Source code for transformers.models.gpt2.configuration_gpt2

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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",
}


[docs]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).. 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"] attribute_map = { "hidden_size": "n_embd", "max_position_embeddings": "n_positions", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } 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, use_cache=True, bos_token_id=50256, eos_token_id=50256, **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.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 super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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