165 lines
7.2 KiB
Python
165 lines
7.2 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright (c) Alibaba, Inc. and its affiliates.
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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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""" BERT model configuration """
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from collections import OrderedDict
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from typing import Mapping
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from transformers.configuration_utils import PretrainedConfig
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from transformers.onnx import OnnxConfig
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from modelscope.utils.logger import get_logger
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logger = get_logger()
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class BertConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a
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[`BertModel`] or a [`TFBertModel`]. It is used to instantiate a BERT model
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according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar
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configuration to that of the BERT
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[bert-base-uncased](https://huggingface.co/bert-base-uncased) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to
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control the model outputs. Read the documentation from [`PretrainedConfig`]
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for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the BERT model. Defines the number of different
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tokens that can be represented by the `inputs_ids` passed when
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calling [`BertModel`] or [`TFBertModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the
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Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward)
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layer in the Transformer encoder.
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hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the
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encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and
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`"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the
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embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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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.
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Typically set this to something large just in case (e.g., 512 or
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1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling
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[`BertModel`] or [`TFBertModel`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for
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initializing all weight 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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position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
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Type of position embedding. Choose one of `"absolute"`,
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`"relative_key"`, `"relative_key_query"`. For positional embeddings
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use `"absolute"`. For more information on `"relative_key"`, please
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refer to [Self-Attention with Relative Position Representations
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(Shaw et al.)](https://arxiv.org/abs/1803.02155). For more
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information on `"relative_key_query"`, please refer to *Method 4* in
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[Improve Transformer Models with Better Relative Position Embeddings
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(Huang et al.)](https://arxiv.org/abs/2009.13658).
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values
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attentions (not used by all models). Only relevant if
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`config.is_decoder=True`.
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classifier_dropout (`float`, *optional*):
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The dropout ratio for the classification head.
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Examples:
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>>> from transformers import BertModel, BertConfig
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>>> # Initializing a BERT bert-base-uncased style configuration
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>>> configuration = BertConfig()
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>>> # Initializing a model from the bert-base-uncased style configuration
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>>> model = BertModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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"""
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model_type = 'bert'
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def __init__(self,
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vocab_size=30522,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act='gelu',
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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position_embedding_type='absolute',
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use_cache=True,
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classifier_dropout=None,
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**kwargs):
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super().__init__(pad_token_id=pad_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.position_embedding_type = position_embedding_type
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self.use_cache = use_cache
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self.classifier_dropout = classifier_dropout
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class BertOnnxConfig(OnnxConfig):
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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return OrderedDict([
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('input_ids', {
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0: 'batch',
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1: 'sequence'
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}),
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('attention_mask', {
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0: 'batch',
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1: 'sequence'
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}),
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('token_type_ids', {
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0: 'batch',
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1: 'sequence'
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}),
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])
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