None, :, None], ) causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype) extended_attention_mask = causal_mask * attention_mask[:, None, :] attention_mask_shape = shape_list(extended_attention_mask) extended_attention_mask = tf.reshape( extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2]) ) if past_key_values[0] is not None: # attention_mask needs to be sliced to the shape `[batch_size, 1, from_seq_length - cached_seq_length, to_seq_length] extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :] else: extended_attention_mask = tf.reshape( attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1]) ) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype) one_cst = tf.constant(1.0, dtype=embedding_output.dtype) ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype) extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) # Copied from `modeling_tf_t5.py` with -1e9 -> -10000 if self.is_decoder and encoder_attention_mask is not None: # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=extended_attention_mask.dtype) num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask)) if num_dims_encoder_attention_mask == 3: encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] if num_dims_encoder_attention_mask == 2: encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask, # tf.transpose(encoder_extended_attention_mask, perm=(-1, -2))) encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.config.num_hidden_layers encoder_outputs = self.encoder( hidden_states=embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None if not return_dict: return ( sequence_output, pooled_output, ) + encoder_outputs[1:] return TFBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) class TFRobertaPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RobertaConfig base_model_prefix = "roberta" @property # Copied from transformers.models.bert.modeling_tf_bert.TFBertPreTrainedModel.dummy_inputs def dummy_inputs(self): """ Dummy inputs to build the network. Returns: `Dict[str, tf.Tensor]`: The dummy inputs. """ dummy = {"input_ids": tf.constant(DUMMY_INPUTS)} # Add `encoder_hidden_states` to make the cross-attention layers' weights initialized if self.config.add_cross_attention: batch_size, seq_len = tf.constant(DUMMY_INPUTS).shape shape = (batch_size, seq_len) + (self.config.hidden_size,) h = tf.random.uniform(shape=shape) dummy["encoder_hidden_states"] = h return dummy @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None), tf.int64, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.int64, name="attention_mask"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) ROBERTA_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`RobertaConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ ROBERTA_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.", ROBERTA_START_DOCSTRING, ) class TFRobertaModel(TFRobertaPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta = TFRobertaMainLayer(config, name="roberta") @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[Tuple, TFBaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation """ outputs = self.roberta( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output def serving_output( self, output: TFBaseModelOutputWithPoolingAndCrossAttentions ) -> TFBaseModelOutputWithPoolingAndCrossAttentions: output_cache = self.config.use_cache and self.config.is_decoder pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None if not (self.config.output_attentions and self.config.add_cross_attention): cross_attns = None return TFBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=output.last_hidden_state, pooler_output=output.pooler_output, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns, ) class TFRobertaLMHead(tf.keras.layers.Layer): """Roberta Head for masked language modeling.""" def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.vocab_size = config.vocab_size self.hidden_size = config.hidden_size self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") self.act = get_tf_activation("gelu") # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = input_embeddings def build(self, input_shape): self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias") super().build(input_shape) def get_output_embeddings(self): return self.decoder def set_output_embeddings(self, value): self.decoder.weight = value self.decoder.vocab_size = shape_list(value)[0] def get_bias(self): return {"bias": self.bias} def set_bias(self, value): self.bias = value["bias"] self.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.layer_norm(hidden_states) # project back to size of vocabulary with bias seq_length = shape_list(tensor=hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states @add_start_docstrings("""RoBERTa Model with a `language modeling` head on top.""", ROBERTA_START_DOCSTRING) class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head") def get_lm_head(self): return self.lm_head def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.lm_head.name @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", expected_output="' Paris'", expected_loss=0.1, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFMaskedLMOutput( loss=loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) class TFRobertaForCausalLM(TFRobertaPreTrainedModel, TFCausalLanguageModelingLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"] def __init__(self, config: RobertaConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) if not config.is_decoder: logger.warning("If you want to use `TFRobertaLMHeadModel` as a standalone, add `is_decoder=True.`") self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.lm_head = TFRobertaLMHead(config, input_embeddings=self.roberta.embeddings, name="lm_head") def get_lm_head(self): return self.lm_head def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.lm_head.name # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.prepare_inputs_for_generation def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = tf.ones(input_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past} @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`. """ outputs = self.roberta( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.lm_head(hidden_states=sequence_output, training=training) loss = None if labels is not None: # shift labels to the left and cut last logit token shifted_logits = logits[:, :-1] labels = labels[:, 1:] loss = self.hf_compute_loss(labels=labels, logits=shifted_logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFCausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output def serving_output(self, output: TFCausalLMOutputWithCrossAttentions) -> TFCausalLMOutputWithCrossAttentions: output_cache = self.config.use_cache and self.config.is_decoder pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None if not (self.config.output_attentions and self.config.add_cross_attention): cross_attns = None return TFCausalLMOutputWithCrossAttentions( logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns ) @staticmethod # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel._reorder_cache def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(tf.gather(past_state, beam_idx, axis=0) for past_state in layer_past),) return reordered_past class TFRobertaClassificationHead(tf.keras.layers.Layer): """Head for sentence-level classification tasks.""" def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = tf.keras.layers.Dropout(classifier_dropout) self.out_proj = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj" ) def call(self, features, training=False): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x, training=training) x = self.dense(x) x = self.dropout(x, training=training) x = self.out_proj(x) return x @add_start_docstrings( """ RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ROBERTA_START_DOCSTRING, ) class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.classifier = TFRobertaClassificationHead(config, name="classifier") @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="cardiffnlp/twitter-roberta-base-emotion", output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="'optimism'", expected_loss=0.08, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.classifier(sequence_output, training=training) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( """ Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ROBERTA_START_DOCSTRING, ) class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"lm_head"] _keys_to_ignore_on_load_missing = [r"dropout"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta = TFRobertaMainLayer(config, name="roberta") self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) self.classifier = tf.keras.layers.Dense( 1, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: tf.Tensor with dummy inputs """ return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)} @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ if input_ids is not None: num_choices = shape_list(input_ids)[1] seq_length = shape_list(input_ids)[2] else: num_choices = shape_list(inputs_embeds)[1] seq_length = shape_list(inputs_embeds)[2] flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None outputs = self.roberta( flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict=return_dict, training=training, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output, training=training) logits = self.classifier(pooled_output) reshaped_logits = tf.reshape(logits, (-1, num_choices)) loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFMultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( """ RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, ROBERTA_START_DOCSTRING, ) class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] _keys_to_ignore_on_load_missing = [r"dropout"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = tf.keras.layers.Dropout(classifier_dropout) self.classifier = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="ydshieh/roberta-large-ner-english", output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']", expected_loss=0.01, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output, training=training) logits = self.classifier(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFTokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( """ RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ROBERTA_START_DOCSTRING, ) class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnsweringLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.qa_outputs = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs" ) @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="ydshieh/roberta-base-squad2", output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, expected_output="' puppet'", expected_loss=0.86, ) def call( self, input_ids: Optional[TFModelInputType] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None, end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]: r""" start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = tf.split(logits, 2, axis=-1) start_logits = tf.squeeze(start_logits, axis=-1) end_logits = tf.squeeze(end_logits, axis=-1) loss = None if start_positions is not None and end_positions is not None: labels = {"start_position": start_positions} labels["end_position"] = end_positions loss = self.hf_compute_loss(labels, (start_logits, end_logits)) if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFQuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/funnel/test_tokenization_funnel.py

# coding=utf-8 # Copyright 2020 HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest from transformers import FunnelTokenizer, FunnelTokenizerFast from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class FunnelTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = FunnelTokenizer rust_tokenizer_class = FunnelTokenizerFast test_rust_tokenizer = True space_between_special_tokens = True def setUp(self): super().setUp() vocab_tokens = [ "<unk>", "<cls>", "<sep>", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) def get_tokenizer(self, **kwargs): return FunnelTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs): return FunnelTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): input_text = "UNwant\u00E9d,running" output_text = "unwanted, running" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.tokenizer_class(self.vocab_file) tokens = tokenizer.tokenize("UNwant\u00E9d,running") self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9]) def test_token_type_ids(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: inputs = tokenizer("UNwant\u00E9d,running") sentence_len = len(inputs["input_ids"]) - 1 self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len) inputs = tokenizer("UNwant\u00E9d,running", "UNwant\u00E9d,running") self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len + [1] * sentence_len)

# coding=utf-8 # Copyright 2020 HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest from transformers import FunnelTokenizer, FunnelTokenizerFast from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class FunnelTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = FunnelTokenizer rust_tokenizer_class = FunnelTokenizerFast test_rust_tokenizer = True space_between_special_tokens = True def setUp(self): super().setUp() vocab_tokens = [ "<unk>", "<cls>", "<sep>", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) def get_tokenizer(self, **kwargs): return FunnelTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs): return FunnelTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): input_text = "UNwant\u00E9d,running" output_text = "unwanted, running" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.tokenizer_class(self.vocab_file) tokens = tokenizer.tokenize("UNwant\u00E9d,running") self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9]) def test_token_type_ids(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: inputs = tokenizer("UNwant\u00E9d,running") sentence_len = len(inputs["input_ids"]) - 1 self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len) inputs = tokenizer("UNwant\u00E9d,running", "UNwant\u00E9d,running") self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len + [1] * sentence_len)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/mvp/modeling_mvp.py

# coding=utf-8 # Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch MVP model.""" import copy import math import random from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, Seq2SeqQuestionAnsweringModelOutput, Seq2SeqSequenceClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_mvp import MvpConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "RUCAIBox/mvp" _CONFIG_FOR_DOC = "MvpConfig" _TOKENIZER_FOR_DOC = "MvpTokenizer" # Base model docstring _EXPECTED_OUTPUT_SHAPE = [1, 8, 1024] MVP_PRETRAINED_MODEL_ARCHIVE_LIST = [ "RUCAIBox/mvp", "RUCAIBox/mvp-data-to-text", "RUCAIBox/mvp-open-dialog", "RUCAIBox/mvp-question-answering", "RUCAIBox/mvp-question-generation", "RUCAIBox/mvp-story", "RUCAIBox/mvp-summarization", "RUCAIBox/mvp-task-dialog", "RUCAIBox/mtl-data-to-text", "RUCAIBox/mtl-multi-task", "RUCAIBox/mtl-open-dialog", "RUCAIBox/mtl-question-answering", "RUCAIBox/mtl-question-generation", "RUCAIBox/mtl-story", "RUCAIBox/mtl-summarization", # See all MVP models at https://huggingface.co/models?filter=mvp ] # Copied from transformers.models.bart.modeling_bart.shift_tokens_right def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): """ Shift input ids one token to the right. """ shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() shifted_input_ids[:, 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min)) mask_cond = torch.arange(mask.size(-1)) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) # Copied from transformers.models.bart.modeling_bart.BartLearnedPositionalEmbedding with Bart->MVP class MvpLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int): # MVP is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 super().__init__(num_embeddings + self.offset, embedding_dim) def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0): """`input_ids' shape is expected to be [bsz x seqlen].""" bsz, seq_len = input_ids.shape[:2] positions = torch.arange( past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device ).expand(bsz, -1) return super().forward(positions + self.offset) class MvpAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, attn_prompt: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) if attn_prompt is not None: key_states = torch.cat([attn_prompt[0].expand(bsz, -1, -1, -1), key_states], dim=2) value_states = torch.cat([attn_prompt[1].expand(bsz, -1, -1, -1), value_states], dim=2) if attention_mask is not None: prompt_mask = torch.zeros(bsz, 1, tgt_len, attn_prompt[0].size(1)).to(attention_mask.device) attention_mask = torch.cat([prompt_mask, attention_mask], dim=(-1)) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned aross GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value class MvpEncoderLayer(nn.Module): def __init__(self, config: MvpConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = MvpAttention( embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, layer_head_mask: torch.FloatTensor, self_attn_prompt: torch.FloatTensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. self_attn_prompt (`torch.FloatTensor`): prompt of self attention of shape `(2, encoder_attention_heads, pro_len, head_dim)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states, attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, attn_prompt=self_attn_prompt, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) if hidden_states.dtype == torch.float16 and ( torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() ): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class MvpDecoderLayer(nn.Module): def __init__(self, config: MvpConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = MvpAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = MvpAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, self_attn_prompt: Optional[torch.Tensor] = None, cross_attn_prompt: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`. self_attn_prompt (`torch.FloatTensor`): prompt of self attention of shape `(2, decoder_attention_heads, pro_len, head_dim)`. cross_attn_prompt (`torch.FloatTensor`): prompt of cross attention of shape `(2, decoder_attention_heads, pro_len, head_dim)`. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, attn_prompt=self_attn_prompt, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, attn_prompt=cross_attn_prompt, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) if use_cache: outputs += (present_key_value,) return outputs # Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->MVP class MvpClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__( self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float, ): super().__init__() self.dense = nn.Linear(input_dim, inner_dim) self.dropout = nn.Dropout(p=pooler_dropout) self.out_proj = nn.Linear(inner_dim, num_classes) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dropout(hidden_states) hidden_states = self.dense(hidden_states) hidden_states = torch.tanh(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states class MvpPrompt(nn.Module): """Layer-wise prompt for encoder or decoder.""" def __init__(self, config, num_layers, num_heads): super().__init__() self.prompt_length = config.prompt_length self.num_layers = num_layers self.num_heads = num_heads self.head_dim = config.d_model // num_heads self.dropout = nn.Dropout(p=config.dropout) self.prompt_embedding = nn.Embedding(config.prompt_length, config.d_model) self.prompt_trans = nn.Sequential( nn.Linear(config.d_model, config.prompt_mid_dim), nn.GELU(), nn.Linear(config.prompt_mid_dim, num_layers * 2 * config.d_model), ) def forward(self, prompt_ids: torch.Tensor) -> Tuple[torch.Tensor]: prompt = self.prompt_trans(self.prompt_embedding(prompt_ids)) prompt = prompt.view(self.prompt_length, self.num_layers * 2, self.num_heads, self.head_dim) prompt = self.dropout(prompt) prompt = prompt.permute([1, 2, 0, 3]).split(2) return prompt class MvpPreTrainedModel(PreTrainedModel): config_class = MvpConfig base_model_prefix = "model" supports_gradient_checkpointing = True _keys_to_ignore_on_load_unexpected = [r"encoder.version", r"decoder.version"] def _init_weights(self, module): std = self.config.init_std if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (MvpDecoder, MvpEncoder, MvpPrompt)): module.gradient_checkpointing = value @property def dummy_inputs(self): pad_token = self.config.pad_token_id input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device) dummy_inputs = { "attention_mask": input_ids.ne(pad_token), "input_ids": input_ids, } return dummy_inputs MVP_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`MvpConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ MVP_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MvpTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`MvpTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) Mvp uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should read [`modeling_mvp._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ MVP_CONDITIONAL_GENERATION_EXAMPLE = r""" Example of summarization: Fine-tuning a model ```python >>> import torch >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp") >>> inputs = tokenizer( ... "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.", ... return_tensors="pt", ... ) >>> labels = tokenizer("Bad Reasons To Quit Your Job", return_tensors="pt")["input_ids"] >>> loss = model(**inputs, labels=labels).loss >>> loss.backward() ``` Inference after the model fine-tuned ```python >>> with torch.no_grad(): ... generated_ids = model.generate(**inputs) >>> generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ``` """ MVP_SEQUENCE_CLASSIFICATION_SAMPLE = r""" Example of single-label classification: Fine-tuning a model on `num_labels` classes ```python >>> import torch >>> from transformers import MvpTokenizer, MvpForSequenceClassification >>> num_labels = 2 # for example, this is a binary classification task >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForSequenceClassification.from_pretrained("RUCAIBox/mvp", num_labels=num_labels) >>> inputs = tokenizer("Classify: Hello, my dog is cute", return_tensors="pt") >>> labels = torch.tensor(1) # the real label for inputs >>> loss = model(**inputs, labels=labels).loss >>> loss.backward() ``` Inference after the model fine-tuned ```python >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> predicted_class_id = logits.argmax() ``` """ MVP_QUESTION_ANSWERING_SAMPLE = r""" Example: Fine-tuning a model for extrative question answering, and our model also supports generative question answering using `BartForConditionalGeneration` ```python >>> import torch >>> from transformers import MvpTokenizer, MvpForQuestionAnswering >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForQuestionAnswering.from_pretrained("RUCAIBox/mvp") >>> inputs = tokenizer( ... "Answer the following question: Who was Jim Henson? [SEP] Jim Henson was a nice puppet", ... return_tensors="pt", ... ) >>> target_start_index = torch.tensor([18]) >>> target_end_index = torch.tensor([19]) >>> loss = model(**inputs, start_positions=target_start_index, end_positions=target_end_index).loss >>> loss.backward() ``` Inference after the model fine-tuned ```python >>> with torch.no_grad(): ... outputs = model(**inputs) >>> answer_start_index = outputs.start_logits.argmax() >>> answer_end_index = outputs.end_logits.argmax() >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] >>> predict_answer = tokenizer.decode(predict_answer_tokens) ``` """ class MvpEncoder(MvpPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`MvpEncoderLayer`]. Args: config: MvpConfig embed_tokens (nn.Embedding): output embedding use_prompt (bool): whether to use prompt """ def __init__( self, config: MvpConfig, embed_tokens: Optional[nn.Embedding] = None, use_prompt: Optional[bool] = False ): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop embed_dim = config.d_model self.padding_idx = config.pad_token_id self.max_source_positions = config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 if embed_tokens is not None: self.embed_tokens = embed_tokens else: self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx) self.embed_positions = MvpLearnedPositionalEmbedding( config.max_position_embeddings, embed_dim, ) self.layers = nn.ModuleList([MvpEncoderLayer(config) for _ in range(config.encoder_layers)]) self.layernorm_embedding = nn.LayerNorm(embed_dim) self.use_prompt = use_prompt if use_prompt: self.prompt_length = config.prompt_length self.self_attn_prompt = MvpPrompt( config, config.encoder_layers, config.encoder_attention_heads, ) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MvpTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input = input_ids input_shape = input.shape input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] input = inputs_embeds[:, :, -1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(input) hidden_states = inputs_embeds + embed_pos hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # layer-wise prompt if self.use_prompt: prompt_ids = torch.arange(self.prompt_length).to(self.device) self_attn_prompt = self.self_attn_prompt(prompt_ids) # expand attention_mask if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.size()[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(encoder_layer), hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), (self_attn_prompt[idx] if self.use_prompt else None), ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), self_attn_prompt=(self_attn_prompt[idx] if self.use_prompt else None), output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class MvpDecoder(MvpPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MvpDecoderLayer`] Args: config: MvpConfig embed_tokens (nn.Embedding): output embedding use_prompt (bool): whether to use prompt """ def __init__( self, config: MvpConfig, embed_tokens: Optional[nn.Embedding] = None, use_prompt: Optional[bool] = False ): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.padding_idx = config.pad_token_id self.max_target_positions = config.max_position_embeddings self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 if embed_tokens is not None: self.embed_tokens = embed_tokens else: self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) self.embed_positions = MvpLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, ) self.layers = nn.ModuleList([MvpDecoderLayer(config) for _ in range(config.decoder_layers)]) self.layernorm_embedding = nn.LayerNorm(config.d_model) self.use_prompt = use_prompt if use_prompt: self.prompt_length = config.prompt_length self.self_attn_prompt = MvpPrompt( config, config.decoder_layers, config.decoder_attention_heads, ) self.cross_attn_prompt = MvpPrompt( config, config.decoder_layers, config.decoder_attention_heads, ) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length ).to(inputs_embeds.device) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MvpTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing cross-attention on hidden heads. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: input = input_ids input_shape = input_ids.shape input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] input = inputs_embeds[:, :, -1] else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale attention_mask = self._prepare_decoder_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) # embed positions positions = self.embed_positions(input, past_key_values_length) hidden_states = inputs_embeds + positions hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # layer-wise prompt if self.use_prompt: prompt_ids = torch.arange(self.prompt_length).to(self.device) self_attn_prompt = self.self_attn_prompt(prompt_ids) cross_attn_prompt = self.cross_attn_prompt(prompt_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None next_decoder_cache = () if use_cache else None # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): if attn_mask is not None: if attn_mask.size()[0] != (len(self.layers)): raise ValueError( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): continue past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs, output_attentions, use_cache) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, head_mask[idx] if head_mask is not None else None, cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, self_attn_prompt[idx] if self.use_prompt else None, cross_attn_prompt[idx] if self.use_prompt else None, None, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=( cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None ), self_attn_prompt=(self_attn_prompt[idx] if self.use_prompt else None), cross_attn_prompt=(cross_attn_prompt[idx] if self.use_prompt else None), past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @add_start_docstrings( "The bare MVP Model outputting raw hidden-states without any specific head on top.", MVP_START_DOCSTRING, ) class MvpModel(MvpPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"final_logits_bias", r"lm_head.weight"] _keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config: MvpConfig): super().__init__(config) padding_idx, vocab_size = config.pad_token_id, config.vocab_size self.use_prompt = config.use_prompt self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx) self.encoder = MvpEncoder(config, self.shared, config.use_prompt) self.decoder = MvpDecoder(config, self.shared, config.use_prompt) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, value): self.shared = value self.encoder.embed_tokens = self.shared self.decoder.embed_tokens = self.shared def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def set_lightweight_tuning(self): assert self.use_prompt, "If you want to use lightweight tuning, make sure that `use_prompt=True`." self.requires_grad_(False) self.encoder.self_attn_prompt.requires_grad_(True) self.decoder.self_attn_prompt.requires_grad_(True) self.decoder.cross_attn_prompt.requires_grad_(True) @add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC, expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqModelOutput]: # different to other models, Mvp automatically creates decoder_input_ids from # input_ids if no decoder_input_ids are provided if decoder_input_ids is None and decoder_inputs_embeds is None: if input_ids is None: raise ValueError( "If no `decoder_input_ids` or `decoder_inputs_embeds` are " "passed, `input_ids` cannot be `None`. Please pass either " "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`." ) decoder_input_ids = shift_tokens_right( input_ids, self.config.pad_token_id, self.config.decoder_start_token_id ) output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The MVP Model with a language modeling head. Can be used for various text generation tasks.", MVP_START_DOCSTRING ) class MvpForConditionalGeneration(MvpPreTrainedModel): _keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] def __init__(self, config: MvpConfig): super().__init__(config) self.model = MvpModel(config) self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) # Initialize weights and apply final processing self.post_init() def get_encoder(self): return self.model.get_encoder() def get_decoder(self): return self.model.get_decoder() def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding: new_embeddings = super().resize_token_embeddings(new_num_tokens) self._resize_final_logits_bias(new_num_tokens) return new_embeddings def _resize_final_logits_bias(self, new_num_tokens: int) -> None: old_num_tokens = self.final_logits_bias.shape[-1] if new_num_tokens <= old_num_tokens: new_bias = self.final_logits_bias[:, :new_num_tokens] else: extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) self.register_buffer("final_logits_bias", new_bias) def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_lightweight_tuning(self): self.model.set_lightweight_tuning() self.lm_head.requires_grad_(False) @add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @add_end_docstrings(MVP_CONDITIONAL_GENERATION_EXAMPLE) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: if use_cache: logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") use_cache = False if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (lm_logits,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return Seq2SeqLMOutput( loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) def prepare_inputs_for_generation( self, decoder_input_ids, past=None, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs ): # cut decoder_input_ids if past is used if past is not None: decoder_input_ids = decoder_input_ids[:, -1:] return { "input_ids": None, # encoder_outputs is defined. input_ids not needed "encoder_outputs": encoder_outputs, "past_key_values": past, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, # change this to avoid caching (presumably for debugging) } def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) @staticmethod def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: # cached cross_attention states don't have to be reordered -> they are always the same reordered_past += ( tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past @add_start_docstrings( """ Mvp model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, MVP_START_DOCSTRING, ) class MvpForSequenceClassification(MvpPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"final_logits_bias", r"lm_head.weight"] _keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] def __init__(self, config: MvpConfig, **kwargs): super().__init__(config, **kwargs) self.model = MvpModel(config) self.classification_head = MvpClassificationHead( config.d_model, config.d_model, config.num_labels, config.classifier_dropout, ) self.model._init_weights(self.classification_head.dense) self.model._init_weights(self.classification_head.out_proj) def set_lightweight_tuning(self): self.model.set_lightweight_tuning() self.classification_head.requires_grad_(False) @add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING) @add_end_docstrings(MVP_SEQUENCE_CLASSIFICATION_SAMPLE) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False if input_ids is None and inputs_embeds is not None: raise NotImplementedError( f"Passing input embeddings is currently not supported for {self.__class__.__name__}" ) outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] # last hidden state eos_mask = input_ids.eq(self.config.eos_token_id) if len(torch.unique_consecutive(eos_mask.sum(1))) > 1: raise ValueError("All examples must have the same number of <eos> tokens.") sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[ :, -1, : ] logits = self.classification_head(sentence_representation) loss = None if labels is not None: if self.config.problem_type is None: if self.config.num_labels == 1: self.config.problem_type = "regression" elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.config.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return Seq2SeqSequenceClassifierOutput( loss=loss, logits=logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ MVP Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, MVP_START_DOCSTRING, ) class MvpForQuestionAnswering(MvpPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"final_logits_bias", r"lm_head.weight"] _keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config): super().__init__(config) config.num_labels = 2 self.num_labels = config.num_labels self.model = MvpModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.model._init_weights(self.qa_outputs) def set_lightweight_tuning(self): self.model.set_lightweight_tuning() self.qa_outputs.requires_grad_(False) @add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING) @add_end_docstrings(MVP_QUESTION_ANSWERING_SAMPLE) def forward( self, input_ids: torch.Tensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if start_positions is not None and end_positions is not None: use_cache = False outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = ( start_logits, end_logits, ) + outputs[1:] return ((total_loss,) + output) if total_loss is not None else output return Seq2SeqQuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) # Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Mvp class MvpDecoderWrapper(MvpPreTrainedModel): """ This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is used in combination with the [`EncoderDecoderModel`] framework. """ def __init__(self, config): super().__init__(config) self.decoder = MvpDecoder(config) def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs) class MvpForCausalLM(MvpPreTrainedModel): _keys_to_ignore_on_load_missing = ["lm_head.weight"] def __init__(self, config): config = copy.deepcopy(config) config.is_decoder = True config.is_encoder_decoder = False super().__init__(config) self.model = MvpDecoderWrapper(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.decoder.embed_tokens def set_input_embeddings(self, value): self.model.decoder.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model.decoder = decoder def get_decoder(self): return self.model.decoder def set_lightweight_tuning(self): self.model.set_lightweight_tuning() self.lm_head.requires_grad_(False) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MvpTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. Returns: Example: ```python >>> from transformers import MvpTokenizer, MvpForCausalLM >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForCausalLM.from_pretrained("RUCAIBox/mvp", add_cross_attention=False) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> list(logits.shape) [1, 8, 50267] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model.decoder( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) logits = self.lm_head(outputs[0]) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs): # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_ids.shape) if past: input_ids = input_ids[:, -1:] # first step, decoder_cached_states are empty return { "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed "attention_mask": attention_mask, "past_key_values": past, "use_cache": use_cache, } @staticmethod def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past

# coding=utf-8 # Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch MVP model.""" import copy import math import random from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, Seq2SeqQuestionAnsweringModelOutput, Seq2SeqSequenceClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_mvp import MvpConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "RUCAIBox/mvp" _CONFIG_FOR_DOC = "MvpConfig" _TOKENIZER_FOR_DOC = "MvpTokenizer" # Base model docstring _EXPECTED_OUTPUT_SHAPE = [1, 8, 1024] MVP_PRETRAINED_MODEL_ARCHIVE_LIST = [ "RUCAIBox/mvp", "RUCAIBox/mvp-data-to-text", "RUCAIBox/mvp-open-dialog", "RUCAIBox/mvp-question-answering", "RUCAIBox/mvp-question-generation", "RUCAIBox/mvp-story", "RUCAIBox/mvp-summarization", "RUCAIBox/mvp-task-dialog", "RUCAIBox/mtl-data-to-text", "RUCAIBox/mtl-multi-task", "RUCAIBox/mtl-open-dialog", "RUCAIBox/mtl-question-answering", "RUCAIBox/mtl-question-generation", "RUCAIBox/mtl-story", "RUCAIBox/mtl-summarization", # See all MVP models at https://huggingface.co/models?filter=mvp ] # Copied from transformers.models.bart.modeling_bart.shift_tokens_right def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): """ Shift input ids one token to the right. """ shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() shifted_input_ids[:, 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min)) mask_cond = torch.arange(mask.size(-1)) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) # Copied from transformers.models.bart.modeling_bart.BartLearnedPositionalEmbedding with Bart->MVP class MvpLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int): # MVP is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 super().__init__(num_embeddings + self.offset, embedding_dim) def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0): """`input_ids' shape is expected to be [bsz x seqlen].""" bsz, seq_len = input_ids.shape[:2] positions = torch.arange( past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device ).expand(bsz, -1) return super().forward(positions + self.offset) class MvpAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, attn_prompt: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) if attn_prompt is not None: key_states = torch.cat([attn_prompt[0].expand(bsz, -1, -1, -1), key_states], dim=2) value_states = torch.cat([attn_prompt[1].expand(bsz, -1, -1, -1), value_states], dim=2) if attention_mask is not None: prompt_mask = torch.zeros(bsz, 1, tgt_len, attn_prompt[0].size(1)).to(attention_mask.device) attention_mask = torch.cat([prompt_mask, attention_mask], dim=(-1)) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned aross GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value class MvpEncoderLayer(nn.Module): def __init__(self, config: MvpConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = MvpAttention( embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, layer_head_mask: torch.FloatTensor, self_attn_prompt: torch.FloatTensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. self_attn_prompt (`torch.FloatTensor`): prompt of self attention of shape `(2, encoder_attention_heads, pro_len, head_dim)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states, attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, attn_prompt=self_attn_prompt, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) if hidden_states.dtype == torch.float16 and ( torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() ): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class MvpDecoderLayer(nn.Module): def __init__(self, config: MvpConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = MvpAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = MvpAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, self_attn_prompt: Optional[torch.Tensor] = None, cross_attn_prompt: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`. self_attn_prompt (`torch.FloatTensor`): prompt of self attention of shape `(2, decoder_attention_heads, pro_len, head_dim)`. cross_attn_prompt (`torch.FloatTensor`): prompt of cross attention of shape `(2, decoder_attention_heads, pro_len, head_dim)`. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, attn_prompt=self_attn_prompt, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, attn_prompt=cross_attn_prompt, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) if use_cache: outputs += (present_key_value,) return outputs # Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->MVP class MvpClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__( self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float, ): super().__init__() self.dense = nn.Linear(input_dim, inner_dim) self.dropout = nn.Dropout(p=pooler_dropout) self.out_proj = nn.Linear(inner_dim, num_classes) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dropout(hidden_states) hidden_states = self.dense(hidden_states) hidden_states = torch.tanh(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states class MvpPrompt(nn.Module): """Layer-wise prompt for encoder or decoder.""" def __init__(self, config, num_layers, num_heads): super().__init__() self.prompt_length = config.prompt_length self.num_layers = num_layers self.num_heads = num_heads self.head_dim = config.d_model // num_heads self.dropout = nn.Dropout(p=config.dropout) self.prompt_embedding = nn.Embedding(config.prompt_length, config.d_model) self.prompt_trans = nn.Sequential( nn.Linear(config.d_model, config.prompt_mid_dim), nn.GELU(), nn.Linear(config.prompt_mid_dim, num_layers * 2 * config.d_model), ) def forward(self, prompt_ids: torch.Tensor) -> Tuple[torch.Tensor]: prompt = self.prompt_trans(self.prompt_embedding(prompt_ids)) prompt = prompt.view(self.prompt_length, self.num_layers * 2, self.num_heads, self.head_dim) prompt = self.dropout(prompt) prompt = prompt.permute([1, 2, 0, 3]).split(2) return prompt class MvpPreTrainedModel(PreTrainedModel): config_class = MvpConfig base_model_prefix = "model" supports_gradient_checkpointing = True _keys_to_ignore_on_load_unexpected = [r"encoder.version", r"decoder.version"] def _init_weights(self, module): std = self.config.init_std if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (MvpDecoder, MvpEncoder, MvpPrompt)): module.gradient_checkpointing = value @property def dummy_inputs(self): pad_token = self.config.pad_token_id input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device) dummy_inputs = { "attention_mask": input_ids.ne(pad_token), "input_ids": input_ids, } return dummy_inputs MVP_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`MvpConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ MVP_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MvpTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`MvpTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) Mvp uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should read [`modeling_mvp._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ MVP_CONDITIONAL_GENERATION_EXAMPLE = r""" Example of summarization: Fine-tuning a model ```python >>> import torch >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp") >>> inputs = tokenizer( ... "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.", ... return_tensors="pt", ... ) >>> labels = tokenizer("Bad Reasons To Quit Your Job", return_tensors="pt")["input_ids"] >>> loss = model(**inputs, labels=labels).loss >>> loss.backward() ``` Inference after the model fine-tuned ```python >>> with torch.no_grad(): ... generated_ids = model.generate(**inputs) >>> generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ``` """ MVP_SEQUENCE_CLASSIFICATION_SAMPLE = r""" Example of single-label classification: Fine-tuning a model on `num_labels` classes ```python >>> import torch >>> from transformers import MvpTokenizer, MvpForSequenceClassification >>> num_labels = 2 # for example, this is a binary classification task >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForSequenceClassification.from_pretrained("RUCAIBox/mvp", num_labels=num_labels) >>> inputs = tokenizer("Classify: Hello, my dog is cute", return_tensors="pt") >>> labels = torch.tensor(1) # the real label for inputs >>> loss = model(**inputs, labels=labels).loss >>> loss.backward() ``` Inference after the model fine-tuned ```python >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> predicted_class_id = logits.argmax() ``` """ MVP_QUESTION_ANSWERING_SAMPLE = r""" Example: Fine-tuning a model for extrative question answering, and our model also supports generative question answering using `BartForConditionalGeneration` ```python >>> import torch >>> from transformers import MvpTokenizer, MvpForQuestionAnswering >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForQuestionAnswering.from_pretrained("RUCAIBox/mvp") >>> inputs = tokenizer( ... "Answer the following question: Who was Jim Henson? [SEP] Jim Henson was a nice puppet", ... return_tensors="pt", ... ) >>> target_start_index = torch.tensor([18]) >>> target_end_index = torch.tensor([19]) >>> loss = model(**inputs, start_positions=target_start_index, end_positions=target_end_index).loss >>> loss.backward() ``` Inference after the model fine-tuned ```python >>> with torch.no_grad(): ... outputs = model(**inputs) >>> answer_start_index = outputs.start_logits.argmax() >>> answer_end_index = outputs.end_logits.argmax() >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] >>> predict_answer = tokenizer.decode(predict_answer_tokens) ``` """ class MvpEncoder(MvpPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`MvpEncoderLayer`]. Args: config: MvpConfig embed_tokens (nn.Embedding): output embedding use_prompt (bool): whether to use prompt """ def __init__( self, config: MvpConfig, embed_tokens: Optional[nn.Embedding] = None, use_prompt: Optional[bool] = False ): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop embed_dim = config.d_model self.padding_idx = config.pad_token_id self.max_source_positions = config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 if embed_tokens is not None: self.embed_tokens = embed_tokens else: self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx) self.embed_positions = MvpLearnedPositionalEmbedding( config.max_position_embeddings, embed_dim, ) self.layers = nn.ModuleList([MvpEncoderLayer(config) for _ in range(config.encoder_layers)]) self.layernorm_embedding = nn.LayerNorm(embed_dim) self.use_prompt = use_prompt if use_prompt: self.prompt_length = config.prompt_length self.self_attn_prompt = MvpPrompt( config, config.encoder_layers, config.encoder_attention_heads, ) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MvpTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input = input_ids input_shape = input.shape input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] input = inputs_embeds[:, :, -1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(input) hidden_states = inputs_embeds + embed_pos hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # layer-wise prompt if self.use_prompt: prompt_ids = torch.arange(self.prompt_length).to(self.device) self_attn_prompt = self.self_attn_prompt(prompt_ids) # expand attention_mask if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.size()[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(encoder_layer), hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), (self_attn_prompt[idx] if self.use_prompt else None), ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), self_attn_prompt=(self_attn_prompt[idx] if self.use_prompt else None), output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class MvpDecoder(MvpPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MvpDecoderLayer`] Args: config: MvpConfig embed_tokens (nn.Embedding): output embedding use_prompt (bool): whether to use prompt """ def __init__( self, config: MvpConfig, embed_tokens: Optional[nn.Embedding] = None, use_prompt: Optional[bool] = False ): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.padding_idx = config.pad_token_id self.max_target_positions = config.max_position_embeddings self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 if embed_tokens is not None: self.embed_tokens = embed_tokens else: self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) self.embed_positions = MvpLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, ) self.layers = nn.ModuleList([MvpDecoderLayer(config) for _ in range(config.decoder_layers)]) self.layernorm_embedding = nn.LayerNorm(config.d_model) self.use_prompt = use_prompt if use_prompt: self.prompt_length = config.prompt_length self.self_attn_prompt = MvpPrompt( config, config.decoder_layers, config.decoder_attention_heads, ) self.cross_attn_prompt = MvpPrompt( config, config.decoder_layers, config.decoder_attention_heads, ) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length ).to(inputs_embeds.device) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MvpTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing cross-attention on hidden heads. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: input = input_ids input_shape = input_ids.shape input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] input = inputs_embeds[:, :, -1] else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale attention_mask = self._prepare_decoder_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) # embed positions positions = self.embed_positions(input, past_key_values_length) hidden_states = inputs_embeds + positions hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # layer-wise prompt if self.use_prompt: prompt_ids = torch.arange(self.prompt_length).to(self.device) self_attn_prompt = self.self_attn_prompt(prompt_ids) cross_attn_prompt = self.cross_attn_prompt(prompt_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None next_decoder_cache = () if use_cache else None # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): if attn_mask is not None: if attn_mask.size()[0] != (len(self.layers)): raise ValueError( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): continue past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs, output_attentions, use_cache) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, head_mask[idx] if head_mask is not None else None, cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, self_attn_prompt[idx] if self.use_prompt else None, cross_attn_prompt[idx] if self.use_prompt else None, None, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=( cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None ), self_attn_prompt=(self_attn_prompt[idx] if self.use_prompt else None), cross_attn_prompt=(cross_attn_prompt[idx] if self.use_prompt else None), past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @add_start_docstrings( "The bare MVP Model outputting raw hidden-states without any specific head on top.", MVP_START_DOCSTRING, ) class MvpModel(MvpPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"final_logits_bias", r"lm_head.weight"] _keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config: MvpConfig): super().__init__(config) padding_idx, vocab_size = config.pad_token_id, config.vocab_size self.use_prompt = config.use_prompt self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx) self.encoder = MvpEncoder(config, self.shared, config.use_prompt) self.decoder = MvpDecoder(config, self.shared, config.use_prompt) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, value): self.shared = value self.encoder.embed_tokens = self.shared self.decoder.embed_tokens = self.shared def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def set_lightweight_tuning(self): assert self.use_prompt, "If you want to use lightweight tuning, make sure that `use_prompt=True`." self.requires_grad_(False) self.encoder.self_attn_prompt.requires_grad_(True) self.decoder.self_attn_prompt.requires_grad_(True) self.decoder.cross_attn_prompt.requires_grad_(True) @add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC, expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqModelOutput]: # different to other models, Mvp automatically creates decoder_input_ids from # input_ids if no decoder_input_ids are provided if decoder_input_ids is None and decoder_inputs_embeds is None: if input_ids is None: raise ValueError( "If no `decoder_input_ids` or `decoder_inputs_embeds` are " "passed, `input_ids` cannot be `None`. Please pass either " "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`." ) decoder_input_ids = shift_tokens_right( input_ids, self.config.pad_token_id, self.config.decoder_start_token_id ) output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The MVP Model with a language modeling head. Can be used for various text generation tasks.", MVP_START_DOCSTRING ) class MvpForConditionalGeneration(MvpPreTrainedModel): _keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] def __init__(self, config: MvpConfig): super().__init__(config) self.model = MvpModel(config) self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) # Initialize weights and apply final processing self.post_init() def get_encoder(self): return self.model.get_encoder() def get_decoder(self): return self.model.get_decoder() def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding: new_embeddings = super().resize_token_embeddings(new_num_tokens) self._resize_final_logits_bias(new_num_tokens) return new_embeddings def _resize_final_logits_bias(self, new_num_tokens: int) -> None: old_num_tokens = self.final_logits_bias.shape[-1] if new_num_tokens <= old_num_tokens: new_bias = self.final_logits_bias[:, :new_num_tokens] else: extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) self.register_buffer("final_logits_bias", new_bias) def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_lightweight_tuning(self): self.model.set_lightweight_tuning() self.lm_head.requires_grad_(False) @add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @add_end_docstrings(MVP_CONDITIONAL_GENERATION_EXAMPLE) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: if use_cache: logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") use_cache = False if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (lm_logits,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return Seq2SeqLMOutput( loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) def prepare_inputs_for_generation( self, decoder_input_ids, past=None, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs ): # cut decoder_input_ids if past is used if past is not None: decoder_input_ids = decoder_input_ids[:, -1:] return { "input_ids": None, # encoder_outputs is defined. input_ids not needed "encoder_outputs": encoder_outputs, "past_key_values": past, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, # change this to avoid caching (presumably for debugging) } def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) @staticmethod def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: # cached cross_attention states don't have to be reordered -> they are always the same reordered_past += ( tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past @add_start_docstrings( """ Mvp model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, MVP_START_DOCSTRING, ) class MvpForSequenceClassification(MvpPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"final_logits_bias", r"lm_head.weight"] _keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] def __init__(self, config: MvpConfig, **kwargs): super().__init__(config, **kwargs) self.model = MvpModel(config) self.classification_head = MvpClassificationHead( config.d_model, config.d_model, config.num_labels, config.classifier_dropout, ) self.model._init_weights(self.classification_head.dense) self.model._init_weights(self.classification_head.out_proj) def set_lightweight_tuning(self): self.model.set_lightweight_tuning() self.classification_head.requires_grad_(False) @add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING) @add_end_docstrings(MVP_SEQUENCE_CLASSIFICATION_SAMPLE) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False if input_ids is None and inputs_embeds is not None: raise NotImplementedError( f"Passing input embeddings is currently not supported for {self.__class__.__name__}" ) outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] # last hidden state eos_mask = input_ids.eq(self.config.eos_token_id) if len(torch.unique_consecutive(eos_mask.sum(1))) > 1: raise ValueError("All examples must have the same number of <eos> tokens.") sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[ :, -1, : ] logits = self.classification_head(sentence_representation) loss = None if labels is not None: if self.config.problem_type is None: if self.config.num_labels == 1: self.config.problem_type = "regression" elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.config.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return Seq2SeqSequenceClassifierOutput( loss=loss, logits=logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ MVP Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, MVP_START_DOCSTRING, ) class MvpForQuestionAnswering(MvpPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"final_logits_bias", r"lm_head.weight"] _keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config): super().__init__(config) config.num_labels = 2 self.num_labels = config.num_labels self.model = MvpModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.model._init_weights(self.qa_outputs) def set_lightweight_tuning(self): self.model.set_lightweight_tuning() self.qa_outputs.requires_grad_(False) @add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING) @add_end_docstrings(MVP_QUESTION_ANSWERING_SAMPLE) def forward( self, input_ids: torch.Tensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if start_positions is not None and end_positions is not None: use_cache = False outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = ( start_logits, end_logits, ) + outputs[1:] return ((total_loss,) + output) if total_loss is not None else output return Seq2SeqQuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) # Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Mvp class MvpDecoderWrapper(MvpPreTrainedModel): """ This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is used in combination with the [`EncoderDecoderModel`] framework. """ def __init__(self, config): super().__init__(config) self.decoder = MvpDecoder(config) def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs) class MvpForCausalLM(MvpPreTrainedModel): _keys_to_ignore_on_load_missing = ["lm_head.weight"] def __init__(self, config): config = copy.deepcopy(config) config.is_decoder = True config.is_encoder_decoder = False super().__init__(config) self.model = MvpDecoderWrapper(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.decoder.embed_tokens def set_input_embeddings(self, value): self.model.decoder.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model.decoder = decoder def get_decoder(self): return self.model.decoder def set_lightweight_tuning(self): self.model.set_lightweight_tuning() self.lm_head.requires_grad_(False) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MvpTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. Returns: Example: ```python >>> from transformers import MvpTokenizer, MvpForCausalLM >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForCausalLM.from_pretrained("RUCAIBox/mvp", add_cross_attention=False) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> list(logits.shape) [1, 8, 50267] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model.decoder( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) logits = self.lm_head(outputs[0]) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs): # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_ids.shape) if past: input_ids = input_ids[:, -1:] # first step, decoder_cached_states are empty return { "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed "attention_mask": attention_mask, "past_key_values": past, "use_cache": use_cache, } @staticmethod def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/tensorflow/language-modeling/run_clm.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for causal language modeling (GPT-2, GPT-Neo...) on a text file or a dataset without using HuggingFace Trainer. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=text-generation """ # You can also adapt this script on your own clm task. Pointers for this are left as comments. import json # region Imports import logging import math import os import random import sys from dataclasses import dataclass, field from itertools import chain from pathlib import Path from typing import Optional import datasets import tensorflow as tf from datasets import load_dataset from sklearn.model_selection import train_test_split import transformers from transformers import ( CONFIG_MAPPING, CONFIG_NAME, TF2_WEIGHTS_NAME, TF_MODEL_FOR_CAUSAL_LM_MAPPING, AutoConfig, AutoTokenizer, HfArgumentParser, PushToHubCallback, TFAutoModelForCausalLM, TFTrainingArguments, create_optimizer, set_seed, ) from transformers.utils import send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/tensorflow/language-modeling/requirements.txt") MODEL_CONFIG_CLASSES = list(TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) # endregion # region Command-line arguments @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_overrides: Optional[str] = field( default=None, metadata={ "help": ( "Override some existing default config settings when a model is trained from scratch. Example: " "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" ) }, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) def __post_init__(self): if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): raise ValueError( "--config_overrides can't be used in combination with --config_name or --model_name_or_path" ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) block_size: Optional[int] = field( default=None, metadata={ "help": ( "Optional input sequence length after tokenization. " "The training dataset will be truncated in block of this size for training. " "Default to the model max input length for single sentence inputs (take into account special tokens)." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) line_by_line: bool = field( default=False, metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) keep_linebreaks: bool = field( default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."} ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." # endregion def main(): # region Argument Parsing parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_clm", model_args, data_args, framework="tensorflow") # Sanity checks if data_args.dataset_name is None and data_args.train_file is None and data_args.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if data_args.train_file is not None: extension = data_args.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file." if data_args.validation_file is not None: extension = data_args.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file." if training_args.output_dir is not None: training_args.output_dir = Path(training_args.output_dir) os.makedirs(training_args.output_dir, exist_ok=True) # endregion # region Checkpoints # Detecting last checkpoint. checkpoint = None if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir: config_path = training_args.output_dir / CONFIG_NAME weights_path = training_args.output_dir / TF2_WEIGHTS_NAME if config_path.is_file() and weights_path.is_file(): checkpoint = training_args.output_dir logger.info( f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this" " behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) else: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to continue regardless." ) # endregion # region Setup logging # accelerator.is_local_main_process is only True for one process per machine. logger.setLevel(logging.INFO) datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() # endregion # If passed along, set the training seed now. if training_args.seed is not None: set_seed(training_args.seed) # region Load datasets # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[:{data_args.validation_split_percentage}%]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) raw_datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: data_files = {} dataset_args = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = ( data_args.train_file.split(".")[-1] if data_args.train_file is not None else data_args.validation_file.split(".")[-1] ) if extension == "txt": extension = "text" dataset_args["keep_linebreaks"] = data_args.keep_linebreaks raw_datasets = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, **dataset_args, ) # If no validation data is there, validation_split_percentage will be used to divide the dataset. if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( extension, data_files=data_files, split=f"train[:{data_args.validation_split_percentage}%]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, **dataset_args, ) raw_datasets["train"] = load_dataset( extension, data_files=data_files, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, **dataset_args, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # endregion # region Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) # endregion # region Dataset preprocessing # First we tokenize all the texts. column_names = raw_datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] def tokenize_function(examples): return tokenizer(examples[text_column_name]) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on dataset", ) if data_args.block_size is None: block_size = tokenizer.model_max_length if block_size > 1024: logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --block_size xxx." ) block_size = 1024 else: if data_args.block_size > tokenizer.model_max_length: logger.warning( f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model" f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}." ) block_size = min(data_args.block_size, tokenizer.model_max_length) # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size. def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= block_size: total_length = (total_length // block_size) * block_size # Split by chunks of max_len. result = { k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items() } result["labels"] = result["input_ids"].copy() return result # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower # to preprocess. # # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map lm_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, desc=f"Grouping texts in chunks of {block_size}", ) train_dataset = lm_datasets["train"] if data_args.validation_file is not None: eval_dataset = lm_datasets["validation"] else: logger.info( f"Validation file not found: using {data_args.validation_split_percentage}% of the dataset as validation" " as provided in data_args" ) train_indices, val_indices = train_test_split( list(range(len(train_dataset))), test_size=data_args.validation_split_percentage / 100 ) eval_dataset = train_dataset.select(val_indices) train_dataset = train_dataset.select(train_indices) if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), min(3, len(train_dataset))): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # endregion with training_args.strategy.scope(): # region Prepare model if checkpoint is not None: model = TFAutoModelForCausalLM.from_pretrained(checkpoint, config=config) elif model_args.model_name_or_path: model = TFAutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, config=config) else: logger.info("Training new model from scratch") model = TFAutoModelForCausalLM.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # endregion # region TF Dataset preparation num_replicas = training_args.strategy.num_replicas_in_sync options = tf.data.Options() options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF # model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in # training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also # use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names # yourself if you use this method, whereas they are automatically inferred from the model input names when # using model.prepare_tf_dataset() # For more info see the docs: # https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset # https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset tf_train_dataset = model.prepare_tf_dataset( train_dataset, shuffle=True, batch_size=num_replicas * training_args.per_device_train_batch_size, ).with_options(options) tf_eval_dataset = model.prepare_tf_dataset( eval_dataset, shuffle=False, batch_size=num_replicas * training_args.per_device_eval_batch_size, drop_remainder=True, ).with_options(options) # endregion # region Optimizer and loss num_train_steps = len(tf_train_dataset) * int(training_args.num_train_epochs) if training_args.warmup_steps > 0: num_warmup_steps = training_args.warmup_steps elif training_args.warmup_ratio > 0: num_warmup_steps = int(num_train_steps * training_args.warmup_ratio) else: num_warmup_steps = 0 # Bias and layernorm weights are automatically excluded from the decay optimizer, lr_schedule = create_optimizer( init_lr=training_args.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, adam_beta1=training_args.adam_beta1, adam_beta2=training_args.adam_beta2, adam_epsilon=training_args.adam_epsilon, weight_decay_rate=training_args.weight_decay, adam_global_clipnorm=training_args.max_grad_norm, ) # no user-specified loss = will use the model internal loss model.compile(optimizer=optimizer, jit_compile=training_args.xla) # endregion # region Preparing push_to_hub and model card push_to_hub_model_id = training_args.push_to_hub_model_id model_name = model_args.model_name_or_path.split("/")[-1] if not push_to_hub_model_id: if data_args.dataset_name is not None: push_to_hub_model_id = f"{model_name}-finetuned-{data_args.dataset_name}" else: push_to_hub_model_id = f"{model_name}-finetuned-clm" model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"} if data_args.dataset_name is not None: model_card_kwargs["dataset_tags"] = data_args.dataset_name if data_args.dataset_config_name is not None: model_card_kwargs["dataset_args"] = data_args.dataset_config_name model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: model_card_kwargs["dataset"] = data_args.dataset_name if training_args.push_to_hub: callbacks = [ PushToHubCallback( output_dir=training_args.output_dir, model_id=push_to_hub_model_id, organization=training_args.push_to_hub_organization, token=training_args.push_to_hub_token, tokenizer=tokenizer, **model_card_kwargs, ) ] else: callbacks = [] # endregion # region Training and validation logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {training_args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}") logger.info(f" Total train batch size = {training_args.per_device_train_batch_size * num_replicas}") # For long training runs, you may wish to use the PushToHub() callback here to save intermediate checkpoints # to the Hugging Face Hub rather than just pushing the finished model. # See https://huggingface.co/docs/transformers/main_classes/keras_callbacks#transformers.PushToHubCallback history = model.fit( tf_train_dataset, validation_data=tf_eval_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks, ) train_loss = history.history["loss"][-1] try: train_perplexity = math.exp(train_loss) except OverflowError: train_perplexity = math.inf logger.info(f" Final train loss: {train_loss:.3f}") logger.info(f" Final train perplexity: {train_perplexity:.3f}") validation_loss = history.history["val_loss"][-1] try: validation_perplexity = math.exp(validation_loss) except OverflowError: validation_perplexity = math.inf logger.info(f" Final validation loss: {validation_loss:.3f}") logger.info(f" Final validation perplexity: {validation_perplexity:.3f}") if training_args.output_dir is not None: output_eval_file = os.path.join(training_args.output_dir, "all_results.json") results_dict = dict() results_dict["train_loss"] = train_loss results_dict["train_perplexity"] = train_perplexity results_dict["eval_loss"] = validation_loss results_dict["eval_perplexity"] = validation_perplexity with open(output_eval_file, "w") as writer: writer.write(json.dumps(results_dict)) # endregion if training_args.output_dir is not None and not training_args.push_to_hub: # If we're not pushing to hub, at least save a local copy when we're done model.save_pretrained(training_args.output_dir) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for causal language modeling (GPT-2, GPT-Neo...) on a text file or a dataset without using HuggingFace Trainer. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=text-generation """ # You can also adapt this script on your own clm task. Pointers for this are left as comments. import json # region Imports import logging import math import os import random import sys from dataclasses import dataclass, field from itertools import chain from pathlib import Path from typing import Optional import datasets import tensorflow as tf from datasets import load_dataset from sklearn.model_selection import train_test_split import transformers from transformers import ( CONFIG_MAPPING, CONFIG_NAME, TF2_WEIGHTS_NAME, TF_MODEL_FOR_CAUSAL_LM_MAPPING, AutoConfig, AutoTokenizer, HfArgumentParser, PushToHubCallback, TFAutoModelForCausalLM, TFTrainingArguments, create_optimizer, set_seed, ) from transformers.utils import send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) require_version("datasets>=1.8.0", "To fix: pip install -r examples/tensorflow/language-modeling/requirements.txt") MODEL_CONFIG_CLASSES = list(TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) # endregion # region Command-line arguments @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_overrides: Optional[str] = field( default=None, metadata={ "help": ( "Override some existing default config settings when a model is trained from scratch. Example: " "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" ) }, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) def __post_init__(self): if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): raise ValueError( "--config_overrides can't be used in combination with --config_name or --model_name_or_path" ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) block_size: Optional[int] = field( default=None, metadata={ "help": ( "Optional input sequence length after tokenization. " "The training dataset will be truncated in block of this size for training. " "Default to the model max input length for single sentence inputs (take into account special tokens)." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) line_by_line: bool = field( default=False, metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) keep_linebreaks: bool = field( default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."} ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." # endregion def main(): # region Argument Parsing parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_clm", model_args, data_args, framework="tensorflow") # Sanity checks if data_args.dataset_name is None and data_args.train_file is None and data_args.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if data_args.train_file is not None: extension = data_args.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file." if data_args.validation_file is not None: extension = data_args.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file." if training_args.output_dir is not None: training_args.output_dir = Path(training_args.output_dir) os.makedirs(training_args.output_dir, exist_ok=True) # endregion # region Checkpoints # Detecting last checkpoint. checkpoint = None if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir: config_path = training_args.output_dir / CONFIG_NAME weights_path = training_args.output_dir / TF2_WEIGHTS_NAME if config_path.is_file() and weights_path.is_file(): checkpoint = training_args.output_dir logger.info( f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this" " behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) else: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to continue regardless." ) # endregion # region Setup logging # accelerator.is_local_main_process is only True for one process per machine. logger.setLevel(logging.INFO) datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() # endregion # If passed along, set the training seed now. if training_args.seed is not None: set_seed(training_args.seed) # region Load datasets # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[:{data_args.validation_split_percentage}%]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) raw_datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: data_files = {} dataset_args = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = ( data_args.train_file.split(".")[-1] if data_args.train_file is not None else data_args.validation_file.split(".")[-1] ) if extension == "txt": extension = "text" dataset_args["keep_linebreaks"] = data_args.keep_linebreaks raw_datasets = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, **dataset_args, ) # If no validation data is there, validation_split_percentage will be used to divide the dataset. if "validation" not in raw_datasets.keys(): raw_datasets["validation"] = load_dataset( extension, data_files=data_files, split=f"train[:{data_args.validation_split_percentage}%]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, **dataset_args, ) raw_datasets["train"] = load_dataset( extension, data_files=data_files, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, **dataset_args, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # endregion # region Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) # endregion # region Dataset preprocessing # First we tokenize all the texts. column_names = raw_datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] def tokenize_function(examples): return tokenizer(examples[text_column_name]) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on dataset", ) if data_args.block_size is None: block_size = tokenizer.model_max_length if block_size > 1024: logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --block_size xxx." ) block_size = 1024 else: if data_args.block_size > tokenizer.model_max_length: logger.warning( f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model" f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}." ) block_size = min(data_args.block_size, tokenizer.model_max_length) # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size. def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= block_size: total_length = (total_length // block_size) * block_size # Split by chunks of max_len. result = { k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items() } result["labels"] = result["input_ids"].copy() return result # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower # to preprocess. # # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map lm_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, desc=f"Grouping texts in chunks of {block_size}", ) train_dataset = lm_datasets["train"] if data_args.validation_file is not None: eval_dataset = lm_datasets["validation"] else: logger.info( f"Validation file not found: using {data_args.validation_split_percentage}% of the dataset as validation" " as provided in data_args" ) train_indices, val_indices = train_test_split( list(range(len(train_dataset))), test_size=data_args.validation_split_percentage / 100 ) eval_dataset = train_dataset.select(val_indices) train_dataset = train_dataset.select(train_indices) if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), min(3, len(train_dataset))): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # endregion with training_args.strategy.scope(): # region Prepare model if checkpoint is not None: model = TFAutoModelForCausalLM.from_pretrained(checkpoint, config=config) elif model_args.model_name_or_path: model = TFAutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, config=config) else: logger.info("Training new model from scratch") model = TFAutoModelForCausalLM.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # endregion # region TF Dataset preparation num_replicas = training_args.strategy.num_replicas_in_sync options = tf.data.Options() options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF # model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in # training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also # use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names # yourself if you use this method, whereas they are automatically inferred from the model input names when # using model.prepare_tf_dataset() # For more info see the docs: # https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset # https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset tf_train_dataset = model.prepare_tf_dataset( train_dataset, shuffle=True, batch_size=num_replicas * training_args.per_device_train_batch_size, ).with_options(options) tf_eval_dataset = model.prepare_tf_dataset( eval_dataset, shuffle=False, batch_size=num_replicas * training_args.per_device_eval_batch_size, drop_remainder=True, ).with_options(options) # endregion # region Optimizer and loss num_train_steps = len(tf_train_dataset) * int(training_args.num_train_epochs) if training_args.warmup_steps > 0: num_warmup_steps = training_args.warmup_steps elif training_args.warmup_ratio > 0: num_warmup_steps = int(num_train_steps * training_args.warmup_ratio) else: num_warmup_steps = 0 # Bias and layernorm weights are automatically excluded from the decay optimizer, lr_schedule = create_optimizer( init_lr=training_args.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, adam_beta1=training_args.adam_beta1, adam_beta2=training_args.adam_beta2, adam_epsilon=training_args.adam_epsilon, weight_decay_rate=training_args.weight_decay, adam_global_clipnorm=training_args.max_grad_norm, ) # no user-specified loss = will use the model internal loss model.compile(optimizer=optimizer, jit_compile=training_args.xla) # endregion # region Preparing push_to_hub and model card push_to_hub_model_id = training_args.push_to_hub_model_id model_name = model_args.model_name_or_path.split("/")[-1] if not push_to_hub_model_id: if data_args.dataset_name is not None: push_to_hub_model_id = f"{model_name}-finetuned-{data_args.dataset_name}" else: push_to_hub_model_id = f"{model_name}-finetuned-clm" model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"} if data_args.dataset_name is not None: model_card_kwargs["dataset_tags"] = data_args.dataset_name if data_args.dataset_config_name is not None: model_card_kwargs["dataset_args"] = data_args.dataset_config_name model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: model_card_kwargs["dataset"] = data_args.dataset_name if training_args.push_to_hub: callbacks = [ PushToHubCallback( output_dir=training_args.output_dir, model_id=push_to_hub_model_id, organization=training_args.push_to_hub_organization, token=training_args.push_to_hub_token, tokenizer=tokenizer, **model_card_kwargs, ) ] else: callbacks = [] # endregion # region Training and validation logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {training_args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}") logger.info(f" Total train batch size = {training_args.per_device_train_batch_size * num_replicas}") # For long training runs, you may wish to use the PushToHub() callback here to save intermediate checkpoints # to the Hugging Face Hub rather than just pushing the finished model. # See https://huggingface.co/docs/transformers/main_classes/keras_callbacks#transformers.PushToHubCallback history = model.fit( tf_train_dataset, validation_data=tf_eval_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks, ) train_loss = history.history["loss"][-1] try: train_perplexity = math.exp(train_loss) except OverflowError: train_perplexity = math.inf logger.info(f" Final train loss: {train_loss:.3f}") logger.info(f" Final train perplexity: {train_perplexity:.3f}") validation_loss = history.history["val_loss"][-1] try: validation_perplexity = math.exp(validation_loss) except OverflowError: validation_perplexity = math.inf logger.info(f" Final validation loss: {validation_loss:.3f}") logger.info(f" Final validation perplexity: {validation_perplexity:.3f}") if training_args.output_dir is not None: output_eval_file = os.path.join(training_args.output_dir, "all_results.json") results_dict = dict() results_dict["train_loss"] = train_loss results_dict["train_perplexity"] = train_perplexity results_dict["eval_loss"] = validation_loss results_dict["eval_perplexity"] = validation_perplexity with open(output_eval_file, "w") as writer: writer.write(json.dumps(results_dict)) # endregion if training_args.output_dir is not None and not training_args.push_to_hub: # If we're not pushing to hub, at least save a local copy when we're done model.save_pretrained(training_args.output_dir) if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/legacy/run_swag.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """BERT finetuning runner. Finetuning the library models for multiple choice on SWAG (Bert). """ import argparse import csv import glob import logging import os import random import numpy as np import torch from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange import transformers from transformers import ( WEIGHTS_NAME, AdamW, AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, get_linear_schedule_with_warmup, ) from transformers.trainer_utils import is_main_process try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) class SwagExample(object): """A single training/test example for the SWAG dataset.""" def __init__(self, swag_id, context_sentence, start_ending, ending_0, ending_1, ending_2, ending_3, label=None): self.swag_id = swag_id self.context_sentence = context_sentence self.start_ending = start_ending self.endings = [ ending_0, ending_1, ending_2, ending_3, ] self.label = label def __str__(self): return self.__repr__() def __repr__(self): attributes = [ "swag_id: {}".format(self.swag_id), "context_sentence: {}".format(self.context_sentence), "start_ending: {}".format(self.start_ending), "ending_0: {}".format(self.endings[0]), "ending_1: {}".format(self.endings[1]), "ending_2: {}".format(self.endings[2]), "ending_3: {}".format(self.endings[3]), ] if self.label is not None: attributes.append("label: {}".format(self.label)) return ", ".join(attributes) class InputFeatures(object): def __init__(self, example_id, choices_features, label): self.example_id = example_id self.choices_features = [ {"input_ids": input_ids, "input_mask": input_mask, "segment_ids": segment_ids} for _, input_ids, input_mask, segment_ids in choices_features ] self.label = label def read_swag_examples(input_file, is_training=True): with open(input_file, "r", encoding="utf-8") as f: lines = list(csv.reader(f)) if is_training and lines[0][-1] != "label": raise ValueError("For training, the input file must contain a label column.") examples = [ SwagExample( swag_id=line[2], context_sentence=line[4], start_ending=line[5], # in the swag dataset, the # common beginning of each # choice is stored in "sent2". ending_0=line[7], ending_1=line[8], ending_2=line[9], ending_3=line[10], label=int(line[11]) if is_training else None, ) for line in lines[1:] # we skip the line with the column names ] return examples def convert_examples_to_features(examples, tokenizer, max_seq_length, is_training): """Loads a data file into a list of `InputBatch`s.""" # Swag is a multiple choice task. To perform this task using Bert, # we will use the formatting proposed in "Improving Language # Understanding by Generative Pre-Training" and suggested by # @jacobdevlin-google in this issue # https://github.com/google-research/bert/issues/38. # # Each choice will correspond to a sample on which we run the # inference. For a given Swag example, we will create the 4 # following inputs: # - [CLS] context [SEP] choice_1 [SEP] # - [CLS] context [SEP] choice_2 [SEP] # - [CLS] context [SEP] choice_3 [SEP] # - [CLS] context [SEP] choice_4 [SEP] # The model will output a single value for each input. To get the # final decision of the model, we will run a softmax over these 4 # outputs. features = [] for example_index, example in tqdm(enumerate(examples)): context_tokens = tokenizer.tokenize(example.context_sentence) start_ending_tokens = tokenizer.tokenize(example.start_ending) choices_features = [] for ending_index, ending in enumerate(example.endings): # We create a copy of the context tokens in order to be # able to shrink it according to ending_tokens context_tokens_choice = context_tokens[:] ending_tokens = start_ending_tokens + tokenizer.tokenize(ending) # Modifies `context_tokens_choice` and `ending_tokens` in # place so that the total length is less than the # specified length. Account for [CLS], [SEP], [SEP] with # "- 3" _truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3) tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"] segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1) input_ids = tokenizer.convert_tokens_to_ids(tokens) input_mask = [1] * len(input_ids) # Zero-pad up to the sequence length. padding = [0] * (max_seq_length - len(input_ids)) input_ids += padding input_mask += padding segment_ids += padding assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length choices_features.append((tokens, input_ids, input_mask, segment_ids)) label = example.label if example_index < 5: logger.info("*** Example ***") logger.info("swag_id: {}".format(example.swag_id)) for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features): logger.info("choice: {}".format(choice_idx)) logger.info("tokens: {}".format(" ".join(tokens))) logger.info("input_ids: {}".format(" ".join(map(str, input_ids)))) logger.info("input_mask: {}".format(" ".join(map(str, input_mask)))) logger.info("segment_ids: {}".format(" ".join(map(str, segment_ids)))) if is_training: logger.info("label: {}".format(label)) features.append(InputFeatures(example_id=example.swag_id, choices_features=choices_features, label=label)) return features def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() def accuracy(out, labels): outputs = np.argmax(out, axis=1) return np.sum(outputs == labels) def select_field(features, field): return [[choice[field] for choice in feature.choices_features] for feature in features] def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False): if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Load data features from cache or dataset file input_file = args.predict_file if evaluate else args.train_file cached_features_file = os.path.join( os.path.dirname(input_file), "cached_{}_{}_{}".format( "dev" if evaluate else "train", list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), ), ) if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", input_file) examples = read_swag_examples(input_file) features = convert_examples_to_features(examples, tokenizer, args.max_seq_length, not evaluate) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor(select_field(features, "input_ids"), dtype=torch.long) all_input_mask = torch.tensor(select_field(features, "input_mask"), dtype=torch.long) all_segment_ids = torch.tensor(select_field(features, "segment_ids"), dtype=torch.long) all_label = torch.tensor([f.label for f in features], dtype=torch.long) if evaluate: dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label) else: dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label) if output_examples: return dataset, examples, features return dataset def train(args, train_dataset, model, tokenizer): """Train the model""" if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = 0 tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) set_seed(args) # Added here for reproductibility for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): model.train() batch = tuple(t.to(args.device) for t in batch) inputs = { "input_ids": batch[0], "attention_mask": batch[1], # 'token_type_ids': None if args.model_type == 'xlm' else batch[2], "token_type_ids": batch[2], "labels": batch[3], } # if args.model_type in ['xlnet', 'xlm']: # inputs.update({'cls_index': batch[5], # 'p_mask': batch[6]}) outputs = model(**inputs) loss = outputs[0] # model outputs are always tuple in transformers (see doc) if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Log metrics if ( args.local_rank == -1 and args.evaluate_during_training ): # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) for key, value in results.items(): tb_writer.add_scalar("eval_{}".format(key), value, global_step) tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step) tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step) logging_loss = tr_loss if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_vocabulary(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix=""): dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True) if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset) eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = { "input_ids": batch[0], "attention_mask": batch[1], # 'token_type_ids': None if args.model_type == 'xlm' else batch[2] # XLM don't use segment_ids "token_type_ids": batch[2], "labels": batch[3], } # if args.model_type in ['xlnet', 'xlm']: # inputs.update({'cls_index': batch[4], # 'p_mask': batch[5]}) outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] eval_loss += tmp_eval_loss.mean().item() logits = logits.detach().cpu().numpy() label_ids = inputs["labels"].to("cpu").numpy() tmp_eval_accuracy = accuracy(logits, label_ids) eval_accuracy += tmp_eval_accuracy nb_eval_steps += 1 nb_eval_examples += inputs["input_ids"].size(0) eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy} output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info("%s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) return result def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--train_file", default=None, type=str, required=True, help="SWAG csv for training. E.g., train.csv" ) parser.add_argument( "--predict_file", default=None, type=str, required=True, help="SWAG csv for predictions. E.g., val.csv or test.csv", ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pretrained model or model identifier from huggingface.co/models", ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints and predictions will be written.", ) # Other parameters parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name" ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--max_seq_length", default=384, type=int, help=( "The maximum total input sequence length after tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded." ), ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step." ) parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation." ) parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform." ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.") parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory" ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help=( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html" ), ) parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.") args = parser.parse_args() if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Set seed set_seed(args) # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path) tokenizer = AutoTokenizer.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, ) model = AutoModelForMultipleChoice.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config ) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Save the trained model and the tokenizer if args.local_rank == -1 or torch.distributed.get_rank() == 0: logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = AutoModelForMultipleChoice.from_pretrained(args.output_dir) tokenizer = AutoTokenizer.from_pretrained(args.output_dir) model.to(args.device) # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory results = {} if args.do_eval and args.local_rank in [-1, 0]: if args.do_train: checkpoints = [args.output_dir] else: # if do_train is False and do_eval is true, load model directly from pretrained. checkpoints = [args.model_name_or_path] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ) logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: # Reload the model global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" model = AutoModelForMultipleChoice.from_pretrained(checkpoint) tokenizer = AutoTokenizer.from_pretrained(checkpoint) model.to(args.device) # Evaluate result = evaluate(args, model, tokenizer, prefix=global_step) result = dict((k + ("_{}".format(global_step) if global_step else ""), v) for k, v in result.items()) results.update(result) logger.info("Results: {}".format(results)) return results if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """BERT finetuning runner. Finetuning the library models for multiple choice on SWAG (Bert). """ import argparse import csv import glob import logging import os import random import numpy as np import torch from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange import transformers from transformers import ( WEIGHTS_NAME, AdamW, AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, get_linear_schedule_with_warmup, ) from transformers.trainer_utils import is_main_process try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) class SwagExample(object): """A single training/test example for the SWAG dataset.""" def __init__(self, swag_id, context_sentence, start_ending, ending_0, ending_1, ending_2, ending_3, label=None): self.swag_id = swag_id self.context_sentence = context_sentence self.start_ending = start_ending self.endings = [ ending_0, ending_1, ending_2, ending_3, ] self.label = label def __str__(self): return self.__repr__() def __repr__(self): attributes = [ "swag_id: {}".format(self.swag_id), "context_sentence: {}".format(self.context_sentence), "start_ending: {}".format(self.start_ending), "ending_0: {}".format(self.endings[0]), "ending_1: {}".format(self.endings[1]), "ending_2: {}".format(self.endings[2]), "ending_3: {}".format(self.endings[3]), ] if self.label is not None: attributes.append("label: {}".format(self.label)) return ", ".join(attributes) class InputFeatures(object): def __init__(self, example_id, choices_features, label): self.example_id = example_id self.choices_features = [ {"input_ids": input_ids, "input_mask": input_mask, "segment_ids": segment_ids} for _, input_ids, input_mask, segment_ids in choices_features ] self.label = label def read_swag_examples(input_file, is_training=True): with open(input_file, "r", encoding="utf-8") as f: lines = list(csv.reader(f)) if is_training and lines[0][-1] != "label": raise ValueError("For training, the input file must contain a label column.") examples = [ SwagExample( swag_id=line[2], context_sentence=line[4], start_ending=line[5], # in the swag dataset, the # common beginning of each # choice is stored in "sent2". ending_0=line[7], ending_1=line[8], ending_2=line[9], ending_3=line[10], label=int(line[11]) if is_training else None, ) for line in lines[1:] # we skip the line with the column names ] return examples def convert_examples_to_features(examples, tokenizer, max_seq_length, is_training): """Loads a data file into a list of `InputBatch`s.""" # Swag is a multiple choice task. To perform this task using Bert, # we will use the formatting proposed in "Improving Language # Understanding by Generative Pre-Training" and suggested by # @jacobdevlin-google in this issue # https://github.com/google-research/bert/issues/38. # # Each choice will correspond to a sample on which we run the # inference. For a given Swag example, we will create the 4 # following inputs: # - [CLS] context [SEP] choice_1 [SEP] # - [CLS] context [SEP] choice_2 [SEP] # - [CLS] context [SEP] choice_3 [SEP] # - [CLS] context [SEP] choice_4 [SEP] # The model will output a single value for each input. To get the # final decision of the model, we will run a softmax over these 4 # outputs. features = [] for example_index, example in tqdm(enumerate(examples)): context_tokens = tokenizer.tokenize(example.context_sentence) start_ending_tokens = tokenizer.tokenize(example.start_ending) choices_features = [] for ending_index, ending in enumerate(example.endings): # We create a copy of the context tokens in order to be # able to shrink it according to ending_tokens context_tokens_choice = context_tokens[:] ending_tokens = start_ending_tokens + tokenizer.tokenize(ending) # Modifies `context_tokens_choice` and `ending_tokens` in # place so that the total length is less than the # specified length. Account for [CLS], [SEP], [SEP] with # "- 3" _truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3) tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"] segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1) input_ids = tokenizer.convert_tokens_to_ids(tokens) input_mask = [1] * len(input_ids) # Zero-pad up to the sequence length. padding = [0] * (max_seq_length - len(input_ids)) input_ids += padding input_mask += padding segment_ids += padding assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length choices_features.append((tokens, input_ids, input_mask, segment_ids)) label = example.label if example_index < 5: logger.info("*** Example ***") logger.info("swag_id: {}".format(example.swag_id)) for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features): logger.info("choice: {}".format(choice_idx)) logger.info("tokens: {}".format(" ".join(tokens))) logger.info("input_ids: {}".format(" ".join(map(str, input_ids)))) logger.info("input_mask: {}".format(" ".join(map(str, input_mask)))) logger.info("segment_ids: {}".format(" ".join(map(str, segment_ids)))) if is_training: logger.info("label: {}".format(label)) features.append(InputFeatures(example_id=example.swag_id, choices_features=choices_features, label=label)) return features def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() def accuracy(out, labels): outputs = np.argmax(out, axis=1) return np.sum(outputs == labels) def select_field(features, field): return [[choice[field] for choice in feature.choices_features] for feature in features] def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False): if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Load data features from cache or dataset file input_file = args.predict_file if evaluate else args.train_file cached_features_file = os.path.join( os.path.dirname(input_file), "cached_{}_{}_{}".format( "dev" if evaluate else "train", list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), ), ) if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", input_file) examples = read_swag_examples(input_file) features = convert_examples_to_features(examples, tokenizer, args.max_seq_length, not evaluate) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor(select_field(features, "input_ids"), dtype=torch.long) all_input_mask = torch.tensor(select_field(features, "input_mask"), dtype=torch.long) all_segment_ids = torch.tensor(select_field(features, "segment_ids"), dtype=torch.long) all_label = torch.tensor([f.label for f in features], dtype=torch.long) if evaluate: dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label) else: dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label) if output_examples: return dataset, examples, features return dataset def train(args, train_dataset, model, tokenizer): """Train the model""" if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = 0 tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) set_seed(args) # Added here for reproductibility for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): model.train() batch = tuple(t.to(args.device) for t in batch) inputs = { "input_ids": batch[0], "attention_mask": batch[1], # 'token_type_ids': None if args.model_type == 'xlm' else batch[2], "token_type_ids": batch[2], "labels": batch[3], } # if args.model_type in ['xlnet', 'xlm']: # inputs.update({'cls_index': batch[5], # 'p_mask': batch[6]}) outputs = model(**inputs) loss = outputs[0] # model outputs are always tuple in transformers (see doc) if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Log metrics if ( args.local_rank == -1 and args.evaluate_during_training ): # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) for key, value in results.items(): tb_writer.add_scalar("eval_{}".format(key), value, global_step) tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step) tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step) logging_loss = tr_loss if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_vocabulary(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix=""): dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True) if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset) eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = { "input_ids": batch[0], "attention_mask": batch[1], # 'token_type_ids': None if args.model_type == 'xlm' else batch[2] # XLM don't use segment_ids "token_type_ids": batch[2], "labels": batch[3], } # if args.model_type in ['xlnet', 'xlm']: # inputs.update({'cls_index': batch[4], # 'p_mask': batch[5]}) outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] eval_loss += tmp_eval_loss.mean().item() logits = logits.detach().cpu().numpy() label_ids = inputs["labels"].to("cpu").numpy() tmp_eval_accuracy = accuracy(logits, label_ids) eval_accuracy += tmp_eval_accuracy nb_eval_steps += 1 nb_eval_examples += inputs["input_ids"].size(0) eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy} output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info("%s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) return result def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--train_file", default=None, type=str, required=True, help="SWAG csv for training. E.g., train.csv" ) parser.add_argument( "--predict_file", default=None, type=str, required=True, help="SWAG csv for predictions. E.g., val.csv or test.csv", ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pretrained model or model identifier from huggingface.co/models", ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints and predictions will be written.", ) # Other parameters parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name" ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--max_seq_length", default=384, type=int, help=( "The maximum total input sequence length after tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded." ), ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step." ) parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation." ) parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform." ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.") parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory" ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help=( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html" ), ) parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.") args = parser.parse_args() if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Set seed set_seed(args) # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path) tokenizer = AutoTokenizer.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, ) model = AutoModelForMultipleChoice.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config ) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Save the trained model and the tokenizer if args.local_rank == -1 or torch.distributed.get_rank() == 0: logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = AutoModelForMultipleChoice.from_pretrained(args.output_dir) tokenizer = AutoTokenizer.from_pretrained(args.output_dir) model.to(args.device) # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory results = {} if args.do_eval and args.local_rank in [-1, 0]: if args.do_train: checkpoints = [args.output_dir] else: # if do_train is False and do_eval is true, load model directly from pretrained. checkpoints = [args.model_name_or_path] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ) logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: # Reload the model global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" model = AutoModelForMultipleChoice.from_pretrained(checkpoint) tokenizer = AutoTokenizer.from_pretrained(checkpoint) model.to(args.device) # Evaluate result = evaluate(args, model, tokenizer, prefix=global_step) result = dict((k + ("_{}".format(global_step) if global_step else ""), v) for k, v in result.items()) results.update(result) logger.info("Results: {}".format(results)) return results if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/donut/configuration_donut_swin.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Donut Swin Transformer model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP = { "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json", # See all Donut models at https://huggingface.co/models?filter=donut-swin } class DonutSwinConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DonutSwinModel`]. It is used to instantiate a Donut model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Donut [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 4): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. embed_dim (`int`, *optional*, defaults to 96): Dimensionality of patch embedding. depths (`list(int)`, *optional*, defaults to [2, 2, 6, 2]): Depth of each layer in the Transformer encoder. num_heads (`list(int)`, *optional*, defaults to [3, 6, 12, 24]): Number of attention heads in each layer of the Transformer encoder. window_size (`int`, *optional*, defaults to 7): Size of windows. mlp_ratio (`float`, *optional*, defaults to 4.0): Ratio of MLP hidden dimensionality to embedding dimensionality. qkv_bias (`bool`, *optional*, defaults to True): Whether or not a learnable bias should be added to the queries, keys and values. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings and encoder. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. drop_path_rate (`float`, *optional*, defaults to 0.1): Stochastic depth rate. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. use_absolute_embeddings (`bool`, *optional*, defaults to False): Whether or not to add absolute position embeddings to the patch embeddings. patch_norm (`bool`, *optional*, defaults to True): Whether or not to add layer normalization after patch embedding. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. Example: ```python >>> from transformers import DonutSwinConfig, DonutSwinModel >>> # Initializing a Donut naver-clova-ix/donut-base style configuration >>> configuration = DonutSwinConfig() >>> # Randomly initializing a model from the naver-clova-ix/donut-base style configuration >>> model = DonutSwinModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "donut-swin" attribute_map = { "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers", } def __init__( self, image_size=224, patch_size=4, num_channels=3, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4.0, qkv_bias=True, hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, drop_path_rate=0.1, hidden_act="gelu", use_absolute_embeddings=False, patch_norm=True, initializer_range=0.02, layer_norm_eps=1e-5, **kwargs ): super().__init__(**kwargs) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.embed_dim = embed_dim self.depths = depths self.num_layers = len(depths) self.num_heads = num_heads self.window_size = window_size self.mlp_ratio = mlp_ratio self.qkv_bias = qkv_bias self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.drop_path_rate = drop_path_rate self.hidden_act = hidden_act self.use_absolute_embeddings = use_absolute_embeddings self.path_norm = patch_norm self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel # this indicates the channel dimension after the last stage of the model self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Donut Swin Transformer model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP = { "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json", # See all Donut models at https://huggingface.co/models?filter=donut-swin } class DonutSwinConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DonutSwinModel`]. It is used to instantiate a Donut model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Donut [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 4): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. embed_dim (`int`, *optional*, defaults to 96): Dimensionality of patch embedding. depths (`list(int)`, *optional*, defaults to [2, 2, 6, 2]): Depth of each layer in the Transformer encoder. num_heads (`list(int)`, *optional*, defaults to [3, 6, 12, 24]): Number of attention heads in each layer of the Transformer encoder. window_size (`int`, *optional*, defaults to 7): Size of windows. mlp_ratio (`float`, *optional*, defaults to 4.0): Ratio of MLP hidden dimensionality to embedding dimensionality. qkv_bias (`bool`, *optional*, defaults to True): Whether or not a learnable bias should be added to the queries, keys and values. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings and encoder. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. drop_path_rate (`float`, *optional*, defaults to 0.1): Stochastic depth rate. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. use_absolute_embeddings (`bool`, *optional*, defaults to False): Whether or not to add absolute position embeddings to the patch embeddings. patch_norm (`bool`, *optional*, defaults to True): Whether or not to add layer normalization after patch embedding. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. Example: ```python >>> from transformers import DonutSwinConfig, DonutSwinModel >>> # Initializing a Donut naver-clova-ix/donut-base style configuration >>> configuration = DonutSwinConfig() >>> # Randomly initializing a model from the naver-clova-ix/donut-base style configuration >>> model = DonutSwinModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "donut-swin" attribute_map = { "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers", } def __init__( self, image_size=224, patch_size=4, num_channels=3, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4.0, qkv_bias=True, hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, drop_path_rate=0.1, hidden_act="gelu", use_absolute_embeddings=False, patch_norm=True, initializer_range=0.02, layer_norm_eps=1e-5, **kwargs ): super().__init__(**kwargs) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.embed_dim = embed_dim self.depths = depths self.num_layers = len(depths) self.num_heads = num_heads self.window_size = window_size self.mlp_ratio = mlp_ratio self.qkv_bias = qkv_bias self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.drop_path_rate = drop_path_rate self.hidden_act = hidden_act self.use_absolute_embeddings = use_absolute_embeddings self.path_norm = patch_norm self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel # this indicates the channel dimension after the last stage of the model self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/herbert/tokenization_herbert_fast.py

