123 lines
4.9 KiB
Python
123 lines
4.9 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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import copy
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from torch.nn import 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 (PeerClassificationHead, PeerModel, PeerPreTrainedModel,
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PeerTopModel)
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logger = logging.get_logger()
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@MODELS.register_module(Tasks.text_classification, module_name=Models.peer)
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@MODELS.register_module(Tasks.nli, module_name=Models.peer)
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@MODELS.register_module(
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Tasks.sentiment_classification, module_name=Models.peer)
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@MODELS.register_module(Tasks.sentence_similarity, module_name=Models.peer)
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@MODELS.register_module(
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Tasks.zero_shot_classification, module_name=Models.peer)
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class PeerForSequenceClassification(PeerPreTrainedModel):
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def __init__(self, config, **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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config_discr_top = copy.deepcopy(config)
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config_shared_bottom = copy.deepcopy(config)
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assert config.num_hidden_layers_shared > 0, 'config.num_hidden_layers_shared should be greater than 0!'
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config_shared_bottom.num_hidden_layers = config.num_hidden_layers_shared
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config_discr_top.num_hidden_layers = config_discr_top.num_hidden_layers \
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- config_discr_top.num_hidden_layers_shared
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self.teams1_shared_bottom = PeerModel(config_shared_bottom)
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self.teams1_discr_top = PeerTopModel(config_discr_top)
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self.classifier = PeerClassificationHead(config)
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self.init_weights()
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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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side_info_sets=dict(),
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return_dict=None,
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):
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r"""
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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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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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hidden_states_discr_bottom = self.teams1_shared_bottom(
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input_ids, attention_mask, token_type_ids, position_ids, head_mask,
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inputs_embeds, output_attentions, output_hidden_states,
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side_info_sets, return_dict)
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hidden_states_discr_top = self.teams1_discr_top(
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hidden_states_discr_bottom[0], input_ids, attention_mask,
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token_type_ids, position_ids, head_mask, inputs_embeds,
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output_attentions, output_hidden_states, side_info_sets,
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return_dict)
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discriminator_hidden_states = hidden_states_discr_top
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sequence_output = discriminator_hidden_states[0]
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logits = self.classifier(sequence_output)
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loss = None
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if labels is not None:
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if self.num_labels == 1:
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# We are doing regression
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loss_fct = MSELoss()
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loss = loss_fct(logits.view(-1), labels.view(-1))
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else:
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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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if not return_dict:
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output = (logits, ) + discriminator_hidden_states[1:]
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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=discriminator_hidden_states.hidden_states,
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attentions=discriminator_hidden_states.attentions,
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)
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