# coding=utf-8 # Copyright 2023 The IndicTrans2 Authors and AI4Bharat 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 IndicTrans config.""" from collections import OrderedDict from typing import Any, Mapping, Optional from transformers import PreTrainedTokenizer from transformers.configuration_utils import PretrainedConfig from transformers.onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast from transformers.onnx.utils import compute_effective_axis_dimension from transformers.utils import TensorType, is_torch_available # Copied from transformers.models.m2m_100.configuration_m2m_100.M2M100Config->IndicTrans class IndicTransConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`IT2Model`]. It is used to instantiate an IT2 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 IT2 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 IT2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`IT2Model`] or 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. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). ```""" model_type = "IndicTrans" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model", } def __init__( self, encoder_vocab_size=None, decoder_vocab_size=None, encoder_embed_dim=512, decoder_embed_dim=512, max_source_positions=210, max_target_positions=210, encoder_layers=6, encoder_ffn_dim=2048, encoder_attention_heads=8, decoder_layers=6, decoder_ffn_dim=2048, decoder_attention_heads=8, encoder_layerdrop=0.00, decoder_layerdrop=0.00, use_cache=True, is_encoder_decoder=True, activation_function="relu", encoder_normalize_before=False, decoder_normalize_before=False, layernorm_embedding=False, share_decoder_input_output_embed=False, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, scale_embedding=True, decoder_start_token_id=2, pad_token_id=1, bos_token_id=0, eos_token_id=2, attn_implementation="eager", **kwargs, ): self.encoder_vocab_size = encoder_vocab_size self.decoder_vocab_size = decoder_vocab_size self.encoder_normalize_before = encoder_normalize_before self.decoder_normalize_before = decoder_normalize_before self.layernorm_embedding = layernorm_embedding self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions self.encoder_embed_dim = encoder_embed_dim self.decoder_embed_dim = decoder_embed_dim 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 self.share_decoder_input_output_embed = share_decoder_input_output_embed self.attn_implementation = attn_implementation 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, ) class IndicTransOnnxConfig(OnnxSeq2SeqConfigWithPast): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: 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") return common_inputs # Copied from BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering # A better name would be _generate_dummy_inputs_for_encoder_and_decoder because sequence classification and question # answering are not supported for IT2, but this name is preserved to be able to check that the copy matches what # was done for BART so that it can be updated if need be. 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_for_default_and_seq2seq_lm 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 + 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 generate_dummy_inputs = _generate_dummy_inputs_for_default_and_seq2seq_lm