# coding=utf-8 # Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List, Optional, Tuple from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_herbert import HerbertTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json" }, "merges_file": { "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt" }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"allegro/herbert-base-cased": 514} PRETRAINED_INIT_CONFIGURATION = {} class HerbertTokenizerFast(PreTrainedTokenizerFast): """ Construct a "Fast" BPE tokenizer for HerBERT (backed by HuggingFace's *tokenizers* library). Peculiarities: - uses BERT's pre-tokenizer: BertPreTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of a punctuation character will be treated separately. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the methods. Users should refer to the superclass for more information regarding methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = HerbertTokenizer def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", sep_token="</s>", **kwargs ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, sep_token=sep_token, **kwargs, ) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An HerBERT, like BERT sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ cls = [self.cls_token_id] sep = [self.sep_token_id] if token_ids_1 is None: return cls + token_ids_0 + sep return cls + token_ids_0 + sep + token_ids_1 + sep def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. HerBERT, like BERT sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files)

# coding=utf-8 # Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List, Optional, Tuple from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_herbert import HerbertTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json" }, "merges_file": { "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt" }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"allegro/herbert-base-cased": 514} PRETRAINED_INIT_CONFIGURATION = {} class HerbertTokenizerFast(PreTrainedTokenizerFast): """ Construct a "Fast" BPE tokenizer for HerBERT (backed by HuggingFace's *tokenizers* library). Peculiarities: - uses BERT's pre-tokenizer: BertPreTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of a punctuation character will be treated separately. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the methods. Users should refer to the superclass for more information regarding methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = HerbertTokenizer def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", sep_token="</s>", **kwargs ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, sep_token=sep_token, **kwargs, ) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An HerBERT, like BERT sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ cls = [self.cls_token_id] sep = [self.sep_token_id] if token_ids_1 is None: return cls + token_ids_0 + sep return cls + token_ids_0 + sep + token_ids_1 + sep def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. HerBERT, like BERT sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./.github/workflows/TROUBLESHOOT.md

# Troubleshooting This is a document explaining how to deal with various issues on github-actions self-hosted CI. The entries may include actually solutions or pointers to Issues that cover those. ## GitHub Actions (self-hosted CI) * Deepspeed - if jit build hangs, clear out `rm -rf ~/.cache/torch_extensions/` reference: https://github.com/huggingface/transformers/pull/12723

# Troubleshooting This is a document explaining how to deal with various issues on github-actions self-hosted CI. The entries may include actually solutions or pointers to Issues that cover those. ## GitHub Actions (self-hosted CI) * Deepspeed - if jit build hangs, clear out `rm -rf ~/.cache/torch_extensions/` reference: https://github.com/huggingface/transformers/pull/12723

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/electra/test_modeling_electra.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import ElectraConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, ElectraForCausalLM, ElectraForMaskedLM, ElectraForMultipleChoice, ElectraForPreTraining, ElectraForQuestionAnswering, ElectraForSequenceClassification, ElectraForTokenClassification, ElectraModel, ) from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST class ElectraModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1) config = self.get_config() return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ) def get_config(self): return ElectraConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, _, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_electra_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): model = ElectraModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_electra_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = ElectraModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_electra_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): model = ElectraForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_electra_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = ElectraForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_electra_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_labels = self.num_labels model = ElectraForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_electra_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_labels = self.num_labels model = ElectraForPreTraining(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length)) def create_and_check_electra_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_labels = self.num_labels model = ElectraForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_electra_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): model = ElectraForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_electra_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_choices = self.num_choices model = ElectraForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class ElectraModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( ( ElectraModel, ElectraForPreTraining, ElectraForMaskedLM, ElectraForCausalLM, ElectraForMultipleChoice, ElectraForTokenClassification, ElectraForSequenceClassification, ElectraForQuestionAnswering, ) if is_torch_available() else () ) fx_compatible = True # special case for ForPreTraining model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = ElectraModelTester(self) self.config_tester = ConfigTester(self, config_class=ElectraConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_electra_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_model(*config_and_inputs) def test_electra_model_as_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_electra_model_as_decoder(*config_and_inputs) def test_electra_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_electra_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_masked_lm(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_token_classification(*config_and_inputs) def test_for_pre_training(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_pretraining(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_sequence_classification(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_question_answering(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_multiple_choice(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ElectraModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_electra_for_causal_lm(*config_and_inputs) @require_torch class ElectraModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head_absolute_embedding(self): model = ElectraModel.from_pretrained("google/electra-small-discriminator") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 256)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import ElectraConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, ElectraForCausalLM, ElectraForMaskedLM, ElectraForMultipleChoice, ElectraForPreTraining, ElectraForQuestionAnswering, ElectraForSequenceClassification, ElectraForTokenClassification, ElectraModel, ) from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST class ElectraModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1) config = self.get_config() return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ) def get_config(self): return ElectraConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, _, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_electra_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): model = ElectraModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_electra_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = ElectraModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_electra_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): model = ElectraForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_electra_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = ElectraForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_electra_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_labels = self.num_labels model = ElectraForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_electra_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_labels = self.num_labels model = ElectraForPreTraining(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length)) def create_and_check_electra_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_labels = self.num_labels model = ElectraForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_electra_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): model = ElectraForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_electra_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_choices = self.num_choices model = ElectraForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class ElectraModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( ( ElectraModel, ElectraForPreTraining, ElectraForMaskedLM, ElectraForCausalLM, ElectraForMultipleChoice, ElectraForTokenClassification, ElectraForSequenceClassification, ElectraForQuestionAnswering, ) if is_torch_available() else () ) fx_compatible = True # special case for ForPreTraining model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = ElectraModelTester(self) self.config_tester = ConfigTester(self, config_class=ElectraConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_electra_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_model(*config_and_inputs) def test_electra_model_as_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_electra_model_as_decoder(*config_and_inputs) def test_electra_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_electra_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_masked_lm(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_token_classification(*config_and_inputs) def test_for_pre_training(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_pretraining(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_sequence_classification(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_question_answering(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_electra_for_multiple_choice(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ElectraModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_electra_for_causal_lm(*config_and_inputs) @require_torch class ElectraModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head_absolute_embedding(self): model = ElectraModel.from_pretrained("google/electra-small-discriminator") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 256)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/deberta/test_tokenization_deberta.py

# coding=utf-8 # Copyright 2019 Hugging Face inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import unittest from transformers import DebertaTokenizer, DebertaTokenizerFast from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES from transformers.testing_utils import slow from ...test_tokenization_common import TokenizerTesterMixin class DebertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = DebertaTokenizer test_rust_tokenizer = True rust_tokenizer_class = DebertaTokenizerFast def setUp(self): super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "[UNK]", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] self.special_tokens_map = {"unk_token": "[UNK]"} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) def get_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): input_text = "lower newer" output_text = "lower newer" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.get_tokenizer() text = "lower newer" bpe_tokens = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"] tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_token_type_ids(self): tokenizer = self.get_tokenizer() tokd = tokenizer("Hello", "World") expected_token_type_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1] self.assertListEqual(tokd["token_type_ids"], expected_token_type_ids) @slow def test_sequence_builders(self): tokenizer = self.tokenizer_class.from_pretrained("microsoft/deberta-base") text = tokenizer.encode("sequence builders", add_special_tokens=False) text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False) encoded_text_from_decode = tokenizer.encode( "sequence builders", add_special_tokens=True, add_prefix_space=False ) encoded_pair_from_decode = tokenizer.encode( "sequence builders", "multi-sequence build", add_special_tokens=True, add_prefix_space=False ) encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) assert encoded_sentence == encoded_text_from_decode assert encoded_pair == encoded_pair_from_decode @slow def test_tokenizer_integration(self): tokenizer_classes = [self.tokenizer_class] if self.test_rust_tokenizer: tokenizer_classes.append(self.rust_tokenizer_class) for tokenizer_class in tokenizer_classes: tokenizer = tokenizer_class.from_pretrained("microsoft/deberta-base") sequences = [ "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations", "ALBERT incorporates two parameter reduction techniques", "The first one is a factorized embedding parameterization. By decomposing the large vocabulary" " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of" " vocabulary embedding.", ] encoding = tokenizer(sequences, padding=True) decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]] # fmt: off expected_encoding = { 'input_ids': [ [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2] ], 'token_type_ids': [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ], 'attention_mask': [ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] ] } # fmt: on expected_decoded_sequence = [ "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations", "ALBERT incorporates two parameter reduction techniques", "The first one is a factorized embedding parameterization. By decomposing the large vocabulary" " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of" " vocabulary embedding.", ] self.assertDictEqual(encoding.data, expected_encoding) for expected, decoded in zip(expected_decoded_sequence, decoded_sequences): self.assertEqual(expected, decoded)

# coding=utf-8 # Copyright 2019 Hugging Face inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import unittest from transformers import DebertaTokenizer, DebertaTokenizerFast from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES from transformers.testing_utils import slow from ...test_tokenization_common import TokenizerTesterMixin class DebertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = DebertaTokenizer test_rust_tokenizer = True rust_tokenizer_class = DebertaTokenizerFast def setUp(self): super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "[UNK]", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] self.special_tokens_map = {"unk_token": "[UNK]"} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) def get_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): input_text = "lower newer" output_text = "lower newer" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.get_tokenizer() text = "lower newer" bpe_tokens = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"] tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_token_type_ids(self): tokenizer = self.get_tokenizer() tokd = tokenizer("Hello", "World") expected_token_type_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1] self.assertListEqual(tokd["token_type_ids"], expected_token_type_ids) @slow def test_sequence_builders(self): tokenizer = self.tokenizer_class.from_pretrained("microsoft/deberta-base") text = tokenizer.encode("sequence builders", add_special_tokens=False) text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False) encoded_text_from_decode = tokenizer.encode( "sequence builders", add_special_tokens=True, add_prefix_space=False ) encoded_pair_from_decode = tokenizer.encode( "sequence builders", "multi-sequence build", add_special_tokens=True, add_prefix_space=False ) encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) assert encoded_sentence == encoded_text_from_decode assert encoded_pair == encoded_pair_from_decode @slow def test_tokenizer_integration(self): tokenizer_classes = [self.tokenizer_class] if self.test_rust_tokenizer: tokenizer_classes.append(self.rust_tokenizer_class) for tokenizer_class in tokenizer_classes: tokenizer = tokenizer_class.from_pretrained("microsoft/deberta-base") sequences = [ "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations", "ALBERT incorporates two parameter reduction techniques", "The first one is a factorized embedding parameterization. By decomposing the large vocabulary" " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of" " vocabulary embedding.", ] encoding = tokenizer(sequences, padding=True) decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]] # fmt: off expected_encoding = { 'input_ids': [ [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2] ], 'token_type_ids': [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ], 'attention_mask': [ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] ] } # fmt: on expected_decoded_sequence = [ "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations", "ALBERT incorporates two parameter reduction techniques", "The first one is a factorized embedding parameterization. By decomposing the large vocabulary" " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of" " vocabulary embedding.", ] self.assertDictEqual(encoding.data, expected_encoding) for expected, decoded in zip(expected_decoded_sequence, decoded_sequences): self.assertEqual(expected, decoded)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/esm/configuration_esm.py

# coding=utf-8 # Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ ESM model configuration""" from dataclasses import asdict, dataclass from typing import Optional from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) # TODO Update this ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json", # See all ESM models at https://huggingface.co/models?filter=esm } class EsmConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the ESM [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*): Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ESMModel`]. mask_token_id (`int`, *optional*): The index of the mask token in the vocabulary. This must be included in the config because of the "mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens. pad_token_id (`int`, *optional*): The index of the padding token in the vocabulary. This must be included in the config because certain parts of the ESM code use this instead of the attention mask. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 1026): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. classifier_dropout (`float`, *optional*): The dropout ratio for the classification head. emb_layer_norm_before (`bool`, *optional*): Whether to apply layer normalization after embeddings but before the main stem of the network. token_dropout (`bool`, defaults to `False`): When this is enabled, masked tokens are treated as if they had been dropped out by input dropout. Examples: ```python >>> from transformers import EsmModel, EsmConfig >>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig() >>> # Initializing a model from the configuration >>> model = ESMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "esm" def __init__( self, vocab_size=None, mask_token_id=None, pad_token_id=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=1026, initializer_range=0.02, layer_norm_eps=1e-12, position_embedding_type="absolute", use_cache=True, classifier_dropout=None, emb_layer_norm_before=None, token_dropout=False, is_folding_model=False, esmfold_config=None, vocab_list=None, **kwargs ): super().__init__(pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.position_embedding_type = position_embedding_type self.use_cache = use_cache self.classifier_dropout = classifier_dropout self.emb_layer_norm_before = emb_layer_norm_before self.token_dropout = token_dropout self.is_folding_model = is_folding_model if is_folding_model: if esmfold_config is None: logger.info("No esmfold_config supplied for folding model, using default values.") esmfold_config = EsmFoldConfig() elif isinstance(esmfold_config, dict): esmfold_config = EsmFoldConfig(**esmfold_config) self.esmfold_config = esmfold_config if vocab_list is None: logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!") self.vocab_list = get_default_vocab_list() else: self.vocab_list = vocab_list else: self.esmfold_config = None self.vocab_list = None if self.esmfold_config is not None and getattr(self.esmfold_config, "use_esm_attn_map", False): raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!") def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = super().to_dict() if isinstance(self.esmfold_config, EsmFoldConfig): output["esmfold_config"] = self.esmfold_config.to_dict() return output @dataclass class EsmFoldConfig: esm_type: str = None fp16_esm: bool = True use_esm_attn_map: bool = False esm_ablate_pairwise: bool = False esm_ablate_sequence: bool = False esm_input_dropout: float = 0 embed_aa: bool = True bypass_lm: bool = False lddt_head_hid_dim: int = 128 trunk: "TrunkConfig" = None def __post_init__(self): if self.trunk is None: self.trunk = TrunkConfig() elif isinstance(self.trunk, dict): self.trunk = TrunkConfig(**self.trunk) def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = asdict(self) output["trunk"] = self.trunk.to_dict() return output @dataclass class TrunkConfig: num_blocks: int = 48 sequence_state_dim: int = 1024 pairwise_state_dim: int = 128 sequence_head_width: int = 32 pairwise_head_width: int = 32 position_bins: int = 32 dropout: float = 0 layer_drop: float = 0 cpu_grad_checkpoint: bool = False max_recycles: int = 4 chunk_size: Optional[int] = 128 structure_module: "StructureModuleConfig" = None def __post_init__(self): if self.structure_module is None: self.structure_module = StructureModuleConfig() elif isinstance(self.structure_module, dict): self.structure_module = StructureModuleConfig(**self.structure_module) if self.max_recycles <= 0: raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}.") if self.sequence_state_dim % self.sequence_state_dim != 0: raise ValueError( "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got" f" {self.sequence_state_dim} and {self.sequence_state_dim}." ) if self.pairwise_state_dim % self.pairwise_state_dim != 0: raise ValueError( "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got" f" {self.pairwise_state_dim} and {self.pairwise_state_dim}." ) sequence_num_heads = self.sequence_state_dim // self.sequence_head_width pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width: raise ValueError( "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got" f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}." ) if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width: raise ValueError( "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got" f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}." ) if self.pairwise_state_dim % 2 != 0: raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.") if self.dropout >= 0.4: raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}.") def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = asdict(self) output["structure_module"] = self.structure_module.to_dict() return output @dataclass class StructureModuleConfig: """ Args: sequence_dim: Single representation channel dimension pairwise_dim: Pair representation channel dimension ipa_dim: IPA hidden channel dimension resnet_dim: Angle resnet (Alg. 23 lines 11-14) hidden channel dimension num_heads_ipa: Number of IPA heads num_qk_points: Number of query/key points to generate during IPA num_v_points: Number of value points to generate during IPA dropout_rate: Dropout rate used throughout the layer num_blocks: Number of structure module blocks num_transition_layers: Number of layers in the single representation transition (Alg. 23 lines 8-9) num_resnet_blocks: Number of blocks in the angle resnet num_angles: Number of angles to generate in the angle resnet trans_scale_factor: Scale of single representation transition hidden dimension epsilon: Small number used in angle resnet normalization inf: Large number used for attention masking """ sequence_dim: int = 384 pairwise_dim: int = 128 ipa_dim: int = 16 resnet_dim: int = 128 num_heads_ipa: int = 12 num_qk_points: int = 4 num_v_points: int = 8 dropout_rate: float = 0.1 num_blocks: int = 8 num_transition_layers: int = 1 num_resnet_blocks: int = 2 num_angles: int = 7 trans_scale_factor: int = 10 epsilon: float = 1e-8 inf: float = 1e5 def to_dict(self): return asdict(self) def get_default_vocab_list(): return ( "<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>", )

# coding=utf-8 # Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ ESM model configuration""" from dataclasses import asdict, dataclass from typing import Optional from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) # TODO Update this ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json", # See all ESM models at https://huggingface.co/models?filter=esm } class EsmConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the ESM [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*): Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ESMModel`]. mask_token_id (`int`, *optional*): The index of the mask token in the vocabulary. This must be included in the config because of the "mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens. pad_token_id (`int`, *optional*): The index of the padding token in the vocabulary. This must be included in the config because certain parts of the ESM code use this instead of the attention mask. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 1026): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. classifier_dropout (`float`, *optional*): The dropout ratio for the classification head. emb_layer_norm_before (`bool`, *optional*): Whether to apply layer normalization after embeddings but before the main stem of the network. token_dropout (`bool`, defaults to `False`): When this is enabled, masked tokens are treated as if they had been dropped out by input dropout. Examples: ```python >>> from transformers import EsmModel, EsmConfig >>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig() >>> # Initializing a model from the configuration >>> model = ESMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "esm" def __init__( self, vocab_size=None, mask_token_id=None, pad_token_id=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=1026, initializer_range=0.02, layer_norm_eps=1e-12, position_embedding_type="absolute", use_cache=True, classifier_dropout=None, emb_layer_norm_before=None, token_dropout=False, is_folding_model=False, esmfold_config=None, vocab_list=None, **kwargs ): super().__init__(pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.position_embedding_type = position_embedding_type self.use_cache = use_cache self.classifier_dropout = classifier_dropout self.emb_layer_norm_before = emb_layer_norm_before self.token_dropout = token_dropout self.is_folding_model = is_folding_model if is_folding_model: if esmfold_config is None: logger.info("No esmfold_config supplied for folding model, using default values.") esmfold_config = EsmFoldConfig() elif isinstance(esmfold_config, dict): esmfold_config = EsmFoldConfig(**esmfold_config) self.esmfold_config = esmfold_config if vocab_list is None: logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!") self.vocab_list = get_default_vocab_list() else: self.vocab_list = vocab_list else: self.esmfold_config = None self.vocab_list = None if self.esmfold_config is not None and getattr(self.esmfold_config, "use_esm_attn_map", False): raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!") def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = super().to_dict() if isinstance(self.esmfold_config, EsmFoldConfig): output["esmfold_config"] = self.esmfold_config.to_dict() return output @dataclass class EsmFoldConfig: esm_type: str = None fp16_esm: bool = True use_esm_attn_map: bool = False esm_ablate_pairwise: bool = False esm_ablate_sequence: bool = False esm_input_dropout: float = 0 embed_aa: bool = True bypass_lm: bool = False lddt_head_hid_dim: int = 128 trunk: "TrunkConfig" = None def __post_init__(self): if self.trunk is None: self.trunk = TrunkConfig() elif isinstance(self.trunk, dict): self.trunk = TrunkConfig(**self.trunk) def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = asdict(self) output["trunk"] = self.trunk.to_dict() return output @dataclass class TrunkConfig: num_blocks: int = 48 sequence_state_dim: int = 1024 pairwise_state_dim: int = 128 sequence_head_width: int = 32 pairwise_head_width: int = 32 position_bins: int = 32 dropout: float = 0 layer_drop: float = 0 cpu_grad_checkpoint: bool = False max_recycles: int = 4 chunk_size: Optional[int] = 128 structure_module: "StructureModuleConfig" = None def __post_init__(self): if self.structure_module is None: self.structure_module = StructureModuleConfig() elif isinstance(self.structure_module, dict): self.structure_module = StructureModuleConfig(**self.structure_module) if self.max_recycles <= 0: raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}.") if self.sequence_state_dim % self.sequence_state_dim != 0: raise ValueError( "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got" f" {self.sequence_state_dim} and {self.sequence_state_dim}." ) if self.pairwise_state_dim % self.pairwise_state_dim != 0: raise ValueError( "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got" f" {self.pairwise_state_dim} and {self.pairwise_state_dim}." ) sequence_num_heads = self.sequence_state_dim // self.sequence_head_width pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width: raise ValueError( "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got" f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}." ) if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width: raise ValueError( "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got" f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}." ) if self.pairwise_state_dim % 2 != 0: raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.") if self.dropout >= 0.4: raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}.") def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = asdict(self) output["structure_module"] = self.structure_module.to_dict() return output @dataclass class StructureModuleConfig: """ Args: sequence_dim: Single representation channel dimension pairwise_dim: Pair representation channel dimension ipa_dim: IPA hidden channel dimension resnet_dim: Angle resnet (Alg. 23 lines 11-14) hidden channel dimension num_heads_ipa: Number of IPA heads num_qk_points: Number of query/key points to generate during IPA num_v_points: Number of value points to generate during IPA dropout_rate: Dropout rate used throughout the layer num_blocks: Number of structure module blocks num_transition_layers: Number of layers in the single representation transition (Alg. 23 lines 8-9) num_resnet_blocks: Number of blocks in the angle resnet num_angles: Number of angles to generate in the angle resnet trans_scale_factor: Scale of single representation transition hidden dimension epsilon: Small number used in angle resnet normalization inf: Large number used for attention masking """ sequence_dim: int = 384 pairwise_dim: int = 128 ipa_dim: int = 16 resnet_dim: int = 128 num_heads_ipa: int = 12 num_qk_points: int = 4 num_v_points: int = 8 dropout_rate: float = 0.1 num_blocks: int = 8 num_transition_layers: int = 1 num_resnet_blocks: int = 2 num_angles: int = 7 trans_scale_factor: int = 10 epsilon: float = 1e-8 inf: float = 1e5 def to_dict(self): return asdict(self) def get_default_vocab_list(): return ( "<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>", )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./.github/workflows/self-past-caller.yml

name: Self-hosted runner (past-ci-caller) on: push: branches: - run-past-ci* jobs: run_past_ci_pytorch_1-11: name: PyTorch 1.11 if: always() uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.11" secrets: inherit run_past_ci_pytorch_1-10: name: PyTorch 1.10 if: always() needs: [run_past_ci_pytorch_1-11] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.10" secrets: inherit run_past_ci_pytorch_1-9: name: PyTorch 1.9 if: always() needs: [run_past_ci_pytorch_1-10] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.9" secrets: inherit run_past_ci_pytorch_1-8: name: PyTorch 1.8 if: always() needs: [run_past_ci_pytorch_1-9] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.8" secrets: inherit run_past_ci_pytorch_1-7: name: PyTorch 1.7 if: always() needs: [run_past_ci_pytorch_1-8] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.7" secrets: inherit run_past_ci_pytorch_1-6: name: PyTorch 1.6 if: always() needs: [run_past_ci_pytorch_1-7] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.6" secrets: inherit run_past_ci_pytorch_1-5: name: PyTorch 1.5 if: always() needs: [run_past_ci_pytorch_1-6] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.5" secrets: inherit run_past_ci_pytorch_1-4: name: PyTorch 1.4 if: always() needs: [run_past_ci_pytorch_1-5] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.4" secrets: inherit run_past_ci_tensorflow_2-8: name: TensorFlow 2.8 if: always() needs: [run_past_ci_pytorch_1-4] uses: ./.github/workflows/self-past.yml with: framework: tensorflow version: "2.8" secrets: inherit run_past_ci_tensorflow_2-7: name: TensorFlow 2.7 if: always() needs: [run_past_ci_tensorflow_2-8] uses: ./.github/workflows/self-past.yml with: framework: tensorflow version: "2.7" secrets: inherit run_past_ci_tensorflow_2-6: name: TensorFlow 2.6 if: always() needs: [run_past_ci_tensorflow_2-7] uses: ./.github/workflows/self-past.yml with: framework: tensorflow version: "2.6" secrets: inherit run_past_ci_tensorflow_2-5: name: TensorFlow 2.5 if: always() needs: [run_past_ci_tensorflow_2-6] uses: ./.github/workflows/self-past.yml with: framework: tensorflow version: "2.5" secrets: inherit run_past_ci_tensorflow_2-4: name: TensorFlow 2.4 if: always() needs: [run_past_ci_tensorflow_2-5] uses: ./.github/workflows/self-past.yml with: framework: tensorflow version: "2.4" secrets: inherit

name: Self-hosted runner (past-ci-caller) on: push: branches: - run-past-ci* jobs: run_past_ci_pytorch_1-11: name: PyTorch 1.11 if: always() uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.11" secrets: inherit run_past_ci_pytorch_1-10: name: PyTorch 1.10 if: always() needs: [run_past_ci_pytorch_1-11] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.10" secrets: inherit run_past_ci_pytorch_1-9: name: PyTorch 1.9 if: always() needs: [run_past_ci_pytorch_1-10] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.9" secrets: inherit run_past_ci_pytorch_1-8: name: PyTorch 1.8 if: always() needs: [run_past_ci_pytorch_1-9] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.8" secrets: inherit run_past_ci_pytorch_1-7: name: PyTorch 1.7 if: always() needs: [run_past_ci_pytorch_1-8] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.7" secrets: inherit run_past_ci_pytorch_1-6: name: PyTorch 1.6 if: always() needs: [run_past_ci_pytorch_1-7] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.6" secrets: inherit run_past_ci_pytorch_1-5: name: PyTorch 1.5 if: always() needs: [run_past_ci_pytorch_1-6] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.5" secrets: inherit run_past_ci_pytorch_1-4: name: PyTorch 1.4 if: always() needs: [run_past_ci_pytorch_1-5] uses: ./.github/workflows/self-past.yml with: framework: pytorch version: "1.4" secrets: inherit run_past_ci_tensorflow_2-8: name: TensorFlow 2.8 if: always() needs: [run_past_ci_pytorch_1-4] uses: ./.github/workflows/self-past.yml with: framework: tensorflow version: "2.8" secrets: inherit run_past_ci_tensorflow_2-7: name: TensorFlow 2.7 if: always() needs: [run_past_ci_tensorflow_2-8] uses: ./.github/workflows/self-past.yml with: framework: tensorflow version: "2.7" secrets: inherit run_past_ci_tensorflow_2-6: name: TensorFlow 2.6 if: always() needs: [run_past_ci_tensorflow_2-7] uses: ./.github/workflows/self-past.yml with: framework: tensorflow version: "2.6" secrets: inherit run_past_ci_tensorflow_2-5: name: TensorFlow 2.5 if: always() needs: [run_past_ci_tensorflow_2-6] uses: ./.github/workflows/self-past.yml with: framework: tensorflow version: "2.5" secrets: inherit run_past_ci_tensorflow_2-4: name: TensorFlow 2.4 if: always() needs: [run_past_ci_tensorflow_2-5] uses: ./.github/workflows/self-past.yml with: framework: tensorflow version: "2.4" secrets: inherit

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/camembert/modeling_camembert.py

# coding=utf-8 # Copyright 2019 Inria, Facebook AI Research and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch CamemBERT model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN, gelu from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_camembert import CamembertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "camembert-base" _CONFIG_FOR_DOC = "CamembertConfig" _TOKENIZER_FOR_DOC = "CamembertTokenizer" CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "camembert-base", "Musixmatch/umberto-commoncrawl-cased-v1", "Musixmatch/umberto-wikipedia-uncased-v1", # See all CamemBERT models at https://huggingface.co/models?filter=camembert ] CAMEMBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CamembertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->Camembert class CamembertEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) # End copy self.padding_idx = config.pad_token_id self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx ) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfAttention with Roberta->Camembert class CamembertSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.is_decoder = config.is_decoder def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) use_cache = past_key_value is not None if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in CamembertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput with Roberta->Camembert class CamembertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaAttention with Roberta->Camembert class CamembertAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self = CamembertSelfAttention(config, position_embedding_type=position_embedding_type) self.output = CamembertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Roberta->Camembert class CamembertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Roberta->Camembert class CamembertOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaLayer with Roberta->Camembert class CamembertLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = CamembertAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = CamembertAttention(config, position_embedding_type="absolute") self.intermediate = CamembertIntermediate(config) self.output = CamembertOutput(config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output # Copied from transformers.models.roberta.modeling_roberta.RobertaEncoder with Roberta->Camembert class CamembertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([CamembertLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertPooler class CamembertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class CamembertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CamembertConfig base_model_prefix = "roberta" supports_gradient_checkpointing = True # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, CamembertEncoder): module.gradient_checkpointing = value def update_keys_to_ignore(self, config, del_keys_to_ignore): """Remove some keys from ignore list""" if not config.tie_word_embeddings: # must make a new list, or the class variable gets modified! self._keys_to_ignore_on_save = [k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore] self._keys_to_ignore_on_load_missing = [ k for k in self._keys_to_ignore_on_load_missing if k not in del_keys_to_ignore ] CAMEMBERT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`CamembertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Camembert class CamembertClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) return x # Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead with Roberta->Camembert class CamembertLMHead(nn.Module): """Camembert Head for masked language modeling.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.decoder = nn.Linear(config.hidden_size, config.vocab_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.decoder.bias = self.bias def forward(self, features, **kwargs): x = self.dense(features) x = gelu(x) x = self.layer_norm(x) # project back to size of vocabulary with bias x = self.decoder(x) return x def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) # For accelerate compatibility and to not break backward compatibility if self.decoder.bias.device.type == "meta": self.decoder.bias = self.bias else: self.bias = self.decoder.bias @add_start_docstrings( "The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.", CAMEMBERT_START_DOCSTRING, ) class CamembertModel(CamembertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in *Attention is all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as a decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 """ _keys_to_ignore_on_load_missing = [r"position_ids"] _no_split_modules = [] # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Camembert def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = CamembertEmbeddings(config) self.encoder = CamembertEncoder(config) self.pooler = CamembertPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_bert.BertModel.forward def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings( """CamemBERT Model with a `language modeling` head on top.""", CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForMaskedLM(CamembertPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `CamembertForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.roberta = CamembertModel(config, add_pooling_layer=False) self.lm_head = CamembertLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", expected_output="' Paris'", expected_loss=0.1, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForSequenceClassification(CamembertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.roberta = CamembertModel(config, add_pooling_layer=False) self.classifier = CamembertClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="cardiffnlp/twitter-roberta-base-emotion", output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="'optimism'", expected_loss=0.08, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForMultipleChoice with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForMultipleChoice(CamembertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.roberta = CamembertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward( CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.roberta( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForTokenClassification(CamembertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = CamembertModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="Jean-Baptiste/roberta-large-ner-english", output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']", expected_loss=0.01, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits` """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForQuestionAnswering(CamembertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = CamembertModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="deepset/roberta-base-squad2", output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, expected_output="' puppet'", expected_loss=0.86, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """CamemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", CAMEMBERT_START_DOCSTRING ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with Roberta->Camembert, ROBERTA->CAMEMBERT, roberta-base->camembert-base class CamembertForCausalLM(CamembertPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if not config.is_decoder: logger.warning("If you want to use `CamembertLMHeadModel` as a standalone, add `is_decoder=True.`") self.roberta = CamembertModel(config, add_pooling_layer=False) self.lm_head = CamembertLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, past_key_values: Tuple[Tuple[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Returns: Example: ```python >>> from transformers import CamembertTokenizer, CamembertForCausalLM, CamembertConfig >>> import torch >>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base") >>> config = CamembertConfig.from_pretrained("camembert-base") >>> config.is_decoder = True >>> model = CamembertForCausalLM.from_pretrained("camembert-base", config=config) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past} def _reorder_cache(self, past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past # Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

# coding=utf-8 # Copyright 2019 Inria, Facebook AI Research and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch CamemBERT model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN, gelu from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_camembert import CamembertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "camembert-base" _CONFIG_FOR_DOC = "CamembertConfig" _TOKENIZER_FOR_DOC = "CamembertTokenizer" CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "camembert-base", "Musixmatch/umberto-commoncrawl-cased-v1", "Musixmatch/umberto-wikipedia-uncased-v1", # See all CamemBERT models at https://huggingface.co/models?filter=camembert ] CAMEMBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CamembertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->Camembert class CamembertEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) # End copy self.padding_idx = config.pad_token_id self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx ) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfAttention with Roberta->Camembert class CamembertSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.is_decoder = config.is_decoder def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) use_cache = past_key_value is not None if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in CamembertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput with Roberta->Camembert class CamembertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaAttention with Roberta->Camembert class CamembertAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self = CamembertSelfAttention(config, position_embedding_type=position_embedding_type) self.output = CamembertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Roberta->Camembert class CamembertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Roberta->Camembert class CamembertOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaLayer with Roberta->Camembert class CamembertLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = CamembertAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = CamembertAttention(config, position_embedding_type="absolute") self.intermediate = CamembertIntermediate(config) self.output = CamembertOutput(config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output # Copied from transformers.models.roberta.modeling_roberta.RobertaEncoder with Roberta->Camembert class CamembertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([CamembertLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertPooler class CamembertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class CamembertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CamembertConfig base_model_prefix = "roberta" supports_gradient_checkpointing = True # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, CamembertEncoder): module.gradient_checkpointing = value def update_keys_to_ignore(self, config, del_keys_to_ignore): """Remove some keys from ignore list""" if not config.tie_word_embeddings: # must make a new list, or the class variable gets modified! self._keys_to_ignore_on_save = [k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore] self._keys_to_ignore_on_load_missing = [ k for k in self._keys_to_ignore_on_load_missing if k not in del_keys_to_ignore ] CAMEMBERT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`CamembertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Camembert class CamembertClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) return x # Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead with Roberta->Camembert class CamembertLMHead(nn.Module): """Camembert Head for masked language modeling.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.decoder = nn.Linear(config.hidden_size, config.vocab_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.decoder.bias = self.bias def forward(self, features, **kwargs): x = self.dense(features) x = gelu(x) x = self.layer_norm(x) # project back to size of vocabulary with bias x = self.decoder(x) return x def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) # For accelerate compatibility and to not break backward compatibility if self.decoder.bias.device.type == "meta": self.decoder.bias = self.bias else: self.bias = self.decoder.bias @add_start_docstrings( "The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.", CAMEMBERT_START_DOCSTRING, ) class CamembertModel(CamembertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in *Attention is all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as a decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 """ _keys_to_ignore_on_load_missing = [r"position_ids"] _no_split_modules = [] # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Camembert def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = CamembertEmbeddings(config) self.encoder = CamembertEncoder(config) self.pooler = CamembertPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_bert.BertModel.forward def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings( """CamemBERT Model with a `language modeling` head on top.""", CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForMaskedLM(CamembertPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `CamembertForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.roberta = CamembertModel(config, add_pooling_layer=False) self.lm_head = CamembertLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", expected_output="' Paris'", expected_loss=0.1, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForSequenceClassification(CamembertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.roberta = CamembertModel(config, add_pooling_layer=False) self.classifier = CamembertClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="cardiffnlp/twitter-roberta-base-emotion", output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="'optimism'", expected_loss=0.08, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForMultipleChoice with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForMultipleChoice(CamembertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.roberta = CamembertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward( CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.roberta( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForTokenClassification(CamembertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = CamembertModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="Jean-Baptiste/roberta-large-ner-english", output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']", expected_loss=0.01, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits` """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForQuestionAnswering(CamembertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = CamembertModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="deepset/roberta-base-squad2", output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, expected_output="' puppet'", expected_loss=0.86, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """CamemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", CAMEMBERT_START_DOCSTRING ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with Roberta->Camembert, ROBERTA->CAMEMBERT, roberta-base->camembert-base class CamembertForCausalLM(CamembertPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if not config.is_decoder: logger.warning("If you want to use `CamembertLMHeadModel` as a standalone, add `is_decoder=True.`") self.roberta = CamembertModel(config, add_pooling_layer=False) self.lm_head = CamembertLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, past_key_values: Tuple[Tuple[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Returns: Example: ```python >>> from transformers import CamembertTokenizer, CamembertForCausalLM, CamembertConfig >>> import torch >>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base") >>> config = CamembertConfig.from_pretrained("camembert-base") >>> config.is_decoder = True >>> model = CamembertForCausalLM.from_pretrained("camembert-base", config=config) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past} def _reorder_cache(self, past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past # Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/utils/notebook.py

# coding=utf-8 # Copyright 2020 Hugging Face # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re import time from typing import Optional import IPython.display as disp from ..trainer_callback import TrainerCallback from ..trainer_utils import IntervalStrategy, has_length def format_time(t): "Format `t` (in seconds) to (h):mm:ss" t = int(t) h, m, s = t // 3600, (t // 60) % 60, t % 60 return f"{h}:{m:02d}:{s:02d}" if h != 0 else f"{m:02d}:{s:02d}" def html_progress_bar(value, total, prefix, label, width=300): # docstyle-ignore return f""" <div> {prefix} <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress> {label} </div> """ def text_to_html_table(items): "Put the texts in `items` in an HTML table." html_code = """<table border="1" class="dataframe">\n""" html_code += """ <thead>\n <tr style="text-align: left;">\n""" for i in items[0]: html_code += f" <th>{i}</th>\n" html_code += " </tr>\n </thead>\n <tbody>\n" for line in items[1:]: html_code += " <tr>\n" for elt in line: elt = f"{elt:.6f}" if isinstance(elt, float) else str(elt) html_code += f" <td>{elt}</td>\n" html_code += " </tr>\n" html_code += " </tbody>\n</table><p>" return html_code class NotebookProgressBar: """ A progress par for display in a notebook. Class attributes (overridden by derived classes) - **warmup** (`int`) -- The number of iterations to do at the beginning while ignoring `update_every`. - **update_every** (`float`) -- Since calling the time takes some time, we only do it every presumed `update_every` seconds. The progress bar uses the average time passed up until now to guess the next value for which it will call the update. Args: total (`int`): The total number of iterations to reach. prefix (`str`, *optional*): A prefix to add before the progress bar. leave (`bool`, *optional*, defaults to `True`): Whether or not to leave the progress bar once it's completed. You can always call the [`~utils.notebook.NotebookProgressBar.close`] method to make the bar disappear. parent ([`~notebook.NotebookTrainingTracker`], *optional*): A parent object (like [`~utils.notebook.NotebookTrainingTracker`]) that spawns progress bars and handle their display. If set, the object passed must have a `display()` method. width (`int`, *optional*, defaults to 300): The width (in pixels) that the bar will take. Example: ```python import time pbar = NotebookProgressBar(100) for val in range(100): pbar.update(val) time.sleep(0.07) pbar.update(100) ```""" warmup = 5 update_every = 0.2 def __init__( self, total: int, prefix: Optional[str] = None, leave: bool = True, parent: Optional["NotebookTrainingTracker"] = None, width: int = 300, ): self.total = total self.prefix = "" if prefix is None else prefix self.leave = leave self.parent = parent self.width = width self.last_value = None self.comment = None self.output = None def update(self, value: int, force_update: bool = False, comment: str = None): """ The main method to update the progress bar to `value`. Args: value (`int`): The value to use. Must be between 0 and `total`. force_update (`bool`, *optional*, defaults to `False`): Whether or not to force and update of the internal state and display (by default, the bar will wait for `value` to reach the value it predicted corresponds to a time of more than the `update_every` attribute since the last update to avoid adding boilerplate). comment (`str`, *optional*): A comment to add on the left of the progress bar. """ self.value = value if comment is not None: self.comment = comment if self.last_value is None: self.start_time = self.last_time = time.time() self.start_value = self.last_value = value self.elapsed_time = self.predicted_remaining = None self.first_calls = self.warmup self.wait_for = 1 self.update_bar(value) elif value <= self.last_value and not force_update: return elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for, self.total): if self.first_calls > 0: self.first_calls -= 1 current_time = time.time() self.elapsed_time = current_time - self.start_time # We could have value = self.start_value if the update is called twixe with the same start value. if value > self.start_value: self.average_time_per_item = self.elapsed_time / (value - self.start_value) else: self.average_time_per_item = None if value >= self.total: value = self.total self.predicted_remaining = None if not self.leave: self.close() elif self.average_time_per_item is not None: self.predicted_remaining = self.average_time_per_item * (self.total - value) self.update_bar(value) self.last_value = value self.last_time = current_time if self.average_time_per_item is None: self.wait_for = 1 else: self.wait_for = max(int(self.update_every / self.average_time_per_item), 1) def update_bar(self, value, comment=None): spaced_value = " " * (len(str(self.total)) - len(str(value))) + str(value) if self.elapsed_time is None: self.label = f"[{spaced_value}/{self.total} : < :" elif self.predicted_remaining is None: self.label = f"[{spaced_value}/{self.total} {format_time(self.elapsed_time)}" else: self.label = ( f"[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <" f" {format_time(self.predicted_remaining)}" ) self.label += f", {1/self.average_time_per_item:.2f} it/s" self.label += "]" if self.comment is None or len(self.comment) == 0 else f", {self.comment}]" self.display() def display(self): self.html_code = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width) if self.parent is not None: # If this is a child bar, the parent will take care of the display. self.parent.display() return if self.output is None: self.output = disp.display(disp.HTML(self.html_code), display_id=True) else: self.output.update(disp.HTML(self.html_code)) def close(self): "Closes the progress bar." if self.parent is None and self.output is not None: self.output.update(disp.HTML("")) class NotebookTrainingTracker(NotebookProgressBar): """ An object tracking the updates of an ongoing training with progress bars and a nice table reporting metrics. Args: num_steps (`int`): The number of steps during training. column_names (`List[str]`, *optional*): The list of column names for the metrics table (will be inferred from the first call to [`~utils.notebook.NotebookTrainingTracker.write_line`] if not set). """ def __init__(self, num_steps, column_names=None): super().__init__(num_steps) self.inner_table = None if column_names is None else [column_names] self.child_bar = None def display(self): self.html_code = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width) if self.inner_table is not None: self.html_code += text_to_html_table(self.inner_table) if self.child_bar is not None: self.html_code += self.child_bar.html_code if self.output is None: self.output = disp.display(disp.HTML(self.html_code), display_id=True) else: self.output.update(disp.HTML(self.html_code)) def write_line(self, values): """ Write the values in the inner table. Args: values (`Dict[str, float]`): The values to display. """ if self.inner_table is None: self.inner_table = [list(values.keys()), list(values.values())] else: columns = self.inner_table[0] if len(self.inner_table) == 1: # We give a chance to update the column names at the first iteration for key in values.keys(): if key not in columns: columns.append(key) self.inner_table[0] = columns self.inner_table.append([values[c] for c in columns]) def add_child(self, total, prefix=None, width=300): """ Add a child progress bar displayed under the table of metrics. The child progress bar is returned (so it can be easily updated). Args: total (`int`): The number of iterations for the child progress bar. prefix (`str`, *optional*): A prefix to write on the left of the progress bar. width (`int`, *optional*, defaults to 300): The width (in pixels) of the progress bar. """ self.child_bar = NotebookProgressBar(total, prefix=prefix, parent=self, width=width) return self.child_bar def remove_child(self): """ Closes the child progress bar. """ self.child_bar = None self.display() class NotebookProgressCallback(TrainerCallback): """ A [`TrainerCallback`] that displays the progress of training or evaluation, optimized for Jupyter Notebooks or Google colab. """ def __init__(self): self.training_tracker = None self.prediction_bar = None self._force_next_update = False def on_train_begin(self, args, state, control, **kwargs): self.first_column = "Epoch" if args.evaluation_strategy == IntervalStrategy.EPOCH else "Step" self.training_loss = 0 self.last_log = 0 column_names = [self.first_column] + ["Training Loss"] if args.evaluation_strategy != IntervalStrategy.NO: column_names.append("Validation Loss") self.training_tracker = NotebookTrainingTracker(state.max_steps, column_names) def on_step_end(self, args, state, control, **kwargs): epoch = int(state.epoch) if int(state.epoch) == state.epoch else f"{state.epoch:.2f}" self.training_tracker.update( state.global_step + 1, comment=f"Epoch {epoch}/{state.num_train_epochs}", force_update=self._force_next_update, ) self._force_next_update = False def on_prediction_step(self, args, state, control, eval_dataloader=None, **kwargs): if not has_length(eval_dataloader): return if self.prediction_bar is None: if self.training_tracker is not None: self.prediction_bar = self.training_tracker.add_child(len(eval_dataloader)) else: self.prediction_bar = NotebookProgressBar(len(eval_dataloader)) self.prediction_bar.update(1) else: self.prediction_bar.update(self.prediction_bar.value + 1) def on_predict(self, args, state, control, **kwargs): if self.prediction_bar is not None: self.prediction_bar.close() self.prediction_bar = None def on_log(self, args, state, control, logs=None, **kwargs): # Only for when there is no evaluation if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs: values = {"Training Loss": logs["loss"]} # First column is necessarily Step sine we're not in epoch eval strategy values["Step"] = state.global_step self.training_tracker.write_line(values) def on_evaluate(self, args, state, control, metrics=None, **kwargs): if self.training_tracker is not None: values = {"Training Loss": "No log", "Validation Loss": "No log"} for log in reversed(state.log_history): if "loss" in log: values["Training Loss"] = log["loss"] break if self.first_column == "Epoch": values["Epoch"] = int(state.epoch) else: values["Step"] = state.global_step metric_key_prefix = "eval" for k in metrics: if k.endswith("_loss"): metric_key_prefix = re.sub(r"\_loss$", "", k) _ = metrics.pop("total_flos", None) _ = metrics.pop("epoch", None) _ = metrics.pop(f"{metric_key_prefix}_runtime", None) _ = metrics.pop(f"{metric_key_prefix}_samples_per_second", None) _ = metrics.pop(f"{metric_key_prefix}_steps_per_second", None) for k, v in metrics.items(): if k == f"{metric_key_prefix}_loss": values["Validation Loss"] = v else: splits = k.split("_") name = " ".join([part.capitalize() for part in splits[1:]]) values[name] = v self.training_tracker.write_line(values) self.training_tracker.remove_child() self.prediction_bar = None # Evaluation takes a long time so we should force the next update. self._force_next_update = True def on_train_end(self, args, state, control, **kwargs): self.training_tracker.update( state.global_step, comment=f"Epoch {int(state.epoch)}/{state.num_train_epochs}", force_update=True ) self.training_tracker = None

