indictrans2-en-indic-dist-200M / configuration_indictrans.py

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	# 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