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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