# coding=utf-8 # Copyright 2020 Hugging Face # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re import time from typing import Optional import IPython.display as disp from ..trainer_callback import TrainerCallback from ..trainer_utils import IntervalStrategy, has_length def format_time(t): "Format `t` (in seconds) to (h):mm:ss" t = int(t) h, m, s = t // 3600, (t // 60) % 60, t % 60 return f"{h}:{m:02d}:{s:02d}" if h != 0 else f"{m:02d}:{s:02d}" def html_progress_bar(value, total, prefix, label, width=300): # docstyle-ignore return f""" <div> {prefix} <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress> {label} </div> """ def text_to_html_table(items): "Put the texts in `items` in an HTML table." html_code = """<table border="1" class="dataframe">\n""" html_code += """ <thead>\n <tr style="text-align: left;">\n""" for i in items[0]: html_code += f" <th>{i}</th>\n" html_code += " </tr>\n </thead>\n <tbody>\n" for line in items[1:]: html_code += " <tr>\n" for elt in line: elt = f"{elt:.6f}" if isinstance(elt, float) else str(elt) html_code += f" <td>{elt}</td>\n" html_code += " </tr>\n" html_code += " </tbody>\n</table><p>" return html_code class NotebookProgressBar: """ A progress par for display in a notebook. Class attributes (overridden by derived classes) - **warmup** (`int`) -- The number of iterations to do at the beginning while ignoring `update_every`. - **update_every** (`float`) -- Since calling the time takes some time, we only do it every presumed `update_every` seconds. The progress bar uses the average time passed up until now to guess the next value for which it will call the update. Args: total (`int`): The total number of iterations to reach. prefix (`str`, *optional*): A prefix to add before the progress bar. leave (`bool`, *optional*, defaults to `True`): Whether or not to leave the progress bar once it's completed. You can always call the [`~utils.notebook.NotebookProgressBar.close`] method to make the bar disappear. parent ([`~notebook.NotebookTrainingTracker`], *optional*): A parent object (like [`~utils.notebook.NotebookTrainingTracker`]) that spawns progress bars and handle their display. If set, the object passed must have a `display()` method. width (`int`, *optional*, defaults to 300): The width (in pixels) that the bar will take. Example: ```python import time pbar = NotebookProgressBar(100) for val in range(100): pbar.update(val) time.sleep(0.07) pbar.update(100) ```""" warmup = 5 update_every = 0.2 def __init__( self, total: int, prefix: Optional[str] = None, leave: bool = True, parent: Optional["NotebookTrainingTracker"] = None, width: int = 300, ): self.total = total self.prefix = "" if prefix is None else prefix self.leave = leave self.parent = parent self.width = width self.last_value = None self.comment = None self.output = None def update(self, value: int, force_update: bool = False, comment: str = None): """ The main method to update the progress bar to `value`. Args: value (`int`): The value to use. Must be between 0 and `total`. force_update (`bool`, *optional*, defaults to `False`): Whether or not to force and update of the internal state and display (by default, the bar will wait for `value` to reach the value it predicted corresponds to a time of more than the `update_every` attribute since the last update to avoid adding boilerplate). comment (`str`, *optional*): A comment to add on the left of the progress bar. """ self.value = value if comment is not None: self.comment = comment if self.last_value is None: self.start_time = self.last_time = time.time() self.start_value = self.last_value = value self.elapsed_time = self.predicted_remaining = None self.first_calls = self.warmup self.wait_for = 1 self.update_bar(value) elif value <= self.last_value and not force_update: return elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for, self.total): if self.first_calls > 0: self.first_calls -= 1 current_time = time.time() self.elapsed_time = current_time - self.start_time # We could have value = self.start_value if the update is called twixe with the same start value. if value > self.start_value: self.average_time_per_item = self.elapsed_time / (value - self.start_value) else: self.average_time_per_item = None if value >= self.total: value = self.total self.predicted_remaining = None if not self.leave: self.close() elif self.average_time_per_item is not None: self.predicted_remaining = self.average_time_per_item * (self.total - value) self.update_bar(value) self.last_value = value self.last_time = current_time if self.average_time_per_item is None: self.wait_for = 1 else: self.wait_for = max(int(self.update_every / self.average_time_per_item), 1) def update_bar(self, value, comment=None): spaced_value = " " * (len(str(self.total)) - len(str(value))) + str(value) if self.elapsed_time is None: self.label = f"[{spaced_value}/{self.total} : < :" elif self.predicted_remaining is None: self.label = f"[{spaced_value}/{self.total} {format_time(self.elapsed_time)}" else: self.label = ( f"[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <" f" {format_time(self.predicted_remaining)}" ) self.label += f", {1/self.average_time_per_item:.2f} it/s" self.label += "]" if self.comment is None or len(self.comment) == 0 else f", {self.comment}]" self.display() def display(self): self.html_code = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width) if self.parent is not None: # If this is a child bar, the parent will take care of the display. self.parent.display() return if self.output is None: self.output = disp.display(disp.HTML(self.html_code), display_id=True) else: self.output.update(disp.HTML(self.html_code)) def close(self): "Closes the progress bar." if self.parent is None and self.output is not None: self.output.update(disp.HTML("")) class NotebookTrainingTracker(NotebookProgressBar): """ An object tracking the updates of an ongoing training with progress bars and a nice table reporting metrics. Args: num_steps (`int`): The number of steps during training. column_names (`List[str]`, *optional*): The list of column names for the metrics table (will be inferred from the first call to [`~utils.notebook.NotebookTrainingTracker.write_line`] if not set). """ def __init__(self, num_steps, column_names=None): super().__init__(num_steps) self.inner_table = None if column_names is None else [column_names] self.child_bar = None def display(self): self.html_code = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width) if self.inner_table is not None: self.html_code += text_to_html_table(self.inner_table) if self.child_bar is not None: self.html_code += self.child_bar.html_code if self.output is None: self.output = disp.display(disp.HTML(self.html_code), display_id=True) else: self.output.update(disp.HTML(self.html_code)) def write_line(self, values): """ Write the values in the inner table. Args: values (`Dict[str, float]`): The values to display. """ if self.inner_table is None: self.inner_table = [list(values.keys()), list(values.values())] else: columns = self.inner_table[0] if len(self.inner_table) == 1: # We give a chance to update the column names at the first iteration for key in values.keys(): if key not in columns: columns.append(key) self.inner_table[0] = columns self.inner_table.append([values[c] for c in columns]) def add_child(self, total, prefix=None, width=300): """ Add a child progress bar displayed under the table of metrics. The child progress bar is returned (so it can be easily updated). Args: total (`int`): The number of iterations for the child progress bar. prefix (`str`, *optional*): A prefix to write on the left of the progress bar. width (`int`, *optional*, defaults to 300): The width (in pixels) of the progress bar. """ self.child_bar = NotebookProgressBar(total, prefix=prefix, parent=self, width=width) return self.child_bar def remove_child(self): """ Closes the child progress bar. """ self.child_bar = None self.display() class NotebookProgressCallback(TrainerCallback): """ A [`TrainerCallback`] that displays the progress of training or evaluation, optimized for Jupyter Notebooks or Google colab. """ def __init__(self): self.training_tracker = None self.prediction_bar = None self._force_next_update = False def on_train_begin(self, args, state, control, **kwargs): self.first_column = "Epoch" if args.evaluation_strategy == IntervalStrategy.EPOCH else "Step" self.training_loss = 0 self.last_log = 0 column_names = [self.first_column] + ["Training Loss"] if args.evaluation_strategy != IntervalStrategy.NO: column_names.append("Validation Loss") self.training_tracker = NotebookTrainingTracker(state.max_steps, column_names) def on_step_end(self, args, state, control, **kwargs): epoch = int(state.epoch) if int(state.epoch) == state.epoch else f"{state.epoch:.2f}" self.training_tracker.update( state.global_step + 1, comment=f"Epoch {epoch}/{state.num_train_epochs}", force_update=self._force_next_update, ) self._force_next_update = False def on_prediction_step(self, args, state, control, eval_dataloader=None, **kwargs): if not has_length(eval_dataloader): return if self.prediction_bar is None: if self.training_tracker is not None: self.prediction_bar = self.training_tracker.add_child(len(eval_dataloader)) else: self.prediction_bar = NotebookProgressBar(len(eval_dataloader)) self.prediction_bar.update(1) else: self.prediction_bar.update(self.prediction_bar.value + 1) def on_predict(self, args, state, control, **kwargs): if self.prediction_bar is not None: self.prediction_bar.close() self.prediction_bar = None def on_log(self, args, state, control, logs=None, **kwargs): # Only for when there is no evaluation if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs: values = {"Training Loss": logs["loss"]} # First column is necessarily Step sine we're not in epoch eval strategy values["Step"] = state.global_step self.training_tracker.write_line(values) def on_evaluate(self, args, state, control, metrics=None, **kwargs): if self.training_tracker is not None: values = {"Training Loss": "No log", "Validation Loss": "No log"} for log in reversed(state.log_history): if "loss" in log: values["Training Loss"] = log["loss"] break if self.first_column == "Epoch": values["Epoch"] = int(state.epoch) else: values["Step"] = state.global_step metric_key_prefix = "eval" for k in metrics: if k.endswith("_loss"): metric_key_prefix = re.sub(r"\_loss$", "", k) _ = metrics.pop("total_flos", None) _ = metrics.pop("epoch", None) _ = metrics.pop(f"{metric_key_prefix}_runtime", None) _ = metrics.pop(f"{metric_key_prefix}_samples_per_second", None) _ = metrics.pop(f"{metric_key_prefix}_steps_per_second", None) for k, v in metrics.items(): if k == f"{metric_key_prefix}_loss": values["Validation Loss"] = v else: splits = k.split("_") name = " ".join([part.capitalize() for part in splits[1:]]) values[name] = v self.training_tracker.write_line(values) self.training_tracker.remove_child() self.prediction_bar = None # Evaluation takes a long time so we should force the next update. self._force_next_update = True def on_train_end(self, args, state, control, **kwargs): self.training_tracker.update( state.global_step, comment=f"Epoch {int(state.epoch)}/{state.num_train_epochs}", force_update=True ) self.training_tracker = None

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./docker/transformers-tensorflow-gpu/Dockerfile

FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04 LABEL maintainer="Hugging Face" ARG DEBIAN_FRONTEND=noninteractive RUN apt update RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg RUN python3 -m pip install --no-cache-dir --upgrade pip ARG REF=main RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-tensorflow,testing] # If set to nothing, will install the latest version ARG TENSORFLOW='' RUN [ ${#TENSORFLOW} -gt 0 ] && VERSION='tensorflow=='$TENSORFLOW'.*' || VERSION='tensorflow'; python3 -m pip install --no-cache-dir -U $VERSION RUN python3 -m pip uninstall -y torch flax RUN python3 -m pip install -U "itsdangerous<2.1.0" # When installing in editable mode, `transformers` is not recognized as a package. # this line must be added in order for python to be aware of transformers. RUN cd transformers && python3 setup.py develop

FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04 LABEL maintainer="Hugging Face" ARG DEBIAN_FRONTEND=noninteractive RUN apt update RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg RUN python3 -m pip install --no-cache-dir --upgrade pip ARG REF=main RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-tensorflow,testing] # If set to nothing, will install the latest version ARG TENSORFLOW='' RUN [ ${#TENSORFLOW} -gt 0 ] && VERSION='tensorflow=='$TENSORFLOW'.*' || VERSION='tensorflow'; python3 -m pip install --no-cache-dir -U $VERSION RUN python3 -m pip uninstall -y torch flax RUN python3 -m pip install -U "itsdangerous<2.1.0" # When installing in editable mode, `transformers` is not recognized as a package. # this line must be added in order for python to be aware of transformers. RUN cd transformers && python3 setup.py develop

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/pplm/requirements.txt

tensorboard scikit-learn seqeval psutil sacrebleu rouge-score tensorflow_datasets pytorch-lightning matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.1.3 fire pytest conllu sentencepiece != 0.1.92 protobuf transformers==3.5.1

tensorboard scikit-learn seqeval psutil sacrebleu rouge-score tensorflow_datasets pytorch-lightning matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.1.3 fire pytest conllu sentencepiece != 0.1.92 protobuf transformers==3.5.1

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/layoutlmv2/modeling_layoutlmv2.py

# coding=utf-8 # Copyright 2021 Microsoft Research The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch LayoutLMv2 model.""" import math from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPooling, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, torch_int_div from ...utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, is_detectron2_available, logging, replace_return_docstrings, requires_backends, ) from .configuration_layoutlmv2 import LayoutLMv2Config # soft dependency if is_detectron2_available(): import detectron2 from detectron2.modeling import META_ARCH_REGISTRY logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "microsoft/layoutlmv2-base-uncased" _CONFIG_FOR_DOC = "LayoutLMv2Config" _TOKENIZER_FOR_DOC = "LayoutLMv2Tokenizer" LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/layoutlmv2-base-uncased", "microsoft/layoutlmv2-large-uncased", # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2 ] class LayoutLMv2Embeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super(LayoutLMv2Embeddings, self).__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size) self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size) self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size) self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) def _calc_spatial_position_embeddings(self, bbox): try: left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0]) upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1]) right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2]) lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3]) except IndexError as e: raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1]) w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0]) spatial_position_embeddings = torch.cat( [ left_position_embeddings, upper_position_embeddings, right_position_embeddings, lower_position_embeddings, h_position_embeddings, w_position_embeddings, ], dim=-1, ) return spatial_position_embeddings class LayoutLMv2SelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.fast_qkv = config.fast_qkv self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.has_relative_attention_bias = config.has_relative_attention_bias self.has_spatial_attention_bias = config.has_spatial_attention_bias if config.fast_qkv: self.qkv_linear = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=False) self.q_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size)) self.v_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size)) else: self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def compute_qkv(self, hidden_states): if self.fast_qkv: qkv = self.qkv_linear(hidden_states) q, k, v = torch.chunk(qkv, 3, dim=-1) if q.ndimension() == self.q_bias.ndimension(): q = q + self.q_bias v = v + self.v_bias else: _sz = (1,) * (q.ndimension() - 1) + (-1,) q = q + self.q_bias.view(*_sz) v = v + self.v_bias.view(*_sz) else: q = self.query(hidden_states) k = self.key(hidden_states) v = self.value(hidden_states) return q, k, v def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None, ): q, k, v = self.compute_qkv(hidden_states) # (B, L, H*D) -> (B, H, L, D) query_layer = self.transpose_for_scores(q) key_layer = self.transpose_for_scores(k) value_layer = self.transpose_for_scores(v) query_layer = query_layer / math.sqrt(self.attention_head_size) # [BSZ, NAT, L, L] attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.has_relative_attention_bias: attention_scores += rel_pos if self.has_spatial_attention_bias: attention_scores += rel_2d_pos attention_scores = attention_scores.float().masked_fill_( attention_mask.to(torch.bool), torch.finfo(attention_scores.dtype).min ) attention_probs = nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32).type_as(value_layer) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class LayoutLMv2Attention(nn.Module): def __init__(self, config): super().__init__() self.self = LayoutLMv2SelfAttention(config) self.output = LayoutLMv2SelfOutput(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None, ): self_outputs = self.self( hidden_states, attention_mask, head_mask, output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class LayoutLMv2SelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->LayoutLMv2 class LayoutLMv2Intermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->LayoutLM class LayoutLMv2Output(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class LayoutLMv2Layer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = LayoutLMv2Attention(config) self.intermediate = LayoutLMv2Intermediate(config) self.output = LayoutLMv2Output(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None, ): self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output def relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on. Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ ret = 0 if bidirectional: num_buckets //= 2 ret += (relative_position > 0).long() * num_buckets n = torch.abs(relative_position) else: n = torch.max(-relative_position, torch.zeros_like(relative_position)) # now n is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = n < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance val_if_large = max_exact + ( torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1)) ret += torch.where(is_small, n, val_if_large) return ret class LayoutLMv2Encoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([LayoutLMv2Layer(config) for _ in range(config.num_hidden_layers)]) self.has_relative_attention_bias = config.has_relative_attention_bias self.has_spatial_attention_bias = config.has_spatial_attention_bias if self.has_relative_attention_bias: self.rel_pos_bins = config.rel_pos_bins self.max_rel_pos = config.max_rel_pos self.rel_pos_onehot_size = config.rel_pos_bins self.rel_pos_bias = nn.Linear(self.rel_pos_onehot_size, config.num_attention_heads, bias=False) if self.has_spatial_attention_bias: self.max_rel_2d_pos = config.max_rel_2d_pos self.rel_2d_pos_bins = config.rel_2d_pos_bins self.rel_2d_pos_onehot_size = config.rel_2d_pos_bins self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_onehot_size, config.num_attention_heads, bias=False) self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_onehot_size, config.num_attention_heads, bias=False) self.gradient_checkpointing = False def _calculate_1d_position_embeddings(self, hidden_states, position_ids): rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1) rel_pos = relative_position_bucket( rel_pos_mat, num_buckets=self.rel_pos_bins, max_distance=self.max_rel_pos, ) rel_pos = nn.functional.one_hot(rel_pos, num_classes=self.rel_pos_onehot_size).type_as(hidden_states) rel_pos = self.rel_pos_bias(rel_pos).permute(0, 3, 1, 2) rel_pos = rel_pos.contiguous() return rel_pos def _calculate_2d_position_embeddings(self, hidden_states, bbox): position_coord_x = bbox[:, :, 0] position_coord_y = bbox[:, :, 3] rel_pos_x_2d_mat = position_coord_x.unsqueeze(-2) - position_coord_x.unsqueeze(-1) rel_pos_y_2d_mat = position_coord_y.unsqueeze(-2) - position_coord_y.unsqueeze(-1) rel_pos_x = relative_position_bucket( rel_pos_x_2d_mat, num_buckets=self.rel_2d_pos_bins, max_distance=self.max_rel_2d_pos, ) rel_pos_y = relative_position_bucket( rel_pos_y_2d_mat, num_buckets=self.rel_2d_pos_bins, max_distance=self.max_rel_2d_pos, ) rel_pos_x = nn.functional.one_hot(rel_pos_x, num_classes=self.rel_2d_pos_onehot_size).type_as(hidden_states) rel_pos_y = nn.functional.one_hot(rel_pos_y, num_classes=self.rel_2d_pos_onehot_size).type_as(hidden_states) rel_pos_x = self.rel_pos_x_bias(rel_pos_x).permute(0, 3, 1, 2) rel_pos_y = self.rel_pos_y_bias(rel_pos_y).permute(0, 3, 1, 2) rel_pos_x = rel_pos_x.contiguous() rel_pos_y = rel_pos_y.contiguous() rel_2d_pos = rel_pos_x + rel_pos_y return rel_2d_pos def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, bbox=None, position_ids=None, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None rel_pos = ( self._calculate_1d_position_embeddings(hidden_states, position_ids) if self.has_relative_attention_bias else None ) rel_2d_pos = ( self._calculate_2d_position_embeddings(hidden_states, bbox) if self.has_spatial_attention_bias else None ) for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, all_hidden_states, all_self_attentions, ] if v is not None ) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class LayoutLMv2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = LayoutLMv2Config pretrained_model_archive_map = LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST base_model_prefix = "layoutlmv2" _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, LayoutLMv2Encoder): module.gradient_checkpointing = value def my_convert_sync_batchnorm(module, process_group=None): # same as `nn.modules.SyncBatchNorm.convert_sync_batchnorm` but allowing converting from `detectron2.layers.FrozenBatchNorm2d` if isinstance(module, torch.nn.modules.batchnorm._BatchNorm): return nn.modules.SyncBatchNorm.convert_sync_batchnorm(module, process_group) module_output = module if isinstance(module, detectron2.layers.FrozenBatchNorm2d): module_output = torch.nn.SyncBatchNorm( num_features=module.num_features, eps=module.eps, affine=True, track_running_stats=True, process_group=process_group, ) module_output.weight = torch.nn.Parameter(module.weight) module_output.bias = torch.nn.Parameter(module.bias) module_output.running_mean = module.running_mean module_output.running_var = module.running_var module_output.num_batches_tracked = torch.tensor(0, dtype=torch.long, device=module.running_mean.device) for name, child in module.named_children(): module_output.add_module(name, my_convert_sync_batchnorm(child, process_group)) del module return module_output class LayoutLMv2VisualBackbone(nn.Module): def __init__(self, config): super().__init__() self.cfg = config.get_detectron2_config() meta_arch = self.cfg.MODEL.META_ARCHITECTURE model = META_ARCH_REGISTRY.get(meta_arch)(self.cfg) assert isinstance(model.backbone, detectron2.modeling.backbone.FPN) self.backbone = model.backbone assert len(self.cfg.MODEL.PIXEL_MEAN) == len(self.cfg.MODEL.PIXEL_STD) num_channels = len(self.cfg.MODEL.PIXEL_MEAN) self.register_buffer( "pixel_mean", torch.Tensor(self.cfg.MODEL.PIXEL_MEAN).view(num_channels, 1, 1), ) self.register_buffer("pixel_std", torch.Tensor(self.cfg.MODEL.PIXEL_STD).view(num_channels, 1, 1)) self.out_feature_key = "p2" if torch.are_deterministic_algorithms_enabled(): logger.warning("using `AvgPool2d` instead of `AdaptiveAvgPool2d`") input_shape = (224, 224) backbone_stride = self.backbone.output_shape()[self.out_feature_key].stride self.pool = nn.AvgPool2d( ( math.ceil(math.ceil(input_shape[0] / backbone_stride) / config.image_feature_pool_shape[0]), math.ceil(math.ceil(input_shape[1] / backbone_stride) / config.image_feature_pool_shape[1]), ) ) else: self.pool = nn.AdaptiveAvgPool2d(config.image_feature_pool_shape[:2]) if len(config.image_feature_pool_shape) == 2: config.image_feature_pool_shape.append(self.backbone.output_shape()[self.out_feature_key].channels) assert self.backbone.output_shape()[self.out_feature_key].channels == config.image_feature_pool_shape[2] def forward(self, images): images_input = ((images if torch.is_tensor(images) else images.tensor) - self.pixel_mean) / self.pixel_std features = self.backbone(images_input) features = features[self.out_feature_key] features = self.pool(features).flatten(start_dim=2).transpose(1, 2).contiguous() return features def synchronize_batch_norm(self): if not ( torch.distributed.is_available() and torch.distributed.is_initialized() and torch.distributed.get_rank() > -1 ): raise RuntimeError("Make sure torch.distributed is set up properly.") self_rank = torch.distributed.get_rank() node_size = torch.cuda.device_count() world_size = torch.distributed.get_world_size() if not (world_size & node_size == 0): raise RuntimeError("Make sure the number of processes can be divided by the number of nodes") node_global_ranks = [list(range(i * node_size, (i + 1) * node_size)) for i in range(world_size // node_size)] sync_bn_groups = [ torch.distributed.new_group(ranks=node_global_ranks[i]) for i in range(world_size // node_size) ] node_rank = self_rank // node_size self.backbone = my_convert_sync_batchnorm(self.backbone, process_group=sync_bn_groups[node_rank]) LAYOUTLMV2_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`LayoutLMv2Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ LAYOUTLMV2_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `{0}`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`LayoutLMv2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*): Bounding boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. image (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `detectron.structures.ImageList` whose `tensors` is of shape `(batch_size, num_channels, height, width)`): Batch of document images. attention_mask (`torch.FloatTensor` of shape `{0}`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `{0}`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `{0}`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class LayoutLMv2Pooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output @add_start_docstrings( "The bare LayoutLMv2 Model transformer outputting raw hidden-states without any specific head on top.", LAYOUTLMV2_START_DOCSTRING, ) class LayoutLMv2Model(LayoutLMv2PreTrainedModel): def __init__(self, config): requires_backends(self, "detectron2") super().__init__(config) self.config = config self.has_visual_segment_embedding = config.has_visual_segment_embedding self.embeddings = LayoutLMv2Embeddings(config) self.visual = LayoutLMv2VisualBackbone(config) self.visual_proj = nn.Linear(config.image_feature_pool_shape[-1], config.hidden_size) if self.has_visual_segment_embedding: self.visual_segment_embedding = nn.Parameter(nn.Embedding(1, config.hidden_size).weight[0]) self.visual_LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.visual_dropout = nn.Dropout(config.hidden_dropout_prob) self.encoder = LayoutLMv2Encoder(config) self.pooler = LayoutLMv2Pooler(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _calc_text_embeddings(self, input_ids, bbox, position_ids, token_type_ids, inputs_embeds=None): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) if inputs_embeds is None: inputs_embeds = self.embeddings.word_embeddings(input_ids) position_embeddings = self.embeddings.position_embeddings(position_ids) spatial_position_embeddings = self.embeddings._calc_spatial_position_embeddings(bbox) token_type_embeddings = self.embeddings.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + position_embeddings + spatial_position_embeddings + token_type_embeddings embeddings = self.embeddings.LayerNorm(embeddings) embeddings = self.embeddings.dropout(embeddings) return embeddings def _calc_img_embeddings(self, image, bbox, position_ids): visual_embeddings = self.visual_proj(self.visual(image)) position_embeddings = self.embeddings.position_embeddings(position_ids) spatial_position_embeddings = self.embeddings._calc_spatial_position_embeddings(bbox) embeddings = visual_embeddings + position_embeddings + spatial_position_embeddings if self.has_visual_segment_embedding: embeddings += self.visual_segment_embedding embeddings = self.visual_LayerNorm(embeddings) embeddings = self.visual_dropout(embeddings) return embeddings def _calc_visual_bbox(self, image_feature_pool_shape, bbox, device, final_shape): visual_bbox_x = torch_int_div( torch.arange( 0, 1000 * (image_feature_pool_shape[1] + 1), 1000, device=device, dtype=bbox.dtype, ), self.config.image_feature_pool_shape[1], ) visual_bbox_y = torch_int_div( torch.arange( 0, 1000 * (self.config.image_feature_pool_shape[0] + 1), 1000, device=device, dtype=bbox.dtype, ), self.config.image_feature_pool_shape[0], ) visual_bbox = torch.stack( [ visual_bbox_x[:-1].repeat(image_feature_pool_shape[0], 1), visual_bbox_y[:-1].repeat(image_feature_pool_shape[1], 1).transpose(0, 1), visual_bbox_x[1:].repeat(image_feature_pool_shape[0], 1), visual_bbox_y[1:].repeat(image_feature_pool_shape[1], 1).transpose(0, 1), ], dim=-1, ).view(-1, bbox.size(-1)) visual_bbox = visual_bbox.repeat(final_shape[0], 1, 1) return visual_bbox def _get_input_shape(self, input_ids=None, inputs_embeds=None): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: return input_ids.size() elif inputs_embeds is not None: return inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") @add_start_docstrings_to_model_forward(LAYOUTLMV2_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, bbox: Optional[torch.LongTensor] = None, image: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Return: Examples: ```python >>> from transformers import LayoutLMv2Processor, LayoutLMv2Model, set_seed >>> from PIL import Image >>> import torch >>> from datasets import load_dataset >>> set_seed(88) >>> processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased") >>> model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased") >>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa") >>> image_path = dataset["test"][0]["file"] >>> image = Image.open(image_path).convert("RGB") >>> encoding = processor(image, return_tensors="pt") >>> outputs = model(**encoding) >>> last_hidden_states = outputs.last_hidden_state >>> last_hidden_states.shape torch.Size([1, 342, 768]) ``` """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict input_shape = self._get_input_shape(input_ids, inputs_embeds) device = input_ids.device if input_ids is not None else inputs_embeds.device visual_shape = list(input_shape) visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1] visual_shape = torch.Size(visual_shape) # needs a new copy of input_shape for tracing. Otherwise wrong dimensions will occur final_shape = list(self._get_input_shape(input_ids, inputs_embeds)) final_shape[1] += visual_shape[1] final_shape = torch.Size(final_shape) visual_bbox = self._calc_visual_bbox(self.config.image_feature_pool_shape, bbox, device, final_shape) final_bbox = torch.cat([bbox, visual_bbox], dim=1) if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) visual_attention_mask = torch.ones(visual_shape, device=device) final_attention_mask = torch.cat([attention_mask, visual_attention_mask], dim=1) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) if position_ids is None: seq_length = input_shape[1] position_ids = self.embeddings.position_ids[:, :seq_length] position_ids = position_ids.expand(input_shape) visual_position_ids = torch.arange(0, visual_shape[1], dtype=torch.long, device=device).repeat( input_shape[0], 1 ) final_position_ids = torch.cat([position_ids, visual_position_ids], dim=1) if bbox is None: bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device) text_layout_emb = self._calc_text_embeddings( input_ids=input_ids, bbox=bbox, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, ) visual_emb = self._calc_img_embeddings( image=image, bbox=visual_bbox, position_ids=visual_position_ids, ) final_emb = torch.cat([text_layout_emb, visual_emb], dim=1) extended_attention_mask = final_attention_mask.unsqueeze(1).unsqueeze(2) extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min if head_mask is not None: if head_mask.dim() == 1: head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1) elif head_mask.dim() == 2: head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) head_mask = head_mask.to(dtype=next(self.parameters()).dtype) else: head_mask = [None] * self.config.num_hidden_layers encoder_outputs = self.encoder( final_emb, extended_attention_mask, bbox=final_bbox, position_ids=final_position_ids, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( """ LayoutLMv2 Model with a sequence classification head on top (a linear layer on top of the concatenation of the final hidden state of the [CLS] token, average-pooled initial visual embeddings and average-pooled final visual embeddings, e.g. for document image classification tasks such as the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset. """, LAYOUTLMV2_START_DOCSTRING, ) class LayoutLMv2ForSequenceClassification(LayoutLMv2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.layoutlmv2 = LayoutLMv2Model(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.layoutlmv2.embeddings.word_embeddings @add_start_docstrings_to_model_forward(LAYOUTLMV2_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, bbox: Optional[torch.LongTensor] = None, image: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Example: ```python >>> from transformers import LayoutLMv2Processor, LayoutLMv2ForSequenceClassification, set_seed >>> from PIL import Image >>> import torch >>> from datasets import load_dataset >>> set_seed(88) >>> dataset = load_dataset("rvl_cdip", split="train", streaming=True) >>> data = next(iter(dataset)) >>> image = data["image"].convert("RGB") >>> processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased") >>> model = LayoutLMv2ForSequenceClassification.from_pretrained( ... "microsoft/layoutlmv2-base-uncased", num_labels=dataset.info.features["label"].num_classes ... ) >>> encoding = processor(image, return_tensors="pt") >>> sequence_label = torch.tensor([data["label"]]) >>> outputs = model(**encoding, labels=sequence_label) >>> loss, logits = outputs.loss, outputs.logits >>> predicted_idx = logits.argmax(dim=-1).item() >>> predicted_answer = dataset.info.features["label"].names[4] >>> predicted_idx, predicted_answer (4, 'advertisement') ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device visual_shape = list(input_shape) visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1] visual_shape = torch.Size(visual_shape) final_shape = list(input_shape) final_shape[1] += visual_shape[1] final_shape = torch.Size(final_shape) visual_bbox = self.layoutlmv2._calc_visual_bbox( self.config.image_feature_pool_shape, bbox, device, final_shape ) visual_position_ids = torch.arange(0, visual_shape[1], dtype=torch.long, device=device).repeat( input_shape[0], 1 ) initial_image_embeddings = self.layoutlmv2._calc_img_embeddings( image=image, bbox=visual_bbox, position_ids=visual_position_ids, ) outputs = self.layoutlmv2( input_ids=input_ids, bbox=bbox, image=image, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] sequence_output, final_image_embeddings = outputs[0][:, :seq_length], outputs[0][:, seq_length:] cls_final_output = sequence_output[:, 0, :] # average-pool the visual embeddings pooled_initial_image_embeddings = initial_image_embeddings.mean(dim=1) pooled_final_image_embeddings = final_image_embeddings.mean(dim=1) # concatenate with cls_final_output sequence_output = torch.cat( [cls_final_output, pooled_initial_image_embeddings, pooled_final_image_embeddings], dim=1 ) sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ LayoutLMv2 Model with a token classification head on top (a linear layer on top of the text part of the hidden states) e.g. for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/), [SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and [Kleister-NDA](https://github.com/applicaai/kleister-nda). """, LAYOUTLMV2_START_DOCSTRING, ) class LayoutLMv2ForTokenClassification(LayoutLMv2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.layoutlmv2 = LayoutLMv2Model(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.layoutlmv2.embeddings.word_embeddings @add_start_docstrings_to_model_forward(LAYOUTLMV2_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, bbox: Optional[torch.LongTensor] = None, image: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. Returns: Example: ```python >>> from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification, set_seed >>> from PIL import Image >>> from datasets import load_dataset >>> set_seed(88) >>> datasets = load_dataset("nielsr/funsd", split="test") >>> labels = datasets.features["ner_tags"].feature.names >>> id2label = {v: k for v, k in enumerate(labels)} >>> processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr") >>> model = LayoutLMv2ForTokenClassification.from_pretrained( ... "microsoft/layoutlmv2-base-uncased", num_labels=len(labels) ... ) >>> data = datasets[0] >>> image = Image.open(data["image_path"]).convert("RGB") >>> words = data["words"] >>> boxes = data["bboxes"] # make sure to normalize your bounding boxes >>> word_labels = data["ner_tags"] >>> encoding = processor( ... image, ... words, ... boxes=boxes, ... word_labels=word_labels, ... padding="max_length", ... truncation=True, ... return_tensors="pt", ... ) >>> outputs = model(**encoding) >>> logits, loss = outputs.logits, outputs.loss >>> predicted_token_class_ids = logits.argmax(-1) >>> predicted_tokens_classes = [id2label[t.item()] for t in predicted_token_class_ids[0]] >>> predicted_tokens_classes[:5] ['B-ANSWER', 'B-HEADER', 'B-HEADER', 'B-HEADER', 'B-HEADER'] ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.layoutlmv2( input_ids=input_ids, bbox=bbox, image=image, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # only take the text part of the output representations sequence_output = outputs[0][:, :seq_length] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ LayoutLMv2 Model with a span classification head on top for extractive question-answering tasks such as [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the text part of the hidden-states output to compute `span start logits` and `span end logits`). """, LAYOUTLMV2_START_DOCSTRING, ) class LayoutLMv2ForQuestionAnswering(LayoutLMv2PreTrainedModel): def __init__(self, config, has_visual_segment_embedding=True): super().__init__(config) self.num_labels = config.num_labels config.has_visual_segment_embedding = has_visual_segment_embedding self.layoutlmv2 = LayoutLMv2Model(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.layoutlmv2.embeddings.word_embeddings @add_start_docstrings_to_model_forward(LAYOUTLMV2_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, bbox: Optional[torch.LongTensor] = None, image: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. Returns: Example: In this example below, we give the LayoutLMv2 model an image (of texts) and ask it a question. It will give us a prediction of what it thinks the answer is (the span of the answer within the texts parsed from the image). ```python >>> from transformers import LayoutLMv2Processor, LayoutLMv2ForQuestionAnswering, set_seed >>> import torch >>> from PIL import Image >>> from datasets import load_dataset >>> set_seed(88) >>> processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased") >>> model = LayoutLMv2ForQuestionAnswering.from_pretrained("microsoft/layoutlmv2-base-uncased") >>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa") >>> image_path = dataset["test"][0]["file"] >>> image = Image.open(image_path).convert("RGB") >>> question = "When is coffee break?" >>> encoding = processor(image, question, return_tensors="pt") >>> outputs = model(**encoding) >>> predicted_start_idx = outputs.start_logits.argmax(-1).item() >>> predicted_end_idx = outputs.end_logits.argmax(-1).item() >>> predicted_start_idx, predicted_end_idx (154, 287) >>> predicted_answer_tokens = encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1] >>> predicted_answer = processor.tokenizer.decode(predicted_answer_tokens) >>> predicted_answer # results are not very good without further fine-tuning 'council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public ... ``` ```python >>> target_start_index = torch.tensor([7]) >>> target_end_index = torch.tensor([14]) >>> outputs = model(**encoding, start_positions=target_start_index, end_positions=target_end_index) >>> predicted_answer_span_start = outputs.start_logits.argmax(-1).item() >>> predicted_answer_span_end = outputs.end_logits.argmax(-1).item() >>> predicted_answer_span_start, predicted_answer_span_end (154, 287) ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.layoutlmv2( input_ids=input_ids, bbox=bbox, image=image, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # only take the text part of the output representations sequence_output = outputs[0][:, :seq_length] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

# coding=utf-8 # Copyright 2021 Microsoft Research The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch LayoutLMv2 model.""" import math from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPooling, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, torch_int_div from ...utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, is_detectron2_available, logging, replace_return_docstrings, requires_backends, ) from .configuration_layoutlmv2 import LayoutLMv2Config # soft dependency if is_detectron2_available(): import detectron2 from detectron2.modeling import META_ARCH_REGISTRY logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "microsoft/layoutlmv2-base-uncased" _CONFIG_FOR_DOC = "LayoutLMv2Config" _TOKENIZER_FOR_DOC = "LayoutLMv2Tokenizer" LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/layoutlmv2-base-uncased", "microsoft/layoutlmv2-large-uncased", # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2 ] class LayoutLMv2Embeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super(LayoutLMv2Embeddings, self).__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size) self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size) self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size) self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) def _calc_spatial_position_embeddings(self, bbox): try: left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0]) upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1]) right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2]) lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3]) except IndexError as e: raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1]) w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0]) spatial_position_embeddings = torch.cat( [ left_position_embeddings, upper_position_embeddings, right_position_embeddings, lower_position_embeddings, h_position_embeddings, w_position_embeddings, ], dim=-1, ) return spatial_position_embeddings class LayoutLMv2SelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.fast_qkv = config.fast_qkv self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.has_relative_attention_bias = config.has_relative_attention_bias self.has_spatial_attention_bias = config.has_spatial_attention_bias if config.fast_qkv: self.qkv_linear = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=False) self.q_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size)) self.v_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size)) else: self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def compute_qkv(self, hidden_states): if self.fast_qkv: qkv = self.qkv_linear(hidden_states) q, k, v = torch.chunk(qkv, 3, dim=-1) if q.ndimension() == self.q_bias.ndimension(): q = q + self.q_bias v = v + self.v_bias else: _sz = (1,) * (q.ndimension() - 1) + (-1,) q = q + self.q_bias.view(*_sz) v = v + self.v_bias.view(*_sz) else: q = self.query(hidden_states) k = self.key(hidden_states) v = self.value(hidden_states) return q, k, v def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None, ): q, k, v = self.compute_qkv(hidden_states) # (B, L, H*D) -> (B, H, L, D) query_layer = self.transpose_for_scores(q) key_layer = self.transpose_for_scores(k) value_layer = self.transpose_for_scores(v) query_layer = query_layer / math.sqrt(self.attention_head_size) # [BSZ, NAT, L, L] attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.has_relative_attention_bias: attention_scores += rel_pos if self.has_spatial_attention_bias: attention_scores += rel_2d_pos attention_scores = attention_scores.float().masked_fill_( attention_mask.to(torch.bool), torch.finfo(attention_scores.dtype).min ) attention_probs = nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32).type_as(value_layer) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class LayoutLMv2Attention(nn.Module): def __init__(self, config): super().__init__() self.self = LayoutLMv2SelfAttention(config) self.output = LayoutLMv2SelfOutput(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None, ): self_outputs = self.self( hidden_states, attention_mask, head_mask, output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class LayoutLMv2SelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->LayoutLMv2 class LayoutLMv2Intermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->LayoutLM class LayoutLMv2Output(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class LayoutLMv2Layer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = LayoutLMv2Attention(config) self.intermediate = LayoutLMv2Intermediate(config) self.output = LayoutLMv2Output(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None, ): self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output def relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on. Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ ret = 0 if bidirectional: num_buckets //= 2 ret += (relative_position > 0).long() * num_buckets n = torch.abs(relative_position) else: n = torch.max(-relative_position, torch.zeros_like(relative_position)) # now n is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = n < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance val_if_large = max_exact + ( torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1)) ret += torch.where(is_small, n, val_if_large) return ret class LayoutLMv2Encoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([LayoutLMv2Layer(config) for _ in range(config.num_hidden_layers)]) self.has_relative_attention_bias = config.has_relative_attention_bias self.has_spatial_attention_bias = config.has_spatial_attention_bias if self.has_relative_attention_bias: self.rel_pos_bins = config.rel_pos_bins self.max_rel_pos = config.max_rel_pos self.rel_pos_onehot_size = config.rel_pos_bins self.rel_pos_bias = nn.Linear(self.rel_pos_onehot_size, config.num_attention_heads, bias=False) if self.has_spatial_attention_bias: self.max_rel_2d_pos = config.max_rel_2d_pos self.rel_2d_pos_bins = config.rel_2d_pos_bins self.rel_2d_pos_onehot_size = config.rel_2d_pos_bins self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_onehot_size, config.num_attention_heads, bias=False) self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_onehot_size, config.num_attention_heads, bias=False) self.gradient_checkpointing = False def _calculate_1d_position_embeddings(self, hidden_states, position_ids): rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1) rel_pos = relative_position_bucket( rel_pos_mat, num_buckets=self.rel_pos_bins, max_distance=self.max_rel_pos, ) rel_pos = nn.functional.one_hot(rel_pos, num_classes=self.rel_pos_onehot_size).type_as(hidden_states) rel_pos = self.rel_pos_bias(rel_pos).permute(0, 3, 1, 2) rel_pos = rel_pos.contiguous() return rel_pos def _calculate_2d_position_embeddings(self, hidden_states, bbox): position_coord_x = bbox[:, :, 0] position_coord_y = bbox[:, :, 3] rel_pos_x_2d_mat = position_coord_x.unsqueeze(-2) - position_coord_x.unsqueeze(-1) rel_pos_y_2d_mat = position_coord_y.unsqueeze(-2) - position_coord_y.unsqueeze(-1) rel_pos_x = relative_position_bucket( rel_pos_x_2d_mat, num_buckets=self.rel_2d_pos_bins, max_distance=self.max_rel_2d_pos, ) rel_pos_y = relative_position_bucket( rel_pos_y_2d_mat, num_buckets=self.rel_2d_pos_bins, max_distance=self.max_rel_2d_pos, ) rel_pos_x = nn.functional.one_hot(rel_pos_x, num_classes=self.rel_2d_pos_onehot_size).type_as(hidden_states) rel_pos_y = nn.functional.one_hot(rel_pos_y, num_classes=self.rel_2d_pos_onehot_size).type_as(hidden_states) rel_pos_x = self.rel_pos_x_bias(rel_pos_x).permute(0, 3, 1, 2) rel_pos_y = self.rel_pos_y_bias(rel_pos_y).permute(0, 3, 1, 2) rel_pos_x = rel_pos_x.contiguous() rel_pos_y = rel_pos_y.contiguous() rel_2d_pos = rel_pos_x + rel_pos_y return rel_2d_pos def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, bbox=None, position_ids=None, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None rel_pos = ( self._calculate_1d_position_embeddings(hidden_states, position_ids) if self.has_relative_attention_bias else None ) rel_2d_pos = ( self._calculate_2d_position_embeddings(hidden_states, bbox) if self.has_spatial_attention_bias else None ) for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, all_hidden_states, all_self_attentions, ] if v is not None ) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class LayoutLMv2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = LayoutLMv2Config pretrained_model_archive_map = LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST base_model_prefix = "layoutlmv2" _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, LayoutLMv2Encoder): module.gradient_checkpointing = value def my_convert_sync_batchnorm(module, process_group=None): # same as `nn.modules.SyncBatchNorm.convert_sync_batchnorm` but allowing converting from `detectron2.layers.FrozenBatchNorm2d` if isinstance(module, torch.nn.modules.batchnorm._BatchNorm): return nn.modules.SyncBatchNorm.convert_sync_batchnorm(module, process_group) module_output = module if isinstance(module, detectron2.layers.FrozenBatchNorm2d): module_output = torch.nn.SyncBatchNorm( num_features=module.num_features, eps=module.eps, affine=True, track_running_stats=True, process_group=process_group, ) module_output.weight = torch.nn.Parameter(module.weight) module_output.bias = torch.nn.Parameter(module.bias) module_output.running_mean = module.running_mean module_output.running_var = module.running_var module_output.num_batches_tracked = torch.tensor(0, dtype=torch.long, device=module.running_mean.device) for name, child in module.named_children(): module_output.add_module(name, my_convert_sync_batchnorm(child, process_group)) del module return module_output class LayoutLMv2VisualBackbone(nn.Module): def __init__(self, config): super().__init__() self.cfg = config.get_detectron2_config() meta_arch = self.cfg.MODEL.META_ARCHITECTURE model = META_ARCH_REGISTRY.get(meta_arch)(self.cfg) assert isinstance(model.backbone, detectron2.modeling.backbone.FPN) self.backbone = model.backbone assert len(self.cfg.MODEL.PIXEL_MEAN) == len(self.cfg.MODEL.PIXEL_STD) num_channels = len(self.cfg.MODEL.PIXEL_MEAN) self.register_buffer( "pixel_mean", torch.Tensor(self.cfg.MODEL.PIXEL_MEAN).view(num_channels, 1, 1), ) self.register_buffer("pixel_std", torch.Tensor(self.cfg.MODEL.PIXEL_STD).view(num_channels, 1, 1)) self.out_feature_key = "p2" if torch.are_deterministic_algorithms_enabled(): logger.warning("using `AvgPool2d` instead of `AdaptiveAvgPool2d`") input_shape = (224, 224) backbone_stride = self.backbone.output_shape()[self.out_feature_key].stride self.pool = nn.AvgPool2d( ( math.ceil(math.ceil(input_shape[0] / backbone_stride) / config.image_feature_pool_shape[0]), math.ceil(math.ceil(input_shape[1] / backbone_stride) / config.image_feature_pool_shape[1]), ) ) else: self.pool = nn.AdaptiveAvgPool2d(config.image_feature_pool_shape[:2]) if len(config.image_feature_pool_shape) == 2: config.image_feature_pool_shape.append(self.backbone.output_shape()[self.out_feature_key].channels) assert self.backbone.output_shape()[self.out_feature_key].channels == config.image_feature_pool_shape[2] def forward(self, images): images_input = ((images if torch.is_tensor(images) else images.tensor) - self.pixel_mean) / self.pixel_std features = self.backbone(images_input) features = features[self.out_feature_key] features = self.pool(features).flatten(start_dim=2).transpose(1, 2).contiguous() return features def synchronize_batch_norm(self): if not ( torch.distributed.is_available() and torch.distributed.is_initialized() and torch.distributed.get_rank() > -1 ): raise RuntimeError("Make sure torch.distributed is set up properly.") self_rank = torch.distributed.get_rank() node_size = torch.cuda.device_count() world_size = torch.distributed.get_world_size() if not (world_size & node_size == 0): raise RuntimeError("Make sure the number of processes can be divided by the number of nodes") node_global_ranks = [list(range(i * node_size, (i + 1) * node_size)) for i in range(world_size // node_size)] sync_bn_groups = [ torch.distributed.new_group(ranks=node_global_ranks[i]) for i in range(world_size // node_size) ] node_rank = self_rank // node_size self.backbone = my_convert_sync_batchnorm(self.backbone, process_group=sync_bn_groups[node_rank]) LAYOUTLMV2_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`LayoutLMv2Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ LAYOUTLMV2_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `{0}`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`LayoutLMv2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*): Bounding boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. image (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `detectron.structures.ImageList` whose `tensors` is of shape `(batch_size, num_channels, height, width)`): Batch of document images. attention_mask (`torch.FloatTensor` of shape `{0}`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `{0}`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `{0}`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class LayoutLMv2Pooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output @add_start_docstrings( "The bare LayoutLMv2 Model transformer outputting raw hidden-states without any specific head on top.", LAYOUTLMV2_START_DOCSTRING, ) class LayoutLMv2Model(LayoutLMv2PreTrainedModel): def __init__(self, config): requires_backends(self, "detectron2") super().__init__(config) self.config = config self.has_visual_segment_embedding = config.has_visual_segment_embedding self.embeddings = LayoutLMv2Embeddings(config) self.visual = LayoutLMv2VisualBackbone(config) self.visual_proj = nn.Linear(config.image_feature_pool_shape[-1], config.hidden_size) if self.has_visual_segment_embedding: self.visual_segment_embedding = nn.Parameter(nn.Embedding(1, config.hidden_size).weight[0]) self.visual_LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.visual_dropout = nn.Dropout(config.hidden_dropout_prob) self.encoder = LayoutLMv2Encoder(config) self.pooler = LayoutLMv2Pooler(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _calc_text_embeddings(self, input_ids, bbox, position_ids, token_type_ids, inputs_embeds=None): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) if inputs_embeds is None: inputs_embeds = self.embeddings.word_embeddings(input_ids) position_embeddings = self.embeddings.position_embeddings(position_ids) spatial_position_embeddings = self.embeddings._calc_spatial_position_embeddings(bbox) token_type_embeddings = self.embeddings.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + position_embeddings + spatial_position_embeddings + token_type_embeddings embeddings = self.embeddings.LayerNorm(embeddings) embeddings = self.embeddings.dropout(embeddings) return embeddings def _calc_img_embeddings(self, image, bbox, position_ids): visual_embeddings = self.visual_proj(self.visual(image)) position_embeddings = self.embeddings.position_embeddings(position_ids) spatial_position_embeddings = self.embeddings._calc_spatial_position_embeddings(bbox) embeddings = visual_embeddings + position_embeddings + spatial_position_embeddings if self.has_visual_segment_embedding: embeddings += self.visual_segment_embedding embeddings = self.visual_LayerNorm(embeddings) embeddings = self.visual_dropout(embeddings) return embeddings def _calc_visual_bbox(self, image_feature_pool_shape, bbox, device, final_shape): visual_bbox_x = torch_int_div( torch.arange( 0, 1000 * (image_feature_pool_shape[1] + 1), 1000, device=device, dtype=bbox.dtype, ), self.config.image_feature_pool_shape[1], ) visual_bbox_y = torch_int_div( torch.arange( 0, 1000 * (self.config.image_feature_pool_shape[0] + 1), 1000, device=device, dtype=bbox.dtype, ), self.config.image_feature_pool_shape[0], ) visual_bbox = torch.stack( [ visual_bbox_x[:-1].repeat(image_feature_pool_shape[0], 1), visual_bbox_y[:-1].repeat(image_feature_pool_shape[1], 1).transpose(0, 1), visual_bbox_x[1:].repeat(image_feature_pool_shape[0], 1), visual_bbox_y[1:].repeat(image_feature_pool_shape[1], 1).transpose(0, 1), ], dim=-1, ).view(-1, bbox.size(-1)) visual_bbox = visual_bbox.repeat(final_shape[0], 1, 1) return visual_bbox def _get_input_shape(self, input_ids=None, inputs_embeds=None): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: return input_ids.size() elif inputs_embeds is not None: return inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") @add_start_docstrings_to_model_forward(LAYOUTLMV2_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, bbox: Optional[torch.LongTensor] = None, image: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Return: Examples: ```python >>> from transformers import LayoutLMv2Processor, LayoutLMv2Model, set_seed >>> from PIL import Image >>> import torch >>> from datasets import load_dataset >>> set_seed(88) >>> processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased") >>> model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased") >>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa") >>> image_path = dataset["test"][0]["file"] >>> image = Image.open(image_path).convert("RGB") >>> encoding = processor(image, return_tensors="pt") >>> outputs = model(**encoding) >>> last_hidden_states = outputs.last_hidden_state >>> last_hidden_states.shape torch.Size([1, 342, 768]) ``` """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict input_shape = self._get_input_shape(input_ids, inputs_embeds) device = input_ids.device if input_ids is not None else inputs_embeds.device visual_shape = list(input_shape) visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1] visual_shape = torch.Size(visual_shape) # needs a new copy of input_shape for tracing. Otherwise wrong dimensions will occur final_shape = list(self._get_input_shape(input_ids, inputs_embeds)) final_shape[1] += visual_shape[1] final_shape = torch.Size(final_shape) visual_bbox = self._calc_visual_bbox(self.config.image_feature_pool_shape, bbox, device, final_shape) final_bbox = torch.cat([bbox, visual_bbox], dim=1) if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) visual_attention_mask = torch.ones(visual_shape, device=device) final_attention_mask = torch.cat([attention_mask, visual_attention_mask], dim=1) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) if position_ids is None: seq_length = input_shape[1] position_ids = self.embeddings.position_ids[:, :seq_length] position_ids = position_ids.expand(input_shape) visual_position_ids = torch.arange(0, visual_shape[1], dtype=torch.long, device=device).repeat( input_shape[0], 1 ) final_position_ids = torch.cat([position_ids, visual_position_ids], dim=1) if bbox is None: bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device) text_layout_emb = self._calc_text_embeddings( input_ids=input_ids, bbox=bbox, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, ) visual_emb = self._calc_img_embeddings( image=image, bbox=visual_bbox, position_ids=visual_position_ids, ) final_emb = torch.cat([text_layout_emb, visual_emb], dim=1) extended_attention_mask = final_attention_mask.unsqueeze(1).unsqueeze(2) extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min if head_mask is not None: if head_mask.dim() == 1: head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1) elif head_mask.dim() == 2: head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) head_mask = head_mask.to(dtype=next(self.parameters()).dtype) else: head_mask = [None] * self.config.num_hidden_layers encoder_outputs = self.encoder( final_emb, extended_attention_mask, bbox=final_bbox, position_ids=final_position_ids, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( """ LayoutLMv2 Model with a sequence classification head on top (a linear layer on top of the concatenation of the final hidden state of the [CLS] token, average-pooled initial visual embeddings and average-pooled final visual embeddings, e.g. for document image classification tasks such as the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset. """, LAYOUTLMV2_START_DOCSTRING, ) class LayoutLMv2ForSequenceClassification(LayoutLMv2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.layoutlmv2 = LayoutLMv2Model(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.layoutlmv2.embeddings.word_embeddings @add_start_docstrings_to_model_forward(LAYOUTLMV2_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, bbox: Optional[torch.LongTensor] = None, image: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Example: ```python >>> from transformers import LayoutLMv2Processor, LayoutLMv2ForSequenceClassification, set_seed >>> from PIL import Image >>> import torch >>> from datasets import load_dataset >>> set_seed(88) >>> dataset = load_dataset("rvl_cdip", split="train", streaming=True) >>> data = next(iter(dataset)) >>> image = data["image"].convert("RGB") >>> processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased") >>> model = LayoutLMv2ForSequenceClassification.from_pretrained( ... "microsoft/layoutlmv2-base-uncased", num_labels=dataset.info.features["label"].num_classes ... ) >>> encoding = processor(image, return_tensors="pt") >>> sequence_label = torch.tensor([data["label"]]) >>> outputs = model(**encoding, labels=sequence_label) >>> loss, logits = outputs.loss, outputs.logits >>> predicted_idx = logits.argmax(dim=-1).item() >>> predicted_answer = dataset.info.features["label"].names[4] >>> predicted_idx, predicted_answer (4, 'advertisement') ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device visual_shape = list(input_shape) visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1] visual_shape = torch.Size(visual_shape) final_shape = list(input_shape) final_shape[1] += visual_shape[1] final_shape = torch.Size(final_shape) visual_bbox = self.layoutlmv2._calc_visual_bbox( self.config.image_feature_pool_shape, bbox, device, final_shape ) visual_position_ids = torch.arange(0, visual_shape[1], dtype=torch.long, device=device).repeat( input_shape[0], 1 ) initial_image_embeddings = self.layoutlmv2._calc_img_embeddings( image=image, bbox=visual_bbox, position_ids=visual_position_ids, ) outputs = self.layoutlmv2( input_ids=input_ids, bbox=bbox, image=image, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] sequence_output, final_image_embeddings = outputs[0][:, :seq_length], outputs[0][:, seq_length:] cls_final_output = sequence_output[:, 0, :] # average-pool the visual embeddings pooled_initial_image_embeddings = initial_image_embeddings.mean(dim=1) pooled_final_image_embeddings = final_image_embeddings.mean(dim=1) # concatenate with cls_final_output sequence_output = torch.cat( [cls_final_output, pooled_initial_image_embeddings, pooled_final_image_embeddings], dim=1 ) sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ LayoutLMv2 Model with a token classification head on top (a linear layer on top of the text part of the hidden states) e.g. for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/), [SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and [Kleister-NDA](https://github.com/applicaai/kleister-nda). """, LAYOUTLMV2_START_DOCSTRING, ) class LayoutLMv2ForTokenClassification(LayoutLMv2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.layoutlmv2 = LayoutLMv2Model(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.layoutlmv2.embeddings.word_embeddings @add_start_docstrings_to_model_forward(LAYOUTLMV2_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, bbox: Optional[torch.LongTensor] = None, image: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. Returns: Example: ```python >>> from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification, set_seed >>> from PIL import Image >>> from datasets import load_dataset >>> set_seed(88) >>> datasets = load_dataset("nielsr/funsd", split="test") >>> labels = datasets.features["ner_tags"].feature.names >>> id2label = {v: k for v, k in enumerate(labels)} >>> processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr") >>> model = LayoutLMv2ForTokenClassification.from_pretrained( ... "microsoft/layoutlmv2-base-uncased", num_labels=len(labels) ... ) >>> data = datasets[0] >>> image = Image.open(data["image_path"]).convert("RGB") >>> words = data["words"] >>> boxes = data["bboxes"] # make sure to normalize your bounding boxes >>> word_labels = data["ner_tags"] >>> encoding = processor( ... image, ... words, ... boxes=boxes, ... word_labels=word_labels, ... padding="max_length", ... truncation=True, ... return_tensors="pt", ... ) >>> outputs = model(**encoding) >>> logits, loss = outputs.logits, outputs.loss >>> predicted_token_class_ids = logits.argmax(-1) >>> predicted_tokens_classes = [id2label[t.item()] for t in predicted_token_class_ids[0]] >>> predicted_tokens_classes[:5] ['B-ANSWER', 'B-HEADER', 'B-HEADER', 'B-HEADER', 'B-HEADER'] ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.layoutlmv2( input_ids=input_ids, bbox=bbox, image=image, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # only take the text part of the output representations sequence_output = outputs[0][:, :seq_length] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ LayoutLMv2 Model with a span classification head on top for extractive question-answering tasks such as [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the text part of the hidden-states output to compute `span start logits` and `span end logits`). """, LAYOUTLMV2_START_DOCSTRING, ) class LayoutLMv2ForQuestionAnswering(LayoutLMv2PreTrainedModel): def __init__(self, config, has_visual_segment_embedding=True): super().__init__(config) self.num_labels = config.num_labels config.has_visual_segment_embedding = has_visual_segment_embedding self.layoutlmv2 = LayoutLMv2Model(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.layoutlmv2.embeddings.word_embeddings @add_start_docstrings_to_model_forward(LAYOUTLMV2_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, bbox: Optional[torch.LongTensor] = None, image: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. Returns: Example: In this example below, we give the LayoutLMv2 model an image (of texts) and ask it a question. It will give us a prediction of what it thinks the answer is (the span of the answer within the texts parsed from the image). ```python >>> from transformers import LayoutLMv2Processor, LayoutLMv2ForQuestionAnswering, set_seed >>> import torch >>> from PIL import Image >>> from datasets import load_dataset >>> set_seed(88) >>> processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased") >>> model = LayoutLMv2ForQuestionAnswering.from_pretrained("microsoft/layoutlmv2-base-uncased") >>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa") >>> image_path = dataset["test"][0]["file"] >>> image = Image.open(image_path).convert("RGB") >>> question = "When is coffee break?" >>> encoding = processor(image, question, return_tensors="pt") >>> outputs = model(**encoding) >>> predicted_start_idx = outputs.start_logits.argmax(-1).item() >>> predicted_end_idx = outputs.end_logits.argmax(-1).item() >>> predicted_start_idx, predicted_end_idx (154, 287) >>> predicted_answer_tokens = encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1] >>> predicted_answer = processor.tokenizer.decode(predicted_answer_tokens) >>> predicted_answer # results are not very good without further fine-tuning 'council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public ... ``` ```python >>> target_start_index = torch.tensor([7]) >>> target_end_index = torch.tensor([14]) >>> outputs = model(**encoding, start_positions=target_start_index, end_positions=target_end_index) >>> predicted_answer_span_start = outputs.start_logits.argmax(-1).item() >>> predicted_answer_span_end = outputs.end_logits.argmax(-1).item() >>> predicted_answer_span_start, predicted_answer_span_end (154, 287) ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.layoutlmv2( input_ids=input_ids, bbox=bbox, image=image, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # only take the text part of the output representations sequence_output = outputs[0][:, :seq_length] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/roformer/configuration_roformer.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ RoFormer model configuration""" from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = { "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json", "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json", "junnyu/roformer_chinese_char_small": ( "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json" ), "junnyu/roformer_chinese_char_base": ( "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json" ), "junnyu/roformer_small_discriminator": ( "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json" ), "junnyu/roformer_small_generator": ( "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json" ), # See all RoFormer models at https://huggingface.co/models?filter=roformer } class RoFormerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`RoFormerModel`]. It is used to instantiate an RoFormer model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the RoFormer [junnyu/roformer_chinese_base](https://huggingface.co/junnyu/roformer_chinese_base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50000): Vocabulary size of the RoFormer model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`RoFormerModel`] or [`TFRoFormerModel`]. embedding_size (`int`, *optional*, defaults to None): Dimensionality of the encoder layers and the pooler layer. Defaults to the `hidden_size` if not provided. hidden_size (`int`, *optional*, defaults to 768): Dimension of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 1536): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 1536). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`RoFormerModel`] or [`TFRoFormerModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. rotary_value (`bool`, *optional*, defaults to `False`): Whether or not apply rotary position embeddings on value layer. Example: ```python >>> from transformers import RoFormerModel, RoFormerConfig >>> # Initializing a RoFormer junnyu/roformer_chinese_base style configuration >>> configuration = RoFormerConfig() >>> # Initializing a model from the junnyu/roformer_chinese_base style configuration >>> model = RoFormerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "roformer" def __init__( self, vocab_size=50000, embedding_size=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=1536, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, rotary_value=False, use_cache=True, **kwargs ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.embedding_size = hidden_size if embedding_size is None else embedding_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.rotary_value = rotary_value self.use_cache = use_cache class RoFormerOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task == "multiple-choice": dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"} else: dynamic_axis = {0: "batch", 1: "sequence"} dynamic_axis = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis), ] )

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ RoFormer model configuration""" from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = { "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json", "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json", "junnyu/roformer_chinese_char_small": ( "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json" ), "junnyu/roformer_chinese_char_base": ( "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json" ), "junnyu/roformer_small_discriminator": ( "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json" ), "junnyu/roformer_small_generator": ( "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json" ), # See all RoFormer models at https://huggingface.co/models?filter=roformer } class RoFormerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`RoFormerModel`]. It is used to instantiate an RoFormer model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the RoFormer [junnyu/roformer_chinese_base](https://huggingface.co/junnyu/roformer_chinese_base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50000): Vocabulary size of the RoFormer model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`RoFormerModel`] or [`TFRoFormerModel`]. embedding_size (`int`, *optional*, defaults to None): Dimensionality of the encoder layers and the pooler layer. Defaults to the `hidden_size` if not provided. hidden_size (`int`, *optional*, defaults to 768): Dimension of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 1536): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 1536). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`RoFormerModel`] or [`TFRoFormerModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. rotary_value (`bool`, *optional*, defaults to `False`): Whether or not apply rotary position embeddings on value layer. Example: ```python >>> from transformers import RoFormerModel, RoFormerConfig >>> # Initializing a RoFormer junnyu/roformer_chinese_base style configuration >>> configuration = RoFormerConfig() >>> # Initializing a model from the junnyu/roformer_chinese_base style configuration >>> model = RoFormerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "roformer" def __init__( self, vocab_size=50000, embedding_size=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=1536, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, rotary_value=False, use_cache=True, **kwargs ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.embedding_size = hidden_size if embedding_size is None else embedding_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.rotary_value = rotary_value self.use_cache = use_cache class RoFormerOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task == "multiple-choice": dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"} else: dynamic_axis = {0: "batch", 1: "sequence"} dynamic_axis = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis), ] )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./docs/source/en/model_doc/wav2vec2-conformer.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Wav2Vec2-Conformer ## Overview The Wav2Vec2-Conformer was added to an updated version of [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino. The official results of the model can be found in Table 3 and Table 4 of the paper. The Wav2Vec2-Conformer weights were released by the Meta AI team within the [Fairseq library](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md#pre-trained-models). Tips: - Wav2Vec2-Conformer follows the same architecture as Wav2Vec2, but replaces the *Attention*-block with a *Conformer*-block as introduced in [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100). - For the same number of layers, Wav2Vec2-Conformer requires more parameters than Wav2Vec2, but also yields an improved word error rate. - Wav2Vec2-Conformer uses the same tokenizer and feature extractor as Wav2Vec2. - Wav2Vec2-Conformer can use either no relative position embeddings, Transformer-XL-like position embeddings, or rotary position embeddings by setting the correct `config.position_embeddings_type`. This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The original code can be found [here](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec). ## Wav2Vec2ConformerConfig [[autodoc]] Wav2Vec2ConformerConfig ## Wav2Vec2Conformer specific outputs [[autodoc]] models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput ## Wav2Vec2ConformerModel [[autodoc]] Wav2Vec2ConformerModel - forward ## Wav2Vec2ConformerForCTC [[autodoc]] Wav2Vec2ConformerForCTC - forward ## Wav2Vec2ConformerForSequenceClassification [[autodoc]] Wav2Vec2ConformerForSequenceClassification - forward ## Wav2Vec2ConformerForAudioFrameClassification [[autodoc]] Wav2Vec2ConformerForAudioFrameClassification - forward ## Wav2Vec2ConformerForXVector [[autodoc]] Wav2Vec2ConformerForXVector - forward ## Wav2Vec2ConformerForPreTraining [[autodoc]] Wav2Vec2ConformerForPreTraining - forward

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Wav2Vec2-Conformer ## Overview The Wav2Vec2-Conformer was added to an updated version of [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino. The official results of the model can be found in Table 3 and Table 4 of the paper. The Wav2Vec2-Conformer weights were released by the Meta AI team within the [Fairseq library](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md#pre-trained-models). Tips: - Wav2Vec2-Conformer follows the same architecture as Wav2Vec2, but replaces the *Attention*-block with a *Conformer*-block as introduced in [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100). - For the same number of layers, Wav2Vec2-Conformer requires more parameters than Wav2Vec2, but also yields an improved word error rate. - Wav2Vec2-Conformer uses the same tokenizer and feature extractor as Wav2Vec2. - Wav2Vec2-Conformer can use either no relative position embeddings, Transformer-XL-like position embeddings, or rotary position embeddings by setting the correct `config.position_embeddings_type`. This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The original code can be found [here](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec). ## Wav2Vec2ConformerConfig [[autodoc]] Wav2Vec2ConformerConfig ## Wav2Vec2Conformer specific outputs [[autodoc]] models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput ## Wav2Vec2ConformerModel [[autodoc]] Wav2Vec2ConformerModel - forward ## Wav2Vec2ConformerForCTC [[autodoc]] Wav2Vec2ConformerForCTC - forward ## Wav2Vec2ConformerForSequenceClassification [[autodoc]] Wav2Vec2ConformerForSequenceClassification - forward ## Wav2Vec2ConformerForAudioFrameClassification [[autodoc]] Wav2Vec2ConformerForAudioFrameClassification - forward ## Wav2Vec2ConformerForXVector [[autodoc]] Wav2Vec2ConformerForXVector - forward ## Wav2Vec2ConformerForPreTraining [[autodoc]] Wav2Vec2ConformerForPreTraining - forward

