183 lines
8.3 KiB
Python
183 lines
8.3 KiB
Python
# coding=utf-8
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# 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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import logging
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from .configuration_utils import PretrainedConfig
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logger = logging.getLogger(__name__)
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GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
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"gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json",
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"gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-config.json",
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"gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-config.json",
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"distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-config.json",
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}
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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 :class:`~transformers.GPT2Model`.
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It is used to instantiate an GPT-2 model according to the specified arguments, defining the model
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architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
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the GPT-2 `small <https://huggingface.co/gpt2>`__ architecture.
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Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
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to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
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for more information.
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Args:
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vocab_size (:obj:`int`, optional, defaults to 50257):
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Vocabulary size of the GPT-2 model. Defines the different tokens that
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can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.GPT2Model`.
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n_positions (:obj:`int`, optional, defaults to 1024):
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The maximum sequence length that this model might ever be used with.
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Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
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n_ctx (:obj:`int`, optional, defaults to 1024):
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Dimensionality of the causal mask (usually same as n_positions).
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n_embd (:obj:`int`, optional, defaults to 768):
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Dimensionality of the embeddings and hidden states.
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n_layer (:obj:`int`, optional, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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n_head (:obj:`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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activation_function (:obj:`str`, optional, defaults to 'gelu'):
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Activation function selected in the list ["relu", "swish", "gelu", "tanh", "gelu_new"].
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resid_pdrop (:obj:`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 (:obj:`int`, optional, defaults to 0.1):
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The dropout ratio for the embeddings.
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attn_pdrop (:obj:`float`, optional, defaults to 0.1):
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The dropout ratio for the attention.
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layer_norm_epsilon (:obj:`float`, optional, defaults to 1e-5):
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The epsilon to use in the layer normalization layers
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initializer_range (:obj:`float`, optional, defaults to 16):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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summary_type (:obj:`string`, optional, defaults to "cls_index"):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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:class:`~transformers.GPT2DoubleHeadsModel`.
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Is 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 (GPT/GPT-2)
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- 'attn' => Not implemented now, use multi-head attention
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summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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:class:`~transformers.GPT2DoubleHeadsModel`.
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Add a projection after the vector extraction
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summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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:class:`~transformers.GPT2DoubleHeadsModel`.
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'tanh' => add a tanh activation to the output, Other => no activation.
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summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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:class:`~transformers.GPT2DoubleHeadsModel`.
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If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
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summary_first_dropout (:obj:`float`, optional, defaults to 0.1):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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:class:`~transformers.GPT2DoubleHeadsModel`.
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Add a dropout before the projection and activation
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Example::
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from transformers import GPT2Model, GPT2Config
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# Initializing a GPT2 configuration
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configuration = GPT2Config()
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# Initializing a model 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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Attributes:
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pretrained_config_archive_map (Dict[str, str]):
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A dictionary containing all the available pre-trained checkpoints.
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"""
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pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
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model_type = "gpt2"
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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_ctx=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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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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bos_token_id=50256,
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eos_token_id=50256,
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**kwargs
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):
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.n_ctx = n_ctx
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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.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.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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@property
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def max_position_embeddings(self):
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return self.n_positions
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@property
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def hidden_size(self):
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return self.n_embd
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@property
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def num_attention_heads(self):
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return self.n_head
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@property
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def num_hidden_layers(self):
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return self.n_layer
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