public/gpt-2/transformers/models/gpt2/configuration_gpt2.py

# coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" OpenAI GPT-2 configuration """
from collections import OrderedDict
from typing import Any, Mapping, Optional

from transformers import PreTrainedTokenizer, TensorType, is_torch_available

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


logger = logging.get_logger(__name__)

GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
    "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
    "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
    "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
    "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
}


class GPT2Config(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a :class:`~transformers.GPT2Model` or a
    :class:`~transformers.TFGPT2Model`. It is used to instantiate a GPT-2 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 GPT-2 `small <https://huggingface.co/gpt2>`__ architecture.

    Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
    outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.


    Args:
        vocab_size (:obj:`int`, `optional`, defaults to 50257):
            Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
            :obj:`inputs_ids` passed when calling :class:`~transformers.GPT2Model` or
            :class:`~transformers.TFGPT2Model`.
        n_positions (:obj:`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).
        n_ctx (:obj:`int`, `optional`, defaults to 1024):
            Dimensionality of the causal mask (usually same as n_positions).
        n_embd (:obj:`int`, `optional`, defaults to 768):
            Dimensionality of the embeddings and hidden states.
        n_layer (:obj:`int`, `optional`, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        n_head (:obj:`int`, `optional`, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        n_inner (:obj:`int`, `optional`, defaults to None):
            Dimensionality of the inner feed-forward layers. :obj:`None` will set it to 4 times n_embd
        activation_function (:obj:`str`, `optional`, defaults to :obj:`"gelu"`):
            Activation function, to be selected in the list :obj:`["relu", "silu", "gelu", "tanh", "gelu_new"]`.
        resid_pdrop (:obj:`float`, `optional`, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        embd_pdrop (:obj:`int`, `optional`, defaults to 0.1):
            The dropout ratio for the embeddings.
        attn_pdrop (:obj:`float`, `optional`, defaults to 0.1):
            The dropout ratio for the attention.
        layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
            The epsilon to use in the layer normalization layers
        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        summary_type (:obj:`string`, `optional`, defaults to :obj:`"cls_index"`):
            Argument used when doing sequence summary, used in the models :class:`~transformers.GPT2DoubleHeadsModel`
            and :class:`~transformers.TFGPT2DoubleHeadsModel`.

            Has to be one of the following options:

                - :obj:`"last"`: Take the last token hidden state (like XLNet).
                - :obj:`"first"`: Take the first token hidden state (like BERT).
                - :obj:`"mean"`: Take the mean of all tokens hidden states.
                - :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
                - :obj:`"attn"`: Not implemented now, use multi-head attention.
        summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`):
            Argument used when doing sequence summary, used in the models :class:`~transformers.GPT2DoubleHeadsModel`
            and :class:`~transformers.TFGPT2DoubleHeadsModel`.

            Whether or not to add a projection after the vector extraction.
        summary_activation (:obj:`str`, `optional`):
            Argument used when doing sequence summary. Used in for the multiple choice head in
            :class:`~transformers.GPT2DoubleHeadsModel`.

            Pass :obj:`"tanh"` for a tanh activation to the output, any other value will result in no activation.
        summary_proj_to_labels (:obj:`bool`, `optional`, defaults to :obj:`True`):
            Argument used when doing sequence summary, used in the models :class:`~transformers.GPT2DoubleHeadsModel`
            and :class:`~transformers.TFGPT2DoubleHeadsModel`.

            Whether the projection outputs should have :obj:`config.num_labels` or :obj:`config.hidden_size` classes.
        summary_first_dropout (:obj:`float`, `optional`, defaults to 0.1):
            Argument used when doing sequence summary, used in the models :class:`~transformers.GPT2DoubleHeadsModel`
            and :class:`~transformers.TFGPT2DoubleHeadsModel`.

            The dropout ratio to be used after the projection and activation.
        scale_attn_weights (:obj:`bool`, `optional`, defaults to :obj:`True`):
            Scale attention weights by dividing by sqrt(hidden_size).
        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.
        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
            Whether or not the model should return the last key/values attentions (not used by all models).

    Example::

        >>> from transformers import GPT2Model, GPT2Config

        >>> # Initializing a GPT2 configuration
        >>> configuration = GPT2Config()

        >>> # Initializing a model from the configuration
        >>> model = GPT2Model(configuration)

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

    model_type = "gpt2"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=50257,
        n_positions=1024,
        n_ctx=1024,
        n_embd=768,
        n_layer=12,
        n_head=12,
        n_inner=None,
        activation_function="gelu_new",
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        summary_type="cls_index",
        summary_use_proj=True,
        summary_activation=None,
        summary_proj_to_labels=True,
        summary_first_dropout=0.1,
        scale_attn_weights=True,
        gradient_checkpointing=False,
        use_cache=True,
        bos_token_id=50256,
        eos_token_id=50256,
        **kwargs
    ):
        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.n_ctx = n_ctx
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.n_inner = n_inner
        self.activation_function = activation_function
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.attn_pdrop = attn_pdrop
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range
        self.summary_type = summary_type
        self.summary_use_proj = summary_use_proj
        self.summary_activation = summary_activation
        self.summary_first_dropout = summary_first_dropout
        self.summary_proj_to_labels = summary_proj_to_labels
        self.gradient_checkpointing = gradient_checkpointing
        self.scale_attn_weights = scale_attn_weights
        self.use_cache = use_cache

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id

    @property
    def max_position_embeddings(self):
        return self.n_positions

    @property
    def hidden_size(self):
        return self.n_embd

    @property
    def num_attention_heads(self):
        return self.n_head

    @property
    def num_hidden_layers(self):
        return self.n_layer


class GPT2OnnxConfig(OnnxConfigWithPast):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        common_inputs = OrderedDict({"input_ids": {0: "batch"}})
        if self.use_past:
            for i in range(self._config.n_layer * 2):
                common_inputs[f"past_key_values.{i}"] = {0: "batch", 2: "sequence"}

            common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
        else:
            common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}

        return common_inputs

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        common_outputs = OrderedDict({"last_hidden_state": {0: "batch", 1: "sequence"}})
        if self.use_past:
            for i in range(self._config.n_layer * 2):
                common_outputs[f"present.{i}"] = {0: "batch", 2: "sequence"}

            return common_outputs

        return common_outputs

    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]:
        common_inputs = super().generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework)

        # We need to order the input in the way they appears in the forward()
        ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})

        # Need to add the past_keys
        if self.use_past:
            if not is_torch_available():
                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
            else:
                import torch

                batch = common_inputs["input_ids"].shape[0]
                ordered_inputs["past_key_values"] = [
                    (
                        torch.zeros((batch, self._config.n_head, 1, self._config.hidden_size // self._config.n_head)),
                        torch.zeros((batch, self._config.n_head, 1, self._config.hidden_size // self._config.n_head)),
                    )
                    for _ in range(self._config.n_layer)
                ]

        ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
        return ordered_inputs