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/yolos/__init__.py

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/barthez/__init__.py

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/contrastive-image-text/run_clip.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2022 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Training a CLIP like dual encoder models using text and vision encoders in the library. The script can be used to train CLIP like models for languages other than English by using a text encoder pre-trained in the desired language. Currently this script supports the following vision and text models: Vision models: ViT(https://huggingface.co/models?filter=vit), CLIP (https://huggingface.co/models?filter=clip) Text models: BERT, ROBERTa (https://huggingface.co/models?filter=fill-mask) """ import logging import os import sys from dataclasses import dataclass, field from typing import Optional import torch from datasets import load_dataset from PIL import Image from torchvision.io import ImageReadMode, read_image from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize from torchvision.transforms.functional import InterpolationMode import transformers from transformers import ( AutoFeatureExtractor, AutoModel, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/contrastive-image-text/requirements.txt") @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) feature_extractor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."}) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) freeze_vision_model: bool = field( default=False, metadata={"help": "Whether to freeze the vision model parameters or not."} ) freeze_text_model: bool = field( default=False, metadata={"help": "Whether to freeze the text model parameters or not."} ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) data_dir: Optional[str] = field(default=None, metadata={"help": "The data directory containing input files."}) image_column: Optional[str] = field( default="image_path", metadata={"help": "The name of the column in the datasets containing the full image file paths."}, ) caption_column: Optional[str] = field( default="caption", metadata={"help": "The name of the column in the datasets containing the image captions."}, ) train_file: Optional[str] = field( default=None, metadata={"help": "The input training data file (a jsonlines file)."} ) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file (a jsonlines file)."}, ) max_seq_length: Optional[int] = field( default=128, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension == "json", "`validation_file` should be a json file." dataset_name_mapping = { "image_caption_dataset.py": ("image_path", "caption"), } # We use torchvision for faster image pre-processing. The transforms are implemented as nn.Module, # so we jit it to be faster. class Transform(torch.nn.Module): def __init__(self, image_size, mean, std): super().__init__() self.transforms = torch.nn.Sequential( Resize([image_size], interpolation=InterpolationMode.BICUBIC), CenterCrop(image_size), ConvertImageDtype(torch.float), Normalize(mean, std), ) def forward(self, x) -> torch.Tensor: """`x` should be an instance of `PIL.Image.Image`""" with torch.no_grad(): x = self.transforms(x) return x def collate_fn(examples): pixel_values = torch.stack([example["pixel_values"] for example in examples]) input_ids = torch.tensor([example["input_ids"] for example in examples], dtype=torch.long) attention_mask = torch.tensor([example["attention_mask"] for example in examples], dtype=torch.long) return { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, "return_loss": True, } def main(): # 1. Parse input arguments # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_clip", model_args, data_args) # 2. Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) log_level = training_args.get_process_log_level() logger.setLevel(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # 3. Detecting last checkpoint and eventualy continue from last checkpoint last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # 4. Load dataset # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files this script will use the first column for the full image path and the second column for the # captions (unless you specify column names for this with the `image_column` and `caption_column` arguments). # if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False, data_dir=data_args.data_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file extension = data_args.train_file.split(".")[-1] if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.validation_file.split(".")[-1] if data_args.test_file is not None: data_files["test"] = data_args.test_file extension = data_args.test_file.split(".")[-1] dataset = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # 5. Load pretrained model, tokenizer, and feature extractor if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) # Load feature_extractor, in this script we only use this to get the mean and std for normalization. feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.feature_extractor_name or model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) model = AutoModel.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) config = model.config def _freeze_params(module): for param in module.parameters(): param.requires_grad = False if model_args.freeze_vision_model: _freeze_params(model.vision_model) if model_args.freeze_text_model: _freeze_params(model.text_model) # set seed for torch dataloaders set_seed(training_args.seed) # Preprocessing the datasets. # We need to tokenize inputs and targets. if training_args.do_train: column_names = dataset["train"].column_names elif training_args.do_eval: column_names = dataset["validation"].column_names elif training_args.do_predict: column_names = dataset["test"].column_names else: logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.") return # 6. Get the column names for input/target. dataset_columns = dataset_name_mapping.get(data_args.dataset_name, None) if data_args.image_column is None: image_column = dataset_columns[0] if dataset_columns is not None else column_names[0] else: image_column = data_args.image_column if image_column not in column_names: raise ValueError( f"--image_column' value '{data_args.image_column}' needs to be one of: {', '.join(column_names)}" ) if data_args.caption_column is None: caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1] else: caption_column = data_args.caption_column if caption_column not in column_names: raise ValueError( f"--caption_column' value '{data_args.caption_column}' needs to be one of: {', '.join(column_names)}" ) # 7. Preprocessing the datasets. # Initialize torchvision transforms and jit it for faster processing. image_transformations = Transform( config.vision_config.image_size, feature_extractor.image_mean, feature_extractor.image_std ) image_transformations = torch.jit.script(image_transformations) # Preprocessing the datasets. # We need to tokenize input captions and transform the images. def tokenize_captions(examples): captions = [caption for caption in examples[caption_column]] text_inputs = tokenizer(captions, max_length=data_args.max_seq_length, padding="max_length", truncation=True) examples["input_ids"] = text_inputs.input_ids examples["attention_mask"] = text_inputs.attention_mask return examples def transform_images(examples): images = [read_image(image_file, mode=ImageReadMode.RGB) for image_file in examples[image_column]] examples["pixel_values"] = [image_transformations(image) for image in images] return examples def filter_corrupt_images(examples): """remove problematic images""" valid_images = [] for image_file in examples[image_column]: try: Image.open(image_file) valid_images.append(True) except Exception: valid_images.append(False) return valid_images if training_args.do_train: if "train" not in dataset: raise ValueError("--do_train requires a train dataset") train_dataset = dataset["train"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) train_dataset = train_dataset.filter( filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers ) train_dataset = train_dataset.map( function=tokenize_captions, batched=True, remove_columns=[col for col in column_names if col != image_column], num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on train dataset", ) # Transform images on the fly as doing it on the whole dataset takes too much time. train_dataset.set_transform(transform_images) if training_args.do_eval: if "validation" not in dataset: raise ValueError("--do_eval requires a train validation") eval_dataset = dataset["validation"] if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) eval_dataset = eval_dataset.filter( filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers ) eval_dataset = eval_dataset.map( function=tokenize_captions, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[col for col in column_names if col != image_column], load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on validation dataset", ) # Transform images on the fly as doing it on the whole dataset takes too much time. eval_dataset.set_transform(transform_images) if training_args.do_predict: if "test" not in dataset: raise ValueError("--do_predict requires a test dataset") test_dataset = dataset["test"] if data_args.max_eval_samples is not None: max_eval_samples = min(len(test_dataset), data_args.max_eval_samples) test_dataset = test_dataset.select(range(max_eval_samples)) test_dataset = test_dataset.filter( filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers ) test_dataset = test_dataset.map( function=tokenize_captions, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[col for col in column_names if col != image_column], load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on test dataset", ) # Transform images on the fly as doing it on the whole dataset takes too much time. test_dataset.set_transform(transform_images) # 8. Initalize our trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, data_collator=collate_fn, ) # 9. Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() # 10. Evaluation if training_args.do_eval: metrics = trainer.evaluate() trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # 11. Write Training Stats and push to hub. kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "contrastive-image-text-modeling"} if data_args.dataset_name is not None: kwargs["dataset_tags"] = data_args.dataset_name if data_args.dataset_config_name is not None: kwargs["dataset_args"] = data_args.dataset_config_name kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: kwargs["dataset"] = data_args.dataset_name if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2022 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Training a CLIP like dual encoder models using text and vision encoders in the library. The script can be used to train CLIP like models for languages other than English by using a text encoder pre-trained in the desired language. Currently this script supports the following vision and text models: Vision models: ViT(https://huggingface.co/models?filter=vit), CLIP (https://huggingface.co/models?filter=clip) Text models: BERT, ROBERTa (https://huggingface.co/models?filter=fill-mask) """ import logging import os import sys from dataclasses import dataclass, field from typing import Optional import torch from datasets import load_dataset from PIL import Image from torchvision.io import ImageReadMode, read_image from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize from torchvision.transforms.functional import InterpolationMode import transformers from transformers import ( AutoFeatureExtractor, AutoModel, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/contrastive-image-text/requirements.txt") @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) feature_extractor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."}) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) freeze_vision_model: bool = field( default=False, metadata={"help": "Whether to freeze the vision model parameters or not."} ) freeze_text_model: bool = field( default=False, metadata={"help": "Whether to freeze the text model parameters or not."} ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) data_dir: Optional[str] = field(default=None, metadata={"help": "The data directory containing input files."}) image_column: Optional[str] = field( default="image_path", metadata={"help": "The name of the column in the datasets containing the full image file paths."}, ) caption_column: Optional[str] = field( default="caption", metadata={"help": "The name of the column in the datasets containing the image captions."}, ) train_file: Optional[str] = field( default=None, metadata={"help": "The input training data file (a jsonlines file)."} ) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file (a jsonlines file)."}, ) max_seq_length: Optional[int] = field( default=128, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension == "json", "`validation_file` should be a json file." dataset_name_mapping = { "image_caption_dataset.py": ("image_path", "caption"), } # We use torchvision for faster image pre-processing. The transforms are implemented as nn.Module, # so we jit it to be faster. class Transform(torch.nn.Module): def __init__(self, image_size, mean, std): super().__init__() self.transforms = torch.nn.Sequential( Resize([image_size], interpolation=InterpolationMode.BICUBIC), CenterCrop(image_size), ConvertImageDtype(torch.float), Normalize(mean, std), ) def forward(self, x) -> torch.Tensor: """`x` should be an instance of `PIL.Image.Image`""" with torch.no_grad(): x = self.transforms(x) return x def collate_fn(examples): pixel_values = torch.stack([example["pixel_values"] for example in examples]) input_ids = torch.tensor([example["input_ids"] for example in examples], dtype=torch.long) attention_mask = torch.tensor([example["attention_mask"] for example in examples], dtype=torch.long) return { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, "return_loss": True, } def main(): # 1. Parse input arguments # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_clip", model_args, data_args) # 2. Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) log_level = training_args.get_process_log_level() logger.setLevel(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # 3. Detecting last checkpoint and eventualy continue from last checkpoint last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # 4. Load dataset # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files this script will use the first column for the full image path and the second column for the # captions (unless you specify column names for this with the `image_column` and `caption_column` arguments). # if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False, data_dir=data_args.data_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file extension = data_args.train_file.split(".")[-1] if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.validation_file.split(".")[-1] if data_args.test_file is not None: data_files["test"] = data_args.test_file extension = data_args.test_file.split(".")[-1] dataset = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # 5. Load pretrained model, tokenizer, and feature extractor if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) # Load feature_extractor, in this script we only use this to get the mean and std for normalization. feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.feature_extractor_name or model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) model = AutoModel.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) config = model.config def _freeze_params(module): for param in module.parameters(): param.requires_grad = False if model_args.freeze_vision_model: _freeze_params(model.vision_model) if model_args.freeze_text_model: _freeze_params(model.text_model) # set seed for torch dataloaders set_seed(training_args.seed) # Preprocessing the datasets. # We need to tokenize inputs and targets. if training_args.do_train: column_names = dataset["train"].column_names elif training_args.do_eval: column_names = dataset["validation"].column_names elif training_args.do_predict: column_names = dataset["test"].column_names else: logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.") return # 6. Get the column names for input/target. dataset_columns = dataset_name_mapping.get(data_args.dataset_name, None) if data_args.image_column is None: image_column = dataset_columns[0] if dataset_columns is not None else column_names[0] else: image_column = data_args.image_column if image_column not in column_names: raise ValueError( f"--image_column' value '{data_args.image_column}' needs to be one of: {', '.join(column_names)}" ) if data_args.caption_column is None: caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1] else: caption_column = data_args.caption_column if caption_column not in column_names: raise ValueError( f"--caption_column' value '{data_args.caption_column}' needs to be one of: {', '.join(column_names)}" ) # 7. Preprocessing the datasets. # Initialize torchvision transforms and jit it for faster processing. image_transformations = Transform( config.vision_config.image_size, feature_extractor.image_mean, feature_extractor.image_std ) image_transformations = torch.jit.script(image_transformations) # Preprocessing the datasets. # We need to tokenize input captions and transform the images. def tokenize_captions(examples): captions = [caption for caption in examples[caption_column]] text_inputs = tokenizer(captions, max_length=data_args.max_seq_length, padding="max_length", truncation=True) examples["input_ids"] = text_inputs.input_ids examples["attention_mask"] = text_inputs.attention_mask return examples def transform_images(examples): images = [read_image(image_file, mode=ImageReadMode.RGB) for image_file in examples[image_column]] examples["pixel_values"] = [image_transformations(image) for image in images] return examples def filter_corrupt_images(examples): """remove problematic images""" valid_images = [] for image_file in examples[image_column]: try: Image.open(image_file) valid_images.append(True) except Exception: valid_images.append(False) return valid_images if training_args.do_train: if "train" not in dataset: raise ValueError("--do_train requires a train dataset") train_dataset = dataset["train"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) train_dataset = train_dataset.filter( filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers ) train_dataset = train_dataset.map( function=tokenize_captions, batched=True, remove_columns=[col for col in column_names if col != image_column], num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on train dataset", ) # Transform images on the fly as doing it on the whole dataset takes too much time. train_dataset.set_transform(transform_images) if training_args.do_eval: if "validation" not in dataset: raise ValueError("--do_eval requires a train validation") eval_dataset = dataset["validation"] if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) eval_dataset = eval_dataset.filter( filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers ) eval_dataset = eval_dataset.map( function=tokenize_captions, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[col for col in column_names if col != image_column], load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on validation dataset", ) # Transform images on the fly as doing it on the whole dataset takes too much time. eval_dataset.set_transform(transform_images) if training_args.do_predict: if "test" not in dataset: raise ValueError("--do_predict requires a test dataset") test_dataset = dataset["test"] if data_args.max_eval_samples is not None: max_eval_samples = min(len(test_dataset), data_args.max_eval_samples) test_dataset = test_dataset.select(range(max_eval_samples)) test_dataset = test_dataset.filter( filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers ) test_dataset = test_dataset.map( function=tokenize_captions, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[col for col in column_names if col != image_column], load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on test dataset", ) # Transform images on the fly as doing it on the whole dataset takes too much time. test_dataset.set_transform(transform_images) # 8. Initalize our trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, data_collator=collate_fn, ) # 9. Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() # 10. Evaluation if training_args.do_eval: metrics = trainer.evaluate() trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # 11. Write Training Stats and push to hub. kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "contrastive-image-text-modeling"} if data_args.dataset_name is not None: kwargs["dataset_tags"] = data_args.dataset_name if data_args.dataset_config_name is not None: kwargs["dataset_args"] = data_args.dataset_config_name kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: kwargs["dataset"] = data_args.dataset_name if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) if __name__ == "__main__": main()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/bert-loses-patience/README.md

# Patience-based Early Exit Patience-based Early Exit (PABEE) is a plug-and-play inference method for pretrained language models. We have already implemented it on BERT and ALBERT. Basically, you can make your LM faster and more robust with PABEE. It can even improve the performance of ALBERT on GLUE. The only sacrifice is that the batch size can only be 1. Learn more in the paper ["BERT Loses Patience: Fast and Robust Inference with Early Exit"](https://arxiv.org/abs/2006.04152) and the official [GitHub repo](https://github.com/JetRunner/PABEE).  ## Training You can fine-tune a pretrained language model (you can choose from BERT and ALBERT) and train the internal classifiers by: ```bash export GLUE_DIR=/path/to/glue_data export TASK_NAME=MRPC python ./run_glue_with_pabee.py \ --model_type albert \ --model_name_or_path bert-base-uncased/albert-base-v2 \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --do_lower_case \ --data_dir "$GLUE_DIR/$TASK_NAME" \ --max_seq_length 128 \ --per_gpu_train_batch_size 32 \ --per_gpu_eval_batch_size 32 \ --learning_rate 2e-5 \ --save_steps 50 \ --logging_steps 50 \ --num_train_epochs 5 \ --output_dir /path/to/save/ \ --evaluate_during_training ``` ## Inference You can inference with different patience settings by: ```bash export GLUE_DIR=/path/to/glue_data export TASK_NAME=MRPC python ./run_glue_with_pabee.py \ --model_type albert \ --model_name_or_path /path/to/save/ \ --task_name $TASK_NAME \ --do_eval \ --do_lower_case \ --data_dir "$GLUE_DIR/$TASK_NAME" \ --max_seq_length 128 \ --per_gpu_eval_batch_size 1 \ --learning_rate 2e-5 \ --logging_steps 50 \ --num_train_epochs 15 \ --output_dir /path/to/save/ \ --eval_all_checkpoints \ --patience 3,4,5,6,7,8 ``` where `patience` can be a list of patience settings, separated by a comma. It will help determine which patience works best. When evaluating on a regression task (STS-B), you may add `--regression_threshold 0.1` to define the regression threshold. ## Results On the GLUE dev set: | Model | \#Param | Speed | CoLA | MNLI | MRPC | QNLI | QQP | RTE | SST\-2 | STS\-B | |--------------|---------|--------|-------|-------|-------|-------|-------|-------|--------|--------| | ALBERT\-base | 12M | | 58\.9 | 84\.6 | 89\.5 | 91\.7 | 89\.6 | 78\.6 | 92\.8 | 89\.5 | | \+PABEE | 12M | 1\.57x | 61\.2 | 85\.1 | 90\.0 | 91\.8 | 89\.6 | 80\.1 | 93\.0 | 90\.1 | | Model | \#Param | Speed\-up | MNLI | SST\-2 | STS\-B | |---------------|---------|-----------|-------|--------|--------| | BERT\-base | 108M | | 84\.5 | 92\.1 | 88\.9 | | \+PABEE | 108M | 1\.62x | 83\.6 | 92\.0 | 88\.7 | | ALBERT\-large | 18M | | 86\.4 | 94\.9 | 90\.4 | | \+PABEE | 18M | 2\.42x | 86\.8 | 95\.2 | 90\.6 | ## Citation If you find this resource useful, please consider citing the following paper: ```bibtex @misc{zhou2020bert, title={BERT Loses Patience: Fast and Robust Inference with Early Exit}, author={Wangchunshu Zhou and Canwen Xu and Tao Ge and Julian McAuley and Ke Xu and Furu Wei}, year={2020}, eprint={2006.04152}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

# Patience-based Early Exit Patience-based Early Exit (PABEE) is a plug-and-play inference method for pretrained language models. We have already implemented it on BERT and ALBERT. Basically, you can make your LM faster and more robust with PABEE. It can even improve the performance of ALBERT on GLUE. The only sacrifice is that the batch size can only be 1. Learn more in the paper ["BERT Loses Patience: Fast and Robust Inference with Early Exit"](https://arxiv.org/abs/2006.04152) and the official [GitHub repo](https://github.com/JetRunner/PABEE).  ## Training You can fine-tune a pretrained language model (you can choose from BERT and ALBERT) and train the internal classifiers by: ```bash export GLUE_DIR=/path/to/glue_data export TASK_NAME=MRPC python ./run_glue_with_pabee.py \ --model_type albert \ --model_name_or_path bert-base-uncased/albert-base-v2 \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --do_lower_case \ --data_dir "$GLUE_DIR/$TASK_NAME" \ --max_seq_length 128 \ --per_gpu_train_batch_size 32 \ --per_gpu_eval_batch_size 32 \ --learning_rate 2e-5 \ --save_steps 50 \ --logging_steps 50 \ --num_train_epochs 5 \ --output_dir /path/to/save/ \ --evaluate_during_training ``` ## Inference You can inference with different patience settings by: ```bash export GLUE_DIR=/path/to/glue_data export TASK_NAME=MRPC python ./run_glue_with_pabee.py \ --model_type albert \ --model_name_or_path /path/to/save/ \ --task_name $TASK_NAME \ --do_eval \ --do_lower_case \ --data_dir "$GLUE_DIR/$TASK_NAME" \ --max_seq_length 128 \ --per_gpu_eval_batch_size 1 \ --learning_rate 2e-5 \ --logging_steps 50 \ --num_train_epochs 15 \ --output_dir /path/to/save/ \ --eval_all_checkpoints \ --patience 3,4,5,6,7,8 ``` where `patience` can be a list of patience settings, separated by a comma. It will help determine which patience works best. When evaluating on a regression task (STS-B), you may add `--regression_threshold 0.1` to define the regression threshold. ## Results On the GLUE dev set: | Model | \#Param | Speed | CoLA | MNLI | MRPC | QNLI | QQP | RTE | SST\-2 | STS\-B | |--------------|---------|--------|-------|-------|-------|-------|-------|-------|--------|--------| | ALBERT\-base | 12M | | 58\.9 | 84\.6 | 89\.5 | 91\.7 | 89\.6 | 78\.6 | 92\.8 | 89\.5 | | \+PABEE | 12M | 1\.57x | 61\.2 | 85\.1 | 90\.0 | 91\.8 | 89\.6 | 80\.1 | 93\.0 | 90\.1 | | Model | \#Param | Speed\-up | MNLI | SST\-2 | STS\-B | |---------------|---------|-----------|-------|--------|--------| | BERT\-base | 108M | | 84\.5 | 92\.1 | 88\.9 | | \+PABEE | 108M | 1\.62x | 83\.6 | 92\.0 | 88\.7 | | ALBERT\-large | 18M | | 86\.4 | 94\.9 | 90\.4 | | \+PABEE | 18M | 2\.42x | 86\.8 | 95\.2 | 90\.6 | ## Citation If you find this resource useful, please consider citing the following paper: ```bibtex @misc{zhou2020bert, title={BERT Loses Patience: Fast and Robust Inference with Early Exit}, author={Wangchunshu Zhou and Canwen Xu and Tao Ge and Julian McAuley and Ke Xu and Furu Wei}, year={2020}, eprint={2006.04152}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/seq2seq-distillation/finetune_pegasus_xsum.sh

#!/usr/bin/env bash export PYTHONPATH="../":"${PYTHONPATH}" # From appendix C of paper https://arxiv.org/abs/1912.08777 # Set --gradient_accumulation_steps so that effective batch size is 256 (2*128, 4*64, 8*32, 16*16) python finetune.py \ --learning_rate=1e-4 \ --do_train \ --do_predict \ --n_val 1000 \ --val_check_interval 0.25 \ --max_source_length 512 --max_target_length 56 \ --freeze_embeds --label_smoothing 0.1 --adafactor --task summarization_xsum \ "$@"

#!/usr/bin/env bash export PYTHONPATH="../":"${PYTHONPATH}" # From appendix C of paper https://arxiv.org/abs/1912.08777 # Set --gradient_accumulation_steps so that effective batch size is 256 (2*128, 4*64, 8*32, 16*16) python finetune.py \ --learning_rate=1e-4 \ --do_train \ --do_predict \ --n_val 1000 \ --val_check_interval 0.25 \ --max_source_length 512 --max_target_length 56 \ --freeze_embeds --label_smoothing 0.1 --adafactor --task summarization_xsum \ "$@"

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/dpt/modeling_dpt.py

# coding=utf-8 # Copyright 2022 Intel Labs, OpenMMLab and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch DPT (Dense Prediction Transformers) model. This implementation is heavily inspired by OpenMMLab's implementation, found here: https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/decode_heads/dpt_head.py. """ import collections.abc import math from typing import List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN from ...file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPooling, DepthEstimatorOutput, SemanticSegmenterOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import logging from .configuration_dpt import DPTConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "DPTConfig" _FEAT_EXTRACTOR_FOR_DOC = "DPTFeatureExtractor" # Base docstring _CHECKPOINT_FOR_DOC = "Intel/dpt-large" _EXPECTED_OUTPUT_SHAPE = [1, 577, 1024] DPT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "Intel/dpt-large", # See all DPT models at https://huggingface.co/models?filter=dpt ] class DPTViTEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. """ def __init__(self, config): super().__init__() self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.patch_embeddings = DPTViTPatchEmbeddings(config) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def _resize_pos_embed(self, posemb, grid_size_height, grid_size_width, start_index=1): posemb_tok = posemb[:, :start_index] posemb_grid = posemb[0, start_index:] old_grid_size = int(math.sqrt(len(posemb_grid))) posemb_grid = posemb_grid.reshape(1, old_grid_size, old_grid_size, -1).permute(0, 3, 1, 2) posemb_grid = nn.functional.interpolate(posemb_grid, size=(grid_size_height, grid_size_width), mode="bilinear") posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, grid_size_height * grid_size_width, -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def forward(self, pixel_values): batch_size, num_channels, height, width = pixel_values.shape # possibly interpolate position encodings to handle varying image sizes patch_size = self.config.patch_size position_embeddings = self._resize_pos_embed( self.position_embeddings, height // patch_size, width // patch_size ) embeddings = self.patch_embeddings(pixel_values) batch_size, seq_len, _ = embeddings.size() # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token embeddings = embeddings + position_embeddings embeddings = self.dropout(embeddings) return embeddings class DPTViTPatchEmbeddings(nn.Module): """ Image to Patch Embedding. """ def __init__(self, config): super().__init__() image_size, patch_size = config.image_size, config.patch_size num_channels, hidden_size = config.num_channels, config.hidden_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_patches = num_patches self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values): batch_size, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) return embeddings # Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->DPT class DPTViTSelfAttention(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->DPT class DPTViTSelfOutput(nn.Module): """ The residual connection is defined in DPTLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: DPTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class DPTViTAttention(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.attention = DPTViTSelfAttention(config) self.output = DPTViTSelfOutput(config) self.pruned_heads = set() # Copied from transformers.models.vit.modeling_vit.ViTAttention.prune_heads def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) # Copied from transformers.models.vit.modeling_vit.ViTAttention.forward def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->DPT class DPTViTIntermediate(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->DPT class DPTViTOutput(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states # copied from transformers.models.vit.modeling_vit.ViTLayer with ViTConfig->DPTConfig, ViTAttention->DPTViTAttention, ViTIntermediate->DPTViTIntermediate, ViTOutput->DPTViTOutput class DPTViTLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: DPTConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = DPTViTAttention(config) self.intermediate = DPTViTIntermediate(config) self.output = DPTViTOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs # copied from transformers.models.vit.modeling_vit.ViTEncoder with ViTConfig -> DPTConfig, ViTLayer->DPTViTLayer class DPTViTEncoder(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([DPTViTLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class DPTReassembleStage(nn.Module): """ This class reassembles the hidden states of the backbone into image-like feature representations at various resolutions. This happens in 3 stages: 1. Map the N + 1 tokens to a set of N tokens, by taking into account the readout ([CLS]) token according to `config.readout_type`. 2. Project the channel dimension of the hidden states according to `config.neck_hidden_sizes`. 3. Resizing the spatial dimensions (height, width). Args: config (`[DPTConfig]`): Model configuration class defining the model architecture. """ def __init__(self, config): super().__init__() self.config = config self.layers = nn.ModuleList() for i, factor in zip(range(len(config.neck_hidden_sizes)), config.reassemble_factors): self.layers.append(DPTReassembleLayer(config, channels=config.neck_hidden_sizes[i], factor=factor)) if config.readout_type == "project": self.readout_projects = nn.ModuleList() for _ in range(len(config.neck_hidden_sizes)): self.readout_projects.append( nn.Sequential(nn.Linear(2 * config.hidden_size, config.hidden_size), ACT2FN[config.hidden_act]) ) def forward(self, hidden_states: List[torch.Tensor]) -> List[torch.Tensor]: """ Args: hidden_states (`List[torch.FloatTensor]`, each of shape `(batch_size, sequence_length + 1, hidden_size)`): List of hidden states from the backbone. """ out = [] for i, hidden_state in enumerate(hidden_states): # reshape to (B, C, H, W) hidden_state, cls_token = hidden_state[:, 1:], hidden_state[:, 0] batch_size, sequence_length, num_channels = hidden_state.shape size = int(math.sqrt(sequence_length)) hidden_state = hidden_state.reshape(batch_size, size, size, num_channels) hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous() feature_shape = hidden_state.shape if self.config.readout_type == "project": # reshape to (B, H*W, C) hidden_state = hidden_state.flatten(2).permute((0, 2, 1)) readout = cls_token.unsqueeze(1).expand_as(hidden_state) # concatenate the readout token to the hidden states and project hidden_state = self.readout_projects[i](torch.cat((hidden_state, readout), -1)) # reshape back to (B, C, H, W) hidden_state = hidden_state.permute(0, 2, 1).reshape(feature_shape) elif self.config.readout_type == "add": hidden_state = hidden_state.flatten(2) + cls_token.unsqueeze(-1) hidden_state = hidden_state.reshape(feature_shape) hidden_state = self.layers[i](hidden_state) out.append(hidden_state) return out class DPTReassembleLayer(nn.Module): def __init__(self, config, channels, factor): super().__init__() # projection self.projection = nn.Conv2d(in_channels=config.hidden_size, out_channels=channels, kernel_size=1) # up/down sampling depending on factor if factor > 1: self.resize = nn.ConvTranspose2d(channels, channels, kernel_size=factor, stride=factor, padding=0) elif factor == 1: self.resize = nn.Identity() elif factor < 1: # so should downsample self.resize = nn.Conv2d(channels, channels, kernel_size=3, stride=int(1 / factor), padding=1) def forward(self, hidden_state): hidden_state = self.projection(hidden_state) hidden_state = self.resize(hidden_state) return hidden_state class DPTFeatureFusionStage(nn.Module): def __init__(self, config): super().__init__() self.layers = nn.ModuleList() for _ in range(len(config.neck_hidden_sizes)): self.layers.append(DPTFeatureFusionLayer(config)) def forward(self, hidden_states): # reversing the hidden_states, we start from the last hidden_states = hidden_states[::-1] fused_hidden_states = [] # first layer only uses the last hidden_state fused_hidden_state = self.layers[0](hidden_states[0]) fused_hidden_states.append(fused_hidden_state) # looping from the last layer to the second for hidden_state, layer in zip(hidden_states[1:], self.layers[1:]): fused_hidden_state = layer(fused_hidden_state, hidden_state) fused_hidden_states.append(fused_hidden_state) return fused_hidden_states class DPTPreActResidualLayer(nn.Module): """ ResidualConvUnit, pre-activate residual unit. Args: config (`[DPTConfig]`): Model configuration class defining the model architecture. """ def __init__(self, config): super().__init__() self.use_batch_norm = config.use_batch_norm_in_fusion_residual self.activation1 = ACT2FN["relu"] self.convolution1 = nn.Conv2d( config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=not self.use_batch_norm, ) self.activation2 = ACT2FN["relu"] self.convolution2 = nn.Conv2d( config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=not self.use_batch_norm, ) if self.use_batch_norm: self.batch_norm1 = nn.BatchNorm2d(config.fusion_hidden_size) self.batch_norm2 = nn.BatchNorm2d(config.fusion_hidden_size) def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: residual = hidden_state hidden_state = self.activation1(hidden_state) hidden_state = self.convolution1(hidden_state) if self.use_batch_norm: hidden_state = self.batch_norm1(hidden_state) hidden_state = self.activation2(hidden_state) hidden_state = self.convolution2(hidden_state) if self.use_batch_norm: hidden_state = self.batch_norm2(hidden_state) return hidden_state + residual class DPTFeatureFusionLayer(nn.Module): """Feature fusion layer, merges feature maps from different stages. Args: config (`[DPTConfig]`): Model configuration class defining the model architecture. align_corners (`bool`, *optional*, defaults to `True`): The align_corner setting for bilinear upsample. """ def __init__(self, config, align_corners=True): super().__init__() self.align_corners = align_corners self.projection = nn.Conv2d(config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=1, bias=True) self.residual_layer1 = DPTPreActResidualLayer(config) self.residual_layer2 = DPTPreActResidualLayer(config) def forward(self, hidden_state, residual=None): if residual is not None: if hidden_state.shape != residual.shape: residual = nn.functional.interpolate( residual, size=(hidden_state.shape[2], hidden_state.shape[3]), mode="bilinear", align_corners=False ) hidden_state = hidden_state + self.residual_layer1(residual) hidden_state = self.residual_layer2(hidden_state) hidden_state = nn.functional.interpolate( hidden_state, scale_factor=2, mode="bilinear", align_corners=self.align_corners ) hidden_state = self.projection(hidden_state) return hidden_state class DPTPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DPTConfig base_model_prefix = "dpt" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, DPTViTEncoder): module.gradient_checkpointing = value DPT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ViTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ DPT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`ViTFeatureExtractor`]. See [`ViTFeatureExtractor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare DPT Model transformer outputting raw hidden-states without any specific head on top.", DPT_START_DOCSTRING, ) class DPTModel(DPTPreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config # vit encoder self.embeddings = DPTViTEmbeddings(config) self.encoder = DPTViTEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = DPTViTPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(DPT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: torch.FloatTensor, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings(pixel_values) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) return head_outputs + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) # Copied from transformers.models.vit.modeling_vit.ViTPooler with ViT->DPT class DPTViTPooler(nn.Module): def __init__(self, config: DPTConfig): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class DPTNeck(nn.Module): """ DPTNeck. A neck is a module that is normally used between the backbone and the head. It takes a list of tensors as input and produces another list of tensors as output. For DPT, it includes 2 stages: * DPTReassembleStage * DPTFeatureFusionStage. Args: config (dict): config dict. """ def __init__(self, config): super().__init__() self.config = config # postprocessing self.reassemble_stage = DPTReassembleStage(config) self.convs = nn.ModuleList() for channel in config.neck_hidden_sizes: self.convs.append(nn.Conv2d(channel, config.fusion_hidden_size, kernel_size=3, padding=1, bias=False)) # fusion self.fusion_stage = DPTFeatureFusionStage(config) def forward(self, hidden_states: List[torch.Tensor]) -> List[torch.Tensor]: if not isinstance(hidden_states, list): raise ValueError("hidden_states should be a list of tensors") if len(hidden_states) != len(self.config.neck_hidden_sizes): raise ValueError("The number of hidden states should be equal to the number of neck hidden sizes.") # postprocess hidden states features = self.reassemble_stage(hidden_states) features = [self.convs[i](feature) for i, feature in enumerate(features)] # fusion blocks output = self.fusion_stage(features) return output class DPTDepthEstimationHead(nn.Module): """ Output head head consisting of 3 convolutional layers. It progressively halves the feature dimension and upsamples the predictions to the input resolution after the first convolutional layer (details can be found in the paper's supplementary material). """ def __init__(self, config): super().__init__() self.config = config features = config.fusion_hidden_size self.head = nn.Sequential( nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True), nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), ACT2FN["relu"], nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), ACT2FN["relu"], ) def forward(self, hidden_states: List[torch.Tensor]) -> torch.Tensor: # use last features hidden_states = hidden_states[self.config.head_in_index] predicted_depth = self.head(hidden_states) predicted_depth = predicted_depth.squeeze(dim=1) return predicted_depth @add_start_docstrings( """ DPT Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2. """, DPT_START_DOCSTRING, ) class DPTForDepthEstimation(DPTPreTrainedModel): def __init__(self, config): super().__init__(config) self.dpt = DPTModel(config, add_pooling_layer=False) # Neck self.neck = DPTNeck(config) # Depth estimation head self.head = DPTDepthEstimationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(DPT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DepthEstimatorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, head_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], DepthEstimatorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Ground truth depth estimation maps for computing the loss. Returns: Examples: ```python >>> from transformers import DPTFeatureExtractor, DPTForDepthEstimation >>> import torch >>> import numpy as np >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large") >>> model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") >>> # prepare image for the model >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) ... predicted_depth = outputs.predicted_depth >>> # interpolate to original size >>> prediction = torch.nn.functional.interpolate( ... predicted_depth.unsqueeze(1), ... size=image.size[::-1], ... mode="bicubic", ... align_corners=False, ... ) >>> # visualize the prediction >>> output = prediction.squeeze().cpu().numpy() >>> formatted = (output * 255 / np.max(output)).astype("uint8") >>> depth = Image.fromarray(formatted) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) outputs = self.dpt( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) hidden_states = outputs.hidden_states if return_dict else outputs[1] # only keep certain features based on config.backbone_out_indices # note that the hidden_states also include the initial embeddings hidden_states = [ feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices ] hidden_states = self.neck(hidden_states) predicted_depth = self.head(hidden_states) loss = None if labels is not None: raise NotImplementedError("Training is not implemented yet") if not return_dict: if output_hidden_states: output = (predicted_depth,) + outputs[1:] else: output = (predicted_depth,) + outputs[2:] return ((loss,) + output) if loss is not None else output return DepthEstimatorOutput( loss=loss, predicted_depth=predicted_depth, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, ) class DPTSemanticSegmentationHead(nn.Module): def __init__(self, config): super().__init__() self.config = config features = config.fusion_hidden_size self.head = nn.Sequential( nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(features), ACT2FN["relu"], nn.Dropout(config.semantic_classifier_dropout), nn.Conv2d(features, config.num_labels, kernel_size=1), nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True), ) def forward(self, hidden_states: List[torch.Tensor]) -> torch.Tensor: # use last features hidden_states = hidden_states[self.config.head_in_index] logits = self.head(hidden_states) return logits class DPTAuxiliaryHead(nn.Module): def __init__(self, config): super().__init__() features = config.fusion_hidden_size self.head = nn.Sequential( nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(features), ACT2FN["relu"], nn.Dropout(0.1, False), nn.Conv2d(features, config.num_labels, kernel_size=1), ) def forward(self, hidden_states): logits = self.head(hidden_states) return logits @add_start_docstrings( """ DPT Model with a semantic segmentation head on top e.g. for ADE20k, CityScapes. """, DPT_START_DOCSTRING, ) class DPTForSemanticSegmentation(DPTPreTrainedModel): def __init__(self, config): super().__init__(config) self.dpt = DPTModel(config, add_pooling_layer=False) # Neck self.neck = DPTNeck(config) # Segmentation head(s) self.head = DPTSemanticSegmentationHead(config) self.auxiliary_head = DPTAuxiliaryHead(config) if config.use_auxiliary_head else None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(DPT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SemanticSegmenterOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import DPTFeatureExtractor, DPTForSemanticSegmentation >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large-ade") >>> model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade") >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) outputs = self.dpt( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) hidden_states = outputs.hidden_states if return_dict else outputs[1] # only keep certain features based on config.backbone_out_indices # note that the hidden_states also include the initial embeddings hidden_states = [ feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices ] hidden_states = self.neck(hidden_states) logits = self.head(hidden_states) auxiliary_logits = None if self.auxiliary_head is not None: auxiliary_logits = self.auxiliary_head(hidden_states[-1]) loss = None if labels is not None: if self.config.num_labels == 1: raise ValueError("The number of labels should be greater than one") else: # upsample logits to the images' original size upsampled_logits = nn.functional.interpolate( logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) if auxiliary_logits is not None: upsampled_auxiliary_logits = nn.functional.interpolate( auxiliary_logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) # compute weighted loss loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index) main_loss = loss_fct(upsampled_logits, labels) auxiliary_loss = loss_fct(upsampled_auxiliary_logits, labels) loss = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss if not return_dict: if output_hidden_states: output = (logits,) + outputs[1:] else: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SemanticSegmenterOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, )

# coding=utf-8 # Copyright 2022 Intel Labs, OpenMMLab and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch DPT (Dense Prediction Transformers) model. This implementation is heavily inspired by OpenMMLab's implementation, found here: https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/decode_heads/dpt_head.py. """ import collections.abc import math from typing import List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN from ...file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPooling, DepthEstimatorOutput, SemanticSegmenterOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import logging from .configuration_dpt import DPTConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "DPTConfig" _FEAT_EXTRACTOR_FOR_DOC = "DPTFeatureExtractor" # Base docstring _CHECKPOINT_FOR_DOC = "Intel/dpt-large" _EXPECTED_OUTPUT_SHAPE = [1, 577, 1024] DPT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "Intel/dpt-large", # See all DPT models at https://huggingface.co/models?filter=dpt ] class DPTViTEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. """ def __init__(self, config): super().__init__() self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.patch_embeddings = DPTViTPatchEmbeddings(config) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def _resize_pos_embed(self, posemb, grid_size_height, grid_size_width, start_index=1): posemb_tok = posemb[:, :start_index] posemb_grid = posemb[0, start_index:] old_grid_size = int(math.sqrt(len(posemb_grid))) posemb_grid = posemb_grid.reshape(1, old_grid_size, old_grid_size, -1).permute(0, 3, 1, 2) posemb_grid = nn.functional.interpolate(posemb_grid, size=(grid_size_height, grid_size_width), mode="bilinear") posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, grid_size_height * grid_size_width, -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def forward(self, pixel_values): batch_size, num_channels, height, width = pixel_values.shape # possibly interpolate position encodings to handle varying image sizes patch_size = self.config.patch_size position_embeddings = self._resize_pos_embed( self.position_embeddings, height // patch_size, width // patch_size ) embeddings = self.patch_embeddings(pixel_values) batch_size, seq_len, _ = embeddings.size() # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token embeddings = embeddings + position_embeddings embeddings = self.dropout(embeddings) return embeddings class DPTViTPatchEmbeddings(nn.Module): """ Image to Patch Embedding. """ def __init__(self, config): super().__init__() image_size, patch_size = config.image_size, config.patch_size num_channels, hidden_size = config.num_channels, config.hidden_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_patches = num_patches self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values): batch_size, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) return embeddings # Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->DPT class DPTViTSelfAttention(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->DPT class DPTViTSelfOutput(nn.Module): """ The residual connection is defined in DPTLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: DPTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class DPTViTAttention(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.attention = DPTViTSelfAttention(config) self.output = DPTViTSelfOutput(config) self.pruned_heads = set() # Copied from transformers.models.vit.modeling_vit.ViTAttention.prune_heads def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) # Copied from transformers.models.vit.modeling_vit.ViTAttention.forward def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->DPT class DPTViTIntermediate(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->DPT class DPTViTOutput(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states # copied from transformers.models.vit.modeling_vit.ViTLayer with ViTConfig->DPTConfig, ViTAttention->DPTViTAttention, ViTIntermediate->DPTViTIntermediate, ViTOutput->DPTViTOutput class DPTViTLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: DPTConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = DPTViTAttention(config) self.intermediate = DPTViTIntermediate(config) self.output = DPTViTOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs # copied from transformers.models.vit.modeling_vit.ViTEncoder with ViTConfig -> DPTConfig, ViTLayer->DPTViTLayer class DPTViTEncoder(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([DPTViTLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class DPTReassembleStage(nn.Module): """ This class reassembles the hidden states of the backbone into image-like feature representations at various resolutions. This happens in 3 stages: 1. Map the N + 1 tokens to a set of N tokens, by taking into account the readout ([CLS]) token according to `config.readout_type`. 2. Project the channel dimension of the hidden states according to `config.neck_hidden_sizes`. 3. Resizing the spatial dimensions (height, width). Args: config (`[DPTConfig]`): Model configuration class defining the model architecture. """ def __init__(self, config): super().__init__() self.config = config self.layers = nn.ModuleList() for i, factor in zip(range(len(config.neck_hidden_sizes)), config.reassemble_factors): self.layers.append(DPTReassembleLayer(config, channels=config.neck_hidden_sizes[i], factor=factor)) if config.readout_type == "project": self.readout_projects = nn.ModuleList() for _ in range(len(config.neck_hidden_sizes)): self.readout_projects.append( nn.Sequential(nn.Linear(2 * config.hidden_size, config.hidden_size), ACT2FN[config.hidden_act]) ) def forward(self, hidden_states: List[torch.Tensor]) -> List[torch.Tensor]: """ Args: hidden_states (`List[torch.FloatTensor]`, each of shape `(batch_size, sequence_length + 1, hidden_size)`): List of hidden states from the backbone. """ out = [] for i, hidden_state in enumerate(hidden_states): # reshape to (B, C, H, W) hidden_state, cls_token = hidden_state[:, 1:], hidden_state[:, 0] batch_size, sequence_length, num_channels = hidden_state.shape size = int(math.sqrt(sequence_length)) hidden_state = hidden_state.reshape(batch_size, size, size, num_channels) hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous() feature_shape = hidden_state.shape if self.config.readout_type == "project": # reshape to (B, H*W, C) hidden_state = hidden_state.flatten(2).permute((0, 2, 1)) readout = cls_token.unsqueeze(1).expand_as(hidden_state) # concatenate the readout token to the hidden states and project hidden_state = self.readout_projects[i](torch.cat((hidden_state, readout), -1)) # reshape back to (B, C, H, W) hidden_state = hidden_state.permute(0, 2, 1).reshape(feature_shape) elif self.config.readout_type == "add": hidden_state = hidden_state.flatten(2) + cls_token.unsqueeze(-1) hidden_state = hidden_state.reshape(feature_shape) hidden_state = self.layers[i](hidden_state) out.append(hidden_state) return out class DPTReassembleLayer(nn.Module): def __init__(self, config, channels, factor): super().__init__() # projection self.projection = nn.Conv2d(in_channels=config.hidden_size, out_channels=channels, kernel_size=1) # up/down sampling depending on factor if factor > 1: self.resize = nn.ConvTranspose2d(channels, channels, kernel_size=factor, stride=factor, padding=0) elif factor == 1: self.resize = nn.Identity() elif factor < 1: # so should downsample self.resize = nn.Conv2d(channels, channels, kernel_size=3, stride=int(1 / factor), padding=1) def forward(self, hidden_state): hidden_state = self.projection(hidden_state) hidden_state = self.resize(hidden_state) return hidden_state class DPTFeatureFusionStage(nn.Module): def __init__(self, config): super().__init__() self.layers = nn.ModuleList() for _ in range(len(config.neck_hidden_sizes)): self.layers.append(DPTFeatureFusionLayer(config)) def forward(self, hidden_states): # reversing the hidden_states, we start from the last hidden_states = hidden_states[::-1] fused_hidden_states = [] # first layer only uses the last hidden_state fused_hidden_state = self.layers[0](hidden_states[0]) fused_hidden_states.append(fused_hidden_state) # looping from the last layer to the second for hidden_state, layer in zip(hidden_states[1:], self.layers[1:]): fused_hidden_state = layer(fused_hidden_state, hidden_state) fused_hidden_states.append(fused_hidden_state) return fused_hidden_states class DPTPreActResidualLayer(nn.Module): """ ResidualConvUnit, pre-activate residual unit. Args: config (`[DPTConfig]`): Model configuration class defining the model architecture. """ def __init__(self, config): super().__init__() self.use_batch_norm = config.use_batch_norm_in_fusion_residual self.activation1 = ACT2FN["relu"] self.convolution1 = nn.Conv2d( config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=not self.use_batch_norm, ) self.activation2 = ACT2FN["relu"] self.convolution2 = nn.Conv2d( config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=not self.use_batch_norm, ) if self.use_batch_norm: self.batch_norm1 = nn.BatchNorm2d(config.fusion_hidden_size) self.batch_norm2 = nn.BatchNorm2d(config.fusion_hidden_size) def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: residual = hidden_state hidden_state = self.activation1(hidden_state) hidden_state = self.convolution1(hidden_state) if self.use_batch_norm: hidden_state = self.batch_norm1(hidden_state) hidden_state = self.activation2(hidden_state) hidden_state = self.convolution2(hidden_state) if self.use_batch_norm: hidden_state = self.batch_norm2(hidden_state) return hidden_state + residual class DPTFeatureFusionLayer(nn.Module): """Feature fusion layer, merges feature maps from different stages. Args: config (`[DPTConfig]`): Model configuration class defining the model architecture. align_corners (`bool`, *optional*, defaults to `True`): The align_corner setting for bilinear upsample. """ def __init__(self, config, align_corners=True): super().__init__() self.align_corners = align_corners self.projection = nn.Conv2d(config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=1, bias=True) self.residual_layer1 = DPTPreActResidualLayer(config) self.residual_layer2 = DPTPreActResidualLayer(config) def forward(self, hidden_state, residual=None): if residual is not None: if hidden_state.shape != residual.shape: residual = nn.functional.interpolate( residual, size=(hidden_state.shape[2], hidden_state.shape[3]), mode="bilinear", align_corners=False ) hidden_state = hidden_state + self.residual_layer1(residual) hidden_state = self.residual_layer2(hidden_state) hidden_state = nn.functional.interpolate( hidden_state, scale_factor=2, mode="bilinear", align_corners=self.align_corners ) hidden_state = self.projection(hidden_state) return hidden_state class DPTPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DPTConfig base_model_prefix = "dpt" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, DPTViTEncoder): module.gradient_checkpointing = value DPT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ViTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ DPT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`ViTFeatureExtractor`]. See [`ViTFeatureExtractor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare DPT Model transformer outputting raw hidden-states without any specific head on top.", DPT_START_DOCSTRING, ) class DPTModel(DPTPreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config # vit encoder self.embeddings = DPTViTEmbeddings(config) self.encoder = DPTViTEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = DPTViTPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(DPT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: torch.FloatTensor, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings(pixel_values) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) return head_outputs + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) # Copied from transformers.models.vit.modeling_vit.ViTPooler with ViT->DPT class DPTViTPooler(nn.Module): def __init__(self, config: DPTConfig): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class DPTNeck(nn.Module): """ DPTNeck. A neck is a module that is normally used between the backbone and the head. It takes a list of tensors as input and produces another list of tensors as output. For DPT, it includes 2 stages: * DPTReassembleStage * DPTFeatureFusionStage. Args: config (dict): config dict. """ def __init__(self, config): super().__init__() self.config = config # postprocessing self.reassemble_stage = DPTReassembleStage(config) self.convs = nn.ModuleList() for channel in config.neck_hidden_sizes: self.convs.append(nn.Conv2d(channel, config.fusion_hidden_size, kernel_size=3, padding=1, bias=False)) # fusion self.fusion_stage = DPTFeatureFusionStage(config) def forward(self, hidden_states: List[torch.Tensor]) -> List[torch.Tensor]: if not isinstance(hidden_states, list): raise ValueError("hidden_states should be a list of tensors") if len(hidden_states) != len(self.config.neck_hidden_sizes): raise ValueError("The number of hidden states should be equal to the number of neck hidden sizes.") # postprocess hidden states features = self.reassemble_stage(hidden_states) features = [self.convs[i](feature) for i, feature in enumerate(features)] # fusion blocks output = self.fusion_stage(features) return output class DPTDepthEstimationHead(nn.Module): """ Output head head consisting of 3 convolutional layers. It progressively halves the feature dimension and upsamples the predictions to the input resolution after the first convolutional layer (details can be found in the paper's supplementary material). """ def __init__(self, config): super().__init__() self.config = config features = config.fusion_hidden_size self.head = nn.Sequential( nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True), nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), ACT2FN["relu"], nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), ACT2FN["relu"], ) def forward(self, hidden_states: List[torch.Tensor]) -> torch.Tensor: # use last features hidden_states = hidden_states[self.config.head_in_index] predicted_depth = self.head(hidden_states) predicted_depth = predicted_depth.squeeze(dim=1) return predicted_depth @add_start_docstrings( """ DPT Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2. """, DPT_START_DOCSTRING, ) class DPTForDepthEstimation(DPTPreTrainedModel): def __init__(self, config): super().__init__(config) self.dpt = DPTModel(config, add_pooling_layer=False) # Neck self.neck = DPTNeck(config) # Depth estimation head self.head = DPTDepthEstimationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(DPT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DepthEstimatorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, head_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], DepthEstimatorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Ground truth depth estimation maps for computing the loss. Returns: Examples: ```python >>> from transformers import DPTFeatureExtractor, DPTForDepthEstimation >>> import torch >>> import numpy as np >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large") >>> model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") >>> # prepare image for the model >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) ... predicted_depth = outputs.predicted_depth >>> # interpolate to original size >>> prediction = torch.nn.functional.interpolate( ... predicted_depth.unsqueeze(1), ... size=image.size[::-1], ... mode="bicubic", ... align_corners=False, ... ) >>> # visualize the prediction >>> output = prediction.squeeze().cpu().numpy() >>> formatted = (output * 255 / np.max(output)).astype("uint8") >>> depth = Image.fromarray(formatted) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) outputs = self.dpt( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) hidden_states = outputs.hidden_states if return_dict else outputs[1] # only keep certain features based on config.backbone_out_indices # note that the hidden_states also include the initial embeddings hidden_states = [ feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices ] hidden_states = self.neck(hidden_states) predicted_depth = self.head(hidden_states) loss = None if labels is not None: raise NotImplementedError("Training is not implemented yet") if not return_dict: if output_hidden_states: output = (predicted_depth,) + outputs[1:] else: output = (predicted_depth,) + outputs[2:] return ((loss,) + output) if loss is not None else output return DepthEstimatorOutput( loss=loss, predicted_depth=predicted_depth, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, ) class DPTSemanticSegmentationHead(nn.Module): def __init__(self, config): super().__init__() self.config = config features = config.fusion_hidden_size self.head = nn.Sequential( nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(features), ACT2FN["relu"], nn.Dropout(config.semantic_classifier_dropout), nn.Conv2d(features, config.num_labels, kernel_size=1), nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True), ) def forward(self, hidden_states: List[torch.Tensor]) -> torch.Tensor: # use last features hidden_states = hidden_states[self.config.head_in_index] logits = self.head(hidden_states) return logits class DPTAuxiliaryHead(nn.Module): def __init__(self, config): super().__init__() features = config.fusion_hidden_size self.head = nn.Sequential( nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(features), ACT2FN["relu"], nn.Dropout(0.1, False), nn.Conv2d(features, config.num_labels, kernel_size=1), ) def forward(self, hidden_states): logits = self.head(hidden_states) return logits @add_start_docstrings( """ DPT Model with a semantic segmentation head on top e.g. for ADE20k, CityScapes. """, DPT_START_DOCSTRING, ) class DPTForSemanticSegmentation(DPTPreTrainedModel): def __init__(self, config): super().__init__(config) self.dpt = DPTModel(config, add_pooling_layer=False) # Neck self.neck = DPTNeck(config) # Segmentation head(s) self.head = DPTSemanticSegmentationHead(config) self.auxiliary_head = DPTAuxiliaryHead(config) if config.use_auxiliary_head else None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(DPT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SemanticSegmenterOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import DPTFeatureExtractor, DPTForSemanticSegmentation >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large-ade") >>> model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade") >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) outputs = self.dpt( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) hidden_states = outputs.hidden_states if return_dict else outputs[1] # only keep certain features based on config.backbone_out_indices # note that the hidden_states also include the initial embeddings hidden_states = [ feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices ] hidden_states = self.neck(hidden_states) logits = self.head(hidden_states) auxiliary_logits = None if self.auxiliary_head is not None: auxiliary_logits = self.auxiliary_head(hidden_states[-1]) loss = None if labels is not None: if self.config.num_labels == 1: raise ValueError("The number of labels should be greater than one") else: # upsample logits to the images' original size upsampled_logits = nn.functional.interpolate( logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) if auxiliary_logits is not None: upsampled_auxiliary_logits = nn.functional.interpolate( auxiliary_logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) # compute weighted loss loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index) main_loss = loss_fct(upsampled_logits, labels) auxiliary_loss = loss_fct(upsampled_auxiliary_logits, labels) loss = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss if not return_dict: if output_hidden_states: output = (logits,) + outputs[1:] else: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SemanticSegmenterOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./utils/check_repo.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import inspect import os import re import warnings from collections import OrderedDict from difflib import get_close_matches from pathlib import Path from transformers import is_flax_available, is_tf_available, is_torch_available from transformers.models.auto import get_values from transformers.utils import ENV_VARS_TRUE_VALUES # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_repo.py PATH_TO_TRANSFORMERS = "src/transformers" PATH_TO_TESTS = "tests" PATH_TO_DOC = "docs/source/en" # Update this list with models that are supposed to be private. PRIVATE_MODELS = [ "DPRSpanPredictor", "LongT5Stack", "RealmBertModel", "T5Stack", "SwitchTransformersStack", "TFDPRSpanPredictor", ] # Update this list for models that are not tested with a comment explaining the reason it should not be. # Being in this list is an exception and should **not** be the rule. IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [ # models to ignore for not tested "CLIPSegDecoder", # Building part of bigger (tested) model. "TableTransformerEncoder", # Building part of bigger (tested) model. "TableTransformerDecoder", # Building part of bigger (tested) model. "TimeSeriesTransformerEncoder", # Building part of bigger (tested) model. "TimeSeriesTransformerDecoder", # Building part of bigger (tested) model. "JukeboxVQVAE", # Building part of bigger (tested) model. "JukeboxPrior", # Building part of bigger (tested) model. "DeformableDetrEncoder", # Building part of bigger (tested) model. "DeformableDetrDecoder", # Building part of bigger (tested) model. "OPTDecoder", # Building part of bigger (tested) model. "WhisperDecoder", # Building part of bigger (tested) model. "WhisperEncoder", # Building part of bigger (tested) model. "DecisionTransformerGPT2Model", # Building part of bigger (tested) model. "SegformerDecodeHead", # Building part of bigger (tested) model. "PLBartEncoder", # Building part of bigger (tested) model. "PLBartDecoder", # Building part of bigger (tested) model. "PLBartDecoderWrapper", # Building part of bigger (tested) model. "BigBirdPegasusEncoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoderWrapper", # Building part of bigger (tested) model. "DetrEncoder", # Building part of bigger (tested) model. "DetrDecoder", # Building part of bigger (tested) model. "DetrDecoderWrapper", # Building part of bigger (tested) model. "ConditionalDetrEncoder", # Building part of bigger (tested) model. "ConditionalDetrDecoder", # Building part of bigger (tested) model. "M2M100Encoder", # Building part of bigger (tested) model. "M2M100Decoder", # Building part of bigger (tested) model. "MCTCTEncoder", # Building part of bigger (tested) model. "Speech2TextEncoder", # Building part of bigger (tested) model. "Speech2TextDecoder", # Building part of bigger (tested) model. "LEDEncoder", # Building part of bigger (tested) model. "LEDDecoder", # Building part of bigger (tested) model. "BartDecoderWrapper", # Building part of bigger (tested) model. "BartEncoder", # Building part of bigger (tested) model. "BertLMHeadModel", # Needs to be setup as decoder. "BlenderbotSmallEncoder", # Building part of bigger (tested) model. "BlenderbotSmallDecoderWrapper", # Building part of bigger (tested) model. "BlenderbotEncoder", # Building part of bigger (tested) model. "BlenderbotDecoderWrapper", # Building part of bigger (tested) model. "MBartEncoder", # Building part of bigger (tested) model. "MBartDecoderWrapper", # Building part of bigger (tested) model. "MegatronBertLMHeadModel", # Building part of bigger (tested) model. "MegatronBertEncoder", # Building part of bigger (tested) model. "MegatronBertDecoder", # Building part of bigger (tested) model. "MegatronBertDecoderWrapper", # Building part of bigger (tested) model. "MvpDecoderWrapper", # Building part of bigger (tested) model. "MvpEncoder", # Building part of bigger (tested) model. "PegasusEncoder", # Building part of bigger (tested) model. "PegasusDecoderWrapper", # Building part of bigger (tested) model. "PegasusXEncoder", # Building part of bigger (tested) model. "PegasusXDecoder", # Building part of bigger (tested) model. "PegasusXDecoderWrapper", # Building part of bigger (tested) model. "DPREncoder", # Building part of bigger (tested) model. "ProphetNetDecoderWrapper", # Building part of bigger (tested) model. "RealmBertModel", # Building part of bigger (tested) model. "RealmReader", # Not regular model. "RealmScorer", # Not regular model. "RealmForOpenQA", # Not regular model. "ReformerForMaskedLM", # Needs to be setup as decoder. "Speech2Text2DecoderWrapper", # Building part of bigger (tested) model. "TFDPREncoder", # Building part of bigger (tested) model. "TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?) "TFRobertaForMultipleChoice", # TODO: fix "TrOCRDecoderWrapper", # Building part of bigger (tested) model. "TFWhisperEncoder", # Building part of bigger (tested) model. "TFWhisperDecoder", # Building part of bigger (tested) model. "SeparableConv1D", # Building part of bigger (tested) model. "FlaxBartForCausalLM", # Building part of bigger (tested) model. "FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM. "OPTDecoderWrapper", "TFSegformerDecodeHead", # Not a regular model. ] # Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't # trigger the common tests. TEST_FILES_WITH_NO_COMMON_TESTS = [ "models/decision_transformer/test_modeling_decision_transformer.py", "models/camembert/test_modeling_camembert.py", "models/mt5/test_modeling_flax_mt5.py", "models/mbart/test_modeling_mbart.py", "models/mt5/test_modeling_mt5.py", "models/pegasus/test_modeling_pegasus.py", "models/camembert/test_modeling_tf_camembert.py", "models/mt5/test_modeling_tf_mt5.py", "models/xlm_roberta/test_modeling_tf_xlm_roberta.py", "models/xlm_roberta/test_modeling_flax_xlm_roberta.py", "models/xlm_prophetnet/test_modeling_xlm_prophetnet.py", "models/xlm_roberta/test_modeling_xlm_roberta.py", "models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py", "models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py", "models/decision_transformer/test_modeling_decision_transformer.py", ] # Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and # should **not** be the rule. IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [ # models to ignore for model xxx mapping "CLIPSegForImageSegmentation", "CLIPSegVisionModel", "CLIPSegTextModel", "EsmForProteinFolding", "TimeSeriesTransformerForPrediction", "JukeboxVQVAE", "JukeboxPrior", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "DPTForDepthEstimation", "DecisionTransformerGPT2Model", "GLPNForDepthEstimation", "ViltForImagesAndTextClassification", "ViltForImageAndTextRetrieval", "ViltForTokenClassification", "ViltForMaskedLM", "XGLMEncoder", "XGLMDecoder", "XGLMDecoderWrapper", "PerceiverForMultimodalAutoencoding", "PerceiverForOpticalFlow", "SegformerDecodeHead", "TFSegformerDecodeHead", "FlaxBeitForMaskedImageModeling", "PLBartEncoder", "PLBartDecoder", "PLBartDecoderWrapper", "BeitForMaskedImageModeling", "CLIPTextModel", "CLIPTextModelWithProjection", "CLIPVisionModel", "CLIPVisionModelWithProjection", "GroupViTTextModel", "GroupViTVisionModel", "TFCLIPTextModel", "TFCLIPVisionModel", "TFGroupViTTextModel", "TFGroupViTVisionModel", "FlaxCLIPTextModel", "FlaxCLIPVisionModel", "FlaxWav2Vec2ForCTC", "DetrForSegmentation", "ConditionalDetrForSegmentation", "DPRReader", "FlaubertForQuestionAnswering", "FlavaImageCodebook", "FlavaTextModel", "FlavaImageModel", "FlavaMultimodalModel", "GPT2DoubleHeadsModel", "LayoutLMForQuestionAnswering", "LukeForMaskedLM", "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", "OpenAIGPTDoubleHeadsModel", "OwlViTTextModel", "OwlViTVisionModel", "OwlViTForObjectDetection", "RagModel", "RagSequenceForGeneration", "RagTokenForGeneration", "RealmEmbedder", "RealmForOpenQA", "RealmScorer", "RealmReader", "TFDPRReader", "TFGPT2DoubleHeadsModel", "TFLayoutLMForQuestionAnswering", "TFOpenAIGPTDoubleHeadsModel", "TFRagModel", "TFRagSequenceForGeneration", "TFRagTokenForGeneration", "Wav2Vec2ForCTC", "HubertForCTC", "SEWForCTC", "SEWDForCTC", "XLMForQuestionAnswering", "XLNetForQuestionAnswering", "SeparableConv1D", "VisualBertForRegionToPhraseAlignment", "VisualBertForVisualReasoning", "VisualBertForQuestionAnswering", "VisualBertForMultipleChoice", "TFWav2Vec2ForCTC", "TFHubertForCTC", "MaskFormerForInstanceSegmentation", "XCLIPVisionModel", "XCLIPTextModel", ] # Update this list for models that have multiple model types for the same # model doc MODEL_TYPE_TO_DOC_MAPPING = OrderedDict( [ ("data2vec-text", "data2vec"), ("data2vec-audio", "data2vec"), ("data2vec-vision", "data2vec"), ("donut-swin", "donut"), ] ) # This is to make sure the transformers module imported is the one in the repo. spec = importlib.util.spec_from_file_location( "transformers", os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), submodule_search_locations=[PATH_TO_TRANSFORMERS], ) transformers = spec.loader.load_module() def check_model_list(): """Check the model list inside the transformers library.""" # Get the models from the directory structure of `src/transformers/models/` models_dir = os.path.join(PATH_TO_TRANSFORMERS, "models") _models = [] for model in os.listdir(models_dir): model_dir = os.path.join(models_dir, model) if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir): _models.append(model) # Get the models from the directory structure of `src/transformers/models/` models = [model for model in dir(transformers.models) if not model.startswith("__")] missing_models = sorted(list(set(_models).difference(models))) if missing_models: raise Exception( f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}." ) # If some modeling modules should be ignored for all checks, they should be added in the nested list # _ignore_modules of this function. def get_model_modules(): """Get the model modules inside the transformers library.""" _ignore_modules = [ "modeling_auto", "modeling_encoder_decoder", "modeling_marian", "modeling_mmbt", "modeling_outputs", "modeling_retribert", "modeling_utils", "modeling_flax_auto", "modeling_flax_encoder_decoder", "modeling_flax_utils", "modeling_speech_encoder_decoder", "modeling_flax_speech_encoder_decoder", "modeling_flax_vision_encoder_decoder", "modeling_transfo_xl_utilities", "modeling_tf_auto", "modeling_tf_encoder_decoder", "modeling_tf_outputs", "modeling_tf_pytorch_utils", "modeling_tf_utils", "modeling_tf_transfo_xl_utilities", "modeling_tf_vision_encoder_decoder", "modeling_vision_encoder_decoder", ] modules = [] for model in dir(transformers.models): # There are some magic dunder attributes in the dir, we ignore them if not model.startswith("__"): model_module = getattr(transformers.models, model) for submodule in dir(model_module): if submodule.startswith("modeling") and submodule not in _ignore_modules: modeling_module = getattr(model_module, submodule) if inspect.ismodule(modeling_module): modules.append(modeling_module) return modules def get_models(module, include_pretrained=False): """Get the objects in module that are models.""" models = [] model_classes = (transformers.PreTrainedModel, transformers.TFPreTrainedModel, transformers.FlaxPreTrainedModel) for attr_name in dir(module): if not include_pretrained and ("Pretrained" in attr_name or "PreTrained" in attr_name): continue attr = getattr(module, attr_name) if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__: models.append((attr_name, attr)) return models def is_a_private_model(model): """Returns True if the model should not be in the main init.""" if model in PRIVATE_MODELS: return True # Wrapper, Encoder and Decoder are all privates if model.endswith("Wrapper"): return True if model.endswith("Encoder"): return True if model.endswith("Decoder"): return True return False def check_models_are_in_init(): """Checks all models defined in the library are in the main init.""" models_not_in_init = [] dir_transformers = dir(transformers) for module in get_model_modules(): models_not_in_init += [ model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers ] # Remove private models models_not_in_init = [model for model in models_not_in_init if not is_a_private_model(model)] if len(models_not_in_init) > 0: raise Exception(f"The following models should be in the main init: {','.join(models_not_in_init)}.") # If some test_modeling files should be ignored when checking models are all tested, they should be added in the # nested list _ignore_files of this function. def get_model_test_files(): """Get the model test files. The returned files should NOT contain the `tests` (i.e. `PATH_TO_TESTS` defined in this script). They will be considered as paths relative to `tests`. A caller has to use `os.path.join(PATH_TO_TESTS, ...)` to access the files. """ _ignore_files = [ "test_modeling_common", "test_modeling_encoder_decoder", "test_modeling_flax_encoder_decoder", "test_modeling_flax_speech_encoder_decoder", "test_modeling_marian", "test_modeling_tf_common", "test_modeling_tf_encoder_decoder", ] test_files = [] # Check both `PATH_TO_TESTS` and `PATH_TO_TESTS/models` model_test_root = os.path.join(PATH_TO_TESTS, "models") model_test_dirs = [] for x in os.listdir(model_test_root): x = os.path.join(model_test_root, x) if os.path.isdir(x): model_test_dirs.append(x) for target_dir in [PATH_TO_TESTS] + model_test_dirs: for file_or_dir in os.listdir(target_dir): path = os.path.join(target_dir, file_or_dir) if os.path.isfile(path): filename = os.path.split(path)[-1] if "test_modeling" in filename and not os.path.splitext(filename)[0] in _ignore_files: file = os.path.join(*path.split(os.sep)[1:]) test_files.append(file) return test_files # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class # for the all_model_classes variable. def find_tested_models(test_file): """Parse the content of test_file to detect what's in all_model_classes""" # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the class with open(os.path.join(PATH_TO_TESTS, test_file), "r", encoding="utf-8", newline="\n") as f: content = f.read() all_models = re.findall(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content) # Check with one less parenthesis as well all_models += re.findall(r"all_model_classes\s+=\s+\(([^\)]*)\)", content) if len(all_models) > 0: model_tested = [] for entry in all_models: for line in entry.split(","): name = line.strip() if len(name) > 0: model_tested.append(name) return model_tested def check_models_are_tested(module, test_file): """Check models defined in module are tested in test_file.""" # XxxPreTrainedModel are not tested defined_models = get_models(module) tested_models = find_tested_models(test_file) if tested_models is None: if test_file.replace(os.path.sep, "/") in TEST_FILES_WITH_NO_COMMON_TESTS: return return [ f"{test_file} should define `all_model_classes` to apply common tests to the models it tests. " + "If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file " + "`utils/check_repo.py`." ] failures = [] for model_name, _ in defined_models: if model_name not in tested_models and model_name not in IGNORE_NON_TESTED: failures.append( f"{model_name} is defined in {module.__name__} but is not tested in " + f"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file." + "If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`" + "in the file `utils/check_repo.py`." ) return failures def check_all_models_are_tested(): """Check all models are properly tested.""" modules = get_model_modules() test_files = get_model_test_files() failures = [] for module in modules: test_file = [file for file in test_files if f"test_{module.__name__.split('.')[-1]}.py" in file] if len(test_file) == 0: failures.append(f"{module.__name__} does not have its corresponding test file {test_file}.") elif len(test_file) > 1: failures.append(f"{module.__name__} has several test files: {test_file}.") else: test_file = test_file[0] new_failures = check_models_are_tested(module, test_file) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def get_all_auto_configured_models(): """Return the list of all models in at least one auto class.""" result = set() # To avoid duplicates we concatenate all model classes in a set. if is_torch_available(): for attr_name in dir(transformers.models.auto.modeling_auto): if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_auto, attr_name))) if is_tf_available(): for attr_name in dir(transformers.models.auto.modeling_tf_auto): if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_tf_auto, attr_name))) if is_flax_available(): for attr_name in dir(transformers.models.auto.modeling_flax_auto): if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_flax_auto, attr_name))) return [cls for cls in result] def ignore_unautoclassed(model_name): """Rules to determine if `name` should be in an auto class.""" # Special white list if model_name in IGNORE_NON_AUTO_CONFIGURED: return True # Encoder and Decoder should be ignored if "Encoder" in model_name or "Decoder" in model_name: return True return False def check_models_are_auto_configured(module, all_auto_models): """Check models defined in module are each in an auto class.""" defined_models = get_models(module) failures = [] for model_name, _ in defined_models: if model_name not in all_auto_models and not ignore_unautoclassed(model_name): failures.append( f"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. " "If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file " "`utils/check_repo.py`." ) return failures def check_all_models_are_auto_configured(): """Check all models are each in an auto class.""" missing_backends = [] if not is_torch_available(): missing_backends.append("PyTorch") if not is_tf_available(): missing_backends.append("TensorFlow") if not is_flax_available(): missing_backends.append("Flax") if len(missing_backends) > 0: missing = ", ".join(missing_backends) if os.getenv("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES: raise Exception( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}." ) else: warnings.warn( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you " "didn't make any change in one of those backends modeling files, you should probably execute the " "command above to be on the safe side." ) modules = get_model_modules() all_auto_models = get_all_auto_configured_models() failures = [] for module in modules: new_failures = check_models_are_auto_configured(module, all_auto_models) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) _re_decorator = re.compile(r"^\s*@(\S+)\s+$") def check_decorator_order(filename): """Check that in the test file `filename` the slow decorator is always last.""" with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() decorator_before = None errors = [] for i, line in enumerate(lines): search = _re_decorator.search(line) if search is not None: decorator_name = search.groups()[0] if decorator_before is not None and decorator_name.startswith("parameterized"): errors.append(i) decorator_before = decorator_name elif decorator_before is not None: decorator_before = None return errors def check_all_decorator_order(): """Check that in all test files, the slow decorator is always last.""" errors = [] for fname in os.listdir(PATH_TO_TESTS): if fname.endswith(".py"): filename = os.path.join(PATH_TO_TESTS, fname) new_errors = check_decorator_order(filename) errors += [f"- {filename}, line {i}" for i in new_errors] if len(errors) > 0: msg = "\n".join(errors) raise ValueError( "The parameterized decorator (and its variants) should always be first, but this is not the case in the" f" following files:\n{msg}" ) def find_all_documented_objects(): """Parse the content of all doc files to detect which classes and functions it documents""" documented_obj = [] for doc_file in Path(PATH_TO_DOC).glob("**/*.rst"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] for doc_file in Path(PATH_TO_DOC).glob("**/*.mdx"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall("\[\[autodoc\]\]\s+(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] return documented_obj # One good reason for not being documented is to be deprecated. Put in this list deprecated objects. DEPRECATED_OBJECTS = [ "AutoModelWithLMHead", "BartPretrainedModel", "DataCollator", "DataCollatorForSOP", "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "PretrainedBartModel", "PretrainedFSMTModel", "SingleSentenceClassificationProcessor", "SquadDataTrainingArguments", "SquadDataset", "SquadExample", "SquadFeatures", "SquadV1Processor", "SquadV2Processor", "TFAutoModelWithLMHead", "TFBartPretrainedModel", "TextDataset", "TextDatasetForNextSentencePrediction", "Wav2Vec2ForMaskedLM", "Wav2Vec2Tokenizer", "glue_compute_metrics", "glue_convert_examples_to_features", "glue_output_modes", "glue_processors", "glue_tasks_num_labels", "squad_convert_examples_to_features", "xnli_compute_metrics", "xnli_output_modes", "xnli_processors", "xnli_tasks_num_labels", "TFTrainer", "TFTrainingArguments", ] # Exceptionally, some objects should not be documented after all rules passed. # ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT! UNDOCUMENTED_OBJECTS = [ "AddedToken", # This is a tokenizers class. "BasicTokenizer", # Internal, should never have been in the main init. "CharacterTokenizer", # Internal, should never have been in the main init. "DPRPretrainedReader", # Like an Encoder. "DummyObject", # Just picked by mistake sometimes. "MecabTokenizer", # Internal, should never have been in the main init. "ModelCard", # Internal type. "SqueezeBertModule", # Internal building block (should have been called SqueezeBertLayer) "TFDPRPretrainedReader", # Like an Encoder. "TransfoXLCorpus", # Internal type. "WordpieceTokenizer", # Internal, should never have been in the main init. "absl", # External module "add_end_docstrings", # Internal, should never have been in the main init. "add_start_docstrings", # Internal, should never have been in the main init. "convert_tf_weight_name_to_pt_weight_name", # Internal used to convert model weights "logger", # Internal logger "logging", # External module "requires_backends", # Internal function ] # This list should be empty. Objects in it should get their own doc page. SHOULD_HAVE_THEIR_OWN_PAGE = [ # Benchmarks "PyTorchBenchmark", "PyTorchBenchmarkArguments", "TensorFlowBenchmark", "TensorFlowBenchmarkArguments", ] def ignore_undocumented(name): """Rules to determine if `name` should be undocumented.""" # NOT DOCUMENTED ON PURPOSE. # Constants uppercase are not documented. if name.isupper(): return True # PreTrainedModels / Encoders / Decoders / Layers / Embeddings / Attention are not documented. if ( name.endswith("PreTrainedModel") or name.endswith("Decoder") or name.endswith("Encoder") or name.endswith("Layer") or name.endswith("Embeddings") or name.endswith("Attention") ): return True # Submodules are not documented. if os.path.isdir(os.path.join(PATH_TO_TRANSFORMERS, name)) or os.path.isfile( os.path.join(PATH_TO_TRANSFORMERS, f"{name}.py") ): return True # All load functions are not documented. if name.startswith("load_tf") or name.startswith("load_pytorch"): return True # is_xxx_available functions are not documented. if name.startswith("is_") and name.endswith("_available"): return True # Deprecated objects are not documented. if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS: return True # MMBT model does not really work. if name.startswith("MMBT"): return True if name in SHOULD_HAVE_THEIR_OWN_PAGE: return True return False def check_all_objects_are_documented(): """Check all models are properly documented.""" documented_objs = find_all_documented_objects() modules = transformers._modules objects = [c for c in dir(transformers) if c not in modules and not c.startswith("_")] undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)] if len(undocumented_objs) > 0: raise Exception( "The following objects are in the public init so should be documented:\n - " + "\n - ".join(undocumented_objs) ) check_docstrings_are_in_md() check_model_type_doc_match() def check_model_type_doc_match(): """Check all doc pages have a corresponding model type.""" model_doc_folder = Path(PATH_TO_DOC) / "model_doc" model_docs = [m.stem for m in model_doc_folder.glob("*.mdx")] model_types = list(transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys()) model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types] errors = [] for m in model_docs: if m not in model_types and m != "auto": close_matches = get_close_matches(m, model_types) error_message = f"{m} is not a proper model identifier." if len(close_matches) > 0: close_matches = "/".join(close_matches) error_message += f" Did you mean {close_matches}?" errors.append(error_message) if len(errors) > 0: raise ValueError( "Some model doc pages do not match any existing model type:\n" + "\n".join(errors) + "\nYou can add any missing model type to the `MODEL_NAMES_MAPPING` constant in " "models/auto/configuration_auto.py." ) # Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`. _re_rst_special_words = re.compile(r":(?:obj|func|class|meth):`([^`]+)`") # Re pattern to catch things between double backquotes. _re_double_backquotes = re.compile(r"(^|[^`])``([^`]+)``([^`]|$)") # Re pattern to catch example introduction. _re_rst_example = re.compile(r"^\s*Example.*::\s*$", flags=re.MULTILINE) def is_rst_docstring(docstring): """ Returns `True` if `docstring` is written in rst. """ if _re_rst_special_words.search(docstring) is not None: return True if _re_double_backquotes.search(docstring) is not None: return True if _re_rst_example.search(docstring) is not None: return True return False def check_docstrings_are_in_md(): """Check all docstrings are in md""" files_with_rst = [] for file in Path(PATH_TO_TRANSFORMERS).glob("**/*.py"): with open(file, encoding="utf-8") as f: code = f.read() docstrings = code.split('"""') for idx, docstring in enumerate(docstrings): if idx % 2 == 0 or not is_rst_docstring(docstring): continue files_with_rst.append(file) break if len(files_with_rst) > 0: raise ValueError( "The following files have docstrings written in rst:\n" + "\n".join([f"- {f}" for f in files_with_rst]) + "\nTo fix this run `doc-builder convert path_to_py_file` after installing `doc-builder`\n" "(`pip install git+https://github.com/huggingface/doc-builder`)" ) def check_repo_quality(): """Check all models are properly tested and documented.""" print("Checking all models are included.") check_model_list() print("Checking all models are public.") check_models_are_in_init() print("Checking all models are properly tested.") check_all_decorator_order() check_all_models_are_tested() print("Checking all objects are properly documented.") check_all_objects_are_documented() print("Checking all models are in at least one auto class.") check_all_models_are_auto_configured() if __name__ == "__main__": check_repo_quality()

