136 lines
7.6 KiB
Python
136 lines
7.6 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright 2021-2022 The Alibaba DAMO NLP Team Authors.
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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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# 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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""" StructBERT model configuration, mainly copied from :class:`~transformers.BertConfig` """
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from transformers import PretrainedConfig
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from modelscope.utils import logger as logging
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logger = logging.get_logger()
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class SbertConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration
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of a :class:`~modelscope.models.nlp.structbert.SbertModel`.
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It is used to instantiate a StructBERT model according to the specified arguments.
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Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
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outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
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Args:
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vocab_size (:obj:`int`, `optional`, defaults to 30522):
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Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
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:obj:`inputs_ids` passed when calling :class:`~transformers.BertModel` or
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:class:`~transformers.TFBertModel`.
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hidden_size (:obj:`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 (:obj:`int`, `optional`, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (:obj:`int`, `optional`, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (:obj:`int`, `optional`, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string,
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:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported.
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hidden_dropout_prob (:obj:`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_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (:obj:`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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type_vocab_size (:obj:`int`, `optional`, defaults to 2):
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The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BertModel` or
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:class:`~transformers.TFBertModel`.
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initializer_range (:obj:`float`, `optional`, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (:obj:`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 (:obj:`str`, `optional`, defaults to :obj:`"absolute"`):
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Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
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:obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
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:obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.)
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<https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to
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`Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)
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<https://arxiv.org/abs/2009.13658>`__.
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use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if ``config.is_decoder=True``.
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classifier_dropout (:obj:`float`, `optional`):
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The dropout ratio for the classification head.
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adv_grad_factor (:obj:`float`, `optional`): This factor will be multiplied by the KL loss grad and then
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the result will be added to the original embedding.
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More details please check:https://arxiv.org/abs/1908.04577
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The range of this value should between 1e-3~1e-7
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adv_bound (:obj:`float`, `optional`): adv_bound is used to cut the top and the bottom bound of
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the produced embedding.
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If not provided, 2 * sigma will be used as the adv_bound factor
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sigma (:obj:`float`, `optional`): The std factor used to produce a 0 mean normal distribution.
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If adv_bound not provided, 2 * sigma will be used as the adv_bound factor
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"""
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model_type = 'structbert'
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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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# adv_grad_factor, used in adv loss.
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# Users can check adv_utils.py for details.
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# if adv_grad_factor set to None, no adv loss will not applied to the model.
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self.adv_grad_factor = 5e-5 if 'adv_grad_factor' not in kwargs else kwargs[
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'adv_grad_factor']
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# sigma value, used in adv loss.
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self.sigma = 5e-6 if 'sigma' not in kwargs else kwargs['sigma']
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# adv_bound value, used in adv loss.
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self.adv_bound = 2 * self.sigma if 'adv_bound' not in kwargs else kwargs[
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'adv_bound']
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