235 lines
11 KiB
Python
235 lines
11 KiB
Python
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# coding=utf-8
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# Copyright 2019-present CNRS, 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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""" Flaubert configuration"""
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from collections import OrderedDict
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from typing import Mapping
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class FlaubertConfig(PretrainedConfig):
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"""
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This is the configuration class to store the configuration of a [`FlaubertModel`] or a [`TFFlaubertModel`]. It is
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used to instantiate a FlauBERT model according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the FlauBERT
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[flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_uncased) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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pre_norm (`bool`, *optional*, defaults to `False`):
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Whether to apply the layer normalization before or after the feed forward layer following the attention in
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each layer (Vaswani et al., Tensor2Tensor for Neural Machine Translation. 2018)
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layerdrop (`float`, *optional*, defaults to 0.0):
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Probability to drop layers during training (Fan et al., Reducing Transformer Depth on Demand with
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Structured Dropout. ICLR 2020)
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vocab_size (`int`, *optional*, defaults to 30145):
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Vocabulary size of the FlauBERT model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`FlaubertModel`] or [`TFFlaubertModel`].
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emb_dim (`int`, *optional*, defaults to 2048):
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Dimensionality of the encoder layers and the pooler layer.
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n_layer (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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n_head (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for the attention mechanism
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gelu_activation (`bool`, *optional*, defaults to `True`):
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Whether or not to use a *gelu* activation instead of *relu*.
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sinusoidal_embeddings (`bool`, *optional*, defaults to `False`):
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Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings.
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causal (`bool`, *optional*, defaults to `False`):
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Whether or not the model should behave in a causal manner. Causal models use a triangular attention mask in
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order to only attend to the left-side context instead if a bidirectional context.
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asm (`bool`, *optional*, defaults to `False`):
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Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction
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layer.
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n_langs (`int`, *optional*, defaults to 1):
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The number of languages the model handles. Set to 1 for monolingual models.
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use_lang_emb (`bool`, *optional*, defaults to `True`)
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Whether to use language embeddings. Some models use additional language embeddings, see [the multilingual
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models page](http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings) for information
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on how to use them.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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embed_init_std (`float`, *optional*, defaults to 2048^-0.5):
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The standard deviation of the truncated_normal_initializer for initializing the embedding matrices.
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init_std (`int`, *optional*, defaults to 50257):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices except the
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embedding matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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bos_index (`int`, *optional*, defaults to 0):
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The index of the beginning of sentence token in the vocabulary.
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eos_index (`int`, *optional*, defaults to 1):
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The index of the end of sentence token in the vocabulary.
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pad_index (`int`, *optional*, defaults to 2):
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The index of the padding token in the vocabulary.
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unk_index (`int`, *optional*, defaults to 3):
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The index of the unknown token in the vocabulary.
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mask_index (`int`, *optional*, defaults to 5):
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The index of the masking token in the vocabulary.
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is_encoder(`bool`, *optional*, defaults to `True`):
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Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
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summary_type (`string`, *optional*, defaults to "first"):
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
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Has to be one of the following options:
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- `"last"`: Take the last token hidden state (like XLNet).
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- `"first"`: Take the first token hidden state (like BERT).
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- `"mean"`: Take the mean of all tokens hidden states.
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- `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
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- `"attn"`: Not implemented now, use multi-head attention.
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summary_use_proj (`bool`, *optional*, defaults to `True`):
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
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Whether or not to add a projection after the vector extraction.
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summary_activation (`str`, *optional*):
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
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Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
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summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
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Used in the sequence classification and multiple choice models.
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Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
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summary_first_dropout (`float`, *optional*, defaults to 0.1):
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Used in the sequence classification and multiple choice models.
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The dropout ratio to be used after the projection and activation.
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start_n_top (`int`, *optional*, defaults to 5):
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Used in the SQuAD evaluation script.
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end_n_top (`int`, *optional*, defaults to 5):
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Used in the SQuAD evaluation script.
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mask_token_id (`int`, *optional*, defaults to 0):
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Model agnostic parameter to identify masked tokens when generating text in an MLM context.
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lang_id (`int`, *optional*, defaults to 1):
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The ID of the language used by the model. This parameter is used when generating text in a given language.
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"""
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model_type = "flaubert"
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attribute_map = {
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"hidden_size": "emb_dim",
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"num_attention_heads": "n_heads",
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"num_hidden_layers": "n_layers",
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"n_words": "vocab_size", # For backward compatibility
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}
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def __init__(
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self,
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pre_norm=False,
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layerdrop=0.0,
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vocab_size=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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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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mask_token_id=0,
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lang_id=0,
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pad_token_id=2,
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bos_token_id=0,
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**kwargs,
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):
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"""Constructs FlaubertConfig."""
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self.pre_norm = pre_norm
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self.layerdrop = layerdrop
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self.vocab_size = vocab_size
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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.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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self.mask_token_id = mask_token_id
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self.lang_id = lang_id
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if "n_words" in kwargs:
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self.n_words = kwargs["n_words"]
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs)
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class FlaubertOnnxConfig(OnnxConfig):
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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if self.task == "multiple-choice":
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dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
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else:
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dynamic_axis = {0: "batch", 1: "sequence"}
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return OrderedDict(
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[
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("input_ids", dynamic_axis),
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("attention_mask", dynamic_axis),
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]
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)
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