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import inspect import os import re import warnings from collections import OrderedDict from difflib import get_close_matches from pathlib import Path from transformers import is_flax_available, is_tf_available, is_torch_available from transformers.models.auto import get_values from transformers.utils import ENV_VARS_TRUE_VALUES # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_repo.py PATH_TO_TRANSFORMERS = "src/transformers" PATH_TO_TESTS = "tests" PATH_TO_DOC = "docs/source/en" # Update this list with models that are supposed to be private. PRIVATE_MODELS = [ "DPRSpanPredictor", "LongT5Stack", "RealmBertModel", "T5Stack", "SwitchTransformersStack", "TFDPRSpanPredictor", ] # Update this list for models that are not tested with a comment explaining the reason it should not be. # Being in this list is an exception and should **not** be the rule. IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [ # models to ignore for not tested "CLIPSegDecoder", # Building part of bigger (tested) model. "TableTransformerEncoder", # Building part of bigger (tested) model. "TableTransformerDecoder", # Building part of bigger (tested) model. "TimeSeriesTransformerEncoder", # Building part of bigger (tested) model. "TimeSeriesTransformerDecoder", # Building part of bigger (tested) model. "JukeboxVQVAE", # Building part of bigger (tested) model. "JukeboxPrior", # Building part of bigger (tested) model. "DeformableDetrEncoder", # Building part of bigger (tested) model. "DeformableDetrDecoder", # Building part of bigger (tested) model. "OPTDecoder", # Building part of bigger (tested) model. "WhisperDecoder", # Building part of bigger (tested) model. "WhisperEncoder", # Building part of bigger (tested) model. "DecisionTransformerGPT2Model", # Building part of bigger (tested) model. "SegformerDecodeHead", # Building part of bigger (tested) model. "PLBartEncoder", # Building part of bigger (tested) model. "PLBartDecoder", # Building part of bigger (tested) model. "PLBartDecoderWrapper", # Building part of bigger (tested) model. "BigBirdPegasusEncoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoderWrapper", # Building part of bigger (tested) model. "DetrEncoder", # Building part of bigger (tested) model. "DetrDecoder", # Building part of bigger (tested) model. "DetrDecoderWrapper", # Building part of bigger (tested) model. "ConditionalDetrEncoder", # Building part of bigger (tested) model. "ConditionalDetrDecoder", # Building part of bigger (tested) model. "M2M100Encoder", # Building part of bigger (tested) model. "M2M100Decoder", # Building part of bigger (tested) model. "MCTCTEncoder", # Building part of bigger (tested) model. "Speech2TextEncoder", # Building part of bigger (tested) model. "Speech2TextDecoder", # Building part of bigger (tested) model. "LEDEncoder", # Building part of bigger (tested) model. "LEDDecoder", # Building part of bigger (tested) model. "BartDecoderWrapper", # Building part of bigger (tested) model. "BartEncoder", # Building part of bigger (tested) model. "BertLMHeadModel", # Needs to be setup as decoder. "BlenderbotSmallEncoder", # Building part of bigger (tested) model. "BlenderbotSmallDecoderWrapper", # Building part of bigger (tested) model. "BlenderbotEncoder", # Building part of bigger (tested) model. "BlenderbotDecoderWrapper", # Building part of bigger (tested) model. "MBartEncoder", # Building part of bigger (tested) model. "MBartDecoderWrapper", # Building part of bigger (tested) model. "MegatronBertLMHeadModel", # Building part of bigger (tested) model. "MegatronBertEncoder", # Building part of bigger (tested) model. "MegatronBertDecoder", # Building part of bigger (tested) model. "MegatronBertDecoderWrapper", # Building part of bigger (tested) model. "MvpDecoderWrapper", # Building part of bigger (tested) model. "MvpEncoder", # Building part of bigger (tested) model. "PegasusEncoder", # Building part of bigger (tested) model. "PegasusDecoderWrapper", # Building part of bigger (tested) model. "PegasusXEncoder", # Building part of bigger (tested) model. "PegasusXDecoder", # Building part of bigger (tested) model. "PegasusXDecoderWrapper", # Building part of bigger (tested) model. "DPREncoder", # Building part of bigger (tested) model. "ProphetNetDecoderWrapper", # Building part of bigger (tested) model. "RealmBertModel", # Building part of bigger (tested) model. "RealmReader", # Not regular model. "RealmScorer", # Not regular model. "RealmForOpenQA", # Not regular model. "ReformerForMaskedLM", # Needs to be setup as decoder. "Speech2Text2DecoderWrapper", # Building part of bigger (tested) model. "TFDPREncoder", # Building part of bigger (tested) model. "TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?) "TFRobertaForMultipleChoice", # TODO: fix "TrOCRDecoderWrapper", # Building part of bigger (tested) model. "TFWhisperEncoder", # Building part of bigger (tested) model. "TFWhisperDecoder", # Building part of bigger (tested) model. "SeparableConv1D", # Building part of bigger (tested) model. "FlaxBartForCausalLM", # Building part of bigger (tested) model. "FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM. "OPTDecoderWrapper", "TFSegformerDecodeHead", # Not a regular model. ] # Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't # trigger the common tests. TEST_FILES_WITH_NO_COMMON_TESTS = [ "models/decision_transformer/test_modeling_decision_transformer.py", "models/camembert/test_modeling_camembert.py", "models/mt5/test_modeling_flax_mt5.py", "models/mbart/test_modeling_mbart.py", "models/mt5/test_modeling_mt5.py", "models/pegasus/test_modeling_pegasus.py", "models/camembert/test_modeling_tf_camembert.py", "models/mt5/test_modeling_tf_mt5.py", "models/xlm_roberta/test_modeling_tf_xlm_roberta.py", "models/xlm_roberta/test_modeling_flax_xlm_roberta.py", "models/xlm_prophetnet/test_modeling_xlm_prophetnet.py", "models/xlm_roberta/test_modeling_xlm_roberta.py", "models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py", "models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py", "models/decision_transformer/test_modeling_decision_transformer.py", ] # Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and # should **not** be the rule. IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [ # models to ignore for model xxx mapping "CLIPSegForImageSegmentation", "CLIPSegVisionModel", "CLIPSegTextModel", "EsmForProteinFolding", "TimeSeriesTransformerForPrediction", "JukeboxVQVAE", "JukeboxPrior", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "DPTForDepthEstimation", "DecisionTransformerGPT2Model", "GLPNForDepthEstimation", "ViltForImagesAndTextClassification", "ViltForImageAndTextRetrieval", "ViltForTokenClassification", "ViltForMaskedLM", "XGLMEncoder", "XGLMDecoder", "XGLMDecoderWrapper", "PerceiverForMultimodalAutoencoding", "PerceiverForOpticalFlow", "SegformerDecodeHead", "TFSegformerDecodeHead", "FlaxBeitForMaskedImageModeling", "PLBartEncoder", "PLBartDecoder", "PLBartDecoderWrapper", "BeitForMaskedImageModeling", "CLIPTextModel", "CLIPTextModelWithProjection", "CLIPVisionModel", "CLIPVisionModelWithProjection", "GroupViTTextModel", "GroupViTVisionModel", "TFCLIPTextModel", "TFCLIPVisionModel", "TFGroupViTTextModel", "TFGroupViTVisionModel", "FlaxCLIPTextModel", "FlaxCLIPVisionModel", "FlaxWav2Vec2ForCTC", "DetrForSegmentation", "ConditionalDetrForSegmentation", "DPRReader", "FlaubertForQuestionAnswering", "FlavaImageCodebook", "FlavaTextModel", "FlavaImageModel", "FlavaMultimodalModel", "GPT2DoubleHeadsModel", "LayoutLMForQuestionAnswering", "LukeForMaskedLM", "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", "OpenAIGPTDoubleHeadsModel", "OwlViTTextModel", "OwlViTVisionModel", "OwlViTForObjectDetection", "RagModel", "RagSequenceForGeneration", "RagTokenForGeneration", "RealmEmbedder", "RealmForOpenQA", "RealmScorer", "RealmReader", "TFDPRReader", "TFGPT2DoubleHeadsModel", "TFLayoutLMForQuestionAnswering", "TFOpenAIGPTDoubleHeadsModel", "TFRagModel", "TFRagSequenceForGeneration", "TFRagTokenForGeneration", "Wav2Vec2ForCTC", "HubertForCTC", "SEWForCTC", "SEWDForCTC", "XLMForQuestionAnswering", "XLNetForQuestionAnswering", "SeparableConv1D", "VisualBertForRegionToPhraseAlignment", "VisualBertForVisualReasoning", "VisualBertForQuestionAnswering", "VisualBertForMultipleChoice", "TFWav2Vec2ForCTC", "TFHubertForCTC", "MaskFormerForInstanceSegmentation", "XCLIPVisionModel", "XCLIPTextModel", ] # Update this list for models that have multiple model types for the same # model doc MODEL_TYPE_TO_DOC_MAPPING = OrderedDict( [ ("data2vec-text", "data2vec"), ("data2vec-audio", "data2vec"), ("data2vec-vision", "data2vec"), ("donut-swin", "donut"), ] ) # This is to make sure the transformers module imported is the one in the repo. spec = importlib.util.spec_from_file_location( "transformers", os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), submodule_search_locations=[PATH_TO_TRANSFORMERS], ) transformers = spec.loader.load_module() def check_model_list(): """Check the model list inside the transformers library.""" # Get the models from the directory structure of `src/transformers/models/` models_dir = os.path.join(PATH_TO_TRANSFORMERS, "models") _models = [] for model in os.listdir(models_dir): model_dir = os.path.join(models_dir, model) if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir): _models.append(model) # Get the models from the directory structure of `src/transformers/models/` models = [model for model in dir(transformers.models) if not model.startswith("__")] missing_models = sorted(list(set(_models).difference(models))) if missing_models: raise Exception( f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}." ) # If some modeling modules should be ignored for all checks, they should be added in the nested list # _ignore_modules of this function. def get_model_modules(): """Get the model modules inside the transformers library.""" _ignore_modules = [ "modeling_auto", "modeling_encoder_decoder", "modeling_marian", "modeling_mmbt", "modeling_outputs", "modeling_retribert", "modeling_utils", "modeling_flax_auto", "modeling_flax_encoder_decoder", "modeling_flax_utils", "modeling_speech_encoder_decoder", "modeling_flax_speech_encoder_decoder", "modeling_flax_vision_encoder_decoder", "modeling_transfo_xl_utilities", "modeling_tf_auto", "modeling_tf_encoder_decoder", "modeling_tf_outputs", "modeling_tf_pytorch_utils", "modeling_tf_utils", "modeling_tf_transfo_xl_utilities", "modeling_tf_vision_encoder_decoder", "modeling_vision_encoder_decoder", ] modules = [] for model in dir(transformers.models): # There are some magic dunder attributes in the dir, we ignore them if not model.startswith("__"): model_module = getattr(transformers.models, model) for submodule in dir(model_module): if submodule.startswith("modeling") and submodule not in _ignore_modules: modeling_module = getattr(model_module, submodule) if inspect.ismodule(modeling_module): modules.append(modeling_module) return modules def get_models(module, include_pretrained=False): """Get the objects in module that are models.""" models = [] model_classes = (transformers.PreTrainedModel, transformers.TFPreTrainedModel, transformers.FlaxPreTrainedModel) for attr_name in dir(module): if not include_pretrained and ("Pretrained" in attr_name or "PreTrained" in attr_name): continue attr = getattr(module, attr_name) if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__: models.append((attr_name, attr)) return models def is_a_private_model(model): """Returns True if the model should not be in the main init.""" if model in PRIVATE_MODELS: return True # Wrapper, Encoder and Decoder are all privates if model.endswith("Wrapper"): return True if model.endswith("Encoder"): return True if model.endswith("Decoder"): return True return False def check_models_are_in_init(): """Checks all models defined in the library are in the main init.""" models_not_in_init = [] dir_transformers = dir(transformers) for module in get_model_modules(): models_not_in_init += [ model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers ] # Remove private models models_not_in_init = [model for model in models_not_in_init if not is_a_private_model(model)] if len(models_not_in_init) > 0: raise Exception(f"The following models should be in the main init: {','.join(models_not_in_init)}.") # If some test_modeling files should be ignored when checking models are all tested, they should be added in the # nested list _ignore_files of this function. def get_model_test_files(): """Get the model test files. The returned files should NOT contain the `tests` (i.e. `PATH_TO_TESTS` defined in this script). They will be considered as paths relative to `tests`. A caller has to use `os.path.join(PATH_TO_TESTS, ...)` to access the files. """ _ignore_files = [ "test_modeling_common", "test_modeling_encoder_decoder", "test_modeling_flax_encoder_decoder", "test_modeling_flax_speech_encoder_decoder", "test_modeling_marian", "test_modeling_tf_common", "test_modeling_tf_encoder_decoder", ] test_files = [] # Check both `PATH_TO_TESTS` and `PATH_TO_TESTS/models` model_test_root = os.path.join(PATH_TO_TESTS, "models") model_test_dirs = [] for x in os.listdir(model_test_root): x = os.path.join(model_test_root, x) if os.path.isdir(x): model_test_dirs.append(x) for target_dir in [PATH_TO_TESTS] + model_test_dirs: for file_or_dir in os.listdir(target_dir): path = os.path.join(target_dir, file_or_dir) if os.path.isfile(path): filename = os.path.split(path)[-1] if "test_modeling" in filename and not os.path.splitext(filename)[0] in _ignore_files: file = os.path.join(*path.split(os.sep)[1:]) test_files.append(file) return test_files # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class # for the all_model_classes variable. def find_tested_models(test_file): """Parse the content of test_file to detect what's in all_model_classes""" # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the class with open(os.path.join(PATH_TO_TESTS, test_file), "r", encoding="utf-8", newline="\n") as f: content = f.read() all_models = re.findall(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content) # Check with one less parenthesis as well all_models += re.findall(r"all_model_classes\s+=\s+\(([^\)]*)\)", content) if len(all_models) > 0: model_tested = [] for entry in all_models: for line in entry.split(","): name = line.strip() if len(name) > 0: model_tested.append(name) return model_tested def check_models_are_tested(module, test_file): """Check models defined in module are tested in test_file.""" # XxxPreTrainedModel are not tested defined_models = get_models(module) tested_models = find_tested_models(test_file) if tested_models is None: if test_file.replace(os.path.sep, "/") in TEST_FILES_WITH_NO_COMMON_TESTS: return return [ f"{test_file} should define `all_model_classes` to apply common tests to the models it tests. " + "If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file " + "`utils/check_repo.py`." ] failures = [] for model_name, _ in defined_models: if model_name not in tested_models and model_name not in IGNORE_NON_TESTED: failures.append( f"{model_name} is defined in {module.__name__} but is not tested in " + f"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file." + "If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`" + "in the file `utils/check_repo.py`." ) return failures def check_all_models_are_tested(): """Check all models are properly tested.""" modules = get_model_modules() test_files = get_model_test_files() failures = [] for module in modules: test_file = [file for file in test_files if f"test_{module.__name__.split('.')[-1]}.py" in file] if len(test_file) == 0: failures.append(f"{module.__name__} does not have its corresponding test file {test_file}.") elif len(test_file) > 1: failures.append(f"{module.__name__} has several test files: {test_file}.") else: test_file = test_file[0] new_failures = check_models_are_tested(module, test_file) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def get_all_auto_configured_models(): """Return the list of all models in at least one auto class.""" result = set() # To avoid duplicates we concatenate all model classes in a set. if is_torch_available(): for attr_name in dir(transformers.models.auto.modeling_auto): if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_auto, attr_name))) if is_tf_available(): for attr_name in dir(transformers.models.auto.modeling_tf_auto): if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_tf_auto, attr_name))) if is_flax_available(): for attr_name in dir(transformers.models.auto.modeling_flax_auto): if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_flax_auto, attr_name))) return [cls for cls in result] def ignore_unautoclassed(model_name): """Rules to determine if `name` should be in an auto class.""" # Special white list if model_name in IGNORE_NON_AUTO_CONFIGURED: return True # Encoder and Decoder should be ignored if "Encoder" in model_name or "Decoder" in model_name: return True return False def check_models_are_auto_configured(module, all_auto_models): """Check models defined in module are each in an auto class.""" defined_models = get_models(module) failures = [] for model_name, _ in defined_models: if model_name not in all_auto_models and not ignore_unautoclassed(model_name): failures.append( f"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. " "If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file " "`utils/check_repo.py`." ) return failures def check_all_models_are_auto_configured(): """Check all models are each in an auto class.""" missing_backends = [] if not is_torch_available(): missing_backends.append("PyTorch") if not is_tf_available(): missing_backends.append("TensorFlow") if not is_flax_available(): missing_backends.append("Flax") if len(missing_backends) > 0: missing = ", ".join(missing_backends) if os.getenv("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES: raise Exception( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}." ) else: warnings.warn( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you " "didn't make any change in one of those backends modeling files, you should probably execute the " "command above to be on the safe side." ) modules = get_model_modules() all_auto_models = get_all_auto_configured_models() failures = [] for module in modules: new_failures = check_models_are_auto_configured(module, all_auto_models) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) _re_decorator = re.compile(r"^\s*@(\S+)\s+$") def check_decorator_order(filename): """Check that in the test file `filename` the slow decorator is always last.""" with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() decorator_before = None errors = [] for i, line in enumerate(lines): search = _re_decorator.search(line) if search is not None: decorator_name = search.groups()[0] if decorator_before is not None and decorator_name.startswith("parameterized"): errors.append(i) decorator_before = decorator_name elif decorator_before is not None: decorator_before = None return errors def check_all_decorator_order(): """Check that in all test files, the slow decorator is always last.""" errors = [] for fname in os.listdir(PATH_TO_TESTS): if fname.endswith(".py"): filename = os.path.join(PATH_TO_TESTS, fname) new_errors = check_decorator_order(filename) errors += [f"- {filename}, line {i}" for i in new_errors] if len(errors) > 0: msg = "\n".join(errors) raise ValueError( "The parameterized decorator (and its variants) should always be first, but this is not the case in the" f" following files:\n{msg}" ) def find_all_documented_objects(): """Parse the content of all doc files to detect which classes and functions it documents""" documented_obj = [] for doc_file in Path(PATH_TO_DOC).glob("**/*.rst"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] for doc_file in Path(PATH_TO_DOC).glob("**/*.mdx"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall("\[\[autodoc\]\]\s+(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] return documented_obj # One good reason for not being documented is to be deprecated. Put in this list deprecated objects. DEPRECATED_OBJECTS = [ "AutoModelWithLMHead", "BartPretrainedModel", "DataCollator", "DataCollatorForSOP", "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "PretrainedBartModel", "PretrainedFSMTModel", "SingleSentenceClassificationProcessor", "SquadDataTrainingArguments", "SquadDataset", "SquadExample", "SquadFeatures", "SquadV1Processor", "SquadV2Processor", "TFAutoModelWithLMHead", "TFBartPretrainedModel", "TextDataset", "TextDatasetForNextSentencePrediction", "Wav2Vec2ForMaskedLM", "Wav2Vec2Tokenizer", "glue_compute_metrics", "glue_convert_examples_to_features", "glue_output_modes", "glue_processors", "glue_tasks_num_labels", "squad_convert_examples_to_features", "xnli_compute_metrics", "xnli_output_modes", "xnli_processors", "xnli_tasks_num_labels", "TFTrainer", "TFTrainingArguments", ] # Exceptionally, some objects should not be documented after all rules passed. # ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT! UNDOCUMENTED_OBJECTS = [ "AddedToken", # This is a tokenizers class. "BasicTokenizer", # Internal, should never have been in the main init. "CharacterTokenizer", # Internal, should never have been in the main init. "DPRPretrainedReader", # Like an Encoder. "DummyObject", # Just picked by mistake sometimes. "MecabTokenizer", # Internal, should never have been in the main init. "ModelCard", # Internal type. "SqueezeBertModule", # Internal building block (should have been called SqueezeBertLayer) "TFDPRPretrainedReader", # Like an Encoder. "TransfoXLCorpus", # Internal type. "WordpieceTokenizer", # Internal, should never have been in the main init. "absl", # External module "add_end_docstrings", # Internal, should never have been in the main init. "add_start_docstrings", # Internal, should never have been in the main init. "convert_tf_weight_name_to_pt_weight_name", # Internal used to convert model weights "logger", # Internal logger "logging", # External module "requires_backends", # Internal function ] # This list should be empty. Objects in it should get their own doc page. SHOULD_HAVE_THEIR_OWN_PAGE = [ # Benchmarks "PyTorchBenchmark", "PyTorchBenchmarkArguments", "TensorFlowBenchmark", "TensorFlowBenchmarkArguments", ] def ignore_undocumented(name): """Rules to determine if `name` should be undocumented.""" # NOT DOCUMENTED ON PURPOSE. # Constants uppercase are not documented. if name.isupper(): return True # PreTrainedModels / Encoders / Decoders / Layers / Embeddings / Attention are not documented. if ( name.endswith("PreTrainedModel") or name.endswith("Decoder") or name.endswith("Encoder") or name.endswith("Layer") or name.endswith("Embeddings") or name.endswith("Attention") ): return True # Submodules are not documented. if os.path.isdir(os.path.join(PATH_TO_TRANSFORMERS, name)) or os.path.isfile( os.path.join(PATH_TO_TRANSFORMERS, f"{name}.py") ): return True # All load functions are not documented. if name.startswith("load_tf") or name.startswith("load_pytorch"): return True # is_xxx_available functions are not documented. if name.startswith("is_") and name.endswith("_available"): return True # Deprecated objects are not documented. if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS: return True # MMBT model does not really work. if name.startswith("MMBT"): return True if name in SHOULD_HAVE_THEIR_OWN_PAGE: return True return False def check_all_objects_are_documented(): """Check all models are properly documented.""" documented_objs = find_all_documented_objects() modules = transformers._modules objects = [c for c in dir(transformers) if c not in modules and not c.startswith("_")] undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)] if len(undocumented_objs) > 0: raise Exception( "The following objects are in the public init so should be documented:\n - " + "\n - ".join(undocumented_objs) ) check_docstrings_are_in_md() check_model_type_doc_match() def check_model_type_doc_match(): """Check all doc pages have a corresponding model type.""" model_doc_folder = Path(PATH_TO_DOC) / "model_doc" model_docs = [m.stem for m in model_doc_folder.glob("*.mdx")] model_types = list(transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys()) model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types] errors = [] for m in model_docs: if m not in model_types and m != "auto": close_matches = get_close_matches(m, model_types) error_message = f"{m} is not a proper model identifier." if len(close_matches) > 0: close_matches = "/".join(close_matches) error_message += f" Did you mean {close_matches}?" errors.append(error_message) if len(errors) > 0: raise ValueError( "Some model doc pages do not match any existing model type:\n" + "\n".join(errors) + "\nYou can add any missing model type to the `MODEL_NAMES_MAPPING` constant in " "models/auto/configuration_auto.py." ) # Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`. _re_rst_special_words = re.compile(r":(?:obj|func|class|meth):`([^`]+)`") # Re pattern to catch things between double backquotes. _re_double_backquotes = re.compile(r"(^|[^`])``([^`]+)``([^`]|$)") # Re pattern to catch example introduction. _re_rst_example = re.compile(r"^\s*Example.*::\s*$", flags=re.MULTILINE) def is_rst_docstring(docstring): """ Returns `True` if `docstring` is written in rst. """ if _re_rst_special_words.search(docstring) is not None: return True if _re_double_backquotes.search(docstring) is not None: return True if _re_rst_example.search(docstring) is not None: return True return False def check_docstrings_are_in_md(): """Check all docstrings are in md""" files_with_rst = [] for file in Path(PATH_TO_TRANSFORMERS).glob("**/*.py"): with open(file, encoding="utf-8") as f: code = f.read() docstrings = code.split('"""') for idx, docstring in enumerate(docstrings): if idx % 2 == 0 or not is_rst_docstring(docstring): continue files_with_rst.append(file) break if len(files_with_rst) > 0: raise ValueError( "The following files have docstrings written in rst:\n" + "\n".join([f"- {f}" for f in files_with_rst]) + "\nTo fix this run `doc-builder convert path_to_py_file` after installing `doc-builder`\n" "(`pip install git+https://github.com/huggingface/doc-builder`)" ) def check_repo_quality(): """Check all models are properly tested and documented.""" print("Checking all models are included.") check_model_list() print("Checking all models are public.") check_models_are_in_init() print("Checking all models are properly tested.") check_all_decorator_order() check_all_models_are_tested() print("Checking all objects are properly documented.") check_all_objects_are_documented() print("Checking all models are in at least one auto class.") check_all_models_are_auto_configured() if __name__ == "__main__": check_repo_quality()

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/conditional_detr/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_timm_available, is_vision_available _import_structure = { "configuration_conditional_detr": [ "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConditionalDetrConfig", "ConditionalDetrOnnxConfig", ] } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_conditional_detr"] = ["ConditionalDetrFeatureExtractor"] try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_conditional_detr"] = [ "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "ConditionalDetrForObjectDetection", "ConditionalDetrForSegmentation", "ConditionalDetrModel", "ConditionalDetrPreTrainedModel", ] if TYPE_CHECKING: from .configuration_conditional_detr import ( CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, ConditionalDetrConfig, ConditionalDetrOnnxConfig, ) try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_conditional_detr import ( CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, ConditionalDetrForObjectDetection, ConditionalDetrForSegmentation, ConditionalDetrModel, ConditionalDetrPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_timm_available, is_vision_available _import_structure = { "configuration_conditional_detr": [ "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConditionalDetrConfig", "ConditionalDetrOnnxConfig", ] } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_conditional_detr"] = ["ConditionalDetrFeatureExtractor"] try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_conditional_detr"] = [ "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "ConditionalDetrForObjectDetection", "ConditionalDetrForSegmentation", "ConditionalDetrModel", "ConditionalDetrPreTrainedModel", ] if TYPE_CHECKING: from .configuration_conditional_detr import ( CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, ConditionalDetrConfig, ConditionalDetrOnnxConfig, ) try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_conditional_detr import ( CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, ConditionalDetrForObjectDetection, ConditionalDetrForSegmentation, ConditionalDetrModel, ConditionalDetrPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/codeparrot/scripts/minhash_deduplication.py

import json import multiprocessing as mp import re from collections import defaultdict from functools import partial from typing import Dict, List, Optional, Set, Tuple, Type from datasets import Dataset from tqdm import tqdm from datasketch import MinHash, MinHashLSH from dpu_utils.utils.iterators import ThreadedIterator NON_ALPHA = re.compile("[^A-Za-z_0-9]") # parameters used in DuplicationIndex MIN_NUM_TOKENS = 10 NUM_PERM = 256 def get_min_hash(tokens: List[str]) -> Optional[MinHash]: """Compute the MinHash of a code snippet.""" if len(tokens) < MIN_NUM_TOKENS: return None min_hash = MinHash(num_perm=NUM_PERM) for token in set(tokens): min_hash.update(token.encode()) return min_hash def get_tokens(code: str) -> Set[str]: """Tokenize a code snippet.""" return set([t for t in NON_ALPHA.split(code) if len(t.strip()) > 0]) class DuplicationIndex: def __init__( self, *, duplication_jaccard_threshold: float = 0.85, ): self._duplication_jaccard_threshold = duplication_jaccard_threshold self._num_perm = NUM_PERM self._index = MinHashLSH(threshold=self._duplication_jaccard_threshold, num_perm=self._num_perm) self._duplicate_clusters = defaultdict(set) def add(self, code_key: Tuple, min_hash: MinHash) -> None: """Add a key to _index (MinHashLSH) the min_hash is used to query closest matches based on the jaccard_threshold. The new key is either added to a existing cluster of one close match, or a new cluster is created. The clusters created in this way, depend on the order of add. Args: code_key (Tuple of (index, repo_name, path)): Theoritically any hasbale key. Here we use a tuple to retrieve the information later. min_hash: MinHash of the code_key. """ close_duplicates = self._index.query(min_hash) if code_key in self._index.keys: print(f"Duplicate key {code_key}") return self._index.insert(code_key, min_hash) if len(close_duplicates) > 0: for base_duplicate in close_duplicates: if base_duplicate in self._duplicate_clusters: self._duplicate_clusters[base_duplicate].add(code_key) break else: self._duplicate_clusters[close_duplicates[0]].add(code_key) def get_duplicate_clusters(self) -> List[List[Dict]]: """Export the duplicate clusters. For each cluster, the first element is the base element of the cluster. The base element has an estimation jaccard similarity higher than the threshold with all the other elements. Returns: duplicate_clusters (List[List[Dict]]): List of duplicate clusters. """ duplicate_clusters = [] for base, duplicates in self._duplicate_clusters.items(): cluster = [base] + list(duplicates) # reformat the cluster to be a list of dict cluster = [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster] duplicate_clusters.append(cluster) return duplicate_clusters def save(self, filepath) -> None: duplicate_clusters = self.get_duplicate_clusters() with open(filepath, "w") as f: json.dump(duplicate_clusters, f) def _compute_min_hash(element): index, data = element min_hash = get_min_hash([t for t in NON_ALPHA.split(data["content"]) if len(t.strip()) > 0]) if min_hash is not None: return (index, data["repo_name"], data["path"]), min_hash def minhash_iter(dataset_iterator: Type[Dataset]): with mp.Pool() as pool: for data in pool.imap_unordered( _compute_min_hash, ThreadedIterator(dataset_iterator, max_queue_size=10000), chunksize=100, ): if data is not None: yield data def make_duplicate_clusters(dataset_iterator: Type[Dataset], jaccard_threshold: float): """Find duplicate clusters in the dataset in two steps: 1. Compute MinHash for each code snippet. MinHash is a tool for fast jaccard similarity estimation. This step is computed using an asynchronous multiprocessing pool, minhash_iter 2. Find duplicate clusters. The computed MinHash is added sequentially to the DuplicationIndex. This step cannot be parallelized. So using asynchronous thread in the previous step helps to speed up the process. """ di = DuplicationIndex(duplication_jaccard_threshold=jaccard_threshold) for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(dataset_iterator)), max_queue_size=100)): di.add(filename, min_hash) # Returns a List[Cluster] where Cluster is List[str] with the filenames. return di.get_duplicate_clusters() def jaccard_similarity(code1: str, code2: str) -> float: """Compute the Jaccard similarity of two code snippets.""" tokens1 = get_tokens(code1) tokens2 = get_tokens(code2) return len(tokens1 & tokens2) / len(tokens1 | tokens2) _shared_dataset = None def _find_cluster_extremes_shared(cluster, jaccard_threshold): """Find a reduced cluster such that each code in the origin cluster is similar to at least one code in the reduced cluster. Two codes are similar if their Jaccard similarity is above the threshold. Args: cluster (List[dict]): cluster is a list of dict, each dict contains the following keys: - base_index - repo_name - path This is a typical output of DuplicationIndex.get_duplicate_clusters() jaccard_threshold (float): threshold for Jaccard similarity. Two codes are similar if their Jaccard similarity is above the threshold. Returns: extremes (List[dict]): A reduced representation of the cluster. The field copies is added to each dict. The copies field indicates the number of similar codes in the cluster for a extreme. """ extremes = [] for element1 in cluster: code1 = _shared_dataset[element1["base_index"]]["content"] for element2 in extremes: code2 = _shared_dataset[element2["base_index"]]["content"] if jaccard_similarity(code1, code2) >= jaccard_threshold: element2["copies"] += 1 break else: element1["copies"] = 1 extremes.append(element1) return extremes def find_extremes(cluster_list, dataset, jaccard_threshold): """Call the _find_cluster_extremes_shared function in a parallel fashion. Args: cluster_list (List[List[Dict]]): each cluster is a list of dicts with the key base_index, referring to the index of the base code in the dataset. dataset (Type[Dataset]): dataset is used to access the content of the code snippets, using the base_index from the cluster_list. dataset is shared between all the processes using a glabal variable (any other way to share the dataset?), otherwise the multi processing is not speeded up. jaccard_threshold (float): the threshold for the jaccard similarity. The default value is 0.85 Returns: extremes_list (List[Dict]): Each cluster is reduced to extremes. See _find_cluster_extremes_shared for the definition of extremes. """ global _shared_dataset _shared_dataset = dataset extremes_list = [] f = partial(_find_cluster_extremes_shared, jaccard_threshold=jaccard_threshold) with mp.Pool() as pool: for extremes in tqdm( pool.imap_unordered( f, cluster_list, ), total=len(cluster_list), ): extremes_list.append(extremes) return extremes_list def deduplicate_dataset( dataset: Type[Dataset], jaccard_threshold: float = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]: """Deduplicate the dataset using minhash and jaccard similarity. This function first generate duplicate clusters, then each cluster is reduced to the extremes that are similar to the other elements in the cluster. Codes are called similar if their Jaccard similarity is greater than jaccard_threshold (0.85 default). Args: dataset (Type[Dataset]): The dataset to deduplicate. jaccard_threshold (float, default=0.85): jaccard threshold to determine if two codes are similar Returns: ds_dedup (Type[Dataset]): The deduplicated dataset. duplicate_clusters (List[List[Dict]]): The list of duplicate clusters. Each cluster is a list of dicts with the following keys: - base_index : int The index of the code in the original dataset. - repo_name : str - path : str - copies : int The number of copies of the code in the cluster. (find_cluster_extremes) - is_extreme : bool Whether the code is an extreme in the cluster. All the codes in the cluster are removed from the dataset except the extremes. Example: >>> from datasets import load_dataset >>> from minhash_deduplication import deduplicate_dataset >>> ds = load_dataset("lvwerra/codeparrot-clean", split="train") >>> ds_dedup, duplicate_clusters = deduplicate_dataset(ds, jaccard_threshold=0.85) """ duplicate_clusters = make_duplicate_clusters(dataset, jaccard_threshold) duplicate_indices = set(x["base_index"] for cluster in duplicate_clusters for x in cluster) extreme_dict = {} extremes_clusters = find_extremes(duplicate_clusters, dataset, jaccard_threshold) for extremes in extremes_clusters: for element in extremes: extreme_dict[element["base_index"]] = element remove_indices = duplicate_indices - set(extreme_dict.keys()) ds_filter = dataset.filter(lambda x, idx: idx not in remove_indices, with_indices=True) # update duplicate_clusters for cluster in duplicate_clusters: for element in cluster: element["is_extreme"] = element["base_index"] in extreme_dict if element["is_extreme"]: element["copies"] = extreme_dict[element["base_index"]]["copies"] print(f"Original dataset size: {len(dataset)}") print(f"Number of duplicate clusters: {len(duplicate_clusters)}") print(f"Files in duplicate cluster: {len(duplicate_indices)}") print(f"Unique files in duplicate cluster: {len(extreme_dict)}") print(f"Filtered dataset size: {len(ds_filter)}") return ds_filter, duplicate_clusters

import json import multiprocessing as mp import re from collections import defaultdict from functools import partial from typing import Dict, List, Optional, Set, Tuple, Type from datasets import Dataset from tqdm import tqdm from datasketch import MinHash, MinHashLSH from dpu_utils.utils.iterators import ThreadedIterator NON_ALPHA = re.compile("[^A-Za-z_0-9]") # parameters used in DuplicationIndex MIN_NUM_TOKENS = 10 NUM_PERM = 256 def get_min_hash(tokens: List[str]) -> Optional[MinHash]: """Compute the MinHash of a code snippet.""" if len(tokens) < MIN_NUM_TOKENS: return None min_hash = MinHash(num_perm=NUM_PERM) for token in set(tokens): min_hash.update(token.encode()) return min_hash def get_tokens(code: str) -> Set[str]: """Tokenize a code snippet.""" return set([t for t in NON_ALPHA.split(code) if len(t.strip()) > 0]) class DuplicationIndex: def __init__( self, *, duplication_jaccard_threshold: float = 0.85, ): self._duplication_jaccard_threshold = duplication_jaccard_threshold self._num_perm = NUM_PERM self._index = MinHashLSH(threshold=self._duplication_jaccard_threshold, num_perm=self._num_perm) self._duplicate_clusters = defaultdict(set) def add(self, code_key: Tuple, min_hash: MinHash) -> None: """Add a key to _index (MinHashLSH) the min_hash is used to query closest matches based on the jaccard_threshold. The new key is either added to a existing cluster of one close match, or a new cluster is created. The clusters created in this way, depend on the order of add. Args: code_key (Tuple of (index, repo_name, path)): Theoritically any hasbale key. Here we use a tuple to retrieve the information later. min_hash: MinHash of the code_key. """ close_duplicates = self._index.query(min_hash) if code_key in self._index.keys: print(f"Duplicate key {code_key}") return self._index.insert(code_key, min_hash) if len(close_duplicates) > 0: for base_duplicate in close_duplicates: if base_duplicate in self._duplicate_clusters: self._duplicate_clusters[base_duplicate].add(code_key) break else: self._duplicate_clusters[close_duplicates[0]].add(code_key) def get_duplicate_clusters(self) -> List[List[Dict]]: """Export the duplicate clusters. For each cluster, the first element is the base element of the cluster. The base element has an estimation jaccard similarity higher than the threshold with all the other elements. Returns: duplicate_clusters (List[List[Dict]]): List of duplicate clusters. """ duplicate_clusters = [] for base, duplicates in self._duplicate_clusters.items(): cluster = [base] + list(duplicates) # reformat the cluster to be a list of dict cluster = [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster] duplicate_clusters.append(cluster) return duplicate_clusters def save(self, filepath) -> None: duplicate_clusters = self.get_duplicate_clusters() with open(filepath, "w") as f: json.dump(duplicate_clusters, f) def _compute_min_hash(element): index, data = element min_hash = get_min_hash([t for t in NON_ALPHA.split(data["content"]) if len(t.strip()) > 0]) if min_hash is not None: return (index, data["repo_name"], data["path"]), min_hash def minhash_iter(dataset_iterator: Type[Dataset]): with mp.Pool() as pool: for data in pool.imap_unordered( _compute_min_hash, ThreadedIterator(dataset_iterator, max_queue_size=10000), chunksize=100, ): if data is not None: yield data def make_duplicate_clusters(dataset_iterator: Type[Dataset], jaccard_threshold: float): """Find duplicate clusters in the dataset in two steps: 1. Compute MinHash for each code snippet. MinHash is a tool for fast jaccard similarity estimation. This step is computed using an asynchronous multiprocessing pool, minhash_iter 2. Find duplicate clusters. The computed MinHash is added sequentially to the DuplicationIndex. This step cannot be parallelized. So using asynchronous thread in the previous step helps to speed up the process. """ di = DuplicationIndex(duplication_jaccard_threshold=jaccard_threshold) for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(dataset_iterator)), max_queue_size=100)): di.add(filename, min_hash) # Returns a List[Cluster] where Cluster is List[str] with the filenames. return di.get_duplicate_clusters() def jaccard_similarity(code1: str, code2: str) -> float: """Compute the Jaccard similarity of two code snippets.""" tokens1 = get_tokens(code1) tokens2 = get_tokens(code2) return len(tokens1 & tokens2) / len(tokens1 | tokens2) _shared_dataset = None def _find_cluster_extremes_shared(cluster, jaccard_threshold): """Find a reduced cluster such that each code in the origin cluster is similar to at least one code in the reduced cluster. Two codes are similar if their Jaccard similarity is above the threshold. Args: cluster (List[dict]): cluster is a list of dict, each dict contains the following keys: - base_index - repo_name - path This is a typical output of DuplicationIndex.get_duplicate_clusters() jaccard_threshold (float): threshold for Jaccard similarity. Two codes are similar if their Jaccard similarity is above the threshold. Returns: extremes (List[dict]): A reduced representation of the cluster. The field copies is added to each dict. The copies field indicates the number of similar codes in the cluster for a extreme. """ extremes = [] for element1 in cluster: code1 = _shared_dataset[element1["base_index"]]["content"] for element2 in extremes: code2 = _shared_dataset[element2["base_index"]]["content"] if jaccard_similarity(code1, code2) >= jaccard_threshold: element2["copies"] += 1 break else: element1["copies"] = 1 extremes.append(element1) return extremes def find_extremes(cluster_list, dataset, jaccard_threshold): """Call the _find_cluster_extremes_shared function in a parallel fashion. Args: cluster_list (List[List[Dict]]): each cluster is a list of dicts with the key base_index, referring to the index of the base code in the dataset. dataset (Type[Dataset]): dataset is used to access the content of the code snippets, using the base_index from the cluster_list. dataset is shared between all the processes using a glabal variable (any other way to share the dataset?), otherwise the multi processing is not speeded up. jaccard_threshold (float): the threshold for the jaccard similarity. The default value is 0.85 Returns: extremes_list (List[Dict]): Each cluster is reduced to extremes. See _find_cluster_extremes_shared for the definition of extremes. """ global _shared_dataset _shared_dataset = dataset extremes_list = [] f = partial(_find_cluster_extremes_shared, jaccard_threshold=jaccard_threshold) with mp.Pool() as pool: for extremes in tqdm( pool.imap_unordered( f, cluster_list, ), total=len(cluster_list), ): extremes_list.append(extremes) return extremes_list def deduplicate_dataset( dataset: Type[Dataset], jaccard_threshold: float = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]: """Deduplicate the dataset using minhash and jaccard similarity. This function first generate duplicate clusters, then each cluster is reduced to the extremes that are similar to the other elements in the cluster. Codes are called similar if their Jaccard similarity is greater than jaccard_threshold (0.85 default). Args: dataset (Type[Dataset]): The dataset to deduplicate. jaccard_threshold (float, default=0.85): jaccard threshold to determine if two codes are similar Returns: ds_dedup (Type[Dataset]): The deduplicated dataset. duplicate_clusters (List[List[Dict]]): The list of duplicate clusters. Each cluster is a list of dicts with the following keys: - base_index : int The index of the code in the original dataset. - repo_name : str - path : str - copies : int The number of copies of the code in the cluster. (find_cluster_extremes) - is_extreme : bool Whether the code is an extreme in the cluster. All the codes in the cluster are removed from the dataset except the extremes. Example: >>> from datasets import load_dataset >>> from minhash_deduplication import deduplicate_dataset >>> ds = load_dataset("lvwerra/codeparrot-clean", split="train") >>> ds_dedup, duplicate_clusters = deduplicate_dataset(ds, jaccard_threshold=0.85) """ duplicate_clusters = make_duplicate_clusters(dataset, jaccard_threshold) duplicate_indices = set(x["base_index"] for cluster in duplicate_clusters for x in cluster) extreme_dict = {} extremes_clusters = find_extremes(duplicate_clusters, dataset, jaccard_threshold) for extremes in extremes_clusters: for element in extremes: extreme_dict[element["base_index"]] = element remove_indices = duplicate_indices - set(extreme_dict.keys()) ds_filter = dataset.filter(lambda x, idx: idx not in remove_indices, with_indices=True) # update duplicate_clusters for cluster in duplicate_clusters: for element in cluster: element["is_extreme"] = element["base_index"] in extreme_dict if element["is_extreme"]: element["copies"] = extreme_dict[element["base_index"]]["copies"] print(f"Original dataset size: {len(dataset)}") print(f"Number of duplicate clusters: {len(duplicate_clusters)}") print(f"Files in duplicate cluster: {len(duplicate_indices)}") print(f"Unique files in duplicate cluster: {len(extreme_dict)}") print(f"Filtered dataset size: {len(ds_filter)}") return ds_filter, duplicate_clusters

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/xlnet/configuration_xlnet.py

# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ XLNet configuration""" import warnings from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json", } class XLNetConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`XLNetModel`] or a [`TFXLNetModel`]. It is used to instantiate a XLNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the XLNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`XLNetModel`] or [`TFXLNetModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers and the pooler layer. n_layer (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. d_inner (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. ff_activation (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. untie_r (`bool`, *optional*, defaults to `True`): Whether or not to untie relative position biases attn_type (`str`, *optional*, defaults to `"bi"`): The attention type used by the model. Set `"bi"` for XLNet, `"uni"` for Transformer-XL. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. mem_len (`int` or `None`, *optional*): The number of tokens to cache. The key/value pairs that have already been pre-computed in a previous forward pass won't be re-computed. See the [quickstart](https://huggingface.co/transformers/quickstart.html#using-the-past) for more information. reuse_len (`int`, *optional*): The number of tokens in the current batch to be cached and reused in the future. bi_data (`bool`, *optional*, defaults to `False`): Whether or not to use bidirectional input pipeline. Usually set to `True` during pretraining and `False` during finetuning. clamp_len (`int`, *optional*, defaults to -1): Clamp all relative distances larger than clamp_len. Setting this attribute to -1 means no clamping. same_length (`bool`, *optional*, defaults to `False`): Whether or not to use the same attention length for each token. summary_type (`str`, *optional*, defaults to "last"): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`boo`, *optional*, defaults to `True`): Used in the sequence classification and multiple choice models. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_last_dropout (`float`, *optional*, defaults to 0.1): Used in the sequence classification and multiple choice models. The dropout ratio to be used after the projection and activation. start_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. end_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. use_mems_eval (`bool`, *optional*, defaults to `True`): Whether or not the model should make use of the recurrent memory mechanism in evaluation mode. use_mems_train (`bool`, *optional*, defaults to `False`): Whether or not the model should make use of the recurrent memory mechanism in train mode. <Tip> For pretraining, it is recommended to set `use_mems_train` to `True`. For fine-tuning, it is recommended to set `use_mems_train` to `False` as discussed [here](https://github.com/zihangdai/xlnet/issues/41#issuecomment-505102587). If `use_mems_train` is set to `True`, one has to make sure that the train batches are correctly pre-processed, *e.g.* `batch_1 = [[This line is], [This is the]]` and `batch_2 = [[ the first line], [ second line]]` and that all batches are of equal size. </Tip> Examples: ```python >>> from transformers import XLNetConfig, XLNetModel >>> # Initializing a XLNet configuration >>> configuration = XLNetConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = XLNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "xlnet" keys_to_ignore_at_inference = ["mems"] attribute_map = { "n_token": "vocab_size", # Backward compatibility "hidden_size": "d_model", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size=32000, d_model=1024, n_layer=24, n_head=16, d_inner=4096, ff_activation="gelu", untie_r=True, attn_type="bi", initializer_range=0.02, layer_norm_eps=1e-12, dropout=0.1, mem_len=512, reuse_len=None, use_mems_eval=True, use_mems_train=False, bi_data=False, clamp_len=-1, same_length=False, summary_type="last", summary_use_proj=True, summary_activation="tanh", summary_last_dropout=0.1, start_n_top=5, end_n_top=5, pad_token_id=5, bos_token_id=1, eos_token_id=2, **kwargs ): """Constructs XLNetConfig.""" self.vocab_size = vocab_size self.d_model = d_model self.n_layer = n_layer self.n_head = n_head if d_model % n_head != 0: raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0") if "d_head" in kwargs: if kwargs["d_head"] != d_model // n_head: raise ValueError( f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" ) self.d_head = d_model // n_head self.ff_activation = ff_activation self.d_inner = d_inner self.untie_r = untie_r self.attn_type = attn_type self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.dropout = dropout self.mem_len = mem_len self.reuse_len = reuse_len self.bi_data = bi_data self.clamp_len = clamp_len self.same_length = same_length self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_last_dropout = summary_last_dropout self.start_n_top = start_n_top self.end_n_top = end_n_top self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.eos_token_id = eos_token_id if "use_cache" in kwargs: warnings.warn( "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`" " instead.", FutureWarning, ) use_mems_eval = kwargs["use_cache"] self.use_mems_eval = use_mems_eval self.use_mems_train = use_mems_train super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) @property def max_position_embeddings(self): logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.") return -1 @max_position_embeddings.setter def max_position_embeddings(self, value): # Message copied from Transformer-XL documentation raise NotImplementedError( f"The model {self.model_type} is one of the few models that has no sequence length limit." )

# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ XLNet configuration""" import warnings from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json", } class XLNetConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`XLNetModel`] or a [`TFXLNetModel`]. It is used to instantiate a XLNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the XLNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`XLNetModel`] or [`TFXLNetModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers and the pooler layer. n_layer (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. d_inner (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. ff_activation (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. untie_r (`bool`, *optional*, defaults to `True`): Whether or not to untie relative position biases attn_type (`str`, *optional*, defaults to `"bi"`): The attention type used by the model. Set `"bi"` for XLNet, `"uni"` for Transformer-XL. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. mem_len (`int` or `None`, *optional*): The number of tokens to cache. The key/value pairs that have already been pre-computed in a previous forward pass won't be re-computed. See the [quickstart](https://huggingface.co/transformers/quickstart.html#using-the-past) for more information. reuse_len (`int`, *optional*): The number of tokens in the current batch to be cached and reused in the future. bi_data (`bool`, *optional*, defaults to `False`): Whether or not to use bidirectional input pipeline. Usually set to `True` during pretraining and `False` during finetuning. clamp_len (`int`, *optional*, defaults to -1): Clamp all relative distances larger than clamp_len. Setting this attribute to -1 means no clamping. same_length (`bool`, *optional*, defaults to `False`): Whether or not to use the same attention length for each token. summary_type (`str`, *optional*, defaults to "last"): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`boo`, *optional*, defaults to `True`): Used in the sequence classification and multiple choice models. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_last_dropout (`float`, *optional*, defaults to 0.1): Used in the sequence classification and multiple choice models. The dropout ratio to be used after the projection and activation. start_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. end_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. use_mems_eval (`bool`, *optional*, defaults to `True`): Whether or not the model should make use of the recurrent memory mechanism in evaluation mode. use_mems_train (`bool`, *optional*, defaults to `False`): Whether or not the model should make use of the recurrent memory mechanism in train mode. <Tip> For pretraining, it is recommended to set `use_mems_train` to `True`. For fine-tuning, it is recommended to set `use_mems_train` to `False` as discussed [here](https://github.com/zihangdai/xlnet/issues/41#issuecomment-505102587). If `use_mems_train` is set to `True`, one has to make sure that the train batches are correctly pre-processed, *e.g.* `batch_1 = [[This line is], [This is the]]` and `batch_2 = [[ the first line], [ second line]]` and that all batches are of equal size. </Tip> Examples: ```python >>> from transformers import XLNetConfig, XLNetModel >>> # Initializing a XLNet configuration >>> configuration = XLNetConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = XLNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "xlnet" keys_to_ignore_at_inference = ["mems"] attribute_map = { "n_token": "vocab_size", # Backward compatibility "hidden_size": "d_model", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size=32000, d_model=1024, n_layer=24, n_head=16, d_inner=4096, ff_activation="gelu", untie_r=True, attn_type="bi", initializer_range=0.02, layer_norm_eps=1e-12, dropout=0.1, mem_len=512, reuse_len=None, use_mems_eval=True, use_mems_train=False, bi_data=False, clamp_len=-1, same_length=False, summary_type="last", summary_use_proj=True, summary_activation="tanh", summary_last_dropout=0.1, start_n_top=5, end_n_top=5, pad_token_id=5, bos_token_id=1, eos_token_id=2, **kwargs ): """Constructs XLNetConfig.""" self.vocab_size = vocab_size self.d_model = d_model self.n_layer = n_layer self.n_head = n_head if d_model % n_head != 0: raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0") if "d_head" in kwargs: if kwargs["d_head"] != d_model // n_head: raise ValueError( f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" ) self.d_head = d_model // n_head self.ff_activation = ff_activation self.d_inner = d_inner self.untie_r = untie_r self.attn_type = attn_type self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.dropout = dropout self.mem_len = mem_len self.reuse_len = reuse_len self.bi_data = bi_data self.clamp_len = clamp_len self.same_length = same_length self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_last_dropout = summary_last_dropout self.start_n_top = start_n_top self.end_n_top = end_n_top self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.eos_token_id = eos_token_id if "use_cache" in kwargs: warnings.warn( "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`" " instead.", FutureWarning, ) use_mems_eval = kwargs["use_cache"] self.use_mems_eval = use_mems_eval self.use_mems_train = use_mems_train super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) @property def max_position_embeddings(self): logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.") return -1 @max_position_embeddings.setter def max_position_embeddings(self, value): # Message copied from Transformer-XL documentation raise NotImplementedError( f"The model {self.model_type} is one of the few models that has no sequence length limit." )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/image_transforms.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union import numpy as np from transformers.utils import TensorType from transformers.utils.import_utils import is_flax_available, is_tf_available, is_torch_available, is_vision_available if is_vision_available(): import PIL from .image_utils import ( ChannelDimension, PILImageResampling, get_channel_dimension_axis, get_image_size, infer_channel_dimension_format, is_jax_tensor, is_tf_tensor, is_torch_tensor, to_numpy_array, ) if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp def to_channel_dimension_format(image: np.ndarray, channel_dim: Union[ChannelDimension, str]) -> np.ndarray: """ Converts `image` to the channel dimension format specified by `channel_dim`. Args: image (`numpy.ndarray`): The image to have its channel dimension set. channel_dim (`ChannelDimension`): The channel dimension format to use. Returns: `np.ndarray`: The image with the channel dimension set to `channel_dim`. """ if not isinstance(image, np.ndarray): raise ValueError(f"Input image must be of type np.ndarray, got {type(image)}") current_channel_dim = infer_channel_dimension_format(image) target_channel_dim = ChannelDimension(channel_dim) if current_channel_dim == target_channel_dim: return image if target_channel_dim == ChannelDimension.FIRST: image = image.transpose((2, 0, 1)) elif target_channel_dim == ChannelDimension.LAST: image = image.transpose((1, 2, 0)) else: raise ValueError("Unsupported channel dimension format: {}".format(channel_dim)) return image def rescale( image: np.ndarray, scale: float, data_format: Optional[ChannelDimension] = None, dtype=np.float32 ) -> np.ndarray: """ Rescales `image` by `scale`. Args: image (`np.ndarray`): The image to rescale. scale (`float`): The scale to use for rescaling the image. data_format (`ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. dtype (`np.dtype`, *optional*, defaults to `np.float32`): The dtype of the output image. Defaults to `np.float32`. Used for backwards compatibility with feature extractors. Returns: `np.ndarray`: The rescaled image. """ if not isinstance(image, np.ndarray): raise ValueError(f"Input image must be of type np.ndarray, got {type(image)}") rescaled_image = image * scale if data_format is not None: rescaled_image = to_channel_dimension_format(rescaled_image, data_format) rescaled_image = rescaled_image.astype(dtype) return rescaled_image def to_pil_image( image: Union[np.ndarray, PIL.Image.Image, "torch.Tensor", "tf.Tensor", "jnp.ndarray"], do_rescale: Optional[bool] = None, ) -> PIL.Image.Image: """ Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed. Args: image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor` or `tf.Tensor`): The image to convert to the `PIL.Image` format. do_rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type, `False` otherwise. Returns: `PIL.Image.Image`: The converted image. """ if isinstance(image, PIL.Image.Image): return image # Convert all tensors to numpy arrays before converting to PIL image if is_torch_tensor(image) or is_tf_tensor(image): image = image.numpy() elif is_jax_tensor(image): image = np.array(image) elif not isinstance(image, np.ndarray): raise ValueError("Input image type not supported: {}".format(type(image))) # If the channel as been moved to first dim, we put it back at the end. image = to_channel_dimension_format(image, ChannelDimension.LAST) # If there is a single channel, we squeeze it, as otherwise PIL can't handle it. image = np.squeeze(image, axis=-1) if image.shape[-1] == 1 else image # PIL.Image can only store uint8 values, so we rescale the image to be between 0 and 255 if needed. do_rescale = isinstance(image.flat[0], float) if do_rescale is None else do_rescale if do_rescale: image = rescale(image, 255) image = image.astype(np.uint8) return PIL.Image.fromarray(image) def get_resize_output_image_size( input_image: np.ndarray, size: Union[int, Tuple[int, int], List[int], Tuple[int]], default_to_square: bool = True, max_size: Optional[int] = None, ) -> tuple: """ Find the target (height, width) dimension of the output image after resizing given the input image and the desired size. Args: input_image (`np.ndarray`): The image to resize. size (`int` or `Tuple[int, int]` or List[int] or Tuple[int]): The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to this. If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size). default_to_square (`bool`, *optional*, defaults to `True`): How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square (`size`,`size`). If set to `False`, will replicate [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize) with support for resizing only the smallest edge and providing an optional `max_size`. max_size (`int`, *optional*): The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than `max_size` after being resized according to `size`, then the image is resized again so that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter than `size`. Only used if `default_to_square` is `False`. Returns: `tuple`: The target (height, width) dimension of the output image after resizing. """ if isinstance(size, (tuple, list)): if len(size) == 2: return tuple(size) elif len(size) == 1: # Perform same logic as if size was an int size = size[0] else: raise ValueError("size must have 1 or 2 elements if it is a list or tuple") if default_to_square: return (size, size) height, width = get_image_size(input_image) short, long = (width, height) if width <= height else (height, width) requested_new_short = size if short == requested_new_short: return (height, width) new_short, new_long = requested_new_short, int(requested_new_short * long / short) if max_size is not None: if max_size <= requested_new_short: raise ValueError( f"max_size = {max_size} must be strictly greater than the requested " f"size for the smaller edge size = {size}" ) if new_long > max_size: new_short, new_long = int(max_size * new_short / new_long), max_size return (new_long, new_short) if width <= height else (new_short, new_long) def resize( image, size: Tuple[int, int], resample=PILImageResampling.BILINEAR, data_format: Optional[ChannelDimension] = None, return_numpy: bool = True, ) -> np.ndarray: """ Resizes `image` to (h, w) specified by `size` using the PIL library. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to resize. size (`Tuple[int, int]`): The size to use for resizing the image. resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. data_format (`ChannelDimension`, *optional*): The channel dimension format of the output image. If `None`, will use the inferred format from the input. return_numpy (`bool`, *optional*, defaults to `True`): Whether or not to return the resized image as a numpy array. If False a `PIL.Image.Image` object is returned. Returns: `np.ndarray`: The resized image. """ if not len(size) == 2: raise ValueError("size must have 2 elements") # For all transformations, we want to keep the same data format as the input image unless otherwise specified. # The resized image from PIL will always have channels last, so find the input format first. data_format = infer_channel_dimension_format(image) if data_format is None else data_format # To maintain backwards compatibility with the resizing done in previous image feature extractors, we use # the pillow library to resize the image and then convert back to numpy if not isinstance(image, PIL.Image.Image): # PIL expects image to have channels last image = to_channel_dimension_format(image, ChannelDimension.LAST) image = to_pil_image(image) height, width = size # PIL images are in the format (width, height) resized_image = image.resize((width, height), resample=resample) if return_numpy: resized_image = np.array(resized_image) # If the input image channel dimension was of size 1, then it is dropped when converting to a PIL image # so we need to add it back if necessary. resized_image = np.expand_dims(resized_image, axis=-1) if resized_image.ndim == 2 else resized_image resized_image = to_channel_dimension_format(resized_image, data_format) return resized_image def normalize( image: np.ndarray, mean: Union[float, Iterable[float]], std: Union[float, Iterable[float]], data_format: Optional[ChannelDimension] = None, ) -> np.ndarray: """ Normalizes `image` using the mean and standard deviation specified by `mean` and `std`. image = (image - mean) / std Args: image (`np.ndarray`): The image to normalize. mean (`float` or `Iterable[float]`): The mean to use for normalization. std (`float` or `Iterable[float]`): The standard deviation to use for normalization. data_format (`ChannelDimension`, *optional*): The channel dimension format of the output image. If `None`, will use the inferred format from the input. """ if isinstance(image, PIL.Image.Image): warnings.warn( "PIL.Image.Image inputs are deprecated and will be removed in v4.26.0. Please use numpy arrays instead.", FutureWarning, ) # Convert PIL image to numpy array with the same logic as in the previous feature extractor normalize - # casting to numpy array and dividing by 255. image = to_numpy_array(image) image = rescale(image, scale=1 / 255) if not isinstance(image, np.ndarray): raise ValueError("image must be a numpy array") input_data_format = infer_channel_dimension_format(image) channel_axis = get_channel_dimension_axis(image) num_channels = image.shape[channel_axis] if isinstance(mean, Iterable): if len(mean) != num_channels: raise ValueError(f"mean must have {num_channels} elements if it is an iterable, got {len(mean)}") else: mean = [mean] * num_channels mean = np.array(mean, dtype=image.dtype) if isinstance(std, Iterable): if len(std) != num_channels: raise ValueError(f"std must have {num_channels} elements if it is an iterable, got {len(std)}") else: std = [std] * num_channels std = np.array(std, dtype=image.dtype) if input_data_format == ChannelDimension.LAST: image = (image - mean) / std else: image = ((image.T - mean) / std).T image = to_channel_dimension_format(image, data_format) if data_format is not None else image return image def center_crop( image: np.ndarray, size: Tuple[int, int], data_format: Optional[Union[str, ChannelDimension]] = None, return_numpy: Optional[bool] = None, ) -> np.ndarray: """ Crops the `image` to the specified `size` using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result will always be of size `size`). Args: image (`np.ndarray`): The image to crop. size (`Tuple[int, int]`): The target size for the cropped image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use the inferred format of the input image. return_numpy (`bool`, *optional*): Whether or not to return the cropped image as a numpy array. Used for backwards compatibility with the previous ImageFeatureExtractionMixin method. - Unset: will return the same type as the input image. - `True`: will return a numpy array. - `False`: will return a `PIL.Image.Image` object. Returns: `np.ndarray`: The cropped image. """ if isinstance(image, PIL.Image.Image): warnings.warn( "PIL.Image.Image inputs are deprecated and will be removed in v4.26.0. Please use numpy arrays instead.", FutureWarning, ) image = to_numpy_array(image) return_numpy = False if return_numpy is None else return_numpy else: return_numpy = True if return_numpy is None else return_numpy if not isinstance(image, np.ndarray): raise ValueError(f"Input image must be of type np.ndarray, got {type(image)}") if not isinstance(size, Iterable) or len(size) != 2: raise ValueError("size must have 2 elements representing the height and width of the output image") input_data_format = infer_channel_dimension_format(image) output_data_format = data_format if data_format is not None else input_data_format # We perform the crop in (C, H, W) format and then convert to the output format image = to_channel_dimension_format(image, ChannelDimension.FIRST) orig_height, orig_width = get_image_size(image) crop_height, crop_width = size crop_height, crop_width = int(crop_height), int(crop_width) # In case size is odd, (image_shape[0] + size[0]) // 2 won't give the proper result. top = (orig_height - crop_height) // 2 bottom = top + crop_height # In case size is odd, (image_shape[1] + size[1]) // 2 won't give the proper result. left = (orig_width - crop_width) // 2 right = left + crop_width # Check if cropped area is within image boundaries if top >= 0 and bottom <= orig_height and left >= 0 and right <= orig_width: image = image[..., top:bottom, left:right] image = to_channel_dimension_format(image, output_data_format) return image # Otherwise, we may need to pad if the image is too small. Oh joy... new_height = max(crop_height, orig_height) new_width = max(crop_width, orig_width) new_shape = image.shape[:-2] + (new_height, new_width) new_image = np.zeros_like(image, shape=new_shape) # If the image is too small, pad it with zeros top_pad = (new_height - orig_height) // 2 bottom_pad = top_pad + orig_height left_pad = (new_width - orig_width) // 2 right_pad = left_pad + orig_width new_image[..., top_pad:bottom_pad, left_pad:right_pad] = image top += top_pad bottom += top_pad left += left_pad right += left_pad new_image = new_image[..., max(0, top) : min(new_height, bottom), max(0, left) : min(new_width, right)] new_image = to_channel_dimension_format(new_image, output_data_format) if not return_numpy: new_image = to_pil_image(new_image) return new_image def _center_to_corners_format_torch(bboxes_center: "torch.Tensor") -> "torch.Tensor": center_x, center_y, width, height = bboxes_center.unbind(-1) bbox_corners = torch.stack( # top left x, top left y, bottom right x, bottom right y [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)], dim=-1, ) return bbox_corners def _center_to_corners_format_numpy(bboxes_center: np.ndarray) -> np.ndarray: center_x, center_y, width, height = bboxes_center.T bboxes_corners = np.stack( # top left x, top left y, bottom right x, bottom right y [center_x - 0.5 * width, center_y - 0.5 * height, center_x + 0.5 * width, center_y + 0.5 * height], axis=-1, ) return bboxes_corners def _center_to_corners_format_tf(bboxes_center: "tf.Tensor") -> "tf.Tensor": center_x, center_y, width, height = tf.unstack(bboxes_center, axis=-1) bboxes_corners = tf.stack( # top left x, top left y, bottom right x, bottom right y [center_x - 0.5 * width, center_y - 0.5 * height, center_x + 0.5 * width, center_y + 0.5 * height], axis=-1, ) return bboxes_corners # 2 functions below inspired by https://github.com/facebookresearch/detr/blob/master/util/box_ops.py def center_to_corners_format(bboxes_center: TensorType) -> TensorType: """ Converts bounding boxes from center format to corners format. center format: contains the coordinate for the center of the box and its width, height dimensions (center_x, center_y, width, height) corners format: contains the coodinates for the top-left and bottom-right corners of the box (top_left_x, top_left_y, bottom_right_x, bottom_right_y) """ # Function is used during model forward pass, so we use the input framework if possible, without # converting to numpy if is_torch_tensor(bboxes_center): return _center_to_corners_format_torch(bboxes_center) elif isinstance(bboxes_center, np.ndarray): return _center_to_corners_format_numpy(bboxes_center) elif is_tf_tensor(bboxes_center): return _center_to_corners_format_tf(bboxes_center) raise ValueError(f"Unsupported input type {type(bboxes_center)}") def _corners_to_center_format_torch(bboxes_corners: "torch.Tensor") -> "torch.Tensor": top_left_x, top_left_y, bottom_right_x, bottom_right_y = bboxes_corners.unbind(-1) b = [ (top_left_x + bottom_right_x) / 2, # center x (top_left_y + bottom_right_y) / 2, # center y (bottom_right_x - top_left_x), # width (bottom_right_y - top_left_y), # height ] return torch.stack(b, dim=-1) def _corners_to_center_format_numpy(bboxes_corners: np.ndarray) -> np.ndarray: top_left_x, top_left_y, bottom_right_x, bottom_right_y = bboxes_corners.T bboxes_center = np.stack( [ (top_left_x + bottom_right_x) / 2, # center x (top_left_y + bottom_right_y) / 2, # center y (bottom_right_x - top_left_x), # width (bottom_right_y - top_left_y), # height ], axis=-1, ) return bboxes_center def _corners_to_center_format_tf(bboxes_corners: "tf.Tensor") -> "tf.Tensor": top_left_x, top_left_y, bottom_right_x, bottom_right_y = tf.unstack(bboxes_corners, axis=-1) bboxes_center = tf.stack( [ (top_left_x + bottom_right_x) / 2, # center x (top_left_y + bottom_right_y) / 2, # center y (bottom_right_x - top_left_x), # width (bottom_right_y - top_left_y), # height ], axis=-1, ) return bboxes_center def corners_to_center_format(bboxes_corners: TensorType) -> TensorType: """ Converts bounding boxes from corners format to center format. corners format: contains the coodinates for the top-left and bottom-right corners of the box (top_left_x, top_left_y, bottom_right_x, bottom_right_y) center format: contains the coordinate for the center of the box and its the width, height dimensions (center_x, center_y, width, height) """ # Inverse function accepts different input types so implemented here too if is_torch_tensor(bboxes_corners): return _corners_to_center_format_torch(bboxes_corners) elif isinstance(bboxes_corners, np.ndarray): return _corners_to_center_format_numpy(bboxes_corners) elif is_tf_tensor(bboxes_corners): return _corners_to_center_format_tf(bboxes_corners) raise ValueError(f"Unsupported input type {type(bboxes_corners)}") # 2 functions below copied from https://github.com/cocodataset/panopticapi/blob/master/panopticapi/utils.py # Copyright (c) 2018, Alexander Kirillov # All rights reserved. def rgb_to_id(color): """ Converts RGB color to unique ID. """ if isinstance(color, np.ndarray) and len(color.shape) == 3: if color.dtype == np.uint8: color = color.astype(np.int32) return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2] return int(color[0] + 256 * color[1] + 256 * 256 * color[2]) def id_to_rgb(id_map): """ Converts unique ID to RGB color. """ if isinstance(id_map, np.ndarray): id_map_copy = id_map.copy() rgb_shape = tuple(list(id_map.shape) + [3]) rgb_map = np.zeros(rgb_shape, dtype=np.uint8) for i in range(3): rgb_map[..., i] = id_map_copy % 256 id_map_copy //= 256 return rgb_map color = [] for _ in range(3): color.append(id_map % 256) id_map //= 256 return color

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union import numpy as np from transformers.utils import TensorType from transformers.utils.import_utils import is_flax_available, is_tf_available, is_torch_available, is_vision_available if is_vision_available(): import PIL from .image_utils import ( ChannelDimension, PILImageResampling, get_channel_dimension_axis, get_image_size, infer_channel_dimension_format, is_jax_tensor, is_tf_tensor, is_torch_tensor, to_numpy_array, ) if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp def to_channel_dimension_format(image: np.ndarray, channel_dim: Union[ChannelDimension, str]) -> np.ndarray: """ Converts `image` to the channel dimension format specified by `channel_dim`. Args: image (`numpy.ndarray`): The image to have its channel dimension set. channel_dim (`ChannelDimension`): The channel dimension format to use. Returns: `np.ndarray`: The image with the channel dimension set to `channel_dim`. """ if not isinstance(image, np.ndarray): raise ValueError(f"Input image must be of type np.ndarray, got {type(image)}") current_channel_dim = infer_channel_dimension_format(image) target_channel_dim = ChannelDimension(channel_dim) if current_channel_dim == target_channel_dim: return image if target_channel_dim == ChannelDimension.FIRST: image = image.transpose((2, 0, 1)) elif target_channel_dim == ChannelDimension.LAST: image = image.transpose((1, 2, 0)) else: raise ValueError("Unsupported channel dimension format: {}".format(channel_dim)) return image def rescale( image: np.ndarray, scale: float, data_format: Optional[ChannelDimension] = None, dtype=np.float32 ) -> np.ndarray: """ Rescales `image` by `scale`. Args: image (`np.ndarray`): The image to rescale. scale (`float`): The scale to use for rescaling the image. data_format (`ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. dtype (`np.dtype`, *optional*, defaults to `np.float32`): The dtype of the output image. Defaults to `np.float32`. Used for backwards compatibility with feature extractors. Returns: `np.ndarray`: The rescaled image. """ if not isinstance(image, np.ndarray): raise ValueError(f"Input image must be of type np.ndarray, got {type(image)}") rescaled_image = image * scale if data_format is not None: rescaled_image = to_channel_dimension_format(rescaled_image, data_format) rescaled_image = rescaled_image.astype(dtype) return rescaled_image def to_pil_image( image: Union[np.ndarray, PIL.Image.Image, "torch.Tensor", "tf.Tensor", "jnp.ndarray"], do_rescale: Optional[bool] = None, ) -> PIL.Image.Image: """ Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed. Args: image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor` or `tf.Tensor`): The image to convert to the `PIL.Image` format. do_rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type, `False` otherwise. Returns: `PIL.Image.Image`: The converted image. """ if isinstance(image, PIL.Image.Image): return image # Convert all tensors to numpy arrays before converting to PIL image if is_torch_tensor(image) or is_tf_tensor(image): image = image.numpy() elif is_jax_tensor(image): image = np.array(image) elif not isinstance(image, np.ndarray): raise ValueError("Input image type not supported: {}".format(type(image))) # If the channel as been moved to first dim, we put it back at the end. image = to_channel_dimension_format(image, ChannelDimension.LAST) # If there is a single channel, we squeeze it, as otherwise PIL can't handle it. image = np.squeeze(image, axis=-1) if image.shape[-1] == 1 else image # PIL.Image can only store uint8 values, so we rescale the image to be between 0 and 255 if needed. do_rescale = isinstance(image.flat[0], float) if do_rescale is None else do_rescale if do_rescale: image = rescale(image, 255) image = image.astype(np.uint8) return PIL.Image.fromarray(image) def get_resize_output_image_size( input_image: np.ndarray, size: Union[int, Tuple[int, int], List[int], Tuple[int]], default_to_square: bool = True, max_size: Optional[int] = None, ) -> tuple: """ Find the target (height, width) dimension of the output image after resizing given the input image and the desired size. Args: input_image (`np.ndarray`): The image to resize. size (`int` or `Tuple[int, int]` or List[int] or Tuple[int]): The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to this. If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size). default_to_square (`bool`, *optional*, defaults to `True`): How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square (`size`,`size`). If set to `False`, will replicate [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize) with support for resizing only the smallest edge and providing an optional `max_size`. max_size (`int`, *optional*): The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than `max_size` after being resized according to `size`, then the image is resized again so that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter than `size`. Only used if `default_to_square` is `False`. Returns: `tuple`: The target (height, width) dimension of the output image after resizing. """ if isinstance(size, (tuple, list)): if len(size) == 2: return tuple(size) elif len(size) == 1: # Perform same logic as if size was an int size = size[0] else: raise ValueError("size must have 1 or 2 elements if it is a list or tuple") if default_to_square: return (size, size) height, width = get_image_size(input_image) short, long = (width, height) if width <= height else (height, width) requested_new_short = size if short == requested_new_short: return (height, width) new_short, new_long = requested_new_short, int(requested_new_short * long / short) if max_size is not None: if max_size <= requested_new_short: raise ValueError( f"max_size = {max_size} must be strictly greater than the requested " f"size for the smaller edge size = {size}" ) if new_long > max_size: new_short, new_long = int(max_size * new_short / new_long), max_size return (new_long, new_short) if width <= height else (new_short, new_long) def resize( image, size: Tuple[int, int], resample=PILImageResampling.BILINEAR, data_format: Optional[ChannelDimension] = None, return_numpy: bool = True, ) -> np.ndarray: """ Resizes `image` to (h, w) specified by `size` using the PIL library. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to resize. size (`Tuple[int, int]`): The size to use for resizing the image. resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. data_format (`ChannelDimension`, *optional*): The channel dimension format of the output image. If `None`, will use the inferred format from the input. return_numpy (`bool`, *optional*, defaults to `True`): Whether or not to return the resized image as a numpy array. If False a `PIL.Image.Image` object is returned. Returns: `np.ndarray`: The resized image. """ if not len(size) == 2: raise ValueError("size must have 2 elements") # For all transformations, we want to keep the same data format as the input image unless otherwise specified. # The resized image from PIL will always have channels last, so find the input format first. data_format = infer_channel_dimension_format(image) if data_format is None else data_format # To maintain backwards compatibility with the resizing done in previous image feature extractors, we use # the pillow library to resize the image and then convert back to numpy if not isinstance(image, PIL.Image.Image): # PIL expects image to have channels last image = to_channel_dimension_format(image, ChannelDimension.LAST) image = to_pil_image(image) height, width = size # PIL images are in the format (width, height) resized_image = image.resize((width, height), resample=resample) if return_numpy: resized_image = np.array(resized_image) # If the input image channel dimension was of size 1, then it is dropped when converting to a PIL image # so we need to add it back if necessary. resized_image = np.expand_dims(resized_image, axis=-1) if resized_image.ndim == 2 else resized_image resized_image = to_channel_dimension_format(resized_image, data_format) return resized_image def normalize( image: np.ndarray, mean: Union[float, Iterable[float]], std: Union[float, Iterable[float]], data_format: Optional[ChannelDimension] = None, ) -> np.ndarray: """ Normalizes `image` using the mean and standard deviation specified by `mean` and `std`. image = (image - mean) / std Args: image (`np.ndarray`): The image to normalize. mean (`float` or `Iterable[float]`): The mean to use for normalization. std (`float` or `Iterable[float]`): The standard deviation to use for normalization. data_format (`ChannelDimension`, *optional*): The channel dimension format of the output image. If `None`, will use the inferred format from the input. """ if isinstance(image, PIL.Image.Image): warnings.warn( "PIL.Image.Image inputs are deprecated and will be removed in v4.26.0. Please use numpy arrays instead.", FutureWarning, ) # Convert PIL image to numpy array with the same logic as in the previous feature extractor normalize - # casting to numpy array and dividing by 255. image = to_numpy_array(image) image = rescale(image, scale=1 / 255) if not isinstance(image, np.ndarray): raise ValueError("image must be a numpy array") input_data_format = infer_channel_dimension_format(image) channel_axis = get_channel_dimension_axis(image) num_channels = image.shape[channel_axis] if isinstance(mean, Iterable): if len(mean) != num_channels: raise ValueError(f"mean must have {num_channels} elements if it is an iterable, got {len(mean)}") else: mean = [mean] * num_channels mean = np.array(mean, dtype=image.dtype) if isinstance(std, Iterable): if len(std) != num_channels: raise ValueError(f"std must have {num_channels} elements if it is an iterable, got {len(std)}") else: std = [std] * num_channels std = np.array(std, dtype=image.dtype) if input_data_format == ChannelDimension.LAST: image = (image - mean) / std else: image = ((image.T - mean) / std).T image = to_channel_dimension_format(image, data_format) if data_format is not None else image return image def center_crop( image: np.ndarray, size: Tuple[int, int], data_format: Optional[Union[str, ChannelDimension]] = None, return_numpy: Optional[bool] = None, ) -> np.ndarray: """ Crops the `image` to the specified `size` using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result will always be of size `size`). Args: image (`np.ndarray`): The image to crop. size (`Tuple[int, int]`): The target size for the cropped image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use the inferred format of the input image. return_numpy (`bool`, *optional*): Whether or not to return the cropped image as a numpy array. Used for backwards compatibility with the previous ImageFeatureExtractionMixin method. - Unset: will return the same type as the input image. - `True`: will return a numpy array. - `False`: will return a `PIL.Image.Image` object. Returns: `np.ndarray`: The cropped image. """ if isinstance(image, PIL.Image.Image): warnings.warn( "PIL.Image.Image inputs are deprecated and will be removed in v4.26.0. Please use numpy arrays instead.", FutureWarning, ) image = to_numpy_array(image) return_numpy = False if return_numpy is None else return_numpy else: return_numpy = True if return_numpy is None else return_numpy if not isinstance(image, np.ndarray): raise ValueError(f"Input image must be of type np.ndarray, got {type(image)}") if not isinstance(size, Iterable) or len(size) != 2: raise ValueError("size must have 2 elements representing the height and width of the output image") input_data_format = infer_channel_dimension_format(image) output_data_format = data_format if data_format is not None else input_data_format # We perform the crop in (C, H, W) format and then convert to the output format image = to_channel_dimension_format(image, ChannelDimension.FIRST) orig_height, orig_width = get_image_size(image) crop_height, crop_width = size crop_height, crop_width = int(crop_height), int(crop_width) # In case size is odd, (image_shape[0] + size[0]) // 2 won't give the proper result. top = (orig_height - crop_height) // 2 bottom = top + crop_height # In case size is odd, (image_shape[1] + size[1]) // 2 won't give the proper result. left = (orig_width - crop_width) // 2 right = left + crop_width # Check if cropped area is within image boundaries if top >= 0 and bottom <= orig_height and left >= 0 and right <= orig_width: image = image[..., top:bottom, left:right] image = to_channel_dimension_format(image, output_data_format) return image # Otherwise, we may need to pad if the image is too small. Oh joy... new_height = max(crop_height, orig_height) new_width = max(crop_width, orig_width) new_shape = image.shape[:-2] + (new_height, new_width) new_image = np.zeros_like(image, shape=new_shape) # If the image is too small, pad it with zeros top_pad = (new_height - orig_height) // 2 bottom_pad = top_pad + orig_height left_pad = (new_width - orig_width) // 2 right_pad = left_pad + orig_width new_image[..., top_pad:bottom_pad, left_pad:right_pad] = image top += top_pad bottom += top_pad left += left_pad right += left_pad new_image = new_image[..., max(0, top) : min(new_height, bottom), max(0, left) : min(new_width, right)] new_image = to_channel_dimension_format(new_image, output_data_format) if not return_numpy: new_image = to_pil_image(new_image) return new_image def _center_to_corners_format_torch(bboxes_center: "torch.Tensor") -> "torch.Tensor": center_x, center_y, width, height = bboxes_center.unbind(-1) bbox_corners = torch.stack( # top left x, top left y, bottom right x, bottom right y [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)], dim=-1, ) return bbox_corners def _center_to_corners_format_numpy(bboxes_center: np.ndarray) -> np.ndarray: center_x, center_y, width, height = bboxes_center.T bboxes_corners = np.stack( # top left x, top left y, bottom right x, bottom right y [center_x - 0.5 * width, center_y - 0.5 * height, center_x + 0.5 * width, center_y + 0.5 * height], axis=-1, ) return bboxes_corners def _center_to_corners_format_tf(bboxes_center: "tf.Tensor") -> "tf.Tensor": center_x, center_y, width, height = tf.unstack(bboxes_center, axis=-1) bboxes_corners = tf.stack( # top left x, top left y, bottom right x, bottom right y [center_x - 0.5 * width, center_y - 0.5 * height, center_x + 0.5 * width, center_y + 0.5 * height], axis=-1, ) return bboxes_corners # 2 functions below inspired by https://github.com/facebookresearch/detr/blob/master/util/box_ops.py def center_to_corners_format(bboxes_center: TensorType) -> TensorType: """ Converts bounding boxes from center format to corners format. center format: contains the coordinate for the center of the box and its width, height dimensions (center_x, center_y, width, height) corners format: contains the coodinates for the top-left and bottom-right corners of the box (top_left_x, top_left_y, bottom_right_x, bottom_right_y) """ # Function is used during model forward pass, so we use the input framework if possible, without # converting to numpy if is_torch_tensor(bboxes_center): return _center_to_corners_format_torch(bboxes_center) elif isinstance(bboxes_center, np.ndarray): return _center_to_corners_format_numpy(bboxes_center) elif is_tf_tensor(bboxes_center): return _center_to_corners_format_tf(bboxes_center) raise ValueError(f"Unsupported input type {type(bboxes_center)}") def _corners_to_center_format_torch(bboxes_corners: "torch.Tensor") -> "torch.Tensor": top_left_x, top_left_y, bottom_right_x, bottom_right_y = bboxes_corners.unbind(-1) b = [ (top_left_x + bottom_right_x) / 2, # center x (top_left_y + bottom_right_y) / 2, # center y (bottom_right_x - top_left_x), # width (bottom_right_y - top_left_y), # height ] return torch.stack(b, dim=-1) def _corners_to_center_format_numpy(bboxes_corners: np.ndarray) -> np.ndarray: top_left_x, top_left_y, bottom_right_x, bottom_right_y = bboxes_corners.T bboxes_center = np.stack( [ (top_left_x + bottom_right_x) / 2, # center x (top_left_y + bottom_right_y) / 2, # center y (bottom_right_x - top_left_x), # width (bottom_right_y - top_left_y), # height ], axis=-1, ) return bboxes_center def _corners_to_center_format_tf(bboxes_corners: "tf.Tensor") -> "tf.Tensor": top_left_x, top_left_y, bottom_right_x, bottom_right_y = tf.unstack(bboxes_corners, axis=-1) bboxes_center = tf.stack( [ (top_left_x + bottom_right_x) / 2, # center x (top_left_y + bottom_right_y) / 2, # center y (bottom_right_x - top_left_x), # width (bottom_right_y - top_left_y), # height ], axis=-1, ) return bboxes_center def corners_to_center_format(bboxes_corners: TensorType) -> TensorType: """ Converts bounding boxes from corners format to center format. corners format: contains the coodinates for the top-left and bottom-right corners of the box (top_left_x, top_left_y, bottom_right_x, bottom_right_y) center format: contains the coordinate for the center of the box and its the width, height dimensions (center_x, center_y, width, height) """ # Inverse function accepts different input types so implemented here too if is_torch_tensor(bboxes_corners): return _corners_to_center_format_torch(bboxes_corners) elif isinstance(bboxes_corners, np.ndarray): return _corners_to_center_format_numpy(bboxes_corners) elif is_tf_tensor(bboxes_corners): return _corners_to_center_format_tf(bboxes_corners) raise ValueError(f"Unsupported input type {type(bboxes_corners)}") # 2 functions below copied from https://github.com/cocodataset/panopticapi/blob/master/panopticapi/utils.py # Copyright (c) 2018, Alexander Kirillov # All rights reserved. def rgb_to_id(color): """ Converts RGB color to unique ID. """ if isinstance(color, np.ndarray) and len(color.shape) == 3: if color.dtype == np.uint8: color = color.astype(np.int32) return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2] return int(color[0] + 256 * color[1] + 256 * 256 * color[2]) def id_to_rgb(id_map): """ Converts unique ID to RGB color. """ if isinstance(id_map, np.ndarray): id_map_copy = id_map.copy() rgb_shape = tuple(list(id_map.shape) + [3]) rgb_map = np.zeros(rgb_shape, dtype=np.uint8) for i in range(3): rgb_map[..., i] = id_map_copy % 256 id_map_copy //= 256 return rgb_map color = [] for _ in range(3): color.append(id_map % 256) id_map //= 256 return color

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/mluke/__init__.py

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/vit_mae/__init__.py

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/seq2seq-distillation/convert_pl_checkpoint_to_hf.py

#!/usr/bin/env python import os from pathlib import Path from typing import Dict, List import fire import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from transformers.utils.logging import get_logger logger = get_logger(__name__) def remove_prefix(text: str, prefix: str): if text.startswith(prefix): return text[len(prefix) :] return text # or whatever def sanitize(sd): return {remove_prefix(k, "model."): v for k, v in sd.items()} def average_state_dicts(state_dicts: List[Dict[str, torch.Tensor]]): new_sd = {} for k in state_dicts[0].keys(): tensors = [sd[k] for sd in state_dicts] new_t = sum(tensors) / len(tensors) assert isinstance(new_t, torch.Tensor) new_sd[k] = new_t return new_sd def convert_pl_to_hf(pl_ckpt_path: str, hf_src_model_dir: str, save_path: str) -> None: """Cleanup a pytorch-lightning .ckpt file or experiment dir and save a huggingface model with that state dict. Silently allows extra pl keys (like teacher.) Puts all ckpt models into CPU RAM at once! Args: pl_ckpt_path (:obj:`str`): Path to a .ckpt file saved by pytorch_lightning or dir containing ckpt files. If a directory is passed, all .ckpt files inside it will be averaged! hf_src_model_dir (:obj:`str`): Path to a directory containing a correctly shaped checkpoint save_path (:obj:`str`): Directory to save the new model """ hf_model = AutoModelForSeq2SeqLM.from_pretrained(hf_src_model_dir) if os.path.isfile(pl_ckpt_path): ckpt_files = [pl_ckpt_path] else: assert os.path.isdir(pl_ckpt_path) ckpt_files = list(Path(pl_ckpt_path).glob("*.ckpt")) assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory" if len(ckpt_files) > 1: logger.info(f"averaging the weights of {ckpt_files}") state_dicts = [sanitize(torch.load(x, map_location="cpu")["state_dict"]) for x in ckpt_files] state_dict = average_state_dicts(state_dicts) missing, unexpected = hf_model.load_state_dict(state_dict, strict=False) assert not missing, f"missing keys: {missing}" hf_model.save_pretrained(save_path) try: tok = AutoTokenizer.from_pretrained(hf_src_model_dir) tok.save_pretrained(save_path) except Exception: pass # dont copy tokenizer if cant if __name__ == "__main__": fire.Fire(convert_pl_to_hf)

#!/usr/bin/env python import os from pathlib import Path from typing import Dict, List import fire import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from transformers.utils.logging import get_logger logger = get_logger(__name__) def remove_prefix(text: str, prefix: str): if text.startswith(prefix): return text[len(prefix) :] return text # or whatever def sanitize(sd): return {remove_prefix(k, "model."): v for k, v in sd.items()} def average_state_dicts(state_dicts: List[Dict[str, torch.Tensor]]): new_sd = {} for k in state_dicts[0].keys(): tensors = [sd[k] for sd in state_dicts] new_t = sum(tensors) / len(tensors) assert isinstance(new_t, torch.Tensor) new_sd[k] = new_t return new_sd def convert_pl_to_hf(pl_ckpt_path: str, hf_src_model_dir: str, save_path: str) -> None: """Cleanup a pytorch-lightning .ckpt file or experiment dir and save a huggingface model with that state dict. Silently allows extra pl keys (like teacher.) Puts all ckpt models into CPU RAM at once! Args: pl_ckpt_path (:obj:`str`): Path to a .ckpt file saved by pytorch_lightning or dir containing ckpt files. If a directory is passed, all .ckpt files inside it will be averaged! hf_src_model_dir (:obj:`str`): Path to a directory containing a correctly shaped checkpoint save_path (:obj:`str`): Directory to save the new model """ hf_model = AutoModelForSeq2SeqLM.from_pretrained(hf_src_model_dir) if os.path.isfile(pl_ckpt_path): ckpt_files = [pl_ckpt_path] else: assert os.path.isdir(pl_ckpt_path) ckpt_files = list(Path(pl_ckpt_path).glob("*.ckpt")) assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory" if len(ckpt_files) > 1: logger.info(f"averaging the weights of {ckpt_files}") state_dicts = [sanitize(torch.load(x, map_location="cpu")["state_dict"]) for x in ckpt_files] state_dict = average_state_dicts(state_dicts) missing, unexpected = hf_model.load_state_dict(state_dict, strict=False) assert not missing, f"missing keys: {missing}" hf_model.save_pretrained(save_path) try: tok = AutoTokenizer.from_pretrained(hf_src_model_dir) tok.save_pretrained(save_path) except Exception: pass # dont copy tokenizer if cant if __name__ == "__main__": fire.Fire(convert_pl_to_hf)

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/mvp/configuration_mvp.py

# coding=utf-8 # Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ MVP model configuration""" import warnings from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) MVP_PRETRAINED_CONFIG_ARCHIVE_MAP = { "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json", } class MvpConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MvpModel`]. It is used to instantiate a MVP model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the MVP [RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50267): Vocabulary size of the MVP model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MvpModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). forced_eos_token_id (`int`, *optional*, defaults to 2): The id of the token to force as the last generated token when `max_length` is reached. Usually set to `eos_token_id`. use_prompt (`bool`, *optional*, defaults to `False`): Whether or not to use prompt. prompt_length (`int`, *optional*, defaults to 100): The length of prompt. prompt_mid_dim (`int`, *optional*, defaults to 800): Dimensionality of the "intermediate" layer in prompt. Example: ```python >>> from transformers import MvpModel, MvpConfig >>> # Initializing a MVP RUCAIBox/mvp style configuration >>> configuration = MvpConfig() >>> # Initializing a model from the RUCAIBox/mvp style configuration >>> model = MvpModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "mvp" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=50267, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, activation_function="gelu", d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, classifier_dropout=0.0, scale_embedding=False, use_cache=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, is_encoder_decoder=True, decoder_start_token_id=2, forced_eos_token_id=2, use_prompt=False, prompt_length=100, prompt_mid_dim=800, **kwargs ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.use_prompt = use_prompt self.prompt_length = prompt_length self.prompt_mid_dim = prompt_mid_dim super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs, ) if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): self.forced_bos_token_id = self.bos_token_id warnings.warn( f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. " "The config can simply be saved and uploaded again to be fixed." )

# coding=utf-8 # Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ MVP model configuration""" import warnings from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) MVP_PRETRAINED_CONFIG_ARCHIVE_MAP = { "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json", } class MvpConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MvpModel`]. It is used to instantiate a MVP model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the MVP [RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50267): Vocabulary size of the MVP model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MvpModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). forced_eos_token_id (`int`, *optional*, defaults to 2): The id of the token to force as the last generated token when `max_length` is reached. Usually set to `eos_token_id`. use_prompt (`bool`, *optional*, defaults to `False`): Whether or not to use prompt. prompt_length (`int`, *optional*, defaults to 100): The length of prompt. prompt_mid_dim (`int`, *optional*, defaults to 800): Dimensionality of the "intermediate" layer in prompt. Example: ```python >>> from transformers import MvpModel, MvpConfig >>> # Initializing a MVP RUCAIBox/mvp style configuration >>> configuration = MvpConfig() >>> # Initializing a model from the RUCAIBox/mvp style configuration >>> model = MvpModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "mvp" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=50267, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, activation_function="gelu", d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, classifier_dropout=0.0, scale_embedding=False, use_cache=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, is_encoder_decoder=True, decoder_start_token_id=2, forced_eos_token_id=2, use_prompt=False, prompt_length=100, prompt_mid_dim=800, **kwargs ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.use_prompt = use_prompt self.prompt_length = prompt_length self.prompt_mid_dim = prompt_mid_dim super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs, ) if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): self.forced_bos_token_id = self.bos_token_id warnings.warn( f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. " "The config can simply be saved and uploaded again to be fixed." )

huggingface/transformers

Fix result saving errors of pytorch examples

# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

li-plus

e627e9b5ae2ba8aae72b507596006e8f85dd2de8

9681f052a165784b4194cba5d4712757fe540e6f

Fix result saving errors of pytorch examples. # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes #20079. This PR fixed potential `KeyError`s on saving results in most PyTorch examples, by prefixing metric keys instead of directly accessing metrics by key. In addition, this PR replaced the argument `--max_length` with `--max_seq_length` in `run_swag_no_trainer.py`, to make the no-trainer version consistent with the trainer version and the README instructions. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. https://github.com/huggingface/transformers/issues/20079 - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @sgugger Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/mluke/tokenization_mluke.py

# coding=utf-8 # Copyright 2021 Studio Ousia and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License """ Tokenization classes for mLUKE.""" import itertools import json import os from collections.abc import Mapping from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...tokenization_utils_base import ( ENCODE_KWARGS_DOCSTRING, AddedToken, BatchEncoding, EncodedInput, PaddingStrategy, TensorType, TextInput, TextInputPair, TruncationStrategy, to_py_obj, ) from ...utils import add_end_docstrings, is_tf_tensor, is_torch_tensor, logging logger = logging.get_logger(__name__) EntitySpan = Tuple[int, int] EntitySpanInput = List[EntitySpan] Entity = str EntityInput = List[Entity] SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "entity_vocab_file": "entity_vocab.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/vocab.json", }, "merges_file": { "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/merges.txt", }, "entity_vocab_file": { "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/entity_vocab.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "studio-ousia/mluke-base": 512, } ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r""" return_token_type_ids (`bool`, *optional*): Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are token type IDs?](../glossary#token-type-ids) return_attention_mask (`bool`, *optional*): Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) return_overflowing_tokens (`bool`, *optional*, defaults to `False`): Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead of returning overflowing tokens. return_special_tokens_mask (`bool`, *optional*, defaults to `False`): Whether or not to return special tokens mask information. return_offsets_mapping (`bool`, *optional*, defaults to `False`): Whether or not to return `(char_start, char_end)` for each token. This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using Python's tokenizer, this method will raise `NotImplementedError`. return_length (`bool`, *optional*, defaults to `False`): Whether or not to return the lengths of the encoded inputs. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. **kwargs: passed to the `self.tokenize()` method Return: [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. [What are input IDs?](../glossary#input-ids) - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or if *"token_type_ids"* is in `self.model_input_names`). [What are token type IDs?](../glossary#token-type-ids) - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`). [What are attention masks?](../glossary#attention-mask) - **entity_ids** -- List of entity ids to be fed to a model. [What are input IDs?](../glossary#input-ids) - **entity_position_ids** -- List of entity positions in the input sequence to be fed to a model. - **entity_token_type_ids** -- List of entity token type ids to be fed to a model (when `return_token_type_ids=True` or if *"entity_token_type_ids"* is in `self.model_input_names`). [What are token type IDs?](../glossary#token-type-ids) - **entity_attention_mask** -- List of indices specifying which entities should be attended to by the model (when `return_attention_mask=True` or if *"entity_attention_mask"* is in `self.model_input_names`). [What are attention masks?](../glossary#attention-mask) - **entity_start_positions** -- List of the start positions of entities in the word token sequence (when `task="entity_span_classification"`). - **entity_end_positions** -- List of the end positions of entities in the word token sequence (when `task="entity_span_classification"`). - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and `return_overflowing_tokens=True`). - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and `return_overflowing_tokens=True`). - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`). - **length** -- The length of the inputs (when `return_length=True`) """ class MLukeTokenizer(PreTrainedTokenizer): """ Adapted from [`XLMRobertaTokenizer`] and [`LukeTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. entity_vocab_file (`str`): Path to the entity vocabulary file. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. task (`str`, *optional*): Task for which you want to prepare sequences. One of `"entity_classification"`, `"entity_pair_classification"`, or `"entity_span_classification"`. If you specify this argument, the entity sequence is automatically created based on the given entity span(s). max_entity_length (`int`, *optional*, defaults to 32): The maximum length of `entity_ids`. max_mention_length (`int`, *optional*, defaults to 30): The maximum number of tokens inside an entity span. entity_token_1 (`str`, *optional*, defaults to `<ent>`): The special token used to represent an entity span in a word token sequence. This token is only used when `task` is set to `"entity_classification"` or `"entity_pair_classification"`. entity_token_2 (`str`, *optional*, defaults to `<ent2>`): The special token used to represent an entity span in a word token sequence. This token is only used when `task` is set to `"entity_pair_classification"`. additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`): Additional special tokens used by the tokenizer. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Attributes: sp_model (`SentencePieceProcessor`): The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, entity_vocab_file, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", task=None, max_entity_length=32, max_mention_length=30, entity_token_1="<ent>", entity_token_2="<ent2>", entity_unk_token="[UNK]", entity_pad_token="[PAD]", entity_mask_token="[MASK]", entity_mask2_token="[MASK2]", sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> None: # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token # we add 2 special tokens for downstream tasks # for more information about lstrip and rstrip, see https://github.com/huggingface/transformers/pull/2778 entity_token_1 = ( AddedToken(entity_token_1, lstrip=False, rstrip=False) if isinstance(entity_token_1, str) else entity_token_1 ) entity_token_2 = ( AddedToken(entity_token_2, lstrip=False, rstrip=False) if isinstance(entity_token_2, str) else entity_token_2 ) kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) kwargs["additional_special_tokens"] += [entity_token_1, entity_token_2] self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, sp_model_kwargs=self.sp_model_kwargs, task=task, max_entity_length=max_entity_length, max_mention_length=max_mention_length, entity_token_1=entity_token_1, entity_token_2=entity_token_2, entity_unk_token=entity_unk_token, entity_pad_token=entity_pad_token, entity_mask_token=entity_mask_token, entity_mask2_token=entity_mask2_token, **kwargs, ) self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(str(vocab_file)) self.vocab_file = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # Mimic fairseq token-to-id alignment for the first 4 token self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3} # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab self.fairseq_offset = 1 self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + self.fairseq_offset self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} with open(entity_vocab_file, encoding="utf-8") as entity_vocab_handle: self.entity_vocab = json.load(entity_vocab_handle) for entity_special_token in [entity_unk_token, entity_pad_token, entity_mask_token, entity_mask2_token]: if entity_special_token not in self.entity_vocab: raise ValueError( f"Specified entity special token ``{entity_special_token}`` is not found in entity_vocab. " f"Probably an incorrect entity vocab file is loaded: {entity_vocab_file}." ) self.entity_unk_token_id = self.entity_vocab[entity_unk_token] self.entity_pad_token_id = self.entity_vocab[entity_pad_token] self.entity_mask_token_id = self.entity_vocab[entity_mask_token] self.entity_mask2_token_id = self.entity_vocab[entity_mask2_token] self.task = task if task is None or task == "entity_span_classification": self.max_entity_length = max_entity_length elif task == "entity_classification": self.max_entity_length = 1 elif task == "entity_pair_classification": self.max_entity_length = 2 else: raise ValueError( f"Task {task} not supported. Select task from ['entity_classification', 'entity_pair_classification'," " 'entity_span_classification'] only." ) self.max_mention_length = max_mention_length def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None state["sp_model_proto"] = self.sp_model.serialized_model_proto() return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.LoadFromSerializedProto(self.sp_model_proto) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.__call__ def __call__( self, text: Union[TextInput, List[TextInput]], text_pair: Optional[Union[TextInput, List[TextInput]]] = None, entity_spans: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None, entity_spans_pair: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None, entities: Optional[Union[EntityInput, List[EntityInput]]] = None, entities_pair: Optional[Union[EntityInput, List[EntityInput]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, is_split_into_words: Optional[bool] = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences, depending on the task you want to prepare them for. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this tokenizer does not support tokenization based on pretokenized strings. text_pair (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this tokenizer does not support tokenization based on pretokenized strings. entity_spans (`List[Tuple[int, int]]`, `List[List[Tuple[int, int]]]`, *optional*): The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each with two integers denoting character-based start and end positions of entities. If you specify `"entity_classification"` or `"entity_pair_classification"` as the `task` argument in the constructor, the length of each sequence must be 1 or 2, respectively. If you specify `entities`, the length of each sequence must be equal to the length of each sequence of `entities`. entity_spans_pair (`List[Tuple[int, int]]`, `List[List[Tuple[int, int]]]`, *optional*): The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each with two integers denoting character-based start and end positions of entities. If you specify the `task` argument in the constructor, this argument is ignored. If you specify `entities_pair`, the length of each sequence must be equal to the length of each sequence of `entities_pair`. entities (`List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of each sequence must be equal to the length of each sequence of `entity_spans`. If you specify `entity_spans` without specifying this argument, the entity sequence or the batch of entity sequences is automatically constructed by filling it with the [MASK] entity. entities_pair (`List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of each sequence must be equal to the length of each sequence of `entity_spans_pair`. If you specify `entity_spans_pair` without specifying this argument, the entity sequence or the batch of entity sequences is automatically constructed by filling it with the [MASK] entity. max_entity_length (`int`, *optional*): The maximum length of `entity_ids`. """ # Input type checking for clearer error is_valid_single_text = isinstance(text, str) is_valid_batch_text = isinstance(text, (list, tuple)) and (len(text) == 0 or (isinstance(text[0], str))) if not (is_valid_single_text or is_valid_batch_text): raise ValueError("text input must be of type `str` (single example) or `List[str]` (batch).") is_valid_single_text_pair = isinstance(text_pair, str) is_valid_batch_text_pair = isinstance(text_pair, (list, tuple)) and ( len(text_pair) == 0 or isinstance(text_pair[0], str) ) if not (text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair): raise ValueError("text_pair input must be of type `str` (single example) or `List[str]` (batch).") is_batched = bool(isinstance(text, (list, tuple))) if is_batched: batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text if entities is None: batch_entities_or_entities_pairs = None else: batch_entities_or_entities_pairs = ( list(zip(entities, entities_pair)) if entities_pair is not None else entities ) if entity_spans is None: batch_entity_spans_or_entity_spans_pairs = None else: batch_entity_spans_or_entity_spans_pairs = ( list(zip(entity_spans, entity_spans_pair)) if entity_spans_pair is not None else entity_spans ) return self.batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, batch_entity_spans_or_entity_spans_pairs=batch_entity_spans_or_entity_spans_pairs, batch_entities_or_entities_pairs=batch_entities_or_entities_pairs, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, max_entity_length=max_entity_length, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.encode_plus( text=text, text_pair=text_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, entities=entities, entities_pair=entities_pair, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, max_entity_length=max_entity_length, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._encode_plus def _encode_plus( self, text: Union[TextInput], text_pair: Optional[Union[TextInput]] = None, entity_spans: Optional[EntitySpanInput] = None, entity_spans_pair: Optional[EntitySpanInput] = None, entities: Optional[EntityInput] = None, entities_pair: Optional[EntityInput] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, is_split_into_words: Optional[bool] = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast. " "More information on available tokenizers at " "https://github.com/huggingface/transformers/pull/2674" ) if is_split_into_words: raise NotImplementedError("is_split_into_words is not supported in this tokenizer.") ( first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans, ) = self._create_input_sequence( text=text, text_pair=text_pair, entities=entities, entities_pair=entities_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, **kwargs, ) # prepare_for_model will create the attention_mask and token_type_ids return self.prepare_for_model( first_ids, pair_ids=second_ids, entity_ids=first_entity_ids, pair_entity_ids=second_entity_ids, entity_token_spans=first_entity_token_spans, pair_entity_token_spans=second_entity_token_spans, add_special_tokens=add_special_tokens, padding=padding_strategy.value, truncation=truncation_strategy.value, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, prepend_batch_axis=True, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, verbose=verbose, ) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._batch_encode_plus def _batch_encode_plus( self, batch_text_or_text_pairs: Union[List[TextInput], List[TextInputPair]], batch_entity_spans_or_entity_spans_pairs: Optional[ Union[List[EntitySpanInput], List[Tuple[EntitySpanInput, EntitySpanInput]]] ] = None, batch_entities_or_entities_pairs: Optional[ Union[List[EntityInput], List[Tuple[EntityInput, EntityInput]]] ] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, is_split_into_words: Optional[bool] = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast." ) if is_split_into_words: raise NotImplementedError("is_split_into_words is not supported in this tokenizer.") # input_ids is a list of tuples (one for each example in the batch) input_ids = [] entity_ids = [] entity_token_spans = [] for index, text_or_text_pair in enumerate(batch_text_or_text_pairs): if not isinstance(text_or_text_pair, (list, tuple)): text, text_pair = text_or_text_pair, None else: text, text_pair = text_or_text_pair entities, entities_pair = None, None if batch_entities_or_entities_pairs is not None: entities_or_entities_pairs = batch_entities_or_entities_pairs[index] if entities_or_entities_pairs: if isinstance(entities_or_entities_pairs[0], str): entities, entities_pair = entities_or_entities_pairs, None else: entities, entities_pair = entities_or_entities_pairs entity_spans, entity_spans_pair = None, None if batch_entity_spans_or_entity_spans_pairs is not None: entity_spans_or_entity_spans_pairs = batch_entity_spans_or_entity_spans_pairs[index] if len(entity_spans_or_entity_spans_pairs) > 0 and isinstance( entity_spans_or_entity_spans_pairs[0], list ): entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs else: entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs, None ( first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans, ) = self._create_input_sequence( text=text, text_pair=text_pair, entities=entities, entities_pair=entities_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, **kwargs, ) input_ids.append((first_ids, second_ids)) entity_ids.append((first_entity_ids, second_entity_ids)) entity_token_spans.append((first_entity_token_spans, second_entity_token_spans)) batch_outputs = self._batch_prepare_for_model( input_ids, batch_entity_ids_pairs=entity_ids, batch_entity_token_spans_pairs=entity_token_spans, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=return_tensors, verbose=verbose, ) return BatchEncoding(batch_outputs) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._check_entity_input_format def _check_entity_input_format(self, entities: Optional[EntityInput], entity_spans: Optional[EntitySpanInput]): if not isinstance(entity_spans, list): raise ValueError("entity_spans should be given as a list") elif len(entity_spans) > 0 and not isinstance(entity_spans[0], tuple): raise ValueError( "entity_spans should be given as a list of tuples containing the start and end character indices" ) if entities is not None: if not isinstance(entities, list): raise ValueError("If you specify entities, they should be given as a list") if len(entities) > 0 and not isinstance(entities[0], str): raise ValueError("If you specify entities, they should be given as a list of entity names") if len(entities) != len(entity_spans): raise ValueError("If you specify entities, entities and entity_spans must be the same length") # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._create_input_sequence def _create_input_sequence( self, text: Union[TextInput], text_pair: Optional[Union[TextInput]] = None, entities: Optional[EntityInput] = None, entities_pair: Optional[EntityInput] = None, entity_spans: Optional[EntitySpanInput] = None, entity_spans_pair: Optional[EntitySpanInput] = None, **kwargs ) -> Tuple[list, list, list, list, list, list]: def get_input_ids(text): tokens = self.tokenize(text, **kwargs) return self.convert_tokens_to_ids(tokens) def get_input_ids_and_entity_token_spans(text, entity_spans): if entity_spans is None: return get_input_ids(text), None cur = 0 input_ids = [] entity_token_spans = [None] * len(entity_spans) split_char_positions = sorted(frozenset(itertools.chain(*entity_spans))) char_pos2token_pos = {} for split_char_position in split_char_positions: orig_split_char_position = split_char_position if ( split_char_position > 0 and text[split_char_position - 1] == " " ): # whitespace should be prepended to the following token split_char_position -= 1 if cur != split_char_position: input_ids += get_input_ids(text[cur:split_char_position]) cur = split_char_position char_pos2token_pos[orig_split_char_position] = len(input_ids) input_ids += get_input_ids(text[cur:]) entity_token_spans = [ (char_pos2token_pos[char_start], char_pos2token_pos[char_end]) for char_start, char_end in entity_spans ] return input_ids, entity_token_spans first_ids, second_ids = None, None first_entity_ids, second_entity_ids = None, None first_entity_token_spans, second_entity_token_spans = None, None if self.task is None: if entity_spans is None: first_ids = get_input_ids(text) else: self._check_entity_input_format(entities, entity_spans) first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) if entities is None: first_entity_ids = [self.entity_mask_token_id] * len(entity_spans) else: first_entity_ids = [self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities] if text_pair is not None: if entity_spans_pair is None: second_ids = get_input_ids(text_pair) else: self._check_entity_input_format(entities_pair, entity_spans_pair) second_ids, second_entity_token_spans = get_input_ids_and_entity_token_spans( text_pair, entity_spans_pair ) if entities_pair is None: second_entity_ids = [self.entity_mask_token_id] * len(entity_spans_pair) else: second_entity_ids = [ self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities_pair ] elif self.task == "entity_classification": if not (isinstance(entity_spans, list) and len(entity_spans) == 1 and isinstance(entity_spans[0], tuple)): raise ValueError( "Entity spans should be a list containing a single tuple " "containing the start and end character indices of an entity" ) first_entity_ids = [self.entity_mask_token_id] first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) # add special tokens to input ids entity_token_start, entity_token_end = first_entity_token_spans[0] first_ids = ( first_ids[:entity_token_end] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_end:] ) first_ids = ( first_ids[:entity_token_start] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_start:] ) first_entity_token_spans = [(entity_token_start, entity_token_end + 2)] elif self.task == "entity_pair_classification": if not ( isinstance(entity_spans, list) and len(entity_spans) == 2 and isinstance(entity_spans[0], tuple) and isinstance(entity_spans[1], tuple) ): raise ValueError( "Entity spans should be provided as a list of two tuples, " "each tuple containing the start and end character indices of an entity" ) head_span, tail_span = entity_spans first_entity_ids = [self.entity_mask_token_id, self.entity_mask2_token_id] first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) head_token_span, tail_token_span = first_entity_token_spans token_span_with_special_token_ids = [ (head_token_span, self.additional_special_tokens_ids[0]), (tail_token_span, self.additional_special_tokens_ids[1]), ] if head_token_span[0] < tail_token_span[0]: first_entity_token_spans[0] = (head_token_span[0], head_token_span[1] + 2) first_entity_token_spans[1] = (tail_token_span[0] + 2, tail_token_span[1] + 4) token_span_with_special_token_ids = reversed(token_span_with_special_token_ids) else: first_entity_token_spans[0] = (head_token_span[0] + 2, head_token_span[1] + 4) first_entity_token_spans[1] = (tail_token_span[0], tail_token_span[1] + 2) for (entity_token_start, entity_token_end), special_token_id in token_span_with_special_token_ids: first_ids = first_ids[:entity_token_end] + [special_token_id] + first_ids[entity_token_end:] first_ids = first_ids[:entity_token_start] + [special_token_id] + first_ids[entity_token_start:] elif self.task == "entity_span_classification": if not (isinstance(entity_spans, list) and len(entity_spans) > 0 and isinstance(entity_spans[0], tuple)): raise ValueError( "Entity spans should be provided as a list of tuples, " "each tuple containing the start and end character indices of an entity" ) first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) first_entity_ids = [self.entity_mask_token_id] * len(entity_spans) else: raise ValueError(f"Task {self.task} not supported") return ( first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._batch_prepare_for_model def _batch_prepare_for_model( self, batch_ids_pairs: List[Tuple[List[int], None]], batch_entity_ids_pairs: List[Tuple[Optional[List[int]], Optional[List[int]]]], batch_entity_token_spans_pairs: List[Tuple[Optional[List[Tuple[int, int]]], Optional[List[Tuple[int, int]]]]], add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[str] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_length: bool = False, verbose: bool = True, ) -> BatchEncoding: """ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens Args: batch_ids_pairs: list of tokenized input ids or input ids pairs batch_entity_ids_pairs: list of entity ids or entity ids pairs batch_entity_token_spans_pairs: list of entity spans or entity spans pairs max_entity_length: The maximum length of the entity sequence. """ batch_outputs = {} for input_ids, entity_ids, entity_token_span_pairs in zip( batch_ids_pairs, batch_entity_ids_pairs, batch_entity_token_spans_pairs ): first_ids, second_ids = input_ids first_entity_ids, second_entity_ids = entity_ids first_entity_token_spans, second_entity_token_spans = entity_token_span_pairs outputs = self.prepare_for_model( first_ids, second_ids, entity_ids=first_entity_ids, pair_entity_ids=second_entity_ids, entity_token_spans=first_entity_token_spans, pair_entity_token_spans=second_entity_token_spans, add_special_tokens=add_special_tokens, padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward truncation=truncation_strategy.value, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=None, # we pad in batch afterward return_attention_mask=False, # we pad in batch afterward return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=None, # We convert the whole batch to tensors at the end prepend_batch_axis=False, verbose=verbose, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) batch_outputs = self.pad( batch_outputs, padding=padding_strategy.value, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) return batch_outputs @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.prepare_for_model def prepare_for_model( self, ids: List[int], pair_ids: Optional[List[int]] = None, entity_ids: Optional[List[int]] = None, pair_entity_ids: Optional[List[int]] = None, entity_token_spans: Optional[List[Tuple[int, int]]] = None, pair_entity_token_spans: Optional[List[Tuple[int, int]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, prepend_batch_axis: bool = False, **kwargs ) -> BatchEncoding: """ Prepares a sequence of input id, entity id and entity span, or a pair of sequences of inputs ids, entity ids, entity spans so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error. Args: ids (`List[int]`): Tokenized input ids of the first sequence. pair_ids (`List[int]`, *optional*): Tokenized input ids of the second sequence. entity_ids (`List[int]`, *optional*): Entity ids of the first sequence. pair_entity_ids (`List[int]`, *optional*): Entity ids of the second sequence. entity_token_spans (`List[Tuple[int, int]]`, *optional*): Entity spans of the first sequence. pair_entity_token_spans (`List[Tuple[int, int]]`, *optional*): Entity spans of the second sequence. max_entity_length (`int`, *optional*): The maximum length of the entity sequence. """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) # Compute lengths pair = bool(pair_ids is not None) len_ids = len(ids) len_pair_ids = len(pair_ids) if pair else 0 if return_token_type_ids and not add_special_tokens: raise ValueError( "Asking to return token_type_ids while setting add_special_tokens to False " "results in an undefined behavior. Please set add_special_tokens to True or " "set return_token_type_ids to None." ) if ( return_overflowing_tokens and truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is not None ): raise ValueError( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`." ) # Load from model defaults if return_token_type_ids is None: return_token_type_ids = "token_type_ids" in self.model_input_names if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names encoded_inputs = {} # Compute the total size of the returned word encodings total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0) # Truncation: Handle max sequence length and max_entity_length overflowing_tokens = [] if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length: # truncate words up to max_length ids, pair_ids, overflowing_tokens = self.truncate_sequences( ids, pair_ids=pair_ids, num_tokens_to_remove=total_len - max_length, truncation_strategy=truncation_strategy, stride=stride, ) if return_overflowing_tokens: encoded_inputs["overflowing_tokens"] = overflowing_tokens encoded_inputs["num_truncated_tokens"] = total_len - max_length # Add special tokens if add_special_tokens: sequence = self.build_inputs_with_special_tokens(ids, pair_ids) token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids) entity_token_offset = 1 # 1 * <s> token pair_entity_token_offset = len(ids) + 3 # 1 * <s> token & 2 * <sep> tokens else: sequence = ids + pair_ids if pair else ids token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else []) entity_token_offset = 0 pair_entity_token_offset = len(ids) # Build output dictionary encoded_inputs["input_ids"] = sequence if return_token_type_ids: encoded_inputs["token_type_ids"] = token_type_ids if return_special_tokens_mask: if add_special_tokens: encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids) else: encoded_inputs["special_tokens_mask"] = [0] * len(sequence) # Set max entity length if not max_entity_length: max_entity_length = self.max_entity_length if entity_ids is not None: total_entity_len = 0 num_invalid_entities = 0 valid_entity_ids = [ent_id for ent_id, span in zip(entity_ids, entity_token_spans) if span[1] <= len(ids)] valid_entity_token_spans = [span for span in entity_token_spans if span[1] <= len(ids)] total_entity_len += len(valid_entity_ids) num_invalid_entities += len(entity_ids) - len(valid_entity_ids) valid_pair_entity_ids, valid_pair_entity_token_spans = None, None if pair_entity_ids is not None: valid_pair_entity_ids = [ ent_id for ent_id, span in zip(pair_entity_ids, pair_entity_token_spans) if span[1] <= len(pair_ids) ] valid_pair_entity_token_spans = [span for span in pair_entity_token_spans if span[1] <= len(pair_ids)] total_entity_len += len(valid_pair_entity_ids) num_invalid_entities += len(pair_entity_ids) - len(valid_pair_entity_ids) if num_invalid_entities != 0: logger.warning( f"{num_invalid_entities} entities are ignored because their entity spans are invalid due to the" " truncation of input tokens" ) if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and total_entity_len > max_entity_length: # truncate entities up to max_entity_length valid_entity_ids, valid_pair_entity_ids, overflowing_entities = self.truncate_sequences( valid_entity_ids, pair_ids=valid_pair_entity_ids, num_tokens_to_remove=total_entity_len - max_entity_length, truncation_strategy=truncation_strategy, stride=stride, ) valid_entity_token_spans = valid_entity_token_spans[: len(valid_entity_ids)] if valid_pair_entity_token_spans is not None: valid_pair_entity_token_spans = valid_pair_entity_token_spans[: len(valid_pair_entity_ids)] if return_overflowing_tokens: encoded_inputs["overflowing_entities"] = overflowing_entities encoded_inputs["num_truncated_entities"] = total_entity_len - max_entity_length final_entity_ids = valid_entity_ids + valid_pair_entity_ids if valid_pair_entity_ids else valid_entity_ids encoded_inputs["entity_ids"] = list(final_entity_ids) entity_position_ids = [] entity_start_positions = [] entity_end_positions = [] for token_spans, offset in ( (valid_entity_token_spans, entity_token_offset), (valid_pair_entity_token_spans, pair_entity_token_offset), ): if token_spans is not None: for start, end in token_spans: start += offset end += offset position_ids = list(range(start, end))[: self.max_mention_length] position_ids += [-1] * (self.max_mention_length - end + start) entity_position_ids.append(position_ids) entity_start_positions.append(start) entity_end_positions.append(end - 1) encoded_inputs["entity_position_ids"] = entity_position_ids if self.task == "entity_span_classification": encoded_inputs["entity_start_positions"] = entity_start_positions encoded_inputs["entity_end_positions"] = entity_end_positions if return_token_type_ids: encoded_inputs["entity_token_type_ids"] = [0] * len(encoded_inputs["entity_ids"]) # Check lengths self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose) # Padding if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask: encoded_inputs = self.pad( encoded_inputs, max_length=max_length, max_entity_length=max_entity_length, padding=padding_strategy.value, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) if return_length: encoded_inputs["length"] = len(encoded_inputs["input_ids"]) batch_outputs = BatchEncoding( encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis ) return batch_outputs # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.pad def pad( self, encoded_inputs: Union[ BatchEncoding, List[BatchEncoding], Dict[str, EncodedInput], Dict[str, List[EncodedInput]], List[Dict[str, EncodedInput]], ], padding: Union[bool, str, PaddingStrategy] = True, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, ) -> BatchEncoding: """ Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) .. note:: If the `encoded_inputs` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the specific device of your tensors however. Args: encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `Dict[str, List[int]]`, `Dict[str, List[List[int]]` or `List[Dict[str, List[int]]]`): Tokenized inputs. Can represent one input ([`BatchEncoding`] or `Dict[str, List[int]]`) or a batch of tokenized inputs (list of [`BatchEncoding`], *Dict[str, List[List[int]]]* or *List[Dict[str, List[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader collate function. Instead of `List[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see the note above for the return type. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). max_entity_length (`int`, *optional*): The maximum length of the entity sequence. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_attention_mask (`bool`, *optional*): Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. """ # If we have a list of dicts, let's convert it in a dict of lists # We do this to allow using this method as a collate_fn function in PyTorch Dataloader if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping): encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys()} # The model's main input name, usually `input_ids`, has be passed for padding if self.model_input_names[0] not in encoded_inputs: raise ValueError( "You should supply an encoding or a list of encodings to this method " f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}" ) required_input = encoded_inputs[self.model_input_names[0]] if not required_input: if return_attention_mask: encoded_inputs["attention_mask"] = [] return encoded_inputs # If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch first_element = required_input[0] if isinstance(first_element, (list, tuple)): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. index = 0 while len(required_input[index]) == 0: index += 1 if index < len(required_input): first_element = required_input[index][0] # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do. if not isinstance(first_element, (int, list, tuple)): if is_tf_tensor(first_element): return_tensors = "tf" if return_tensors is None else return_tensors elif is_torch_tensor(first_element): return_tensors = "pt" if return_tensors is None else return_tensors elif isinstance(first_element, np.ndarray): return_tensors = "np" if return_tensors is None else return_tensors else: raise ValueError( f"type of {first_element} unknown: {type(first_element)}. " "Should be one of a python, numpy, pytorch or tensorflow object." ) for key, value in encoded_inputs.items(): encoded_inputs[key] = to_py_obj(value) # Convert padding_strategy in PaddingStrategy padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies( padding=padding, max_length=max_length, verbose=verbose ) if max_entity_length is None: max_entity_length = self.max_entity_length required_input = encoded_inputs[self.model_input_names[0]] if required_input and not isinstance(required_input[0], (list, tuple)): encoded_inputs = self._pad( encoded_inputs, max_length=max_length, max_entity_length=max_entity_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) return BatchEncoding(encoded_inputs, tensor_type=return_tensors) batch_size = len(required_input) if any(len(v) != batch_size for v in encoded_inputs.values()): raise ValueError("Some items in the output dictionary have a different batch size than others.") if padding_strategy == PaddingStrategy.LONGEST: max_length = max(len(inputs) for inputs in required_input) max_entity_length = ( max(len(inputs) for inputs in encoded_inputs["entity_ids"]) if "entity_ids" in encoded_inputs else 0 ) padding_strategy = PaddingStrategy.MAX_LENGTH batch_outputs = {} for i in range(batch_size): inputs = dict((k, v[i]) for k, v in encoded_inputs.items()) outputs = self._pad( inputs, max_length=max_length, max_entity_length=max_entity_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) return BatchEncoding(batch_outputs, tensor_type=return_tensors) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._pad def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, max_entity_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: """ Pad encoded inputs (on left/right and up to predefined length or max length in the batch) Args: encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. max_entity_length: The maximum length of the entity sequence. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability >= 7.5 (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) """ entities_provided = bool("entity_ids" in encoded_inputs) # Load from model defaults if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names if padding_strategy == PaddingStrategy.LONGEST: max_length = len(encoded_inputs["input_ids"]) if entities_provided: max_entity_length = len(encoded_inputs["entity_ids"]) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of if ( entities_provided and max_entity_length is not None and pad_to_multiple_of is not None and (max_entity_length % pad_to_multiple_of != 0) ): max_entity_length = ((max_entity_length // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and ( len(encoded_inputs["input_ids"]) != max_length or (entities_provided and len(encoded_inputs["entity_ids"]) != max_entity_length) ) # Initialize attention mask if not present. if return_attention_mask and "attention_mask" not in encoded_inputs: encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) if entities_provided and return_attention_mask and "entity_attention_mask" not in encoded_inputs: encoded_inputs["entity_attention_mask"] = [1] * len(encoded_inputs["entity_ids"]) if needs_to_be_padded: difference = max_length - len(encoded_inputs["input_ids"]) if entities_provided: entity_difference = max_entity_length - len(encoded_inputs["entity_ids"]) if self.padding_side == "right": if return_attention_mask: encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference if entities_provided: encoded_inputs["entity_attention_mask"] = ( encoded_inputs["entity_attention_mask"] + [0] * entity_difference ) if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"] + [0] * difference if entities_provided: encoded_inputs["entity_token_type_ids"] = ( encoded_inputs["entity_token_type_ids"] + [0] * entity_difference ) if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference if entities_provided: encoded_inputs["entity_ids"] = ( encoded_inputs["entity_ids"] + [self.entity_pad_token_id] * entity_difference ) encoded_inputs["entity_position_ids"] = ( encoded_inputs["entity_position_ids"] + [[-1] * self.max_mention_length] * entity_difference ) if self.task == "entity_span_classification": encoded_inputs["entity_start_positions"] = ( encoded_inputs["entity_start_positions"] + [0] * entity_difference ) encoded_inputs["entity_end_positions"] = ( encoded_inputs["entity_end_positions"] + [0] * entity_difference ) elif self.padding_side == "left": if return_attention_mask: encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] if entities_provided: encoded_inputs["entity_attention_mask"] = [0] * entity_difference + encoded_inputs[ "entity_attention_mask" ] if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = [0] * difference + encoded_inputs["token_type_ids"] if entities_provided: encoded_inputs["entity_token_type_ids"] = [0] * entity_difference + encoded_inputs[ "entity_token_type_ids" ] if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"] if entities_provided: encoded_inputs["entity_ids"] = [self.entity_pad_token_id] * entity_difference + encoded_inputs[ "entity_ids" ] encoded_inputs["entity_position_ids"] = [ [-1] * self.max_mention_length ] * entity_difference + encoded_inputs["entity_position_ids"] if self.task == "entity_span_classification": encoded_inputs["entity_start_positions"] = [0] * entity_difference + encoded_inputs[ "entity_start_positions" ] encoded_inputs["entity_end_positions"] = [0] * entity_difference + encoded_inputs[ "entity_end_positions" ] else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str, str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): copyfile(self.vocab_file, out_vocab_file) entity_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["entity_vocab_file"] ) with open(entity_vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.entity_vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return out_vocab_file, entity_vocab_file # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.build_inputs_with_special_tokens def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLM-RoBERTa sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.get_special_tokens_mask def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.create_token_type_ids_from_sequences def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] @property # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.vocab_size def vocab_size(self): return len(self.sp_model) + self.fairseq_offset + 1 # Add the <mask> token # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.get_vocab def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer._tokenize def _tokenize(self, text: str) -> List[str]: return self.sp_model.encode(text, out_type=str) # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer._convert_token_to_id def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] spm_id = self.sp_model.PieceToId(token) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (strings for sub-words) in a single string.""" out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string

