189 lines
8.5 KiB
Python
189 lines
8.5 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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# 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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import torch
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import torch.nn as nn
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import torch.utils.checkpoint
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from modelscope.metainfo import Models
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from modelscope.models.builder import MODELS
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from modelscope.outputs import AttentionTextClassificationModelOutput
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from modelscope.utils import logger as logging
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from modelscope.utils.constant import Tasks
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from .backbone import MGeo, MGeoPreTrainedModel
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logger = logging.get_logger()
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@MODELS.register_module(Tasks.text_classification, module_name=Models.mgeo)
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@MODELS.register_module(Tasks.nli, module_name=Models.mgeo)
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@MODELS.register_module(
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Tasks.sentiment_classification, module_name=Models.mgeo)
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@MODELS.register_module(Tasks.sentence_similarity, module_name=Models.mgeo)
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@MODELS.register_module(
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Tasks.zero_shot_classification, module_name=Models.mgeo)
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class MGeoForSequenceClassification(MGeoPreTrainedModel):
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def __init__(self, config, finetune_mode: str = 'single-modal', **kwargs):
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super().__init__(config)
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self.num_labels = config.num_labels
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self.config = config
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setattr(self, self.base_model_prefix,
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MGeo(config, finetune_mode, add_pooling_layer=True))
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classifier_dropout = (
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config.classifier_dropout if config.classifier_dropout is not None
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else config.hidden_dropout_prob)
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self.dropout = nn.Dropout(classifier_dropout)
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self.classifier = nn.Linear(config.hidden_size, config.num_labels)
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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input_ids=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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labels=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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gis_list=None,
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gis_tp=None,
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):
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r"""
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Args:
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input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary.
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Indices can be obtained using :class:`~modelscope.models.nlp.structbert.SbertTokenizer`. See
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:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
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details.
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attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
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Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
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Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
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1]``:
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- 0 corresponds to a `sentence A` token,
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- 1 corresponds to a `sentence B` token.
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position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
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config.max_position_embeddings - 1]``.
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head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
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Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
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Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
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This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
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vectors than the model's internal embedding lookup matrix.
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output_attentions (:obj:`bool`, `optional`):
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Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
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tensors for more detail.
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output_hidden_states (:obj:`bool`, `optional`):
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Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
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more detail.
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return_dict (:obj:`bool`, `optional`):
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Whether or not to return a :class:`~transformers.ModelOutput` instead of a plain tuple.
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labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
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Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
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config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
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If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
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Returns:
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Returns `modelscope.outputs.AttentionTextClassificationModelOutput`
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Examples:
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>>> from modelscope.models import Model
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>>> from modelscope.preprocessors import Preprocessor
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>>> model = Model.from_pretrained('damo/nlp_structbert_sentence-similarity_chinese-base')
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>>> preprocessor = Preprocessor.from_pretrained('damo/nlp_structbert_sentence-similarity_chinese-base')
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>>> print(model(**preprocessor(('This is a test', 'This is also a test'))))
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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outputs = self.base_model.forward(
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input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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gis_list=gis_list,
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gis_tp=gis_tp,
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use_token_type=True,
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)
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pooled_output = outputs[1]
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pooled_output = self.dropout(pooled_output)
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logits = self.classifier(pooled_output)
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loss = None
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if labels is not None:
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if self.config.problem_type is None:
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if self.num_labels == 1:
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self.config.problem_type = 'regression'
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elif self.num_labels > 1 and (labels.dtype == torch.long
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or labels.dtype == torch.int):
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self.config.problem_type = 'single_label_classification'
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else:
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self.config.problem_type = 'multi_label_classification'
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if self.config.problem_type == 'regression':
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loss_fct = MSELoss()
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if self.num_labels == 1:
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loss = loss_fct(logits.squeeze(), labels.squeeze())
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else:
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loss = loss_fct(logits, labels)
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elif self.config.problem_type == 'single_label_classification':
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(
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logits.view(-1, self.num_labels), labels.view(-1))
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elif self.config.problem_type == 'multi_label_classification':
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loss_fct = BCEWithLogitsLoss()
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loss = loss_fct(logits, labels)
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if not return_dict:
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output = (logits, ) + outputs[2:]
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return ((loss, ) + output) if loss is not None else output
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return AttentionTextClassificationModelOutput(
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loss=loss,
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logits=logits,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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