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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 """ | |
| from __future__ import absolute_import, division, print_function, unicode_literals | |
| import json | |
| import logging | |
| import sys | |
| from io import open | |
| 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): | |
| """ | |
| Configuration class to store the configuration of a `OpenAIGPTModel`. | |
| Args: | |
| vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `OpenAIGPTModel` or a configuration json file. | |
| n_special: The number of special tokens to learn during fine-tuning ('[SEP]', '[CLF]', ...) | |
| n_positions: Number of positional embeddings. | |
| n_ctx: Size of the causal mask (usually same as n_positions). | |
| n_embd: Dimensionality of the embeddings and hidden states. | |
| n_layer: Number of hidden layers in the Transformer encoder. | |
| n_head: Number of attention heads for each attention layer in | |
| the Transformer encoder. | |
| afn: The non-linear activation function (function or string) in the | |
| encoder and pooler. If string, "gelu", "relu" and "swish" are supported. | |
| resid_pdrop: The dropout probabilitiy for all fully connected | |
| layers in the embeddings, encoder, and pooler. | |
| attn_pdrop: The dropout ratio for the attention | |
| probabilities. | |
| embd_pdrop: The dropout ratio for the embeddings. | |
| layer_norm_epsilon: epsilon to use in the layer norm layers | |
| initializer_range: The sttdev of the truncated_normal_initializer for | |
| initializing all weight matrices. | |
| predict_special_tokens: should we predict special tokens (when the model has a LM head) | |
| """ | |
| pretrained_config_archive_map = OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP | |
| def __init__( | |
| self, | |
| vocab_size_or_config_json_file=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, | |
| num_labels=1, | |
| summary_type='cls_index', | |
| summary_use_proj=True, | |
| summary_activation=None, | |
| summary_proj_to_labels=True, | |
| summary_first_dropout=0.1, | |
| **kwargs | |
| ): | |
| """Constructs OpenAIGPTConfig. | |
| """ | |
| super(OpenAIGPTConfig, self).__init__(**kwargs) | |
| if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2 | |
| and isinstance(vocab_size_or_config_json_file, unicode)): | |
| with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader: | |
| json_config = json.loads(reader.read()) | |
| for key, value in json_config.items(): | |
| self.__dict__[key] = value | |
| elif isinstance(vocab_size_or_config_json_file, int): | |
| self.vocab_size = vocab_size_or_config_json_file | |
| 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.num_labels = num_labels | |
| 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 | |
| else: | |
| raise ValueError( | |
| "First argument must be either a vocabulary size (int)" | |
| "or the path to a pretrained model config file (str)" | |
| ) | |
| 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 | |