# coding=utf-8 # Copyright 2021 Studio Ousia and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License """ Tokenization classes for mLUKE.""" import itertools import json import os from collections.abc import Mapping from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...tokenization_utils_base import ( ENCODE_KWARGS_DOCSTRING, AddedToken, BatchEncoding, EncodedInput, PaddingStrategy, TensorType, TextInput, TextInputPair, TruncationStrategy, to_py_obj, ) from ...utils import add_end_docstrings, is_tf_tensor, is_torch_tensor, logging logger = logging.get_logger(__name__) EntitySpan = Tuple[int, int] EntitySpanInput = List[EntitySpan] Entity = str EntityInput = List[Entity] SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "entity_vocab_file": "entity_vocab.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/vocab.json", }, "merges_file": { "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/merges.txt", }, "entity_vocab_file": { "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/entity_vocab.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "studio-ousia/mluke-base": 512, } ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r""" return_token_type_ids (`bool`, *optional*): Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are token type IDs?](../glossary#token-type-ids) return_attention_mask (`bool`, *optional*): Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) return_overflowing_tokens (`bool`, *optional*, defaults to `False`): Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead of returning overflowing tokens. return_special_tokens_mask (`bool`, *optional*, defaults to `False`): Whether or not to return special tokens mask information. return_offsets_mapping (`bool`, *optional*, defaults to `False`): Whether or not to return `(char_start, char_end)` for each token. This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using Python's tokenizer, this method will raise `NotImplementedError`. return_length (`bool`, *optional*, defaults to `False`): Whether or not to return the lengths of the encoded inputs. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. **kwargs: passed to the `self.tokenize()` method Return: [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. [What are input IDs?](../glossary#input-ids) - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or if *"token_type_ids"* is in `self.model_input_names`). [What are token type IDs?](../glossary#token-type-ids) - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`). [What are attention masks?](../glossary#attention-mask) - **entity_ids** -- List of entity ids to be fed to a model. [What are input IDs?](../glossary#input-ids) - **entity_position_ids** -- List of entity positions in the input sequence to be fed to a model. - **entity_token_type_ids** -- List of entity token type ids to be fed to a model (when `return_token_type_ids=True` or if *"entity_token_type_ids"* is in `self.model_input_names`). [What are token type IDs?](../glossary#token-type-ids) - **entity_attention_mask** -- List of indices specifying which entities should be attended to by the model (when `return_attention_mask=True` or if *"entity_attention_mask"* is in `self.model_input_names`). [What are attention masks?](../glossary#attention-mask) - **entity_start_positions** -- List of the start positions of entities in the word token sequence (when `task="entity_span_classification"`). - **entity_end_positions** -- List of the end positions of entities in the word token sequence (when `task="entity_span_classification"`). - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and `return_overflowing_tokens=True`). - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and `return_overflowing_tokens=True`). - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`). - **length** -- The length of the inputs (when `return_length=True`) """ class MLukeTokenizer(PreTrainedTokenizer): """ Adapted from [`XLMRobertaTokenizer`] and [`LukeTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. entity_vocab_file (`str`): Path to the entity vocabulary file. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. task (`str`, *optional*): Task for which you want to prepare sequences. One of `"entity_classification"`, `"entity_pair_classification"`, or `"entity_span_classification"`. If you specify this argument, the entity sequence is automatically created based on the given entity span(s). max_entity_length (`int`, *optional*, defaults to 32): The maximum length of `entity_ids`. max_mention_length (`int`, *optional*, defaults to 30): The maximum number of tokens inside an entity span. entity_token_1 (`str`, *optional*, defaults to `<ent>`): The special token used to represent an entity span in a word token sequence. This token is only used when `task` is set to `"entity_classification"` or `"entity_pair_classification"`. entity_token_2 (`str`, *optional*, defaults to `<ent2>`): The special token used to represent an entity span in a word token sequence. This token is only used when `task` is set to `"entity_pair_classification"`. additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`): Additional special tokens used by the tokenizer. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Attributes: sp_model (`SentencePieceProcessor`): The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, entity_vocab_file, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", task=None, max_entity_length=32, max_mention_length=30, entity_token_1="<ent>", entity_token_2="<ent2>", entity_unk_token="[UNK]", entity_pad_token="[PAD]", entity_mask_token="[MASK]", entity_mask2_token="[MASK2]", sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> None: # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token # we add 2 special tokens for downstream tasks # for more information about lstrip and rstrip, see https://github.com/huggingface/transformers/pull/2778 entity_token_1 = ( AddedToken(entity_token_1, lstrip=False, rstrip=False) if isinstance(entity_token_1, str) else entity_token_1 ) entity_token_2 = ( AddedToken(entity_token_2, lstrip=False, rstrip=False) if isinstance(entity_token_2, str) else entity_token_2 ) kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) kwargs["additional_special_tokens"] += [entity_token_1, entity_token_2] self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, sp_model_kwargs=self.sp_model_kwargs, task=task, max_entity_length=max_entity_length, max_mention_length=max_mention_length, entity_token_1=entity_token_1, entity_token_2=entity_token_2, entity_unk_token=entity_unk_token, entity_pad_token=entity_pad_token, entity_mask_token=entity_mask_token, entity_mask2_token=entity_mask2_token, **kwargs, ) self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(str(vocab_file)) self.vocab_file = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # Mimic fairseq token-to-id alignment for the first 4 token self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3} # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab self.fairseq_offset = 1 self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + self.fairseq_offset self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} with open(entity_vocab_file, encoding="utf-8") as entity_vocab_handle: self.entity_vocab = json.load(entity_vocab_handle) for entity_special_token in [entity_unk_token, entity_pad_token, entity_mask_token, entity_mask2_token]: if entity_special_token not in self.entity_vocab: raise ValueError( f"Specified entity special token ``{entity_special_token}`` is not found in entity_vocab. " f"Probably an incorrect entity vocab file is loaded: {entity_vocab_file}." ) self.entity_unk_token_id = self.entity_vocab[entity_unk_token] self.entity_pad_token_id = self.entity_vocab[entity_pad_token] self.entity_mask_token_id = self.entity_vocab[entity_mask_token] self.entity_mask2_token_id = self.entity_vocab[entity_mask2_token] self.task = task if task is None or task == "entity_span_classification": self.max_entity_length = max_entity_length elif task == "entity_classification": self.max_entity_length = 1 elif task == "entity_pair_classification": self.max_entity_length = 2 else: raise ValueError( f"Task {task} not supported. Select task from ['entity_classification', 'entity_pair_classification'," " 'entity_span_classification'] only." ) self.max_mention_length = max_mention_length def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None state["sp_model_proto"] = self.sp_model.serialized_model_proto() return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.LoadFromSerializedProto(self.sp_model_proto) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.__call__ def __call__( self, text: Union[TextInput, List[TextInput]], text_pair: Optional[Union[TextInput, List[TextInput]]] = None, entity_spans: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None, entity_spans_pair: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None, entities: Optional[Union[EntityInput, List[EntityInput]]] = None, entities_pair: Optional[Union[EntityInput, List[EntityInput]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, is_split_into_words: Optional[bool] = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences, depending on the task you want to prepare them for. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this tokenizer does not support tokenization based on pretokenized strings. text_pair (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this tokenizer does not support tokenization based on pretokenized strings. entity_spans (`List[Tuple[int, int]]`, `List[List[Tuple[int, int]]]`, *optional*): The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each with two integers denoting character-based start and end positions of entities. If you specify `"entity_classification"` or `"entity_pair_classification"` as the `task` argument in the constructor, the length of each sequence must be 1 or 2, respectively. If you specify `entities`, the length of each sequence must be equal to the length of each sequence of `entities`. entity_spans_pair (`List[Tuple[int, int]]`, `List[List[Tuple[int, int]]]`, *optional*): The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each with two integers denoting character-based start and end positions of entities. If you specify the `task` argument in the constructor, this argument is ignored. If you specify `entities_pair`, the length of each sequence must be equal to the length of each sequence of `entities_pair`. entities (`List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of each sequence must be equal to the length of each sequence of `entity_spans`. If you specify `entity_spans` without specifying this argument, the entity sequence or the batch of entity sequences is automatically constructed by filling it with the [MASK] entity. entities_pair (`List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of each sequence must be equal to the length of each sequence of `entity_spans_pair`. If you specify `entity_spans_pair` without specifying this argument, the entity sequence or the batch of entity sequences is automatically constructed by filling it with the [MASK] entity. max_entity_length (`int`, *optional*): The maximum length of `entity_ids`. """ # Input type checking for clearer error is_valid_single_text = isinstance(text, str) is_valid_batch_text = isinstance(text, (list, tuple)) and (len(text) == 0 or (isinstance(text[0], str))) if not (is_valid_single_text or is_valid_batch_text): raise ValueError("text input must be of type `str` (single example) or `List[str]` (batch).") is_valid_single_text_pair = isinstance(text_pair, str) is_valid_batch_text_pair = isinstance(text_pair, (list, tuple)) and ( len(text_pair) == 0 or isinstance(text_pair[0], str) ) if not (text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair): raise ValueError("text_pair input must be of type `str` (single example) or `List[str]` (batch).") is_batched = bool(isinstance(text, (list, tuple))) if is_batched: batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text if entities is None: batch_entities_or_entities_pairs = None else: batch_entities_or_entities_pairs = ( list(zip(entities, entities_pair)) if entities_pair is not None else entities ) if entity_spans is None: batch_entity_spans_or_entity_spans_pairs = None else: batch_entity_spans_or_entity_spans_pairs = ( list(zip(entity_spans, entity_spans_pair)) if entity_spans_pair is not None else entity_spans ) return self.batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, batch_entity_spans_or_entity_spans_pairs=batch_entity_spans_or_entity_spans_pairs, batch_entities_or_entities_pairs=batch_entities_or_entities_pairs, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, max_entity_length=max_entity_length, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.encode_plus( text=text, text_pair=text_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, entities=entities, entities_pair=entities_pair, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, max_entity_length=max_entity_length, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._encode_plus def _encode_plus( self, text: Union[TextInput], text_pair: Optional[Union[TextInput]] = None, entity_spans: Optional[EntitySpanInput] = None, entity_spans_pair: Optional[EntitySpanInput] = None, entities: Optional[EntityInput] = None, entities_pair: Optional[EntityInput] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, is_split_into_words: Optional[bool] = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast. " "More information on available tokenizers at " "https://github.com/huggingface/transformers/pull/2674" ) if is_split_into_words: raise NotImplementedError("is_split_into_words is not supported in this tokenizer.") ( first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans, ) = self._create_input_sequence( text=text, text_pair=text_pair, entities=entities, entities_pair=entities_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, **kwargs, ) # prepare_for_model will create the attention_mask and token_type_ids return self.prepare_for_model( first_ids, pair_ids=second_ids, entity_ids=first_entity_ids, pair_entity_ids=second_entity_ids, entity_token_spans=first_entity_token_spans, pair_entity_token_spans=second_entity_token_spans, add_special_tokens=add_special_tokens, padding=padding_strategy.value, truncation=truncation_strategy.value, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, prepend_batch_axis=True, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, verbose=verbose, ) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._batch_encode_plus def _batch_encode_plus( self, batch_text_or_text_pairs: Union[List[TextInput], List[TextInputPair]], batch_entity_spans_or_entity_spans_pairs: Optional[ Union[List[EntitySpanInput], List[Tuple[EntitySpanInput, EntitySpanInput]]] ] = None, batch_entities_or_entities_pairs: Optional[ Union[List[EntityInput], List[Tuple[EntityInput, EntityInput]]] ] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, is_split_into_words: Optional[bool] = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast." ) if is_split_into_words: raise NotImplementedError("is_split_into_words is not supported in this tokenizer.") # input_ids is a list of tuples (one for each example in the batch) input_ids = [] entity_ids = [] entity_token_spans = [] for index, text_or_text_pair in enumerate(batch_text_or_text_pairs): if not isinstance(text_or_text_pair, (list, tuple)): text, text_pair = text_or_text_pair, None else: text, text_pair = text_or_text_pair entities, entities_pair = None, None if batch_entities_or_entities_pairs is not None: entities_or_entities_pairs = batch_entities_or_entities_pairs[index] if entities_or_entities_pairs: if isinstance(entities_or_entities_pairs[0], str): entities, entities_pair = entities_or_entities_pairs, None else: entities, entities_pair = entities_or_entities_pairs entity_spans, entity_spans_pair = None, None if batch_entity_spans_or_entity_spans_pairs is not None: entity_spans_or_entity_spans_pairs = batch_entity_spans_or_entity_spans_pairs[index] if len(entity_spans_or_entity_spans_pairs) > 0 and isinstance( entity_spans_or_entity_spans_pairs[0], list ): entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs else: entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs, None ( first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans, ) = self._create_input_sequence( text=text, text_pair=text_pair, entities=entities, entities_pair=entities_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, **kwargs, ) input_ids.append((first_ids, second_ids)) entity_ids.append((first_entity_ids, second_entity_ids)) entity_token_spans.append((first_entity_token_spans, second_entity_token_spans)) batch_outputs = self._batch_prepare_for_model( input_ids, batch_entity_ids_pairs=entity_ids, batch_entity_token_spans_pairs=entity_token_spans, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=return_tensors, verbose=verbose, ) return BatchEncoding(batch_outputs) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._check_entity_input_format def _check_entity_input_format(self, entities: Optional[EntityInput], entity_spans: Optional[EntitySpanInput]): if not isinstance(entity_spans, list): raise ValueError("entity_spans should be given as a list") elif len(entity_spans) > 0 and not isinstance(entity_spans[0], tuple): raise ValueError( "entity_spans should be given as a list of tuples containing the start and end character indices" ) if entities is not None: if not isinstance(entities, list): raise ValueError("If you specify entities, they should be given as a list") if len(entities) > 0 and not isinstance(entities[0], str): raise ValueError("If you specify entities, they should be given as a list of entity names") if len(entities) != len(entity_spans): raise ValueError("If you specify entities, entities and entity_spans must be the same length") # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._create_input_sequence def _create_input_sequence( self, text: Union[TextInput], text_pair: Optional[Union[TextInput]] = None, entities: Optional[EntityInput] = None, entities_pair: Optional[EntityInput] = None, entity_spans: Optional[EntitySpanInput] = None, entity_spans_pair: Optional[EntitySpanInput] = None, **kwargs ) -> Tuple[list, list, list, list, list, list]: def get_input_ids(text): tokens = self.tokenize(text, **kwargs) return self.convert_tokens_to_ids(tokens) def get_input_ids_and_entity_token_spans(text, entity_spans): if entity_spans is None: return get_input_ids(text), None cur = 0 input_ids = [] entity_token_spans = [None] * len(entity_spans) split_char_positions = sorted(frozenset(itertools.chain(*entity_spans))) char_pos2token_pos = {} for split_char_position in split_char_positions: orig_split_char_position = split_char_position if ( split_char_position > 0 and text[split_char_position - 1] == " " ): # whitespace should be prepended to the following token split_char_position -= 1 if cur != split_char_position: input_ids += get_input_ids(text[cur:split_char_position]) cur = split_char_position char_pos2token_pos[orig_split_char_position] = len(input_ids) input_ids += get_input_ids(text[cur:]) entity_token_spans = [ (char_pos2token_pos[char_start], char_pos2token_pos[char_end]) for char_start, char_end in entity_spans ] return input_ids, entity_token_spans first_ids, second_ids = None, None first_entity_ids, second_entity_ids = None, None first_entity_token_spans, second_entity_token_spans = None, None if self.task is None: if entity_spans is None: first_ids = get_input_ids(text) else: self._check_entity_input_format(entities, entity_spans) first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) if entities is None: first_entity_ids = [self.entity_mask_token_id] * len(entity_spans) else: first_entity_ids = [self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities] if text_pair is not None: if entity_spans_pair is None: second_ids = get_input_ids(text_pair) else: self._check_entity_input_format(entities_pair, entity_spans_pair) second_ids, second_entity_token_spans = get_input_ids_and_entity_token_spans( text_pair, entity_spans_pair ) if entities_pair is None: second_entity_ids = [self.entity_mask_token_id] * len(entity_spans_pair) else: second_entity_ids = [ self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities_pair ] elif self.task == "entity_classification": if not (isinstance(entity_spans, list) and len(entity_spans) == 1 and isinstance(entity_spans[0], tuple)): raise ValueError( "Entity spans should be a list containing a single tuple " "containing the start and end character indices of an entity" ) first_entity_ids = [self.entity_mask_token_id] first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) # add special tokens to input ids entity_token_start, entity_token_end = first_entity_token_spans[0] first_ids = ( first_ids[:entity_token_end] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_end:] ) first_ids = ( first_ids[:entity_token_start] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_start:] ) first_entity_token_spans = [(entity_token_start, entity_token_end + 2)] elif self.task == "entity_pair_classification": if not ( isinstance(entity_spans, list) and len(entity_spans) == 2 and isinstance(entity_spans[0], tuple) and isinstance(entity_spans[1], tuple) ): raise ValueError( "Entity spans should be provided as a list of two tuples, " "each tuple containing the start and end character indices of an entity" ) head_span, tail_span = entity_spans first_entity_ids = [self.entity_mask_token_id, self.entity_mask2_token_id] first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) head_token_span, tail_token_span = first_entity_token_spans token_span_with_special_token_ids = [ (head_token_span, self.additional_special_tokens_ids[0]), (tail_token_span, self.additional_special_tokens_ids[1]), ] if head_token_span[0] < tail_token_span[0]: first_entity_token_spans[0] = (head_token_span[0], head_token_span[1] + 2) first_entity_token_spans[1] = (tail_token_span[0] + 2, tail_token_span[1] + 4) token_span_with_special_token_ids = reversed(token_span_with_special_token_ids) else: first_entity_token_spans[0] = (head_token_span[0] + 2, head_token_span[1] + 4) first_entity_token_spans[1] = (tail_token_span[0], tail_token_span[1] + 2) for (entity_token_start, entity_token_end), special_token_id in token_span_with_special_token_ids: first_ids = first_ids[:entity_token_end] + [special_token_id] + first_ids[entity_token_end:] first_ids = first_ids[:entity_token_start] + [special_token_id] + first_ids[entity_token_start:] elif self.task == "entity_span_classification": if not (isinstance(entity_spans, list) and len(entity_spans) > 0 and isinstance(entity_spans[0], tuple)): raise ValueError( "Entity spans should be provided as a list of tuples, " "each tuple containing the start and end character indices of an entity" ) first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) first_entity_ids = [self.entity_mask_token_id] * len(entity_spans) else: raise ValueError(f"Task {self.task} not supported") return ( first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._batch_prepare_for_model def _batch_prepare_for_model( self, batch_ids_pairs: List[Tuple[List[int], None]], batch_entity_ids_pairs: List[Tuple[Optional[List[int]], Optional[List[int]]]], batch_entity_token_spans_pairs: List[Tuple[Optional[List[Tuple[int, int]]], Optional[List[Tuple[int, int]]]]], add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[str] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_length: bool = False, verbose: bool = True, ) -> BatchEncoding: """ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens Args: batch_ids_pairs: list of tokenized input ids or input ids pairs batch_entity_ids_pairs: list of entity ids or entity ids pairs batch_entity_token_spans_pairs: list of entity spans or entity spans pairs max_entity_length: The maximum length of the entity sequence. """ batch_outputs = {} for input_ids, entity_ids, entity_token_span_pairs in zip( batch_ids_pairs, batch_entity_ids_pairs, batch_entity_token_spans_pairs ): first_ids, second_ids = input_ids first_entity_ids, second_entity_ids = entity_ids first_entity_token_spans, second_entity_token_spans = entity_token_span_pairs outputs = self.prepare_for_model( first_ids, second_ids, entity_ids=first_entity_ids, pair_entity_ids=second_entity_ids, entity_token_spans=first_entity_token_spans, pair_entity_token_spans=second_entity_token_spans, add_special_tokens=add_special_tokens, padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward truncation=truncation_strategy.value, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=None, # we pad in batch afterward return_attention_mask=False, # we pad in batch afterward return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=None, # We convert the whole batch to tensors at the end prepend_batch_axis=False, verbose=verbose, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) batch_outputs = self.pad( batch_outputs, padding=padding_strategy.value, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) return batch_outputs @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.prepare_for_model def prepare_for_model( self, ids: List[int], pair_ids: Optional[List[int]] = None, entity_ids: Optional[List[int]] = None, pair_entity_ids: Optional[List[int]] = None, entity_token_spans: Optional[List[Tuple[int, int]]] = None, pair_entity_token_spans: Optional[List[Tuple[int, int]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, prepend_batch_axis: bool = False, **kwargs ) -> BatchEncoding: """ Prepares a sequence of input id, entity id and entity span, or a pair of sequences of inputs ids, entity ids, entity spans so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error. Args: ids (`List[int]`): Tokenized input ids of the first sequence. pair_ids (`List[int]`, *optional*): Tokenized input ids of the second sequence. entity_ids (`List[int]`, *optional*): Entity ids of the first sequence. pair_entity_ids (`List[int]`, *optional*): Entity ids of the second sequence. entity_token_spans (`List[Tuple[int, int]]`, *optional*): Entity spans of the first sequence. pair_entity_token_spans (`List[Tuple[int, int]]`, *optional*): Entity spans of the second sequence. max_entity_length (`int`, *optional*): The maximum length of the entity sequence. """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) # Compute lengths pair = bool(pair_ids is not None) len_ids = len(ids) len_pair_ids = len(pair_ids) if pair else 0 if return_token_type_ids and not add_special_tokens: raise ValueError( "Asking to return token_type_ids while setting add_special_tokens to False " "results in an undefined behavior. Please set add_special_tokens to True or " "set return_token_type_ids to None." ) if ( return_overflowing_tokens and truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is not None ): raise ValueError( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`." ) # Load from model defaults if return_token_type_ids is None: return_token_type_ids = "token_type_ids" in self.model_input_names if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names encoded_inputs = {} # Compute the total size of the returned word encodings total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0) # Truncation: Handle max sequence length and max_entity_length overflowing_tokens = [] if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length: # truncate words up to max_length ids, pair_ids, overflowing_tokens = self.truncate_sequences( ids, pair_ids=pair_ids, num_tokens_to_remove=total_len - max_length, truncation_strategy=truncation_strategy, stride=stride, ) if return_overflowing_tokens: encoded_inputs["overflowing_tokens"] = overflowing_tokens encoded_inputs["num_truncated_tokens"] = total_len - max_length # Add special tokens if add_special_tokens: sequence = self.build_inputs_with_special_tokens(ids, pair_ids) token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids) entity_token_offset = 1 # 1 * <s> token pair_entity_token_offset = len(ids) + 3 # 1 * <s> token & 2 * <sep> tokens else: sequence = ids + pair_ids if pair else ids token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else []) entity_token_offset = 0 pair_entity_token_offset = len(ids) # Build output dictionary encoded_inputs["input_ids"] = sequence if return_token_type_ids: encoded_inputs["token_type_ids"] = token_type_ids if return_special_tokens_mask: if add_special_tokens: encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids) else: encoded_inputs["special_tokens_mask"] = [0] * len(sequence) # Set max entity length if not max_entity_length: max_entity_length = self.max_entity_length if entity_ids is not None: total_entity_len = 0 num_invalid_entities = 0 valid_entity_ids = [ent_id for ent_id, span in zip(entity_ids, entity_token_spans) if span[1] <= len(ids)] valid_entity_token_spans = [span for span in entity_token_spans if span[1] <= len(ids)] total_entity_len += len(valid_entity_ids) num_invalid_entities += len(entity_ids) - len(valid_entity_ids) valid_pair_entity_ids, valid_pair_entity_token_spans = None, None if pair_entity_ids is not None: valid_pair_entity_ids = [ ent_id for ent_id, span in zip(pair_entity_ids, pair_entity_token_spans) if span[1] <= len(pair_ids) ] valid_pair_entity_token_spans = [span for span in pair_entity_token_spans if span[1] <= len(pair_ids)] total_entity_len += len(valid_pair_entity_ids) num_invalid_entities += len(pair_entity_ids) - len(valid_pair_entity_ids) if num_invalid_entities != 0: logger.warning( f"{num_invalid_entities} entities are ignored because their entity spans are invalid due to the" " truncation of input tokens" ) if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and total_entity_len > max_entity_length: # truncate entities up to max_entity_length valid_entity_ids, valid_pair_entity_ids, overflowing_entities = self.truncate_sequences( valid_entity_ids, pair_ids=valid_pair_entity_ids, num_tokens_to_remove=total_entity_len - max_entity_length, truncation_strategy=truncation_strategy, stride=stride, ) valid_entity_token_spans = valid_entity_token_spans[: len(valid_entity_ids)] if valid_pair_entity_token_spans is not None: valid_pair_entity_token_spans = valid_pair_entity_token_spans[: len(valid_pair_entity_ids)] if return_overflowing_tokens: encoded_inputs["overflowing_entities"] = overflowing_entities encoded_inputs["num_truncated_entities"] = total_entity_len - max_entity_length final_entity_ids = valid_entity_ids + valid_pair_entity_ids if valid_pair_entity_ids else valid_entity_ids encoded_inputs["entity_ids"] = list(final_entity_ids) entity_position_ids = [] entity_start_positions = [] entity_end_positions = [] for token_spans, offset in ( (valid_entity_token_spans, entity_token_offset), (valid_pair_entity_token_spans, pair_entity_token_offset), ): if token_spans is not None: for start, end in token_spans: start += offset end += offset position_ids = list(range(start, end))[: self.max_mention_length] position_ids += [-1] * (self.max_mention_length - end + start) entity_position_ids.append(position_ids) entity_start_positions.append(start) entity_end_positions.append(end - 1) encoded_inputs["entity_position_ids"] = entity_position_ids if self.task == "entity_span_classification": encoded_inputs["entity_start_positions"] = entity_start_positions encoded_inputs["entity_end_positions"] = entity_end_positions if return_token_type_ids: encoded_inputs["entity_token_type_ids"] = [0] * len(encoded_inputs["entity_ids"]) # Check lengths self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose) # Padding if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask: encoded_inputs = self.pad( encoded_inputs, max_length=max_length, max_entity_length=max_entity_length, padding=padding_strategy.value, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) if return_length: encoded_inputs["length"] = len(encoded_inputs["input_ids"]) batch_outputs = BatchEncoding( encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis ) return batch_outputs # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.pad def pad( self, encoded_inputs: Union[ BatchEncoding, List[BatchEncoding], Dict[str, EncodedInput], Dict[str, List[EncodedInput]], List[Dict[str, EncodedInput]], ], padding: Union[bool, str, PaddingStrategy] = True, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, ) -> BatchEncoding: """ Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) .. note:: If the `encoded_inputs` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the specific device of your tensors however. Args: encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `Dict[str, List[int]]`, `Dict[str, List[List[int]]` or `List[Dict[str, List[int]]]`): Tokenized inputs. Can represent one input ([`BatchEncoding`] or `Dict[str, List[int]]`) or a batch of tokenized inputs (list of [`BatchEncoding`], *Dict[str, List[List[int]]]* or *List[Dict[str, List[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader collate function. Instead of `List[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see the note above for the return type. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). max_entity_length (`int`, *optional*): The maximum length of the entity sequence. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_attention_mask (`bool`, *optional*): Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. """ # If we have a list of dicts, let's convert it in a dict of lists # We do this to allow using this method as a collate_fn function in PyTorch Dataloader if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping): encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys()} # The model's main input name, usually `input_ids`, has be passed for padding if self.model_input_names[0] not in encoded_inputs: raise ValueError( "You should supply an encoding or a list of encodings to this method " f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}" ) required_input = encoded_inputs[self.model_input_names[0]] if not required_input: if return_attention_mask: encoded_inputs["attention_mask"] = [] return encoded_inputs # If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch first_element = required_input[0] if isinstance(first_element, (list, tuple)): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. index = 0 while len(required_input[index]) == 0: index += 1 if index < len(required_input): first_element = required_input[index][0] # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do. if not isinstance(first_element, (int, list, tuple)): if is_tf_tensor(first_element): return_tensors = "tf" if return_tensors is None else return_tensors elif is_torch_tensor(first_element): return_tensors = "pt" if return_tensors is None else return_tensors elif isinstance(first_element, np.ndarray): return_tensors = "np" if return_tensors is None else return_tensors else: raise ValueError( f"type of {first_element} unknown: {type(first_element)}. " "Should be one of a python, numpy, pytorch or tensorflow object." ) for key, value in encoded_inputs.items(): encoded_inputs[key] = to_py_obj(value) # Convert padding_strategy in PaddingStrategy padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies( padding=padding, max_length=max_length, verbose=verbose ) if max_entity_length is None: max_entity_length = self.max_entity_length required_input = encoded_inputs[self.model_input_names[0]] if required_input and not isinstance(required_input[0], (list, tuple)): encoded_inputs = self._pad( encoded_inputs, max_length=max_length, max_entity_length=max_entity_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) return BatchEncoding(encoded_inputs, tensor_type=return_tensors) batch_size = len(required_input) if any(len(v) != batch_size for v in encoded_inputs.values()): raise ValueError("Some items in the output dictionary have a different batch size than others.") if padding_strategy == PaddingStrategy.LONGEST: max_length = max(len(inputs) for inputs in required_input) max_entity_length = ( max(len(inputs) for inputs in encoded_inputs["entity_ids"]) if "entity_ids" in encoded_inputs else 0 ) padding_strategy = PaddingStrategy.MAX_LENGTH batch_outputs = {} for i in range(batch_size): inputs = dict((k, v[i]) for k, v in encoded_inputs.items()) outputs = self._pad( inputs, max_length=max_length, max_entity_length=max_entity_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) return BatchEncoding(batch_outputs, tensor_type=return_tensors) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._pad def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, max_entity_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: """ Pad encoded inputs (on left/right and up to predefined length or max length in the batch) Args: encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. max_entity_length: The maximum length of the entity sequence. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability >= 7.5 (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) """ entities_provided = bool("entity_ids" in encoded_inputs) # Load from model defaults if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names if padding_strategy == PaddingStrategy.LONGEST: max_length = len(encoded_inputs["input_ids"]) if entities_provided: max_entity_length = len(encoded_inputs["entity_ids"]) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of if ( entities_provided and max_entity_length is not None and pad_to_multiple_of is not None and (max_entity_length % pad_to_multiple_of != 0) ): max_entity_length = ((max_entity_length // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and ( len(encoded_inputs["input_ids"]) != max_length or (entities_provided and len(encoded_inputs["entity_ids"]) != max_entity_length) ) # Initialize attention mask if not present. if return_attention_mask and "attention_mask" not in encoded_inputs: encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) if entities_provided and return_attention_mask and "entity_attention_mask" not in encoded_inputs: encoded_inputs["entity_attention_mask"] = [1] * len(encoded_inputs["entity_ids"]) if needs_to_be_padded: difference = max_length - len(encoded_inputs["input_ids"]) if entities_provided: entity_difference = max_entity_length - len(encoded_inputs["entity_ids"]) if self.padding_side == "right": if return_attention_mask: encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference if entities_provided: encoded_inputs["entity_attention_mask"] = ( encoded_inputs["entity_attention_mask"] + [0] * entity_difference ) if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"] + [0] * difference if entities_provided: encoded_inputs["entity_token_type_ids"] = ( encoded_inputs["entity_token_type_ids"] + [0] * entity_difference ) if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference if entities_provided: encoded_inputs["entity_ids"] = ( encoded_inputs["entity_ids"] + [self.entity_pad_token_id] * entity_difference ) encoded_inputs["entity_position_ids"] = ( encoded_inputs["entity_position_ids"] + [[-1] * self.max_mention_length] * entity_difference ) if self.task == "entity_span_classification": encoded_inputs["entity_start_positions"] = ( encoded_inputs["entity_start_positions"] + [0] * entity_difference ) encoded_inputs["entity_end_positions"] = ( encoded_inputs["entity_end_positions"] + [0] * entity_difference ) elif self.padding_side == "left": if return_attention_mask: encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] if entities_provided: encoded_inputs["entity_attention_mask"] = [0] * entity_difference + encoded_inputs[ "entity_attention_mask" ] if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = [0] * difference + encoded_inputs["token_type_ids"] if entities_provided: encoded_inputs["entity_token_type_ids"] = [0] * entity_difference + encoded_inputs[ "entity_token_type_ids" ] if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"] if entities_provided: encoded_inputs["entity_ids"] = [self.entity_pad_token_id] * entity_difference + encoded_inputs[ "entity_ids" ] encoded_inputs["entity_position_ids"] = [ [-1] * self.max_mention_length ] * entity_difference + encoded_inputs["entity_position_ids"] if self.task == "entity_span_classification": encoded_inputs["entity_start_positions"] = [0] * entity_difference + encoded_inputs[ "entity_start_positions" ] encoded_inputs["entity_end_positions"] = [0] * entity_difference + encoded_inputs[ "entity_end_positions" ] else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str, str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): copyfile(self.vocab_file, out_vocab_file) entity_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["entity_vocab_file"] ) with open(entity_vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.entity_vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return out_vocab_file, entity_vocab_file # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.build_inputs_with_special_tokens def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLM-RoBERTa sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.get_special_tokens_mask def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.create_token_type_ids_from_sequences def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] @property # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.vocab_size def vocab_size(self): return len(self.sp_model) + self.fairseq_offset + 1 # Add the <mask> token # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.get_vocab def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer._tokenize def _tokenize(self, text: str) -> List[str]: return self.sp_model.encode(text, out_type=str) # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer._convert_token_to_id def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] spm_id = self.sp_model.PieceToId(token) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (strings for sub-words) in a single string.""" out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/pipelines/audio_classification.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import subprocess from typing import Union import numpy as np import requests from ..utils import add_end_docstrings, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, Pipeline if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING logger = logging.get_logger(__name__) def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array: """ Helper function to read an audio file through ffmpeg. """ ar = f"{sampling_rate}" ac = "1" format_for_conversion = "f32le" ffmpeg_command = [ "ffmpeg", "-i", "pipe:0", "-ac", ac, "-ar", ar, "-f", format_for_conversion, "-hide_banner", "-loglevel", "quiet", "pipe:1", ] try: ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) except FileNotFoundError: raise ValueError("ffmpeg was not found but is required to load audio files from filename") output_stream = ffmpeg_process.communicate(bpayload) out_bytes = output_stream[0] audio = np.frombuffer(out_bytes, np.float32) if audio.shape[0] == 0: raise ValueError("Malformed soundfile") return audio @add_end_docstrings(PIPELINE_INIT_ARGS) class AudioClassificationPipeline(Pipeline): """ Audio classification pipeline using any `AutoModelForAudioClassification`. This pipeline predicts the class of a raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio formats. Example: ```python >>> from transformers import pipeline >>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks") >>> result = classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac") >>> # Simplify results, different torch versions might alter the scores slightly. >>> from transformers.testing_utils import nested_simplify >>> nested_simplify(result) [{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}] ``` [Using pipelines in a webserver or with a dataset](../pipeline_tutorial) This pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"audio-classification"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=audio-classification). """ def __init__(self, *args, **kwargs): # Default, might be overriden by the model.config. kwargs["top_k"] = 5 super().__init__(*args, **kwargs) if self.framework != "pt": raise ValueError(f"The {self.__class__} is only available in PyTorch.") self.check_model_type(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING) def __call__( self, inputs: Union[np.ndarray, bytes, str], **kwargs, ): """ Classify the sequence(s) given as inputs. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more information. Args: inputs (`np.ndarray` or `bytes` or `str`): The inputs is either a raw waveform (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`) at the correct sampling rate (no further check will be done) or a `str` that is the filename of the audio file, the file will be read at the correct sampling rate to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system. If *inputs* is `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the same way. top_k (`int`, *optional*, defaults to None): The number of top labels that will be returned by the pipeline. If the provided number is `None` or higher than the number of labels available in the model configuration, it will default to the number of labels. Return: A list of `dict` with the following keys: - **label** (`str`) -- The label predicted. - **score** (`float`) -- The corresponding probability. """ return super().__call__(inputs, **kwargs) def _sanitize_parameters(self, top_k=None, **kwargs): # No parameters on this pipeline right now postprocess_params = {} if top_k is not None: if top_k > self.model.config.num_labels: top_k = self.model.config.num_labels postprocess_params["top_k"] = top_k return {}, {}, postprocess_params def preprocess(self, inputs): if isinstance(inputs, str): if inputs.startswith("http://") or inputs.startswith("https://"): # We need to actually check for a real protocol, otherwise it's impossible to use a local file # like http_huggingface_co.png inputs = requests.get(inputs).content else: with open(inputs, "rb") as f: inputs = f.read() if isinstance(inputs, bytes): inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate) if not isinstance(inputs, np.ndarray): raise ValueError("We expect a numpy ndarray as input") if len(inputs.shape) != 1: raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline") processed = self.feature_extractor( inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt" ) return processed def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) return model_outputs def postprocess(self, model_outputs, top_k=5): probs = model_outputs.logits[0].softmax(-1) scores, ids = probs.topk(top_k) scores = scores.tolist() ids = ids.tolist() labels = [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)] return labels

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import subprocess from typing import Union import numpy as np import requests from ..utils import add_end_docstrings, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, Pipeline if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING logger = logging.get_logger(__name__) def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array: """ Helper function to read an audio file through ffmpeg. """ ar = f"{sampling_rate}" ac = "1" format_for_conversion = "f32le" ffmpeg_command = [ "ffmpeg", "-i", "pipe:0", "-ac", ac, "-ar", ar, "-f", format_for_conversion, "-hide_banner", "-loglevel", "quiet", "pipe:1", ] try: ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) except FileNotFoundError: raise ValueError("ffmpeg was not found but is required to load audio files from filename") output_stream = ffmpeg_process.communicate(bpayload) out_bytes = output_stream[0] audio = np.frombuffer(out_bytes, np.float32) if audio.shape[0] == 0: raise ValueError("Malformed soundfile") return audio @add_end_docstrings(PIPELINE_INIT_ARGS) class AudioClassificationPipeline(Pipeline): """ Audio classification pipeline using any `AutoModelForAudioClassification`. This pipeline predicts the class of a raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio formats. Example: ```python >>> from transformers import pipeline >>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks") >>> classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac") [{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}] ``` [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial) This pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"audio-classification"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=audio-classification). """ def __init__(self, *args, **kwargs): # Default, might be overriden by the model.config. kwargs["top_k"] = 5 super().__init__(*args, **kwargs) if self.framework != "pt": raise ValueError(f"The {self.__class__} is only available in PyTorch.") self.check_model_type(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING) def __call__( self, inputs: Union[np.ndarray, bytes, str], **kwargs, ): """ Classify the sequence(s) given as inputs. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more information. Args: inputs (`np.ndarray` or `bytes` or `str`): The inputs is either a raw waveform (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`) at the correct sampling rate (no further check will be done) or a `str` that is the filename of the audio file, the file will be read at the correct sampling rate to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system. If *inputs* is `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the same way. top_k (`int`, *optional*, defaults to None): The number of top labels that will be returned by the pipeline. If the provided number is `None` or higher than the number of labels available in the model configuration, it will default to the number of labels. Return: A list of `dict` with the following keys: - **label** (`str`) -- The label predicted. - **score** (`float`) -- The corresponding probability. """ return super().__call__(inputs, **kwargs) def _sanitize_parameters(self, top_k=None, **kwargs): # No parameters on this pipeline right now postprocess_params = {} if top_k is not None: if top_k > self.model.config.num_labels: top_k = self.model.config.num_labels postprocess_params["top_k"] = top_k return {}, {}, postprocess_params def preprocess(self, inputs): if isinstance(inputs, str): if inputs.startswith("http://") or inputs.startswith("https://"): # We need to actually check for a real protocol, otherwise it's impossible to use a local file # like http_huggingface_co.png inputs = requests.get(inputs).content else: with open(inputs, "rb") as f: inputs = f.read() if isinstance(inputs, bytes): inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate) if not isinstance(inputs, np.ndarray): raise ValueError("We expect a numpy ndarray as input") if len(inputs.shape) != 1: raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline") processed = self.feature_extractor( inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt" ) return processed def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) return model_outputs def postprocess(self, model_outputs, top_k=5): probs = model_outputs.logits[0].softmax(-1) scores, ids = probs.topk(top_k) scores = scores.tolist() ids = ids.tolist() labels = [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)] return labels

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/pipelines/automatic_speech_recognition.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import defaultdict from typing import TYPE_CHECKING, Dict, Optional, Union import numpy as np import requests from ..utils import is_torch_available, logging from .audio_utils import ffmpeg_read from .base import ChunkPipeline if TYPE_CHECKING: from ...feature_extraction_sequence_utils import SequenceFeatureExtractor logger = logging.get_logger(__name__) if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_CTC_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING def rescale_stride(stride, ratio): """ Rescales the stride values from audio space to tokens/logits space. (160_000, 16_000, 16_000) -> (2000, 200, 200) for instance. """ # Shape is [B, SEQ] for tokens # [B, SEQ, V] for logits new_strides = [] for input_n, left, right in stride: token_n = int(round(input_n * ratio)) left = int(round(left / input_n * token_n)) right = int(round(right / input_n * token_n)) new_stride = (token_n, left, right) new_strides.append(new_stride) return new_strides def chunk_iter(inputs, feature_extractor, chunk_len, stride_left, stride_right): inputs_len = inputs.shape[0] step = chunk_len - stride_left - stride_right for i in range(0, inputs_len, step): # add start and end paddings to the chunk chunk = inputs[i : i + chunk_len] processed = feature_extractor(chunk, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt") _stride_left = 0 if i == 0 else stride_left is_last = i + step + stride_left >= inputs_len _stride_right = 0 if is_last else stride_right if "input_features" in processed: processed_len = processed["input_features"].shape[-1] elif "input_values" in processed: processed_len = processed["input_values"].shape[-1] chunk_len = chunk.shape[0] stride = (chunk_len, _stride_left, _stride_right) if processed_len != chunk.shape[-1]: ratio = processed_len / chunk_len stride = rescale_stride([stride], ratio)[0] if chunk.shape[0] > _stride_left: yield {"is_last": is_last, "stride": stride, **processed} def _find_longest_common_sequence(sequences, tokenizer): # TODO Use a faster algorithm this can probably be done in O(n) # using suffix array. # It might be tedious to do because of fault tolerance. # We actually have a really good property which is that the total sequence # MUST be those subsequences in order. # Also the algorithm should be more tolerant to errors. sequence = [tok_id for tok_id in sequences[0][0].tolist() if tok_id not in tokenizer.all_special_ids] for new_seq in sequences[1:]: new_sequence = [tok_id for tok_id in new_seq[0].tolist() if tok_id not in tokenizer.all_special_ids] index = 0 max_ = 0.0 for i in range(1, len(new_sequence) + 1): # epsilon to favor long perfect matches eps = i / 10000.0 matches = np.sum(np.array(sequence[-i:]) == np.array(new_sequence[:i])) matching = matches / i + eps if matches > 1 and matching > max_: index = i max_ = matching sequence.extend(new_sequence[index:]) return np.array(sequence) class AutomaticSpeechRecognitionPipeline(ChunkPipeline): """ Pipeline that aims at extracting spoken text contained within some audio. The input can be either a raw waveform or a audio file. In case of the audio file, ffmpeg should be installed for to support multiple audio formats Example: ```python >>> from transformers import pipeline >>> transcriber = pipeline(model="openai/whisper-base") >>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac") {'text': ' He hoped there would be stew for dinner, turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick, peppered flour fat and sauce.'} ``` [Using pipelines in a webserver or with a dataset](../pipeline_tutorial) Arguments: model ([`PreTrainedModel`] or [`TFPreTrainedModel`]): The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow. tokenizer ([`PreTrainedTokenizer`]): The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from [`PreTrainedTokenizer`]. feature_extractor ([`SequenceFeatureExtractor`]): The feature extractor that will be used by the pipeline to encode waveform for the model. chunk_length_s (`float`, *optional*, defaults to 0): The input length for in each chunk. If `chunk_length_s = 0` then chunking is disabled (default). Only available for CTC models, e.g. [`Wav2Vec2ForCTC`]. <Tip> For more information on how to effectively use `chunk_length_s`, please have a look at the [ASR chunking blog post](https://huggingface.co/blog/asr-chunking). </Tip> stride_length_s (`float`, *optional*, defaults to `chunk_length_s / 6`): The length of stride on the left and right of each chunk. Used only with `chunk_length_s > 0`. This enables the model to *see* more context and infer letters better than without this context but the pipeline discards the stride bits at the end to make the final reconstitution as perfect as possible. <Tip> For more information on how to effectively use `stride_length_s`, please have a look at the [ASR chunking blog post](https://huggingface.co/blog/asr-chunking). </Tip> framework (`str`, *optional*): The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be installed. If no framework is specified, will default to the one currently installed. If no framework is specified and both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is provided. device (`int`, *optional*, defaults to -1): Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on the associated CUDA device id. decoder (`pyctcdecode.BeamSearchDecoderCTC`, *optional*): [PyCTCDecode's BeamSearchDecoderCTC](https://github.com/kensho-technologies/pyctcdecode/blob/2fd33dc37c4111417e08d89ccd23d28e9b308d19/pyctcdecode/decoder.py#L180) can be passed for language model boosted decoding. See [`Wav2Vec2ProcessorWithLM`] for more information. """ def __init__(self, feature_extractor: Union["SequenceFeatureExtractor", str], *args, **kwargs): super().__init__(*args, **kwargs) self.feature_extractor = feature_extractor if self.model.__class__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.values(): self.type = "seq2seq" elif ( feature_extractor._processor_class and feature_extractor._processor_class.endswith("WithLM") and kwargs.get("decoder", None) is not None ): self.decoder = kwargs["decoder"] self.type = "ctc_with_lm" else: self.type = "ctc" if self.framework == "tf": raise ValueError("The AutomaticSpeechRecognitionPipeline is only available in PyTorch.") self.check_model_type(dict(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.items() + MODEL_FOR_CTC_MAPPING.items())) def __call__( self, inputs: Union[np.ndarray, bytes, str], **kwargs, ): """ Transcribe the audio sequence(s) given as inputs to text. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more information. Args: inputs (`np.ndarray` or `bytes` or `str` or `dict`): The inputs is either : - `str` that is the filename of the audio file, the file will be read at the correct sampling rate to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system. - `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the same way. - (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`) Raw audio at the correct sampling rate (no further check will be done) - `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this pipeline do the resampling. The dict must be in the format `{"sampling_rate": int, "raw": np.array}` with optionally a `"stride": (left: int, right: int)` than can ask the pipeline to treat the first `left` samples and last `right` samples to be ignored in decoding (but used at inference to provide more context to the model). Only use `stride` with CTC models. return_timestamps (*optional*, `str`): Only available for pure CTC models. If set to `"char"`, the pipeline will return `timestamps` along the text for every character in the text. For instance if you get `[{"text": "h", "timestamps": (0.5,0.6), {"text": "i", "timestamps": (0.7, .9)}]`, then it means the model predicts that the letter "h" was pronounced after `0.5` and before `0.6` seconds. If set to `"word"`, the pipeline will return `timestamps` along the text for every word in the text. For instance if you get `[{"text": "hi ", "timestamps": (0.5,0.9), {"text": "there", "timestamps": (1.0, .1.5)}]`, then it means the model predicts that the word "hi" was pronounced after `0.5` and before `0.9` seconds. Return: `Dict`: A dictionary with the following keys: - **text** (`str` ) -- The recognized text. - **chunks** (*optional(, `List[Dict]`) When using `return_timestamps`, the `chunks` will become a list containing all the various text chunks identified by the model, *e.g.* `[{"text": "hi ", "timestamps": (0.5,0.9), {"text": "there", "timestamps": (1.0, 1.5)}]`. The original full text can roughly be recovered by doing `"".join(chunk["text"] for chunk in output["chunks"])`. """ return super().__call__(inputs, **kwargs) def _sanitize_parameters(self, **kwargs): # No parameters on this pipeline right now preprocess_params = {} if "chunk_length_s" in kwargs: preprocess_params["chunk_length_s"] = kwargs["chunk_length_s"] if "stride_length_s" in kwargs: preprocess_params["stride_length_s"] = kwargs["stride_length_s"] if "ignore_warning" in kwargs: preprocess_params["ignore_warning"] = kwargs["ignore_warning"] postprocess_params = {} if "decoder_kwargs" in kwargs: postprocess_params["decoder_kwargs"] = kwargs["decoder_kwargs"] if "return_timestamps" in kwargs: postprocess_params["return_timestamps"] = kwargs["return_timestamps"] return preprocess_params, {}, postprocess_params def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None, ignore_warning=False): if isinstance(inputs, str): if inputs.startswith("http://") or inputs.startswith("https://"): # We need to actually check for a real protocol, otherwise it's impossible to use a local file # like http_huggingface_co.png inputs = requests.get(inputs).content else: with open(inputs, "rb") as f: inputs = f.read() if isinstance(inputs, bytes): inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate) stride = None extra = {} if isinstance(inputs, dict): stride = inputs.pop("stride", None) # Accepting `"array"` which is the key defined in `datasets` for # better integration if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)): raise ValueError( "When passing a dictionnary to AutomaticSpeechRecognitionPipeline, the dict needs to contain a " '"raw" key containing the numpy array representing the audio and a "sampling_rate" key, ' "containing the sampling_rate associated with that array" ) _inputs = inputs.pop("raw", None) if _inputs is None: _inputs = inputs.pop("array", None) in_sampling_rate = inputs.pop("sampling_rate") extra = inputs inputs = _inputs if in_sampling_rate != self.feature_extractor.sampling_rate: import torch from torchaudio import functional as F inputs = F.resample( torch.from_numpy(inputs), in_sampling_rate, self.feature_extractor.sampling_rate ).numpy() ratio = self.feature_extractor.sampling_rate / in_sampling_rate else: ratio = 1 if stride is not None: if stride[0] + stride[1] > inputs.shape[0]: raise ValueError("Stride is too large for input") # Stride needs to get the chunk length here, it's going to get # swallowed by the `feature_extractor` later, and then batching # can add extra data in the inputs, so we need to keep track # of the original length in the stride so we can cut properly. stride = (inputs.shape[0], int(round(stride[0] * ratio)), int(round(stride[1] * ratio))) if not isinstance(inputs, np.ndarray): raise ValueError(f"We expect a numpy ndarray as input, got `{type(inputs)}`") if len(inputs.shape) != 1: raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline") if chunk_length_s: if self.type == "seq2seq" and not ignore_warning: logger.warning( "Using `chunk_length_s` is very experimental with seq2seq models. The results will not necessarily" " be entirely accurate and will have caveats. More information:" " https://github.com/huggingface/transformers/pull/20104. Ignore this warning with pipeline(...," " ignore_warning=True)" ) self._preprocess_params["ignore_warning"] = True if stride_length_s is None: stride_length_s = chunk_length_s / 6 if isinstance(stride_length_s, (int, float)): stride_length_s = [stride_length_s, stride_length_s] # XXX: Carefuly, this variable will not exist in `seq2seq` setting. # Currently chunking is not possible at this level for `seq2seq` so # it's ok. align_to = getattr(self.model.config, "inputs_to_logits_ratio", 1) chunk_len = int(round(chunk_length_s * self.feature_extractor.sampling_rate / align_to) * align_to) stride_left = int(round(stride_length_s[0] * self.feature_extractor.sampling_rate / align_to) * align_to) stride_right = int(round(stride_length_s[1] * self.feature_extractor.sampling_rate / align_to) * align_to) if chunk_len < stride_left + stride_right: raise ValueError("Chunk length must be superior to stride length") # make sure that for item in chunk_iter(inputs, self.feature_extractor, chunk_len, stride_left, stride_right): yield item else: processed = self.feature_extractor( inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt" ) if stride is not None: if self.model.__class__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.values(): raise ValueError("Stride is only usable with CTC models, try removing it") processed["stride"] = stride yield {"is_last": True, **processed, **extra} def _forward(self, model_inputs): is_last = model_inputs.pop("is_last") if self.type == "seq2seq": encoder = self.model.get_encoder() # Consume values so we can let extra information flow freely through # the pipeline (important for `partial` in microphone) if "input_features" in model_inputs: inputs = model_inputs.pop("input_features") elif "input_values" in model_inputs: inputs = model_inputs.pop("input_values") else: raise ValueError( "Seq2Seq speech recognition model requires either a " f"`input_features` or `input_values` key, but only has {model_inputs.keys()}" ) # we need to pass `processed.get("attention_mask")` here since audio encoder # attention mask length is different from expected text decoder `encoder_attention_mask` length # `generate` magic to create the mask automatically won't work, we basically need to help # it here. attention_mask = model_inputs.pop("attention_mask", None) tokens = self.model.generate( encoder_outputs=encoder(inputs, attention_mask=attention_mask), attention_mask=attention_mask, ) out = {"tokens": tokens} else: stride = model_inputs.pop("stride", None) input_values = model_inputs.pop("input_values") attention_mask = model_inputs.pop("attention_mask", None) outputs = self.model(input_values=input_values, attention_mask=attention_mask) logits = outputs.logits if self.type == "ctc_with_lm": out = {"logits": logits} else: out = {"tokens": logits.argmax(dim=-1)} if stride is not None: # Send stride to `postprocess`. # it needs to be handled there where # the pieces are to be concatenated. ratio = 1 / self.model.config.inputs_to_logits_ratio if isinstance(stride, tuple): out["stride"] = rescale_stride([stride], ratio)[0] else: out["stride"] = rescale_stride(stride, ratio) # Leftover extra = model_inputs return {"is_last": is_last, **out, **extra} def postprocess(self, model_outputs, decoder_kwargs: Optional[Dict] = None, return_timestamps=None): # Optional return types optional = {} if return_timestamps and self.type == "seq2seq": raise ValueError("We cannot return_timestamps yet on non-ctc models !") if return_timestamps == "char" and self.type == "ctc_with_lm": raise ValueError("CTC with LM cannot return `char` timestamps, only `words`") final_items = [] key = "logits" if self.type == "ctc_with_lm" else "tokens" stride = None for outputs in model_outputs: items = outputs[key].numpy() stride = outputs.pop("stride", None) if stride is not None and self.type in {"ctc", "ctc_with_lm"}: total_n, left, right = stride # Total_n might be < logits.shape[1] # because of padding, that's why # we need to reconstruct this information # This won't work with left padding (which doesn't exist right now) right_n = total_n - right items = items[:, left:right_n] final_items.append(items) if stride and self.type == "seq2seq": items = _find_longest_common_sequence(final_items, self.tokenizer) else: items = np.concatenate(final_items, axis=1) items = items.squeeze(0) if self.type == "ctc_with_lm": if decoder_kwargs is None: decoder_kwargs = {} beams = self.decoder.decode_beams(items, **decoder_kwargs) text = beams[0][0] if return_timestamps: # Simply cast from pyctcdecode format to wav2vec2 format to leverage # pre-existing code later chunk_offset = beams[0][2] word_offsets = [] for word, (start_offset, end_offset) in chunk_offset: word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) else: skip_special_tokens = self.type != "ctc" text = self.tokenizer.decode(items, skip_special_tokens=skip_special_tokens) if return_timestamps: char_offsets = self.tokenizer.decode( items, skip_special_tokens=skip_special_tokens, output_char_offsets=True )["char_offsets"] if return_timestamps == "word": word_offsets = self.tokenizer._get_word_offsets( char_offsets, self.tokenizer.replace_word_delimiter_char ) if return_timestamps: if return_timestamps == "word": offsets = word_offsets else: offsets = char_offsets chunks = [] for item in offsets: start = item["start_offset"] * self.model.config.inputs_to_logits_ratio start /= self.feature_extractor.sampling_rate stop = item["end_offset"] * self.model.config.inputs_to_logits_ratio stop /= self.feature_extractor.sampling_rate chunks.append({"text": item[return_timestamps], "timestamp": (start, stop)}) optional["chunks"] = chunks extra = defaultdict(list) for output in model_outputs: output.pop("tokens", None) output.pop("logits", None) output.pop("is_last", None) for k, v in output.items(): extra[k].append(v) return {"text": text, **optional, **extra}

