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- # coding=utf-8
- # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
- # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- """ OpenAI GPT-2 configuration """
-
- from fastNLP.transformers.torch.configuration_utils import PretrainedConfig
-
- __all__ = [
- "GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP",
- "GPT2Config",
- ]
-
- 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)..
- 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)
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