lib/model_zoo/optimus_models/configuration_gpt2.py

# 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.
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""" OpenAI GPT-2 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__)

GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
                                      "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json",
                                      "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-config.json"}

class GPT2Config(PretrainedConfig):
    """Configuration class to store the configuration of a `GPT2Model`.

    Args:
        vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
        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.
        layer_norm_epsilon: epsilon to use in the layer norm layers
        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.
        initializer_range: The sttdev of the truncated_normal_initializer for
            initializing all weight matrices.
    """
    pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP

    def __init__(
        self,
        vocab_size_or_config_json_file=50257,
        n_positions=1024,
        n_ctx=1024,
        n_embd=768,
        n_layer=12,
        n_head=12,
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,

        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 GPT2Config.

        Args:
            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
            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.
            layer_norm_epsilon: epsilon to use in the layer norm layers
            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.
            initializer_range: The sttdev of the truncated_normal_initializer for
                initializing all weight matrices.
        """
        super(GPT2Config, 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.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.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)"
            )

    @property
    def max_position_embeddings(self):
        return self.n_positions

    @property
    def hidden_size(self):
        return self.n_embd

    @property
    def num_attention_heads(self):
        return self.n_head

    @property
    def num_hidden_layers(self):
        return self.n_layer