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import defaultdict from typing import TYPE_CHECKING, Dict, Optional, Union import numpy as np import requests from ..utils import is_torch_available, logging from .audio_utils import ffmpeg_read from .base import ChunkPipeline if TYPE_CHECKING: from ...feature_extraction_sequence_utils import SequenceFeatureExtractor logger = logging.get_logger(__name__) if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_CTC_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING def rescale_stride(stride, ratio): """ Rescales the stride values from audio space to tokens/logits space. (160_000, 16_000, 16_000) -> (2000, 200, 200) for instance. """ # Shape is [B, SEQ] for tokens # [B, SEQ, V] for logits new_strides = [] for input_n, left, right in stride: token_n = int(round(input_n * ratio)) left = int(round(left / input_n * token_n)) right = int(round(right / input_n * token_n)) new_stride = (token_n, left, right) new_strides.append(new_stride) return new_strides def chunk_iter(inputs, feature_extractor, chunk_len, stride_left, stride_right): inputs_len = inputs.shape[0] step = chunk_len - stride_left - stride_right for i in range(0, inputs_len, step): # add start and end paddings to the chunk chunk = inputs[i : i + chunk_len] processed = feature_extractor(chunk, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt") _stride_left = 0 if i == 0 else stride_left is_last = i + step + stride_left >= inputs_len _stride_right = 0 if is_last else stride_right if "input_features" in processed: processed_len = processed["input_features"].shape[-1] elif "input_values" in processed: processed_len = processed["input_values"].shape[-1] chunk_len = chunk.shape[0] stride = (chunk_len, _stride_left, _stride_right) if processed_len != chunk.shape[-1]: ratio = processed_len / chunk_len stride = rescale_stride([stride], ratio)[0] if chunk.shape[0] > _stride_left: yield {"is_last": is_last, "stride": stride, **processed} def _find_longest_common_sequence(sequences, tokenizer): # TODO Use a faster algorithm this can probably be done in O(n) # using suffix array. # It might be tedious to do because of fault tolerance. # We actually have a really good property which is that the total sequence # MUST be those subsequences in order. # Also the algorithm should be more tolerant to errors. sequence = [tok_id for tok_id in sequences[0][0].tolist() if tok_id not in tokenizer.all_special_ids] for new_seq in sequences[1:]: new_sequence = [tok_id for tok_id in new_seq[0].tolist() if tok_id not in tokenizer.all_special_ids] index = 0 max_ = 0.0 for i in range(1, len(new_sequence) + 1): # epsilon to favor long perfect matches eps = i / 10000.0 matches = np.sum(np.array(sequence[-i:]) == np.array(new_sequence[:i])) matching = matches / i + eps if matches > 1 and matching > max_: index = i max_ = matching sequence.extend(new_sequence[index:]) return np.array(sequence) class AutomaticSpeechRecognitionPipeline(ChunkPipeline): """ Pipeline that aims at extracting spoken text contained within some audio. The input can be either a raw waveform or a audio file. In case of the audio file, ffmpeg should be installed for to support multiple audio formats Example: ```python >>> from transformers import pipeline >>> transcriber = pipeline(model="openai/whisper-base") >>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac") {'text': ' He hoped there would be stew for dinner, turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick, peppered flour fat and sauce.'} ``` [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial) Arguments: model ([`PreTrainedModel`] or [`TFPreTrainedModel`]): The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow. tokenizer ([`PreTrainedTokenizer`]): The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from [`PreTrainedTokenizer`]. feature_extractor ([`SequenceFeatureExtractor`]): The feature extractor that will be used by the pipeline to encode waveform for the model. chunk_length_s (`float`, *optional*, defaults to 0): The input length for in each chunk. If `chunk_length_s = 0` then chunking is disabled (default). Only available for CTC models, e.g. [`Wav2Vec2ForCTC`]. <Tip> For more information on how to effectively use `chunk_length_s`, please have a look at the [ASR chunking blog post](https://huggingface.co/blog/asr-chunking). </Tip> stride_length_s (`float`, *optional*, defaults to `chunk_length_s / 6`): The length of stride on the left and right of each chunk. Used only with `chunk_length_s > 0`. This enables the model to *see* more context and infer letters better than without this context but the pipeline discards the stride bits at the end to make the final reconstitution as perfect as possible. <Tip> For more information on how to effectively use `stride_length_s`, please have a look at the [ASR chunking blog post](https://huggingface.co/blog/asr-chunking). </Tip> framework (`str`, *optional*): The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be installed. If no framework is specified, will default to the one currently installed. If no framework is specified and both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is provided. device (`int`, *optional*, defaults to -1): Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on the associated CUDA device id. decoder (`pyctcdecode.BeamSearchDecoderCTC`, *optional*): [PyCTCDecode's BeamSearchDecoderCTC](https://github.com/kensho-technologies/pyctcdecode/blob/2fd33dc37c4111417e08d89ccd23d28e9b308d19/pyctcdecode/decoder.py#L180) can be passed for language model boosted decoding. See [`Wav2Vec2ProcessorWithLM`] for more information. """ def __init__(self, feature_extractor: Union["SequenceFeatureExtractor", str], *args, **kwargs): super().__init__(*args, **kwargs) self.feature_extractor = feature_extractor if self.model.__class__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.values(): self.type = "seq2seq" elif ( feature_extractor._processor_class and feature_extractor._processor_class.endswith("WithLM") and kwargs.get("decoder", None) is not None ): self.decoder = kwargs["decoder"] self.type = "ctc_with_lm" else: self.type = "ctc" if self.framework == "tf": raise ValueError("The AutomaticSpeechRecognitionPipeline is only available in PyTorch.") self.check_model_type(dict(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.items() + MODEL_FOR_CTC_MAPPING.items())) def __call__( self, inputs: Union[np.ndarray, bytes, str], **kwargs, ): """ Transcribe the audio sequence(s) given as inputs to text. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more information. Args: inputs (`np.ndarray` or `bytes` or `str` or `dict`): The inputs is either : - `str` that is the filename of the audio file, the file will be read at the correct sampling rate to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system. - `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the same way. - (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`) Raw audio at the correct sampling rate (no further check will be done) - `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this pipeline do the resampling. The dict must be in the format `{"sampling_rate": int, "raw": np.array}` with optionally a `"stride": (left: int, right: int)` than can ask the pipeline to treat the first `left` samples and last `right` samples to be ignored in decoding (but used at inference to provide more context to the model). Only use `stride` with CTC models. return_timestamps (*optional*, `str`): Only available for pure CTC models. If set to `"char"`, the pipeline will return `timestamps` along the text for every character in the text. For instance if you get `[{"text": "h", "timestamps": (0.5,0.6), {"text": "i", "timestamps": (0.7, .9)}]`, then it means the model predicts that the letter "h" was pronounced after `0.5` and before `0.6` seconds. If set to `"word"`, the pipeline will return `timestamps` along the text for every word in the text. For instance if you get `[{"text": "hi ", "timestamps": (0.5,0.9), {"text": "there", "timestamps": (1.0, .1.5)}]`, then it means the model predicts that the word "hi" was pronounced after `0.5` and before `0.9` seconds. Return: `Dict`: A dictionary with the following keys: - **text** (`str` ) -- The recognized text. - **chunks** (*optional(, `List[Dict]`) When using `return_timestamps`, the `chunks` will become a list containing all the various text chunks identified by the model, *e.g.* `[{"text": "hi ", "timestamps": (0.5,0.9), {"text": "there", "timestamps": (1.0, 1.5)}]`. The original full text can roughly be recovered by doing `"".join(chunk["text"] for chunk in output["chunks"])`. """ return super().__call__(inputs, **kwargs) def _sanitize_parameters(self, **kwargs): # No parameters on this pipeline right now preprocess_params = {} if "chunk_length_s" in kwargs: preprocess_params["chunk_length_s"] = kwargs["chunk_length_s"] if "stride_length_s" in kwargs: preprocess_params["stride_length_s"] = kwargs["stride_length_s"] if "ignore_warning" in kwargs: preprocess_params["ignore_warning"] = kwargs["ignore_warning"] postprocess_params = {} if "decoder_kwargs" in kwargs: postprocess_params["decoder_kwargs"] = kwargs["decoder_kwargs"] if "return_timestamps" in kwargs: postprocess_params["return_timestamps"] = kwargs["return_timestamps"] return preprocess_params, {}, postprocess_params def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None, ignore_warning=False): if isinstance(inputs, str): if inputs.startswith("http://") or inputs.startswith("https://"): # We need to actually check for a real protocol, otherwise it's impossible to use a local file # like http_huggingface_co.png inputs = requests.get(inputs).content else: with open(inputs, "rb") as f: inputs = f.read() if isinstance(inputs, bytes): inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate) stride = None extra = {} if isinstance(inputs, dict): stride = inputs.pop("stride", None) # Accepting `"array"` which is the key defined in `datasets` for # better integration if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)): raise ValueError( "When passing a dictionnary to AutomaticSpeechRecognitionPipeline, the dict needs to contain a " '"raw" key containing the numpy array representing the audio and a "sampling_rate" key, ' "containing the sampling_rate associated with that array" ) _inputs = inputs.pop("raw", None) if _inputs is None: _inputs = inputs.pop("array", None) in_sampling_rate = inputs.pop("sampling_rate") extra = inputs inputs = _inputs if in_sampling_rate != self.feature_extractor.sampling_rate: import torch from torchaudio import functional as F inputs = F.resample( torch.from_numpy(inputs), in_sampling_rate, self.feature_extractor.sampling_rate ).numpy() ratio = self.feature_extractor.sampling_rate / in_sampling_rate else: ratio = 1 if stride is not None: if stride[0] + stride[1] > inputs.shape[0]: raise ValueError("Stride is too large for input") # Stride needs to get the chunk length here, it's going to get # swallowed by the `feature_extractor` later, and then batching # can add extra data in the inputs, so we need to keep track # of the original length in the stride so we can cut properly. stride = (inputs.shape[0], int(round(stride[0] * ratio)), int(round(stride[1] * ratio))) if not isinstance(inputs, np.ndarray): raise ValueError(f"We expect a numpy ndarray as input, got `{type(inputs)}`") if len(inputs.shape) != 1: raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline") if chunk_length_s: if self.type == "seq2seq" and not ignore_warning: logger.warning( "Using `chunk_length_s` is very experimental with seq2seq models. The results will not necessarily" " be entirely accurate and will have caveats. More information:" " https://github.com/huggingface/transformers/pull/20104. Ignore this warning with pipeline(...," " ignore_warning=True)" ) self._preprocess_params["ignore_warning"] = True if stride_length_s is None: stride_length_s = chunk_length_s / 6 if isinstance(stride_length_s, (int, float)): stride_length_s = [stride_length_s, stride_length_s] # XXX: Carefuly, this variable will not exist in `seq2seq` setting. # Currently chunking is not possible at this level for `seq2seq` so # it's ok. align_to = getattr(self.model.config, "inputs_to_logits_ratio", 1) chunk_len = int(round(chunk_length_s * self.feature_extractor.sampling_rate / align_to) * align_to) stride_left = int(round(stride_length_s[0] * self.feature_extractor.sampling_rate / align_to) * align_to) stride_right = int(round(stride_length_s[1] * self.feature_extractor.sampling_rate / align_to) * align_to) if chunk_len < stride_left + stride_right: raise ValueError("Chunk length must be superior to stride length") # make sure that for item in chunk_iter(inputs, self.feature_extractor, chunk_len, stride_left, stride_right): yield item else: processed = self.feature_extractor( inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt" ) if stride is not None: if self.model.__class__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.values(): raise ValueError("Stride is only usable with CTC models, try removing it") processed["stride"] = stride yield {"is_last": True, **processed, **extra} def _forward(self, model_inputs): is_last = model_inputs.pop("is_last") if self.type == "seq2seq": encoder = self.model.get_encoder() # Consume values so we can let extra information flow freely through # the pipeline (important for `partial` in microphone) if "input_features" in model_inputs: inputs = model_inputs.pop("input_features") elif "input_values" in model_inputs: inputs = model_inputs.pop("input_values") else: raise ValueError( "Seq2Seq speech recognition model requires either a " f"`input_features` or `input_values` key, but only has {model_inputs.keys()}" ) # we need to pass `processed.get("attention_mask")` here since audio encoder # attention mask length is different from expected text decoder `encoder_attention_mask` length # `generate` magic to create the mask automatically won't work, we basically need to help # it here. attention_mask = model_inputs.pop("attention_mask", None) tokens = self.model.generate( encoder_outputs=encoder(inputs, attention_mask=attention_mask), attention_mask=attention_mask, ) out = {"tokens": tokens} else: stride = model_inputs.pop("stride", None) input_values = model_inputs.pop("input_values") attention_mask = model_inputs.pop("attention_mask", None) outputs = self.model(input_values=input_values, attention_mask=attention_mask) logits = outputs.logits if self.type == "ctc_with_lm": out = {"logits": logits} else: out = {"tokens": logits.argmax(dim=-1)} if stride is not None: # Send stride to `postprocess`. # it needs to be handled there where # the pieces are to be concatenated. ratio = 1 / self.model.config.inputs_to_logits_ratio if isinstance(stride, tuple): out["stride"] = rescale_stride([stride], ratio)[0] else: out["stride"] = rescale_stride(stride, ratio) # Leftover extra = model_inputs return {"is_last": is_last, **out, **extra} def postprocess(self, model_outputs, decoder_kwargs: Optional[Dict] = None, return_timestamps=None): # Optional return types optional = {} if return_timestamps and self.type == "seq2seq": raise ValueError("We cannot return_timestamps yet on non-ctc models !") if return_timestamps == "char" and self.type == "ctc_with_lm": raise ValueError("CTC with LM cannot return `char` timestamps, only `words`") final_items = [] key = "logits" if self.type == "ctc_with_lm" else "tokens" stride = None for outputs in model_outputs: items = outputs[key].numpy() stride = outputs.pop("stride", None) if stride is not None and self.type in {"ctc", "ctc_with_lm"}: total_n, left, right = stride # Total_n might be < logits.shape[1] # because of padding, that's why # we need to reconstruct this information # This won't work with left padding (which doesn't exist right now) right_n = total_n - right items = items[:, left:right_n] final_items.append(items) if stride and self.type == "seq2seq": items = _find_longest_common_sequence(final_items, self.tokenizer) else: items = np.concatenate(final_items, axis=1) items = items.squeeze(0) if self.type == "ctc_with_lm": if decoder_kwargs is None: decoder_kwargs = {} beams = self.decoder.decode_beams(items, **decoder_kwargs) text = beams[0][0] if return_timestamps: # Simply cast from pyctcdecode format to wav2vec2 format to leverage # pre-existing code later chunk_offset = beams[0][2] word_offsets = [] for word, (start_offset, end_offset) in chunk_offset: word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) else: skip_special_tokens = self.type != "ctc" text = self.tokenizer.decode(items, skip_special_tokens=skip_special_tokens) if return_timestamps: char_offsets = self.tokenizer.decode( items, skip_special_tokens=skip_special_tokens, output_char_offsets=True )["char_offsets"] if return_timestamps == "word": word_offsets = self.tokenizer._get_word_offsets( char_offsets, self.tokenizer.replace_word_delimiter_char ) if return_timestamps: if return_timestamps == "word": offsets = word_offsets else: offsets = char_offsets chunks = [] for item in offsets: start = item["start_offset"] * self.model.config.inputs_to_logits_ratio start /= self.feature_extractor.sampling_rate stop = item["end_offset"] * self.model.config.inputs_to_logits_ratio stop /= self.feature_extractor.sampling_rate chunks.append({"text": item[return_timestamps], "timestamp": (start, stop)}) optional["chunks"] = chunks extra = defaultdict(list) for output in model_outputs: output.pop("tokens", None) output.pop("logits", None) output.pop("is_last", None) for k, v in output.items(): extra[k].append(v) return {"text": text, **optional, **extra}

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/pipelines/conversational.py

import uuid from typing import Any, Dict, List, Optional, Union from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, Pipeline if is_tf_available(): import tensorflow as tf if is_torch_available(): import torch logger = logging.get_logger(__name__) class Conversation: """ Utility class containing a conversation and its history. This class is meant to be used as an input to the [`ConversationalPipeline`]. The conversation contains a number of utility function to manage the addition of new user input and generated model responses. A conversation needs to contain an unprocessed user input before being passed to the [`ConversationalPipeline`]. This user input is either created when the class is instantiated, or by calling `conversational_pipeline.append_response("input")` after a conversation turn. Arguments: text (`str`, *optional*): The initial user input to start the conversation. If not provided, a user input needs to be provided manually using the [`~Conversation.add_user_input`] method before the conversation can begin. conversation_id (`uuid.UUID`, *optional*): Unique identifier for the conversation. If not provided, a random UUID4 id will be assigned to the conversation. past_user_inputs (`List[str]`, *optional*): Eventual past history of the conversation of the user. You don't need to pass it manually if you use the pipeline interactively but if you want to recreate history you need to set both `past_user_inputs` and `generated_responses` with equal length lists of strings generated_responses (`List[str]`, *optional*): Eventual past history of the conversation of the model. You don't need to pass it manually if you use the pipeline interactively but if you want to recreate history you need to set both `past_user_inputs` and `generated_responses` with equal length lists of strings Usage: ```python conversation = Conversation("Going to the movies tonight - any suggestions?") # Steps usually performed by the model when generating a response: # 1. Mark the user input as processed (moved to the history) conversation.mark_processed() # 2. Append a mode response conversation.append_response("The Big lebowski.") conversation.add_user_input("Is it good?") ```""" def __init__( self, text: str = None, conversation_id: uuid.UUID = None, past_user_inputs=None, generated_responses=None ): if not conversation_id: conversation_id = uuid.uuid4() if past_user_inputs is None: past_user_inputs = [] if generated_responses is None: generated_responses = [] self.uuid: uuid.UUID = conversation_id self.past_user_inputs: List[str] = past_user_inputs self.generated_responses: List[str] = generated_responses self.new_user_input: Optional[str] = text def __eq__(self, other): if not isinstance(other, Conversation): return False if self.uuid == other.uuid: return True return ( self.new_user_input == other.new_user_input and self.past_user_inputs == other.past_user_inputs and self.generated_responses == other.generated_responses ) def add_user_input(self, text: str, overwrite: bool = False): """ Add a user input to the conversation for the next round. This populates the internal `new_user_input` field. Args: text (`str`): The user input for the next conversation round. overwrite (`bool`, *optional*, defaults to `False`): Whether or not existing and unprocessed user input should be overwritten when this function is called. """ if self.new_user_input: if overwrite: logger.warning( f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten ' f'with: "{text}".' ) self.new_user_input = text else: logger.warning( f'User input added while unprocessed input was existing: "{self.new_user_input}" new input ' f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input' ) else: self.new_user_input = text def mark_processed(self): """ Mark the conversation as processed (moves the content of `new_user_input` to `past_user_inputs`) and empties the `new_user_input` field. """ if self.new_user_input: self.past_user_inputs.append(self.new_user_input) self.new_user_input = None def append_response(self, response: str): """ Append a response to the list of generated responses. Args: response (`str`): The model generated response. """ self.generated_responses.append(response) def iter_texts(self): """ Iterates over all blobs of the conversation. Returns: Iterator of (is_user, text_chunk) in chronological order of the conversation. `is_user` is a `bool`, `text_chunks` is a `str`. """ for user_input, generated_response in zip(self.past_user_inputs, self.generated_responses): yield True, user_input yield False, generated_response if self.new_user_input: yield True, self.new_user_input def __repr__(self): """ Generates a string representation of the conversation. Return: `str`: Example: Conversation id: 7d15686b-dc94-49f2-9c4b-c9eac6a1f114 user >> Going to the movies tonight - any suggestions? bot >> The Big Lebowski """ output = f"Conversation id: {self.uuid} \n" for is_user, text in self.iter_texts(): name = "user" if is_user else "bot" output += f"{name} >> {text} \n" return output @add_end_docstrings( PIPELINE_INIT_ARGS, r""" min_length_for_response (`int`, *optional*, defaults to 32): The minimum length (in number of tokens) for a response. minimum_tokens (`int`, *optional*, defaults to 10): The minimum length of tokens to leave for a response. """, ) class ConversationalPipeline(Pipeline): """ Multi-turn conversational pipeline. Example: ```python >>> from transformers import pipeline, Conversation >>> chatbot = pipeline(model="microsoft/DialoGPT-medium") >>> conversation = Conversation("Going to the movies tonight - any suggestions?") >>> conversation = chatbot(conversation) >>> conversation.generated_responses[-1] 'The Big Lebowski' >>> conversation.add_user_input("Is it an action movie?") >>> conversation = chatbot(conversation) >>> conversation.generated_responses[-1] "It's a comedy." ``` [Using pipelines in a webserver or with a dataset](../pipeline_tutorial) This conversational pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"conversational"`. The models that this pipeline can use are models that have been fine-tuned on a multi-turn conversational task, currently: *'microsoft/DialoGPT-small'*, *'microsoft/DialoGPT-medium'*, *'microsoft/DialoGPT-large'*. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=conversational). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if self.tokenizer.pad_token_id is None: self.tokenizer.pad_token = self.tokenizer.eos_token def _sanitize_parameters( self, min_length_for_response=None, minimum_tokens=None, clean_up_tokenization_spaces=None, **generate_kwargs ): preprocess_params = {} forward_params = {} postprocess_params = {} if min_length_for_response is not None: preprocess_params["min_length_for_response"] = min_length_for_response if minimum_tokens is not None: forward_params["minimum_tokens"] = minimum_tokens if "max_length" in generate_kwargs: forward_params["max_length"] = generate_kwargs["max_length"] # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length) if clean_up_tokenization_spaces is not None: postprocess_params["clean_up_tokenization_spaces"] = clean_up_tokenization_spaces if generate_kwargs: forward_params.update(generate_kwargs) return preprocess_params, forward_params, postprocess_params def __call__(self, conversations: Union[Conversation, List[Conversation]], num_workers=0, **kwargs): r""" Generate responses for the conversation(s) given as inputs. Args: conversations (a [`Conversation`] or a list of [`Conversation`]): Conversations to generate responses for. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to clean up the potential extra spaces in the text output. generate_kwargs: Additional keyword arguments to pass along to the generate method of the model (see the generate method corresponding to your framework [here](./model#generative-models)). Returns: [`Conversation`] or a list of [`Conversation`]: Conversation(s) with updated generated responses for those containing a new user input. """ # XXX: num_workers==0 is required to be backward compatible # Otherwise the threads will require a Conversation copy. # This will definitely hinder performance on GPU, but has to be opted # in because of this BC change. outputs = super().__call__(conversations, num_workers=num_workers, **kwargs) if isinstance(outputs, list) and len(outputs) == 1: return outputs[0] return outputs def preprocess(self, conversation: Conversation, min_length_for_response=32) -> Dict[str, Any]: if not isinstance(conversation, Conversation): raise ValueError("ConversationalPipeline, expects Conversation as inputs") if conversation.new_user_input is None: raise ValueError( f"Conversation with UUID {type(conversation.uuid)} does not contain new user input to process. " "Add user inputs with the conversation's `add_user_input` method" ) if hasattr(self.tokenizer, "_build_conversation_input_ids"): input_ids = self.tokenizer._build_conversation_input_ids(conversation) else: # If the tokenizer cannot handle conversations, we default to only the old version input_ids = self._legacy_parse_and_tokenize(conversation) if self.framework == "pt": input_ids = torch.LongTensor([input_ids]) elif self.framework == "tf": input_ids = tf.constant([input_ids]) return {"input_ids": input_ids, "conversation": conversation} def _forward(self, model_inputs, minimum_tokens=10, **generate_kwargs): max_length = generate_kwargs.get("max_length", self.model.config.max_length) n = model_inputs["input_ids"].shape[1] if max_length - minimum_tokens < n: logger.warning(f"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})") trim = max_length - minimum_tokens model_inputs["input_ids"] = model_inputs["input_ids"][:, -trim:] if "attention_mask" in model_inputs: model_inputs["attention_mask"] = model_inputs["attention_mask"][:, -trim:] conversation = model_inputs.pop("conversation") generate_kwargs["max_length"] = max_length output_ids = self.model.generate(**model_inputs, **generate_kwargs) if self.model.config.is_encoder_decoder: start_position = 1 else: start_position = n return {"output_ids": output_ids[:, start_position:], "conversation": conversation} def postprocess(self, model_outputs, clean_up_tokenization_spaces=True): output_ids = model_outputs["output_ids"] answer = self.tokenizer.decode( output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=clean_up_tokenization_spaces, ) conversation = model_outputs["conversation"] conversation.mark_processed() conversation.append_response(answer) return conversation def _legacy_parse_and_tokenize(self, conversation: Conversation) -> Dict: eos_token_id = self.tokenizer.eos_token_id input_ids = [] for is_user, text in conversation.iter_texts(): if eos_token_id is not None: input_ids.extend(self.tokenizer.encode(text, add_special_tokens=False) + [eos_token_id]) else: input_ids.extend(self.tokenizer.encode(text, add_special_tokens=False)) if len(input_ids) > self.tokenizer.model_max_length: input_ids = input_ids[-self.tokenizer.model_max_length :] return input_ids

import uuid from typing import Any, Dict, List, Optional, Union from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, Pipeline if is_tf_available(): import tensorflow as tf if is_torch_available(): import torch logger = logging.get_logger(__name__) class Conversation: """ Utility class containing a conversation and its history. This class is meant to be used as an input to the [`ConversationalPipeline`]. The conversation contains a number of utility function to manage the addition of new user input and generated model responses. A conversation needs to contain an unprocessed user input before being passed to the [`ConversationalPipeline`]. This user input is either created when the class is instantiated, or by calling `conversational_pipeline.append_response("input")` after a conversation turn. Arguments: text (`str`, *optional*): The initial user input to start the conversation. If not provided, a user input needs to be provided manually using the [`~Conversation.add_user_input`] method before the conversation can begin. conversation_id (`uuid.UUID`, *optional*): Unique identifier for the conversation. If not provided, a random UUID4 id will be assigned to the conversation. past_user_inputs (`List[str]`, *optional*): Eventual past history of the conversation of the user. You don't need to pass it manually if you use the pipeline interactively but if you want to recreate history you need to set both `past_user_inputs` and `generated_responses` with equal length lists of strings generated_responses (`List[str]`, *optional*): Eventual past history of the conversation of the model. You don't need to pass it manually if you use the pipeline interactively but if you want to recreate history you need to set both `past_user_inputs` and `generated_responses` with equal length lists of strings Usage: ```python conversation = Conversation("Going to the movies tonight - any suggestions?") # Steps usually performed by the model when generating a response: # 1. Mark the user input as processed (moved to the history) conversation.mark_processed() # 2. Append a mode response conversation.append_response("The Big lebowski.") conversation.add_user_input("Is it good?") ```""" def __init__( self, text: str = None, conversation_id: uuid.UUID = None, past_user_inputs=None, generated_responses=None ): if not conversation_id: conversation_id = uuid.uuid4() if past_user_inputs is None: past_user_inputs = [] if generated_responses is None: generated_responses = [] self.uuid: uuid.UUID = conversation_id self.past_user_inputs: List[str] = past_user_inputs self.generated_responses: List[str] = generated_responses self.new_user_input: Optional[str] = text def __eq__(self, other): if not isinstance(other, Conversation): return False if self.uuid == other.uuid: return True return ( self.new_user_input == other.new_user_input and self.past_user_inputs == other.past_user_inputs and self.generated_responses == other.generated_responses ) def add_user_input(self, text: str, overwrite: bool = False): """ Add a user input to the conversation for the next round. This populates the internal `new_user_input` field. Args: text (`str`): The user input for the next conversation round. overwrite (`bool`, *optional*, defaults to `False`): Whether or not existing and unprocessed user input should be overwritten when this function is called. """ if self.new_user_input: if overwrite: logger.warning( f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten ' f'with: "{text}".' ) self.new_user_input = text else: logger.warning( f'User input added while unprocessed input was existing: "{self.new_user_input}" new input ' f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input' ) else: self.new_user_input = text def mark_processed(self): """ Mark the conversation as processed (moves the content of `new_user_input` to `past_user_inputs`) and empties the `new_user_input` field. """ if self.new_user_input: self.past_user_inputs.append(self.new_user_input) self.new_user_input = None def append_response(self, response: str): """ Append a response to the list of generated responses. Args: response (`str`): The model generated response. """ self.generated_responses.append(response) def iter_texts(self): """ Iterates over all blobs of the conversation. Returns: Iterator of (is_user, text_chunk) in chronological order of the conversation. `is_user` is a `bool`, `text_chunks` is a `str`. """ for user_input, generated_response in zip(self.past_user_inputs, self.generated_responses): yield True, user_input yield False, generated_response if self.new_user_input: yield True, self.new_user_input def __repr__(self): """ Generates a string representation of the conversation. Return: `str`: Example: Conversation id: 7d15686b-dc94-49f2-9c4b-c9eac6a1f114 user >> Going to the movies tonight - any suggestions? bot >> The Big Lebowski """ output = f"Conversation id: {self.uuid} \n" for is_user, text in self.iter_texts(): name = "user" if is_user else "bot" output += f"{name} >> {text} \n" return output @add_end_docstrings( PIPELINE_INIT_ARGS, r""" min_length_for_response (`int`, *optional*, defaults to 32): The minimum length (in number of tokens) for a response. minimum_tokens (`int`, *optional*, defaults to 10): The minimum length of tokens to leave for a response. """, ) class ConversationalPipeline(Pipeline): """ Multi-turn conversational pipeline. Example: ```python >>> from transformers import pipeline, Conversation >>> chatbot = pipeline(model="microsoft/DialoGPT-medium") >>> conversation = Conversation("Going to the movies tonight - any suggestions?") >>> conversation = chatbot(conversation) >>> conversation.generated_responses[-1] 'The Big Lebowski' >>> conversation.add_user_input("Is it an action movie?") >>> conversation = chatbot(conversation) >>> conversation.generated_responses[-1] "It's a comedy." ``` [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial) This conversational pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"conversational"`. The models that this pipeline can use are models that have been fine-tuned on a multi-turn conversational task, currently: *'microsoft/DialoGPT-small'*, *'microsoft/DialoGPT-medium'*, *'microsoft/DialoGPT-large'*. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=conversational). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if self.tokenizer.pad_token_id is None: self.tokenizer.pad_token = self.tokenizer.eos_token def _sanitize_parameters( self, min_length_for_response=None, minimum_tokens=None, clean_up_tokenization_spaces=None, **generate_kwargs ): preprocess_params = {} forward_params = {} postprocess_params = {} if min_length_for_response is not None: preprocess_params["min_length_for_response"] = min_length_for_response if minimum_tokens is not None: forward_params["minimum_tokens"] = minimum_tokens if "max_length" in generate_kwargs: forward_params["max_length"] = generate_kwargs["max_length"] # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length) if clean_up_tokenization_spaces is not None: postprocess_params["clean_up_tokenization_spaces"] = clean_up_tokenization_spaces if generate_kwargs: forward_params.update(generate_kwargs) return preprocess_params, forward_params, postprocess_params def __call__(self, conversations: Union[Conversation, List[Conversation]], num_workers=0, **kwargs): r""" Generate responses for the conversation(s) given as inputs. Args: conversations (a [`Conversation`] or a list of [`Conversation`]): Conversations to generate responses for. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to clean up the potential extra spaces in the text output. generate_kwargs: Additional keyword arguments to pass along to the generate method of the model (see the generate method corresponding to your framework [here](./model#generative-models)). Returns: [`Conversation`] or a list of [`Conversation`]: Conversation(s) with updated generated responses for those containing a new user input. """ # XXX: num_workers==0 is required to be backward compatible # Otherwise the threads will require a Conversation copy. # This will definitely hinder performance on GPU, but has to be opted # in because of this BC change. outputs = super().__call__(conversations, num_workers=num_workers, **kwargs) if isinstance(outputs, list) and len(outputs) == 1: return outputs[0] return outputs def preprocess(self, conversation: Conversation, min_length_for_response=32) -> Dict[str, Any]: if not isinstance(conversation, Conversation): raise ValueError("ConversationalPipeline, expects Conversation as inputs") if conversation.new_user_input is None: raise ValueError( f"Conversation with UUID {type(conversation.uuid)} does not contain new user input to process. " "Add user inputs with the conversation's `add_user_input` method" ) if hasattr(self.tokenizer, "_build_conversation_input_ids"): input_ids = self.tokenizer._build_conversation_input_ids(conversation) else: # If the tokenizer cannot handle conversations, we default to only the old version input_ids = self._legacy_parse_and_tokenize(conversation) if self.framework == "pt": input_ids = torch.LongTensor([input_ids]) elif self.framework == "tf": input_ids = tf.constant([input_ids]) return {"input_ids": input_ids, "conversation": conversation} def _forward(self, model_inputs, minimum_tokens=10, **generate_kwargs): max_length = generate_kwargs.get("max_length", self.model.config.max_length) n = model_inputs["input_ids"].shape[1] if max_length - minimum_tokens < n: logger.warning(f"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})") trim = max_length - minimum_tokens model_inputs["input_ids"] = model_inputs["input_ids"][:, -trim:] if "attention_mask" in model_inputs: model_inputs["attention_mask"] = model_inputs["attention_mask"][:, -trim:] conversation = model_inputs.pop("conversation") generate_kwargs["max_length"] = max_length output_ids = self.model.generate(**model_inputs, **generate_kwargs) if self.model.config.is_encoder_decoder: start_position = 1 else: start_position = n return {"output_ids": output_ids[:, start_position:], "conversation": conversation} def postprocess(self, model_outputs, clean_up_tokenization_spaces=True): output_ids = model_outputs["output_ids"] answer = self.tokenizer.decode( output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=clean_up_tokenization_spaces, ) conversation = model_outputs["conversation"] conversation.mark_processed() conversation.append_response(answer) return conversation def _legacy_parse_and_tokenize(self, conversation: Conversation) -> Dict: eos_token_id = self.tokenizer.eos_token_id input_ids = [] for is_user, text in conversation.iter_texts(): if eos_token_id is not None: input_ids.extend(self.tokenizer.encode(text, add_special_tokens=False) + [eos_token_id]) else: input_ids.extend(self.tokenizer.encode(text, add_special_tokens=False)) if len(input_ids) > self.tokenizer.model_max_length: input_ids = input_ids[-self.tokenizer.model_max_length :] return input_ids

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/pipelines/depth_estimation.py

from typing import List, Union import numpy as np from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): import torch from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING logger = logging.get_logger(__name__) @add_end_docstrings(PIPELINE_INIT_ARGS) class DepthEstimationPipeline(Pipeline): """ Depth estimation pipeline using any `AutoModelForDepthEstimation`. This pipeline predicts the depth of an image. Example: ```python >>> from transformers import pipeline >>> depth_estimator = pipeline(task="depth-estimation", model="Intel/dpt-large") >>> output = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg") >>> # This is a tensor with the values being the depth expressed in meters for each pixel >>> output["predicted_depth"].shape torch.Size([1, 384, 384]) ``` [Using pipelines in a webserver or with a dataset](../pipeline_tutorial) This depth estimation pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"depth-estimation"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=depth-estimation). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) requires_backends(self, "vision") self.check_model_type(MODEL_FOR_DEPTH_ESTIMATION_MAPPING) def __call__(self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs): """ Assign labels to the image(s) passed as inputs. Args: images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images, which must then be passed as a string. Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL images. top_k (`int`, *optional*, defaults to 5): The number of top labels that will be returned by the pipeline. If the provided number is higher than the number of labels available in the model configuration, it will default to the number of labels. Return: A dictionary or a list of dictionaries containing result. If the input is a single image, will return a dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to the images. The dictionaries contain the following keys: - **label** (`str`) -- The label identified by the model. - **score** (`int`) -- The score attributed by the model for that label. """ return super().__call__(images, **kwargs) def _sanitize_parameters(self, **kwargs): return {}, {}, {} def preprocess(self, image): image = load_image(image) self.image_size = image.size model_inputs = self.feature_extractor(images=image, return_tensors=self.framework) return model_inputs def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) return model_outputs def postprocess(self, model_outputs): predicted_depth = model_outputs.predicted_depth prediction = torch.nn.functional.interpolate( predicted_depth.unsqueeze(1), size=self.image_size[::-1], mode="bicubic", align_corners=False ) output = prediction.squeeze().cpu().numpy() formatted = (output * 255 / np.max(output)).astype("uint8") depth = Image.fromarray(formatted) output_dict = {} output_dict["predicted_depth"] = predicted_depth output_dict["depth"] = depth return output_dict

from typing import List, Union import numpy as np from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): import torch from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING logger = logging.get_logger(__name__) @add_end_docstrings(PIPELINE_INIT_ARGS) class DepthEstimationPipeline(Pipeline): """ Depth estimation pipeline using any `AutoModelForDepthEstimation`. This pipeline predicts the depth of an image. Example: ```python >>> from transformers import pipeline >>> depth_estimator = pipeline(task="depth-estimation", model="Intel/dpt-large") >>> output = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg") >>> # This is a tensor with the values being the depth expressed in meters for each pixel >>> output["predicted_depth"].shape torch.Size([1, 384, 384]) ``` [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial) This depth estimation pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"depth-estimation"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=depth-estimation). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) requires_backends(self, "vision") self.check_model_type(MODEL_FOR_DEPTH_ESTIMATION_MAPPING) def __call__(self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs): """ Assign labels to the image(s) passed as inputs. Args: images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images, which must then be passed as a string. Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL images. top_k (`int`, *optional*, defaults to 5): The number of top labels that will be returned by the pipeline. If the provided number is higher than the number of labels available in the model configuration, it will default to the number of labels. Return: A dictionary or a list of dictionaries containing result. If the input is a single image, will return a dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to the images. The dictionaries contain the following keys: - **label** (`str`) -- The label identified by the model. - **score** (`int`) -- The score attributed by the model for that label. """ return super().__call__(images, **kwargs) def _sanitize_parameters(self, **kwargs): return {}, {}, {} def preprocess(self, image): image = load_image(image) self.image_size = image.size model_inputs = self.feature_extractor(images=image, return_tensors=self.framework) return model_inputs def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) return model_outputs def postprocess(self, model_outputs): predicted_depth = model_outputs.predicted_depth prediction = torch.nn.functional.interpolate( predicted_depth.unsqueeze(1), size=self.image_size[::-1], mode="bicubic", align_corners=False ) output = prediction.squeeze().cpu().numpy() formatted = (output * 255 / np.max(output)).astype("uint8") depth = Image.fromarray(formatted) output_dict = {} output_dict["predicted_depth"] = predicted_depth output_dict["depth"] = depth return output_dict

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/pipelines/document_question_answering.py

# Copyright 2022 The Impira Team and the HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re from typing import List, Optional, Tuple, Union import numpy as np from ..utils import ( ExplicitEnum, add_end_docstrings, is_pytesseract_available, is_torch_available, is_vision_available, logging, ) from .base import PIPELINE_INIT_ARGS, ChunkPipeline from .question_answering import select_starts_ends if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): import torch from ..models.auto.modeling_auto import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING TESSERACT_LOADED = False if is_pytesseract_available(): TESSERACT_LOADED = True import pytesseract logger = logging.get_logger(__name__) # normalize_bbox() and apply_tesseract() are derived from apply_tesseract in models/layoutlmv3/feature_extraction_layoutlmv3.py. # However, because the pipeline may evolve from what layoutlmv3 currently does, it's copied (vs. imported) to avoid creating an # unnecessary dependency. def normalize_box(box, width, height): return [ int(1000 * (box[0] / width)), int(1000 * (box[1] / height)), int(1000 * (box[2] / width)), int(1000 * (box[3] / height)), ] def apply_tesseract(image: "Image.Image", lang: Optional[str], tesseract_config: Optional[str]): """Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes.""" # apply OCR data = pytesseract.image_to_data(image, lang=lang, output_type="dict", config=tesseract_config) words, left, top, width, height = data["text"], data["left"], data["top"], data["width"], data["height"] # filter empty words and corresponding coordinates irrelevant_indices = [idx for idx, word in enumerate(words) if not word.strip()] words = [word for idx, word in enumerate(words) if idx not in irrelevant_indices] left = [coord for idx, coord in enumerate(left) if idx not in irrelevant_indices] top = [coord for idx, coord in enumerate(top) if idx not in irrelevant_indices] width = [coord for idx, coord in enumerate(width) if idx not in irrelevant_indices] height = [coord for idx, coord in enumerate(height) if idx not in irrelevant_indices] # turn coordinates into (left, top, left+width, top+height) format actual_boxes = [] for x, y, w, h in zip(left, top, width, height): actual_box = [x, y, x + w, y + h] actual_boxes.append(actual_box) image_width, image_height = image.size # finally, normalize the bounding boxes normalized_boxes = [] for box in actual_boxes: normalized_boxes.append(normalize_box(box, image_width, image_height)) if len(words) != len(normalized_boxes): raise ValueError("Not as many words as there are bounding boxes") return words, normalized_boxes class ModelType(ExplicitEnum): LayoutLM = "layoutlm" LayoutLMv2andv3 = "layoutlmv2andv3" VisionEncoderDecoder = "vision_encoder_decoder" @add_end_docstrings(PIPELINE_INIT_ARGS) class DocumentQuestionAnsweringPipeline(ChunkPipeline): # TODO: Update task_summary docs to include an example with document QA and then update the first sentence """ Document Question Answering pipeline using any `AutoModelForDocumentQuestionAnswering`. The inputs/outputs are similar to the (extractive) question answering pipeline; however, the pipeline takes an image (and optional OCR'd words/boxes) as input instead of text context. Example: ```python >>> from transformers import pipeline >>> document_qa = pipeline(model="impira/layoutlm-document-qa") >>> result = document_qa( ... image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png", ... question="What is the invoice number?", ... ) >>> result[0]["answer"] '1110212019' ``` [Using pipelines in a webserver or with a dataset](../pipeline_tutorial) This document question answering pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"document-question-answering"`. The models that this pipeline can use are models that have been fine-tuned on a document question answering task. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=document-question-answering). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if self.model.config.__class__.__name__ == "VisionEncoderDecoderConfig": self.model_type = ModelType.VisionEncoderDecoder if self.model.config.encoder.model_type != "donut-swin": raise ValueError("Currently, the only supported VisionEncoderDecoder model is Donut") else: self.check_model_type(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING) if self.model.config.__class__.__name__ == "LayoutLMConfig": self.model_type = ModelType.LayoutLM else: self.model_type = ModelType.LayoutLMv2andv3 def _sanitize_parameters( self, padding=None, doc_stride=None, max_question_len=None, lang: Optional[str] = None, tesseract_config: Optional[str] = None, max_answer_len=None, max_seq_len=None, top_k=None, handle_impossible_answer=None, **kwargs, ): preprocess_params, postprocess_params = {}, {} if padding is not None: preprocess_params["padding"] = padding if doc_stride is not None: preprocess_params["doc_stride"] = doc_stride if max_question_len is not None: preprocess_params["max_question_len"] = max_question_len if max_seq_len is not None: preprocess_params["max_seq_len"] = max_seq_len if lang is not None: preprocess_params["lang"] = lang if tesseract_config is not None: preprocess_params["tesseract_config"] = tesseract_config if top_k is not None: if top_k < 1: raise ValueError(f"top_k parameter should be >= 1 (got {top_k})") postprocess_params["top_k"] = top_k if max_answer_len is not None: if max_answer_len < 1: raise ValueError(f"max_answer_len parameter should be >= 1 (got {max_answer_len}") postprocess_params["max_answer_len"] = max_answer_len if handle_impossible_answer is not None: postprocess_params["handle_impossible_answer"] = handle_impossible_answer return preprocess_params, {}, postprocess_params def __call__( self, image: Union["Image.Image", str], question: Optional[str] = None, word_boxes: Tuple[str, List[float]] = None, **kwargs, ): """ Answer the question(s) given as inputs by using the document(s). A document is defined as an image and an optional list of (word, box) tuples which represent the text in the document. If the `word_boxes` are not provided, it will use the Tesseract OCR engine (if available) to extract the words and boxes automatically for LayoutLM-like models which require them as input. For Donut, no OCR is run. You can invoke the pipeline several ways: - `pipeline(image=image, question=question)` - `pipeline(image=image, question=question, word_boxes=word_boxes)` - `pipeline([{"image": image, "question": question}])` - `pipeline([{"image": image, "question": question, "word_boxes": word_boxes}])` Args: image (`str` or `PIL.Image`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images. If given a single image, it can be broadcasted to multiple questions. question (`str`): A question to ask of the document. word_boxes (`List[str, Tuple[float, float, float, float]]`, *optional*): A list of words and bounding boxes (normalized 0->1000). If you provide this optional input, then the pipeline will use these words and boxes instead of running OCR on the image to derive them for models that need them (e.g. LayoutLM). This allows you to reuse OCR'd results across many invocations of the pipeline without having to re-run it each time. top_k (`int`, *optional*, defaults to 1): The number of answers to return (will be chosen by order of likelihood). Note that we return less than top_k answers if there are not enough options available within the context. doc_stride (`int`, *optional*, defaults to 128): If the words in the document are too long to fit with the question for the model, it will be split in several chunks with some overlap. This argument controls the size of that overlap. max_answer_len (`int`, *optional*, defaults to 15): The maximum length of predicted answers (e.g., only answers with a shorter length are considered). max_seq_len (`int`, *optional*, defaults to 384): The maximum length of the total sentence (context + question) in tokens of each chunk passed to the model. The context will be split in several chunks (using `doc_stride` as overlap) if needed. max_question_len (`int`, *optional*, defaults to 64): The maximum length of the question after tokenization. It will be truncated if needed. handle_impossible_answer (`bool`, *optional*, defaults to `False`): Whether or not we accept impossible as an answer. lang (`str`, *optional*): Language to use while running OCR. Defaults to english. tesseract_config (`str`, *optional*): Additional flags to pass to tesseract while running OCR. Return: A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys: - **score** (`float`) -- The probability associated to the answer. - **start** (`int`) -- The start word index of the answer (in the OCR'd version of the input or provided `word_boxes`). - **end** (`int`) -- The end word index of the answer (in the OCR'd version of the input or provided `word_boxes`). - **answer** (`str`) -- The answer to the question. - **words** (`list[int]`) -- The index of each word/box pair that is in the answer """ if isinstance(question, str): inputs = {"question": question, "image": image} if word_boxes is not None: inputs["word_boxes"] = word_boxes else: inputs = image return super().__call__(inputs, **kwargs) def preprocess( self, input, padding="do_not_pad", doc_stride=None, max_seq_len=None, word_boxes: Tuple[str, List[float]] = None, lang=None, tesseract_config="", ): # NOTE: This code mirrors the code in question answering and will be implemented in a follow up PR # to support documents with enough tokens that overflow the model's window if max_seq_len is None: max_seq_len = self.tokenizer.model_max_length if doc_stride is None: doc_stride = min(max_seq_len // 2, 256) image = None image_features = {} if input.get("image", None) is not None: image = load_image(input["image"]) if self.feature_extractor is not None: image_features.update(self.feature_extractor(images=image, return_tensors=self.framework)) elif self.model_type == ModelType.VisionEncoderDecoder: raise ValueError("If you are using a VisionEncoderDecoderModel, you must provide a feature extractor") words, boxes = None, None if not self.model_type == ModelType.VisionEncoderDecoder: if "word_boxes" in input: words = [x[0] for x in input["word_boxes"]] boxes = [x[1] for x in input["word_boxes"]] elif "words" in image_features and "boxes" in image_features: words = image_features.pop("words")[0] boxes = image_features.pop("boxes")[0] elif image is not None: if not TESSERACT_LOADED: raise ValueError( "If you provide an image without word_boxes, then the pipeline will run OCR using Tesseract," " but pytesseract is not available" ) if TESSERACT_LOADED: words, boxes = apply_tesseract(image, lang=lang, tesseract_config=tesseract_config) else: raise ValueError( "You must provide an image or word_boxes. If you provide an image, the pipeline will automatically" " run OCR to derive words and boxes" ) if self.tokenizer.padding_side != "right": raise ValueError( "Document question answering only supports tokenizers whose padding side is 'right', not" f" {self.tokenizer.padding_side}" ) if self.model_type == ModelType.VisionEncoderDecoder: task_prompt = f'<s_docvqa><s_question>{input["question"]}</s_question><s_answer>' # Adapted from https://huggingface.co/spaces/nielsr/donut-docvqa/blob/main/app.py encoding = { "inputs": image_features["pixel_values"], "decoder_input_ids": self.tokenizer( task_prompt, add_special_tokens=False, return_tensors=self.framework ).input_ids, "return_dict_in_generate": True, } yield { **encoding, "p_mask": None, "word_ids": None, "words": None, "output_attentions": True, "is_last": True, } else: tokenizer_kwargs = {} if self.model_type == ModelType.LayoutLM: tokenizer_kwargs["text"] = input["question"].split() tokenizer_kwargs["text_pair"] = words tokenizer_kwargs["is_split_into_words"] = True else: tokenizer_kwargs["text"] = [input["question"]] tokenizer_kwargs["text_pair"] = [words] tokenizer_kwargs["boxes"] = [boxes] encoding = self.tokenizer( padding=padding, max_length=max_seq_len, stride=doc_stride, return_token_type_ids=True, truncation="only_second", return_overflowing_tokens=True, **tokenizer_kwargs, ) encoding.pop("overflow_to_sample_mapping") # We do not use this num_spans = len(encoding["input_ids"]) # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer) # We put 0 on the tokens from the context and 1 everywhere else (question and special tokens) # This logic mirrors the logic in the question_answering pipeline p_mask = [[tok != 1 for tok in encoding.sequence_ids(span_id)] for span_id in range(num_spans)] for span_idx in range(num_spans): if self.framework == "pt": span_encoding = {k: torch.tensor(v[span_idx : span_idx + 1]) for (k, v) in encoding.items()} if "pixel_values" in image_features: span_encoding["image"] = image_features["pixel_values"] else: raise ValueError("Unsupported: Tensorflow preprocessing for DocumentQuestionAnsweringPipeline") input_ids_span_idx = encoding["input_ids"][span_idx] # keep the cls_token unmasked (some models use it to indicate unanswerable questions) if self.tokenizer.cls_token_id is not None: cls_indices = np.nonzero(np.array(input_ids_span_idx) == self.tokenizer.cls_token_id)[0] for cls_index in cls_indices: p_mask[span_idx][cls_index] = 0 # For each span, place a bounding box [0,0,0,0] for question and CLS tokens, [1000,1000,1000,1000] # for SEP tokens, and the word's bounding box for words in the original document. if "boxes" not in tokenizer_kwargs: bbox = [] for input_id, sequence_id, word_id in zip( encoding.input_ids[span_idx], encoding.sequence_ids(span_idx), encoding.word_ids(span_idx), ): if sequence_id == 1: bbox.append(boxes[word_id]) elif input_id == self.tokenizer.sep_token_id: bbox.append([1000] * 4) else: bbox.append([0] * 4) if self.framework == "pt": span_encoding["bbox"] = torch.tensor(bbox).unsqueeze(0) elif self.framework == "tf": raise ValueError("Unsupported: Tensorflow preprocessing for DocumentQuestionAnsweringPipeline") yield { **span_encoding, "p_mask": p_mask[span_idx], "word_ids": encoding.word_ids(span_idx), "words": words, "is_last": span_idx == num_spans - 1, } def _forward(self, model_inputs): p_mask = model_inputs.pop("p_mask", None) word_ids = model_inputs.pop("word_ids", None) words = model_inputs.pop("words", None) is_last = model_inputs.pop("is_last", False) if self.model_type == ModelType.VisionEncoderDecoder: model_outputs = self.model.generate(**model_inputs) else: model_outputs = self.model(**model_inputs) model_outputs = {k: v for (k, v) in model_outputs.items()} model_outputs["p_mask"] = p_mask model_outputs["word_ids"] = word_ids model_outputs["words"] = words model_outputs["attention_mask"] = model_inputs.get("attention_mask", None) model_outputs["is_last"] = is_last return model_outputs def postprocess(self, model_outputs, top_k=1, **kwargs): if self.model_type == ModelType.VisionEncoderDecoder: answers = [self.postprocess_encoder_decoder_single(o) for o in model_outputs] else: answers = self.postprocess_extractive_qa(model_outputs, top_k=top_k, **kwargs) answers = sorted(answers, key=lambda x: x.get("score", 0), reverse=True)[:top_k] return answers def postprocess_encoder_decoder_single(self, model_outputs, **kwargs): sequence = self.tokenizer.batch_decode(model_outputs["sequences"])[0] # TODO: A lot of this logic is specific to Donut and should probably be handled in the tokenizer # (see https://github.com/huggingface/transformers/pull/18414/files#r961747408 for more context). sequence = sequence.replace(self.tokenizer.eos_token, "").replace(self.tokenizer.pad_token, "") sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token ret = { "answer": None, } answer = re.search(r"<s_answer>(.*)</s_answer>", sequence) if answer is not None: ret["answer"] = answer.group(1).strip() return ret def postprocess_extractive_qa( self, model_outputs, top_k=1, handle_impossible_answer=False, max_answer_len=15, **kwargs ): min_null_score = 1000000 # large and positive answers = [] for output in model_outputs: words = output["words"] starts, ends, scores, min_null_score = select_starts_ends( start=output["start_logits"], end=output["end_logits"], p_mask=output["p_mask"], attention_mask=output["attention_mask"].numpy() if output.get("attention_mask", None) is not None else None, min_null_score=min_null_score, top_k=top_k, handle_impossible_answer=handle_impossible_answer, max_answer_len=max_answer_len, ) word_ids = output["word_ids"] for start, end, score in zip(starts, ends, scores): word_start, word_end = word_ids[start], word_ids[end] if word_start is not None and word_end is not None: answers.append( { "score": float(score), "answer": " ".join(words[word_start : word_end + 1]), "start": word_start, "end": word_end, } ) if handle_impossible_answer: answers.append({"score": min_null_score, "answer": "", "start": 0, "end": 0}) return answers

# Copyright 2022 The Impira Team and the HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re from typing import List, Optional, Tuple, Union import numpy as np from ..utils import ( ExplicitEnum, add_end_docstrings, is_pytesseract_available, is_torch_available, is_vision_available, logging, ) from .base import PIPELINE_INIT_ARGS, ChunkPipeline from .question_answering import select_starts_ends if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): import torch from ..models.auto.modeling_auto import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING TESSERACT_LOADED = False if is_pytesseract_available(): TESSERACT_LOADED = True import pytesseract logger = logging.get_logger(__name__) # normalize_bbox() and apply_tesseract() are derived from apply_tesseract in models/layoutlmv3/feature_extraction_layoutlmv3.py. # However, because the pipeline may evolve from what layoutlmv3 currently does, it's copied (vs. imported) to avoid creating an # unnecessary dependency. def normalize_box(box, width, height): return [ int(1000 * (box[0] / width)), int(1000 * (box[1] / height)), int(1000 * (box[2] / width)), int(1000 * (box[3] / height)), ] def apply_tesseract(image: "Image.Image", lang: Optional[str], tesseract_config: Optional[str]): """Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes.""" # apply OCR data = pytesseract.image_to_data(image, lang=lang, output_type="dict", config=tesseract_config) words, left, top, width, height = data["text"], data["left"], data["top"], data["width"], data["height"] # filter empty words and corresponding coordinates irrelevant_indices = [idx for idx, word in enumerate(words) if not word.strip()] words = [word for idx, word in enumerate(words) if idx not in irrelevant_indices] left = [coord for idx, coord in enumerate(left) if idx not in irrelevant_indices] top = [coord for idx, coord in enumerate(top) if idx not in irrelevant_indices] width = [coord for idx, coord in enumerate(width) if idx not in irrelevant_indices] height = [coord for idx, coord in enumerate(height) if idx not in irrelevant_indices] # turn coordinates into (left, top, left+width, top+height) format actual_boxes = [] for x, y, w, h in zip(left, top, width, height): actual_box = [x, y, x + w, y + h] actual_boxes.append(actual_box) image_width, image_height = image.size # finally, normalize the bounding boxes normalized_boxes = [] for box in actual_boxes: normalized_boxes.append(normalize_box(box, image_width, image_height)) if len(words) != len(normalized_boxes): raise ValueError("Not as many words as there are bounding boxes") return words, normalized_boxes class ModelType(ExplicitEnum): LayoutLM = "layoutlm" LayoutLMv2andv3 = "layoutlmv2andv3" VisionEncoderDecoder = "vision_encoder_decoder" @add_end_docstrings(PIPELINE_INIT_ARGS) class DocumentQuestionAnsweringPipeline(ChunkPipeline): # TODO: Update task_summary docs to include an example with document QA and then update the first sentence """ Document Question Answering pipeline using any `AutoModelForDocumentQuestionAnswering`. The inputs/outputs are similar to the (extractive) question answering pipeline; however, the pipeline takes an image (and optional OCR'd words/boxes) as input instead of text context. Example: ```python >>> from transformers import pipeline >>> document_qa = pipeline(model="impira/layoutlm-document-qa") >>> result = document_qa( ... image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png", ... question="What is the invoice number?", ... ) >>> result[0]["answer"] '1110212019' ``` [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial) This document question answering pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"document-question-answering"`. The models that this pipeline can use are models that have been fine-tuned on a document question answering task. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=document-question-answering). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if self.model.config.__class__.__name__ == "VisionEncoderDecoderConfig": self.model_type = ModelType.VisionEncoderDecoder if self.model.config.encoder.model_type != "donut-swin": raise ValueError("Currently, the only supported VisionEncoderDecoder model is Donut") else: self.check_model_type(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING) if self.model.config.__class__.__name__ == "LayoutLMConfig": self.model_type = ModelType.LayoutLM else: self.model_type = ModelType.LayoutLMv2andv3 def _sanitize_parameters( self, padding=None, doc_stride=None, max_question_len=None, lang: Optional[str] = None, tesseract_config: Optional[str] = None, max_answer_len=None, max_seq_len=None, top_k=None, handle_impossible_answer=None, **kwargs, ): preprocess_params, postprocess_params = {}, {} if padding is not None: preprocess_params["padding"] = padding if doc_stride is not None: preprocess_params["doc_stride"] = doc_stride if max_question_len is not None: preprocess_params["max_question_len"] = max_question_len if max_seq_len is not None: preprocess_params["max_seq_len"] = max_seq_len if lang is not None: preprocess_params["lang"] = lang if tesseract_config is not None: preprocess_params["tesseract_config"] = tesseract_config if top_k is not None: if top_k < 1: raise ValueError(f"top_k parameter should be >= 1 (got {top_k})") postprocess_params["top_k"] = top_k if max_answer_len is not None: if max_answer_len < 1: raise ValueError(f"max_answer_len parameter should be >= 1 (got {max_answer_len}") postprocess_params["max_answer_len"] = max_answer_len if handle_impossible_answer is not None: postprocess_params["handle_impossible_answer"] = handle_impossible_answer return preprocess_params, {}, postprocess_params def __call__( self, image: Union["Image.Image", str], question: Optional[str] = None, word_boxes: Tuple[str, List[float]] = None, **kwargs, ): """ Answer the question(s) given as inputs by using the document(s). A document is defined as an image and an optional list of (word, box) tuples which represent the text in the document. If the `word_boxes` are not provided, it will use the Tesseract OCR engine (if available) to extract the words and boxes automatically for LayoutLM-like models which require them as input. For Donut, no OCR is run. You can invoke the pipeline several ways: - `pipeline(image=image, question=question)` - `pipeline(image=image, question=question, word_boxes=word_boxes)` - `pipeline([{"image": image, "question": question}])` - `pipeline([{"image": image, "question": question, "word_boxes": word_boxes}])` Args: image (`str` or `PIL.Image`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images. If given a single image, it can be broadcasted to multiple questions. question (`str`): A question to ask of the document. word_boxes (`List[str, Tuple[float, float, float, float]]`, *optional*): A list of words and bounding boxes (normalized 0->1000). If you provide this optional input, then the pipeline will use these words and boxes instead of running OCR on the image to derive them for models that need them (e.g. LayoutLM). This allows you to reuse OCR'd results across many invocations of the pipeline without having to re-run it each time. top_k (`int`, *optional*, defaults to 1): The number of answers to return (will be chosen by order of likelihood). Note that we return less than top_k answers if there are not enough options available within the context. doc_stride (`int`, *optional*, defaults to 128): If the words in the document are too long to fit with the question for the model, it will be split in several chunks with some overlap. This argument controls the size of that overlap. max_answer_len (`int`, *optional*, defaults to 15): The maximum length of predicted answers (e.g., only answers with a shorter length are considered). max_seq_len (`int`, *optional*, defaults to 384): The maximum length of the total sentence (context + question) in tokens of each chunk passed to the model. The context will be split in several chunks (using `doc_stride` as overlap) if needed. max_question_len (`int`, *optional*, defaults to 64): The maximum length of the question after tokenization. It will be truncated if needed. handle_impossible_answer (`bool`, *optional*, defaults to `False`): Whether or not we accept impossible as an answer. lang (`str`, *optional*): Language to use while running OCR. Defaults to english. tesseract_config (`str`, *optional*): Additional flags to pass to tesseract while running OCR. Return: A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys: - **score** (`float`) -- The probability associated to the answer. - **start** (`int`) -- The start word index of the answer (in the OCR'd version of the input or provided `word_boxes`). - **end** (`int`) -- The end word index of the answer (in the OCR'd version of the input or provided `word_boxes`). - **answer** (`str`) -- The answer to the question. - **words** (`list[int]`) -- The index of each word/box pair that is in the answer """ if isinstance(question, str): inputs = {"question": question, "image": image} if word_boxes is not None: inputs["word_boxes"] = word_boxes else: inputs = image return super().__call__(inputs, **kwargs) def preprocess( self, input, padding="do_not_pad", doc_stride=None, max_seq_len=None, word_boxes: Tuple[str, List[float]] = None, lang=None, tesseract_config="", ): # NOTE: This code mirrors the code in question answering and will be implemented in a follow up PR # to support documents with enough tokens that overflow the model's window if max_seq_len is None: max_seq_len = self.tokenizer.model_max_length if doc_stride is None: doc_stride = min(max_seq_len // 2, 256) image = None image_features = {} if input.get("image", None) is not None: image = load_image(input["image"]) if self.feature_extractor is not None: image_features.update(self.feature_extractor(images=image, return_tensors=self.framework)) elif self.model_type == ModelType.VisionEncoderDecoder: raise ValueError("If you are using a VisionEncoderDecoderModel, you must provide a feature extractor") words, boxes = None, None if not self.model_type == ModelType.VisionEncoderDecoder: if "word_boxes" in input: words = [x[0] for x in input["word_boxes"]] boxes = [x[1] for x in input["word_boxes"]] elif "words" in image_features and "boxes" in image_features: words = image_features.pop("words")[0] boxes = image_features.pop("boxes")[0] elif image is not None: if not TESSERACT_LOADED: raise ValueError( "If you provide an image without word_boxes, then the pipeline will run OCR using Tesseract," " but pytesseract is not available" ) if TESSERACT_LOADED: words, boxes = apply_tesseract(image, lang=lang, tesseract_config=tesseract_config) else: raise ValueError( "You must provide an image or word_boxes. If you provide an image, the pipeline will automatically" " run OCR to derive words and boxes" ) if self.tokenizer.padding_side != "right": raise ValueError( "Document question answering only supports tokenizers whose padding side is 'right', not" f" {self.tokenizer.padding_side}" ) if self.model_type == ModelType.VisionEncoderDecoder: task_prompt = f'<s_docvqa><s_question>{input["question"]}</s_question><s_answer>' # Adapted from https://huggingface.co/spaces/nielsr/donut-docvqa/blob/main/app.py encoding = { "inputs": image_features["pixel_values"], "decoder_input_ids": self.tokenizer( task_prompt, add_special_tokens=False, return_tensors=self.framework ).input_ids, "return_dict_in_generate": True, } yield { **encoding, "p_mask": None, "word_ids": None, "words": None, "output_attentions": True, "is_last": True, } else: tokenizer_kwargs = {} if self.model_type == ModelType.LayoutLM: tokenizer_kwargs["text"] = input["question"].split() tokenizer_kwargs["text_pair"] = words tokenizer_kwargs["is_split_into_words"] = True else: tokenizer_kwargs["text"] = [input["question"]] tokenizer_kwargs["text_pair"] = [words] tokenizer_kwargs["boxes"] = [boxes] encoding = self.tokenizer( padding=padding, max_length=max_seq_len, stride=doc_stride, return_token_type_ids=True, truncation="only_second", return_overflowing_tokens=True, **tokenizer_kwargs, ) encoding.pop("overflow_to_sample_mapping") # We do not use this num_spans = len(encoding["input_ids"]) # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer) # We put 0 on the tokens from the context and 1 everywhere else (question and special tokens) # This logic mirrors the logic in the question_answering pipeline p_mask = [[tok != 1 for tok in encoding.sequence_ids(span_id)] for span_id in range(num_spans)] for span_idx in range(num_spans): if self.framework == "pt": span_encoding = {k: torch.tensor(v[span_idx : span_idx + 1]) for (k, v) in encoding.items()} if "pixel_values" in image_features: span_encoding["image"] = image_features["pixel_values"] else: raise ValueError("Unsupported: Tensorflow preprocessing for DocumentQuestionAnsweringPipeline") input_ids_span_idx = encoding["input_ids"][span_idx] # keep the cls_token unmasked (some models use it to indicate unanswerable questions) if self.tokenizer.cls_token_id is not None: cls_indices = np.nonzero(np.array(input_ids_span_idx) == self.tokenizer.cls_token_id)[0] for cls_index in cls_indices: p_mask[span_idx][cls_index] = 0 # For each span, place a bounding box [0,0,0,0] for question and CLS tokens, [1000,1000,1000,1000] # for SEP tokens, and the word's bounding box for words in the original document. if "boxes" not in tokenizer_kwargs: bbox = [] for input_id, sequence_id, word_id in zip( encoding.input_ids[span_idx], encoding.sequence_ids(span_idx), encoding.word_ids(span_idx), ): if sequence_id == 1: bbox.append(boxes[word_id]) elif input_id == self.tokenizer.sep_token_id: bbox.append([1000] * 4) else: bbox.append([0] * 4) if self.framework == "pt": span_encoding["bbox"] = torch.tensor(bbox).unsqueeze(0) elif self.framework == "tf": raise ValueError("Unsupported: Tensorflow preprocessing for DocumentQuestionAnsweringPipeline") yield { **span_encoding, "p_mask": p_mask[span_idx], "word_ids": encoding.word_ids(span_idx), "words": words, "is_last": span_idx == num_spans - 1, } def _forward(self, model_inputs): p_mask = model_inputs.pop("p_mask", None) word_ids = model_inputs.pop("word_ids", None) words = model_inputs.pop("words", None) is_last = model_inputs.pop("is_last", False) if self.model_type == ModelType.VisionEncoderDecoder: model_outputs = self.model.generate(**model_inputs) else: model_outputs = self.model(**model_inputs) model_outputs = {k: v for (k, v) in model_outputs.items()} model_outputs["p_mask"] = p_mask model_outputs["word_ids"] = word_ids model_outputs["words"] = words model_outputs["attention_mask"] = model_inputs.get("attention_mask", None) model_outputs["is_last"] = is_last return model_outputs def postprocess(self, model_outputs, top_k=1, **kwargs): if self.model_type == ModelType.VisionEncoderDecoder: answers = [self.postprocess_encoder_decoder_single(o) for o in model_outputs] else: answers = self.postprocess_extractive_qa(model_outputs, top_k=top_k, **kwargs) answers = sorted(answers, key=lambda x: x.get("score", 0), reverse=True)[:top_k] return answers def postprocess_encoder_decoder_single(self, model_outputs, **kwargs): sequence = self.tokenizer.batch_decode(model_outputs["sequences"])[0] # TODO: A lot of this logic is specific to Donut and should probably be handled in the tokenizer # (see https://github.com/huggingface/transformers/pull/18414/files#r961747408 for more context). sequence = sequence.replace(self.tokenizer.eos_token, "").replace(self.tokenizer.pad_token, "") sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token ret = { "answer": None, } answer = re.search(r"<s_answer>(.*)</s_answer>", sequence) if answer is not None: ret["answer"] = answer.group(1).strip() return ret def postprocess_extractive_qa( self, model_outputs, top_k=1, handle_impossible_answer=False, max_answer_len=15, **kwargs ): min_null_score = 1000000 # large and positive answers = [] for output in model_outputs: words = output["words"] starts, ends, scores, min_null_score = select_starts_ends( start=output["start_logits"], end=output["end_logits"], p_mask=output["p_mask"], attention_mask=output["attention_mask"].numpy() if output.get("attention_mask", None) is not None else None, min_null_score=min_null_score, top_k=top_k, handle_impossible_answer=handle_impossible_answer, max_answer_len=max_answer_len, ) word_ids = output["word_ids"] for start, end, score in zip(starts, ends, scores): word_start, word_end = word_ids[start], word_ids[end] if word_start is not None and word_end is not None: answers.append( { "score": float(score), "answer": " ".join(words[word_start : word_end + 1]), "start": word_start, "end": word_end, } ) if handle_impossible_answer: answers.append({"score": min_null_score, "answer": "", "start": 0, "end": 0}) return answers