232 lines
11 KiB
Python
232 lines
11 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
|
|
# Copyright 2021-2022 The Alibaba DAMO NLP Team Authors.
|
|
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
|
|
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
|
# 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,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.utils.checkpoint
|
|
from torch.nn import CrossEntropyLoss
|
|
|
|
from modelscope.metainfo import Models
|
|
from modelscope.models.builder import MODELS
|
|
from modelscope.outputs import AttentionTokenClassificationModelOutput
|
|
from modelscope.utils import logger as logging
|
|
from modelscope.utils.constant import Tasks
|
|
from .adv_utils import compute_adv_loss
|
|
from .backbone import SbertModel, SbertPreTrainedModel
|
|
from .configuration import SbertConfig
|
|
|
|
logger = logging.get_logger()
|
|
|
|
|
|
@MODELS.register_module(
|
|
Tasks.token_classification, module_name=Models.structbert)
|
|
@MODELS.register_module(Tasks.word_segmentation, module_name=Models.structbert)
|
|
@MODELS.register_module(Tasks.part_of_speech, module_name=Models.structbert)
|
|
class SbertForTokenClassification(SbertPreTrainedModel):
|
|
r"""StructBERT Model with a token classification head on top (a linear layer on top of the hidden-states output)
|
|
e.g. for Named-Entity-Recognition (NER) tasks.
|
|
|
|
This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
|
|
methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
|
|
pruning heads etc.)
|
|
|
|
This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
|
|
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
|
|
general usage and behavior.
|
|
|
|
Preprocessor:
|
|
This is the token-classification model of StructBERT, the preprocessor of this model
|
|
is `modelscope.preprocessors.TokenClassificationTransformersPreprocessor`.
|
|
|
|
Trainer:
|
|
This model is a normal PyTorch model, and can be trained by variable trainers, like EpochBasedTrainer,
|
|
NlpEpochBasedTrainer, or trainers from other frameworks.
|
|
The preferred trainer in modelscope is NlpEpochBasedTrainer.
|
|
|
|
Parameters:
|
|
config (:class:`~modelscope.models.nlp.structbert.SbertConfig`): Model configuration class with
|
|
all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
|
|
weights.
|
|
"""
|
|
|
|
_keys_to_ignore_on_load_unexpected = [r'pooler']
|
|
|
|
def __init__(self, config: SbertConfig, **kwargs):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
if self.config.adv_grad_factor is None:
|
|
logger.warning(
|
|
'Adv parameters not set, skipping compute_adv_loss.')
|
|
setattr(self, self.base_model_prefix,
|
|
SbertModel(config, add_pooling_layer=False))
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None
|
|
else config.hidden_dropout_prob)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
self.init_weights()
|
|
|
|
def _forward_call(self, **kwargs):
|
|
outputs = self.bert(**kwargs)
|
|
sequence_output = outputs[0]
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
outputs['logits'] = logits
|
|
outputs.kwargs = kwargs
|
|
return outputs
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
offset_mapping=None,
|
|
label_mask=None,
|
|
):
|
|
r"""
|
|
Args:
|
|
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using :class:`~modelscope.models.nlp.structbert.SbertTokenizer`. See
|
|
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
|
|
for details.
|
|
|
|
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
|
|
1]``:
|
|
|
|
- 0 corresponds to a `sentence A` token,
|
|
- 1 corresponds to a `sentence B` token.
|
|
|
|
position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
|
|
``[0, config.max_position_embeddings - 1]``.
|
|
|
|
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`,
|
|
`optional`):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`,
|
|
`optional`):
|
|
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded
|
|
representation. This is useful if you want more control over how to convert :obj:`input_ids` indices
|
|
into associated vectors than the model's internal embedding lookup matrix.
|
|
output_attentions (:obj:`bool`, `optional`):
|
|
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
|
|
returned tensors for more detail.
|
|
output_hidden_states (:obj:`bool`, `optional`):
|
|
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors
|
|
for more detail.
|
|
return_dict (:obj:`bool`, `optional`):
|
|
Whether or not to return a :class:`~transformers.ModelOutput` instead of a plain tuple.
|
|
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
|
Labels for computing the token classification loss. Indices should be in
|
|
``[0, ..., config.num_labels - 1]``.
|
|
offset_mapping (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
|
Indices of positions of each input sequence tokens in the sentence.
|
|
Selected in the range ``[0, sequence_length - 1]``.
|
|
label_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
|
Mask to avoid performing attention on padding token indices. Mask
|
|
values selected in ``[0, 1]``:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
Returns:
|
|
Returns `modelscope.outputs.AttentionTokenClassificationModelOutput`
|
|
|
|
Examples:
|
|
>>> from modelscope.models import Model
|
|
>>> from modelscope.preprocessors import Preprocessor
|
|
>>> model = Model.from_pretrained('damo/nlp_structbert_word-segmentation_chinese-base')
|
|
>>> preprocessor = Preprocessor.from_pretrained('damo/nlp_structbert_word-segmentation_chinese-base')
|
|
>>> print(model(**preprocessor(('This is a test', 'This is also a test'))))
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
if not return_dict:
|
|
logger.error('Return tuple in sbert is not supported now.')
|
|
|
|
outputs = self._forward_call(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict)
|
|
|
|
logits = outputs.logits
|
|
embedding_output = outputs.embedding_output
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
# Only keep active parts of the loss
|
|
if attention_mask is not None:
|
|
active_loss = attention_mask.view(-1) == 1
|
|
active_logits = logits.view(-1, self.num_labels)
|
|
active_labels = torch.where(
|
|
active_loss, labels.view(-1),
|
|
torch.tensor(loss_fct.ignore_index).type_as(labels))
|
|
loss = loss_fct(active_logits, active_labels)
|
|
else:
|
|
loss = loss_fct(
|
|
logits.view(-1, self.num_labels), labels.view(-1))
|
|
if self.config.adv_grad_factor is not None and self.training:
|
|
loss = compute_adv_loss(
|
|
embedding=embedding_output,
|
|
model=self._forward_call,
|
|
ori_logits=logits,
|
|
ori_loss=loss,
|
|
adv_bound=self.config.adv_bound,
|
|
adv_grad_factor=self.config.adv_grad_factor,
|
|
sigma=self.config.sigma,
|
|
with_attention_mask=attention_mask is not None,
|
|
**outputs.kwargs)
|
|
|
|
return AttentionTokenClassificationModelOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
offset_mapping=offset_mapping,
|
|
label_mask=label_mask,
|
|
)
|