transformers/pytorch_transformers/configuration_xlnet.py

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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,
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""" XLNet 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__)

XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json",
    'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
}


class XLNetConfig(PretrainedConfig):
    """Configuration class to store the configuration of a ``XLNetModel``.

    Args:
        vocab_size_or_config_json_file: Vocabulary size of ``inputs_ids`` in ``XLNetModel``.
        d_model: Size of the encoder layers and the pooler layer.
        n_layer: Number of hidden layers in the Transformer encoder.
        n_head: Number of attention heads for each attention layer in
            the Transformer encoder.
        d_inner: The size of the "intermediate" (i.e., feed-forward)
            layer in the Transformer encoder.
        ff_activation: The non-linear activation function (function or string) in the
            encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
        untie_r: untie relative position biases
        attn_type: 'bi' for XLNet, 'uni' for Transformer-XL

        dropout: The dropout probabilitiy for all fully connected
            layers in the embeddings, encoder, and pooler.
        dropatt: The dropout ratio for the attention
            probabilities.
        initializer_range: The sttdev of the truncated_normal_initializer for
            initializing all weight matrices.
        layer_norm_eps: The epsilon used by LayerNorm.

        dropout: float, dropout rate.
        dropatt: float, dropout rate on attention probabilities.
        init: str, the initialization scheme, either "normal" or "uniform".
        init_range: float, initialize the parameters with a uniform distribution
            in [-init_range, init_range]. Only effective when init="uniform".
        init_std: float, initialize the parameters with a normal distribution
            with mean 0 and stddev init_std. Only effective when init="normal".
        mem_len: int, the number of tokens to cache.
        reuse_len: int, the number of tokens in the currect batch to be cached
            and reused in the future.
        bi_data: bool, whether to use bidirectional input pipeline.
            Usually set to True during pretraining and False during finetuning.
        clamp_len: int, clamp all relative distances larger than clamp_len.
            -1 means no clamping.
        same_length: bool, whether to use the same attention length for each token.
        finetuning_task: name of the glue task on which the model was fine-tuned if any
    """
    pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP

    def __init__(self,
                 vocab_size_or_config_json_file=32000,
                 d_model=1024,
                 n_layer=24,
                 n_head=16,
                 d_inner=4096,
                 ff_activation="gelu",
                 untie_r=True,
                 attn_type="bi",

                 initializer_range=0.02,
                 layer_norm_eps=1e-12,

                 dropout=0.1,
                 mem_len=None,
                 reuse_len=None,
                 bi_data=False,
                 clamp_len=-1,
                 same_length=False,

                 finetuning_task=None,
                 num_labels=2,
                 summary_type='last',
                 summary_use_proj=True,
                 summary_activation='tanh',
                 summary_last_dropout=0.1,
                 start_n_top=5,
                 end_n_top=5,
                 **kwargs):
        """Constructs XLNetConfig.
        """
        super(XLNetConfig, 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():
                setattr(config, key, value)
        elif isinstance(vocab_size_or_config_json_file, int):
            self.n_token = vocab_size_or_config_json_file
            self.d_model = d_model
            self.n_layer = n_layer
            self.n_head = n_head
            assert d_model % n_head == 0
            self.d_head = d_model // n_head
            self.ff_activation = ff_activation
            self.d_inner = d_inner
            self.untie_r = untie_r
            self.attn_type = attn_type

            self.initializer_range = initializer_range
            self.layer_norm_eps = layer_norm_eps

            self.dropout = dropout
            self.mem_len = mem_len
            self.reuse_len = reuse_len
            self.bi_data = bi_data
            self.clamp_len = clamp_len
            self.same_length = same_length

            self.finetuning_task = finetuning_task
            self.num_labels = num_labels
            self.summary_type = summary_type
            self.summary_use_proj = summary_use_proj
            self.summary_activation = summary_activation
            self.summary_last_dropout = summary_last_dropout
            self.start_n_top = start_n_top
            self.end_n_top = end_n_top
        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 -1

    @property
    def vocab_size(self):
        return self.n_token

    @vocab_size.setter
    def vocab_size(self, value):
        self.n_token = value

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

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

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