# 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. """UniRecConfig model configuration""" 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, logging logger = logging.get_logger(__name__) class UniRecConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`M2M100Model`]. It is used to instantiate an M2M100 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 M2M100 [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) 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 M2M100 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`M2M100Model`] 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). Example: ```python >>> from transformers import M2M100Config, M2M100Model >>> # Initializing a M2M100 facebook/m2m100_418M style configuration >>> configuration = M2M100Config() >>> # Initializing a model (with random weights) from the facebook/m2m100_418M style configuration >>> model = M2M100Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = 'm2m_100' 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=50000, max_position_embeddings=3072, decoder_layers=6, decoder_ffn_dim=1536, decoder_attention_heads=6, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function='relu', d_model=384, dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=0, scale_embedding=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, depths=[2, 2, 9, 2], dims=[64, 128, 256, 384], mixer=[['Conv'] * 2, ['Conv'] * 2, ['Conv'] * 6 + ['FGlobal', 'Global', 'Global'], ['Global'] * 2], num_heads=[2, 4, 4, 6], sub_k=[[2, 2], [2, 2], [2, 2], [2, 2]], mlp_ratio=4, kernel_size=[3, 3], drop_path_rate=0.1, label_smoothing=0.1, torch_dtype='bfloat16', **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model 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.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.depths = depths self.dims = dims self.mixer = mixer self.num_heads = num_heads self.sub_k = sub_k self.mlp_ratio = mlp_ratio self.kernel_size = kernel_size self.drop_path_rate = drop_path_rate self.label_smoothing = label_smoothing self.torch_dtype = torch_dtype 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 UniRecOnnxConfig(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 M2M100, 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 __all__ = ['M2M100Config', 'M2M100OnnxConfig']