[BIG] pytorch-transformers => transformers
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thomwolf
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transformers/configuration_xlm.py
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181
transformers/configuration_xlm.py
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# coding=utf-8
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# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
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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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""" XLM configuration """
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from __future__ import absolute_import, division, print_function, unicode_literals
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import json
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import logging
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import sys
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from io import open
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from .configuration_utils import PretrainedConfig
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logger = logging.getLogger(__name__)
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XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",
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'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-config.json",
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'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-config.json",
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'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-config.json",
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'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-config.json",
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'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-config.json",
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'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-config.json",
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'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-config.json",
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'xlm-mlm-17-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-config.json",
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'xlm-mlm-100-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-config.json",
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}
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class XLMConfig(PretrainedConfig):
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"""Configuration class to store the configuration of a `XLMModel`.
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Args:
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vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `XLMModel`.
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d_model: Size of the encoder layers and the pooler layer.
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n_layer: Number of hidden layers in the Transformer encoder.
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n_head: Number of attention heads for each attention layer in
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the Transformer encoder.
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d_inner: The size of the "intermediate" (i.e., feed-forward)
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layer in the Transformer encoder.
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ff_activation: The non-linear activation function (function or string) in the
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encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
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untie_r: untie relative position biases
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attn_type: 'bi' for XLM, 'uni' for Transformer-XL
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dropout: The dropout probabilitiy for all fully connected
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layers in the embeddings, encoder, and pooler.
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max_position_embeddings: The maximum sequence length that this model might
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ever be used with. Typically set this to something large just in case
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(e.g., 512 or 1024 or 2048).
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initializer_range: The sttdev of the truncated_normal_initializer for
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initializing all weight matrices.
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layer_norm_eps: The epsilon used by LayerNorm.
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dropout: float, dropout rate.
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init: str, the initialization scheme, either "normal" or "uniform".
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init_range: float, initialize the parameters with a uniform distribution
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in [-init_range, init_range]. Only effective when init="uniform".
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init_std: float, initialize the parameters with a normal distribution
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with mean 0 and stddev init_std. Only effective when init="normal".
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mem_len: int, the number of tokens to cache.
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reuse_len: int, the number of tokens in the currect batch to be cached
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and reused in the future.
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bi_data: bool, whether to use bidirectional input pipeline.
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Usually set to True during pretraining and False during finetuning.
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clamp_len: int, clamp all relative distances larger than clamp_len.
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-1 means no clamping.
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same_length: bool, whether to use the same attention length for each token.
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"""
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pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
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def __init__(self,
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vocab_size_or_config_json_file=30145,
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emb_dim=2048,
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n_layers=12,
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n_heads=16,
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dropout=0.1,
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attention_dropout=0.1,
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gelu_activation=True,
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sinusoidal_embeddings=False,
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causal=False,
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asm=False,
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n_langs=1,
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use_lang_emb=True,
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max_position_embeddings=512,
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embed_init_std=2048 ** -0.5,
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layer_norm_eps=1e-12,
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init_std=0.02,
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bos_index=0,
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eos_index=1,
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pad_index=2,
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unk_index=3,
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mask_index=5,
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is_encoder=True,
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finetuning_task=None,
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num_labels=2,
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summary_type='first',
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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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start_n_top=5,
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end_n_top=5,
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**kwargs):
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"""Constructs XLMConfig.
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"""
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super(XLMConfig, self).__init__(**kwargs)
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if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
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and isinstance(vocab_size_or_config_json_file, unicode)):
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with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
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json_config = json.loads(reader.read())
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for key, value in json_config.items():
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self.__dict__[key] = value
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elif isinstance(vocab_size_or_config_json_file, int):
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self.n_words = vocab_size_or_config_json_file
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self.emb_dim = emb_dim
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self.n_layers = n_layers
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self.n_heads = n_heads
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.gelu_activation = gelu_activation
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self.sinusoidal_embeddings = sinusoidal_embeddings
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self.causal = causal
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self.asm = asm
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self.n_langs = n_langs
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self.use_lang_emb = use_lang_emb
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self.layer_norm_eps = layer_norm_eps
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self.bos_index = bos_index
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self.eos_index = eos_index
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self.pad_index = pad_index
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self.unk_index = unk_index
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self.mask_index = mask_index
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self.is_encoder = is_encoder
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self.max_position_embeddings = max_position_embeddings
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self.embed_init_std = embed_init_std
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self.init_std = init_std
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self.finetuning_task = finetuning_task
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self.num_labels = num_labels
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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_proj_to_labels = summary_proj_to_labels
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self.summary_first_dropout = summary_first_dropout
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self.start_n_top = start_n_top
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self.end_n_top = end_n_top
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else:
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raise ValueError("First argument must be either a vocabulary size (int)"
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" or the path to a pretrained model config file (str)")
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@property
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def vocab_size(self):
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return self.n_words
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@vocab_size.setter
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def vocab_size(self, value):
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self.n_words = value
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@property
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def hidden_size(self):
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return self.emb_dim
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@property
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def num_attention_heads(self):
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return self.n_heads
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@property
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def num_hidden_layers(self):
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return self.n_layers
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