openrec/modeling/transformers_modeling/configuration_unirec.py

# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""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']