configuration_bigbird_pegasus.py

# coding=utf-8
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""" BigBirdPegasus model configuration"""

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


logger = logging.get_logger(__name__)


class BigBirdPegasusConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`BigBirdPegasusModel`]. It is used to instantiate
    an BigBirdPegasus 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 BigBirdPegasus
    [google/bigbird-pegasus-large-arxiv](https://huggingface.co/google/bigbird-pegasus-large-arxiv) 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 96103):
            Vocabulary size of the BigBirdPegasus model. Defines the number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`BigBirdPegasusModel`].
        d_model (`int`, *optional*, defaults to 1024):
            Dimension of the layers and the pooler layer.
        encoder_layers (`int`, *optional*, defaults to 16):
            Number of encoder layers.
        decoder_layers (`int`, *optional*, defaults to 16):
            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):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu_new"`):
            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 4096):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 1024 or 2048 or 4096).
        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).
        attention_type (`str`, *optional*, defaults to `"block_sparse"`)
            Whether to use block sparse attention (with n complexity) as introduced in paper or original attention
            layer (with n^2 complexity) in encoder. Possible values are `"original_full"` and `"block_sparse"`.
        use_bias (`bool`, *optional*, defaults to `False`)
            Whether to use bias in query, key, value.
        block_size (`int`, *optional*, defaults to 64)
            Size of each block. Useful only when `attention_type == "block_sparse"`.
        num_random_blocks (`int`, *optional*, defaults to 3)
            Each query is going to attend these many number of random blocks. Useful only when `attention_type ==
            "block_sparse"`.
        scale_embeddings (`bool`, *optional*, defaults to `True`)
            Whether to rescale embeddings with (hidden_size ** 0.5).

    Example:

    ```python
    >>> from transformers import BigBirdPegasusConfig, BigBirdPegasusModel

    >>> # Initializing a BigBirdPegasus bigbird-pegasus-base style configuration
    >>> configuration = BigBirdPegasusConfig()

    >>> # Initializing a model (with random weights) from the bigbird-pegasus-base style configuration
    >>> model = BigBirdPegasusModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "bigbird_pegasus"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "num_attention_heads": "encoder_attention_heads",
        "hidden_size": "d_model",
        "attention_probs_dropout_prob": "attention_dropout",
    }

    def __init__(
        self,
        vocab_size=96103,
        max_position_embeddings=4096,
        encoder_layers=16,
        encoder_ffn_dim=4096,
        encoder_attention_heads=16,
        decoder_layers=16,
        decoder_ffn_dim=4096,
        decoder_attention_heads=16,
        encoder_layerdrop=0.0,
        decoder_layerdrop=0.0,
        use_cache=True,
        is_encoder_decoder=True,
        activation_function="gelu_new",
        d_model=1024,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        decoder_start_token_id=2,
        classifier_dropout=0.0,
        scale_embedding=True,
        pad_token_id=0,
        bos_token_id=2,
        eos_token_id=1,
        attention_type="block_sparse",  # only for encoder
        block_size=64,
        num_random_blocks=3,
        use_bias=False,
        **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.classifier_dropout = classifier_dropout
        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

        # extra config
        self.attention_type = attention_type
        self.block_size = block_size
        self.num_random_blocks = num_random_blocks
        self.use_bias = use_bias

        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,
        )


# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig
class BigBirdPegasusOnnxConfig(OnnxSeq2SeqConfigWithPast):
    @property
    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":
            # TODO: figure this case out.
            common_inputs = OrderedDict(
                [
                    ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                    ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ]
            )
            if self.use_past:
                num_encoder_layers, _ = self.num_layers
                for i in range(num_encoder_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

    @property
    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 + 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

    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
            # Not using the same length for past_key_values
            past_key_values_length = seqlen + 2
            num_encoder_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_encoder_layers)
            ]
        return common_inputs

    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

    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

    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
            )