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195 lines
8.9 KiB
195 lines
8.9 KiB
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""OpenAI GPT-2 configuration"""
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from ...configuration_utils import PreTrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class GPT2Config(PreTrainedConfig):
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"""
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This is the configuration class to store the configuration of a [`GPT2Model`]. It is used to
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instantiate a GPT-2 model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the GPT-2
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[openai-community/gpt2](https://huggingface.co/openai-community/gpt2) architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50257):
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Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`GPT2Model`].
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n_positions (`int`, *optional*, defaults to 1024):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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n_embd (`int`, *optional*, defaults to 768):
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Dimensionality of the embeddings and hidden states.
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n_layer (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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n_head (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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n_inner (`int`, *optional*):
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Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
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activation_function (`str`, *optional*, defaults to `"gelu_new"`):
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Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
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resid_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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embd_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the embeddings.
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attn_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
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The epsilon to use in the layer normalization layers.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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summary_type (`string`, *optional*, defaults to `"cls_index"`):
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Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`].
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Has to be one of the following options:
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- `"last"`: Take the last token hidden state (like XLNet).
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- `"first"`: Take the first token hidden state (like BERT).
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- `"mean"`: Take the mean of all tokens hidden states.
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- `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
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- `"attn"`: Not implemented now, use multi-head attention.
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summary_use_proj (`bool`, *optional*, defaults to `True`):
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Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`].
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Whether or not to add a projection after the vector extraction.
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summary_activation (`str`, *optional*):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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[`GPT2DoubleHeadsModel`].
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Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
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summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
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Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`].
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Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
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summary_first_dropout (`float`, *optional*, defaults to 0.1):
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Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`].
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The dropout ratio to be used after the projection and activation.
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scale_attn_weights (`bool`, *optional*, defaults to `True`):
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Scale attention weights by dividing by sqrt(hidden_size)..
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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bos_token_id (`int`, *optional*, defaults to 50256):
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Id of the beginning of sentence token in the vocabulary.
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eos_token_id (`int`, *optional*, defaults to 50256):
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Id of the end of sentence token in the vocabulary.
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pad_token_id (`int`, *optional*):
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Padding token id.
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scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
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Whether to additionally scale attention weights by `1 / layer_idx + 1`.
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reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
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Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
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dot-product/softmax to float() when training with mixed precision.
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add_cross_attention (`bool`, *optional*, defaults to `False`):
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Whether cross-attention layers should be added to the model.
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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie weight embeddings
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Example:
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```python
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>>> from transformers import GPT2Config, GPT2Model
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>>> # Initializing a GPT2 configuration
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>>> configuration = GPT2Config()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = GPT2Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "gpt2"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"hidden_size": "n_embd",
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"max_position_embeddings": "n_positions",
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"num_attention_heads": "n_head",
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"num_hidden_layers": "n_layer",
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}
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def __init__(
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self,
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vocab_size=50257,
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n_positions=1024,
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n_embd=768,
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n_layer=12,
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n_head=12,
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n_inner=None,
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activation_function="gelu_new",
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resid_pdrop=0.1,
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embd_pdrop=0.1,
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attn_pdrop=0.1,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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summary_type="cls_index",
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summary_use_proj=True,
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summary_activation=None,
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summary_proj_to_labels=True,
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summary_first_dropout=0.1,
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scale_attn_weights=True,
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use_cache=True,
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bos_token_id=50256,
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eos_token_id=50256,
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pad_token_id=None,
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scale_attn_by_inverse_layer_idx=False,
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reorder_and_upcast_attn=False,
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add_cross_attention=False,
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tie_word_embeddings=True,
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**kwargs,
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):
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self.add_cross_attention = add_cross_attention
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self.tie_word_embeddings = tie_word_embeddings
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self.vocab_size = vocab_size
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_head = n_head
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self.n_inner = n_inner
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self.activation_function = activation_function
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attn_pdrop = attn_pdrop
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.summary_type = summary_type
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self.summary_use_proj = summary_use_proj
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self.summary_activation = summary_activation
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self.summary_first_dropout = summary_first_dropout
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self.summary_proj_to_labels = summary_proj_to_labels
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self.scale_attn_weights = scale_attn_weights
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self.use_cache = use_cache
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self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
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self.reorder_and_upcast_attn = reorder_and_upcast_attn
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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super().__init__(**kwargs)
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__all__ = ["GPT2Config"]
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