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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 configuration """ | |
| import logging | |
| from .configuration_utils import PretrainedConfig | |
| logger = logging.getLogger(__name__) | |
| OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-config.json" | |
| } | |
| class OpenAIGPTConfig(PretrainedConfig): | |
| """ | |
| This is the configuration class to store the configuration of an :class:`~transformers.OpenAIGPTModel`. | |
| It is used to instantiate an GPT 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 <https://huggingface.co/openai-gpt>`__ architecture from OpenAI. | |
| 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 40478): | |
| Vocabulary size of the GPT model. Defines the different tokens that | |
| can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.CTRLModel`. | |
| n_positions (:obj:`int`, optional, defaults to 512): | |
| 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 512): | |
| 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. | |
| afn (:obj:`str` or :obj:`function`, optional, defaults to "gelu"): | |
| The non-linear activation function (function or string) in the encoder and pooler. | |
| If string, "gelu", "relu", "swish" and "gelu_new" are supported. | |
| 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. | |
| predict_special_tokens (:obj:`boolean`, optional, defaults to :obj:`True`): | |
| Whether special tokens should be predicted when the model is has a language modeling head. | |
| summary_type (:obj:`string`, optional, defaults to "cls_index"): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| :class:`~transformers.OpenAIGPTDoubleHeadsModel`. | |
| Is one of the following options: | |
| - 'last' => take the last token hidden state (like XLNet) | |
| - 'first' => take the first token hidden state (like Bert) | |
| - 'mean' => take the mean of all tokens hidden states | |
| - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2) | |
| - 'attn' => Not implemented now, use multi-head attention | |
| summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| :class:`~transformers.OpenAIGPTDoubleHeadsModel`. | |
| Add a projection after the vector extraction | |
| summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| :class:`~transformers.OpenAIGPTDoubleHeadsModel`. | |
| 'tanh' => add a tanh activation to the output, Other => no activation. | |
| summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| :class:`~transformers.OpenAIGPTDoubleHeadsModel`. | |
| If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False. | |
| summary_first_dropout (:obj:`float`, optional, defaults to 0.1): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| :class:`~transformers.OpenAIGPTDoubleHeadsModel`. | |
| Add a dropout before the projection and activation | |
| Example:: | |
| from transformers import OpenAIGPTConfig, OpenAIGPTModel | |
| # Initializing a GPT configuration | |
| configuration = OpenAIGPTConfig() | |
| # Initializing a model from the configuration | |
| model = OpenAIGPTModel(configuration) | |
| # Accessing the model configuration | |
| configuration = model.config | |
| Attributes: | |
| pretrained_config_archive_map (Dict[str, str]): | |
| A dictionary containing all the available pre-trained checkpoints. | |
| """ | |
| pretrained_config_archive_map = OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP | |
| model_type = "openai-gpt" | |
| def __init__( | |
| self, | |
| vocab_size=40478, | |
| n_positions=512, | |
| n_ctx=512, | |
| n_embd=768, | |
| n_layer=12, | |
| n_head=12, | |
| afn="gelu", | |
| resid_pdrop=0.1, | |
| embd_pdrop=0.1, | |
| attn_pdrop=0.1, | |
| layer_norm_epsilon=1e-5, | |
| initializer_range=0.02, | |
| predict_special_tokens=True, | |
| summary_type="cls_index", | |
| summary_use_proj=True, | |
| summary_activation=None, | |
| summary_proj_to_labels=True, | |
| summary_first_dropout=0.1, | |
| **kwargs | |
| ): | |
| super().__init__(**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.afn = afn | |
| 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.predict_special_tokens = predict_special_tokens | |
| 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 | |
| def max_position_embeddings(self): | |
| return self.n_positions | |
| def hidden_size(self): | |
| return self.n_embd | |
| def num_attention_heads(self): | |
| return self.n_head | |
| def num_hidden_layers(self): | |
| return self.n_layer | |