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# 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. | |
""" Blenderbot model configuration""" | |
from collections import OrderedDict | |
from typing import Any, Mapping, Optional | |
from ... import PreTrainedTokenizer | |
from ...configuration_utils import PretrainedConfig | |
from ...file_utils import TensorType, is_torch_available | |
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast | |
from ...onnx.utils import compute_effective_axis_dimension | |
from ...utils import logging | |
logger = logging.get_logger(__name__) | |
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json", | |
# See all Blenderbot models at https://huggingface.co/models?filter=blenderbot | |
} | |
class BlenderbotConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`BlenderbotModel`]. It is used to instantiate an | |
Blenderbot 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 Blenderbot | |
[facebook/blenderbot-3B](https://huggingface.co/facebook/blenderbot-3B) 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 Blenderbot model. Defines the number of different tokens that can be represented by | |
the `inputs_ids` passed when calling [`BlenderbotModel`] or [`TFBlenderbotModel`]. | |
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. | |
max_position_embeddings (`int`, *optional*, defaults to 128): | |
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`. | |
Example: | |
```python | |
>>> from transformers import BlenderbotConfig, BlenderbotModel | |
>>> # Initializing a Blenderbot facebook/blenderbot-3B style configuration | |
>>> configuration = BlenderbotConfig() | |
>>> # Initializing a model (with random weights) from the facebook/blenderbot-3B style configuration | |
>>> model = BlenderbotModel(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "blenderbot" | |
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=8008, | |
max_position_embeddings=128, | |
encoder_layers=2, | |
encoder_ffn_dim=10240, | |
encoder_attention_heads=32, | |
decoder_layers=24, | |
decoder_ffn_dim=10240, | |
decoder_attention_heads=32, | |
encoder_layerdrop=0.0, | |
decoder_layerdrop=0.0, | |
use_cache=True, | |
is_encoder_decoder=True, | |
activation_function="gelu", | |
d_model=2560, | |
dropout=0.1, | |
attention_dropout=0.0, | |
activation_dropout=0.0, | |
init_std=0.02, | |
decoder_start_token_id=1, | |
scale_embedding=False, | |
pad_token_id=0, | |
bos_token_id=1, | |
eos_token_id=2, | |
encoder_no_repeat_ngram_size=3, | |
forced_eos_token_id=2, | |
**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.use_cache = use_cache | |
self.num_hidden_layers = encoder_layers | |
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True | |
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, | |
encoder_no_repeat_ngram_size=encoder_no_repeat_ngram_size, | |
forced_eos_token_id=forced_eos_token_id, | |
**kwargs, | |
) | |
class BlenderbotOnnxConfig(OnnxSeq2SeqConfigWithPast): | |
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": | |
common_inputs = OrderedDict( | |
[ | |
("input_ids", {0: "batch", 1: "encoder_sequence"}), | |
("attention_mask", {0: "batch", 1: "encoder_sequence"}), | |
] | |
) | |
if self.use_past: | |
_, num_decoder_layers = self.num_layers | |
for i in range(num_decoder_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 | |
# 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_sequence_classification_and_question_answering( | |
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_sequence_classification_and_question_answering( | |
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 | |
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"] = [] | |
_, num_decoder_layers = self.num_layers | |
for _ in range(num_decoder_layers): | |
common_inputs["past_key_values"].append( | |
( | |
torch.zeros(decoder_shape), | |
torch.zeros(decoder_shape), | |
torch.zeros(encoder_shape), | |
torch.zeros(encoder_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_sequence_classification_and_question_answering( | |
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 | |
past_key_values_length = seqlen | |
_, num_decoder_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_decoder_layers) | |
] | |
return common_inputs | |
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering | |
def _generate_dummy_inputs_for_sequence_classification_and_question_answering( | |
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 | |
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.generate_dummy_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 | |
) | |
elif self.task == "causal-lm": | |
common_inputs = self._generate_dummy_inputs_for_causal_lm( | |
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework | |
) | |
else: | |
common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( | |
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 | |
) | |
def fill_with_past_key_values_(self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str): | |
if direction not in ["inputs", "outputs"]: | |
raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given') | |
name = "past_key_values" if direction == "inputs" else "present" | |
_, num_decoder_layers = self.num_layers | |
encoder_sequence = "past_encoder_sequence" | |
decoder_sequence = "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence" | |
for i in range(num_decoder_layers): | |
inputs_or_outputs[f"{name}.{i}.decoder.key"] = {0: "batch", 2: decoder_sequence} | |
inputs_or_outputs[f"{name}.{i}.decoder.value"] = {0: "batch", 2: decoder_sequence} | |
inputs_or_outputs[f"{name}.{i}.encoder.key"] = {0: "batch", 2: encoder_sequence} | |
inputs_or_outputs[f"{name}.{i}.encoder.value"] = {0: "batch", 2: encoder_sequence} | |