|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
""" OpenAI GPT-2 configuration """ |
|
|
|
from ...configuration_utils import PretrainedConfig |
|
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 |
|
|
