288 lines
12 KiB
Python
288 lines
12 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 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 CrossEntropyLoss
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from transformers.activations import ACT2FN
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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 AttentionFillMaskModelOutput
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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 SbertModel, SbertPreTrainedModel
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from .configuration import SbertConfig
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logger = logging.get_logger()
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class SbertPredictionHeadTransform(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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if isinstance(config.hidden_act, str):
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self.transform_act_fn = ACT2FN[config.hidden_act]
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else:
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self.transform_act_fn = config.hidden_act
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self.LayerNorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.transform_act_fn(hidden_states)
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hidden_states = self.LayerNorm(hidden_states)
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return hidden_states
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class SbertLMPredictionHead(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.transform = SbertPredictionHeadTransform(config)
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# The output weights are the same as the input embeddings, but there is
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# an output-only bias for each token.
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self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
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def forward(self, hidden_states):
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hidden_states = self.transform(hidden_states)
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hidden_states = self.decoder(hidden_states)
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return hidden_states
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class SbertOnlyMLMHead(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.predictions = SbertLMPredictionHead(config)
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def forward(self, sequence_output):
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prediction_scores = self.predictions(sequence_output)
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return prediction_scores
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class SbertPreTrainingHeads(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.predictions = SbertLMPredictionHead(config)
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self.seq_relationship = nn.Linear(config.hidden_size, 2)
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def forward(self, sequence_output, pooled_output):
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prediction_scores = self.predictions(sequence_output)
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seq_relationship_score = self.seq_relationship(pooled_output)
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return prediction_scores, seq_relationship_score
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@MODELS.register_module(Tasks.fill_mask, module_name=Models.structbert)
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class SbertForMaskedLM(SbertPreTrainedModel):
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r"""StructBERT Model with a `language modeling` head on top.
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This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
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methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
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pruning heads etc.)
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This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
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subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
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general usage and behavior.
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Preprocessor:
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This is the fill_mask model of StructBERT, the preprocessor of this model
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is `modelscope.preprocessors.FillMaskTransformersPreprocessor`.
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Parameters:
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config (:class:`~modelscope.models.nlp.structbert.SbertConfig`): Model configuration class with
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all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the
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configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
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weights.
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"""
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_keys_to_ignore_on_load_unexpected = [r'pooler']
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_keys_to_ignore_on_load_missing = [
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r'position_ids', r'predictions.decoder.bias'
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]
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def __init__(self, config: SbertConfig, **kwargs):
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super().__init__(config)
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if config.is_decoder:
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logger.warning(
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'If you want to use `SbertForMaskedLM` make sure `config.is_decoder=False` for '
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'bi-directional self-attention.')
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self.bert = SbertModel(config)
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self.cls = SbertOnlyMLMHead(config)
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self.init_weights()
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def get_output_embeddings(self):
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return self.cls.predictions.decoder
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def set_output_embeddings(self, new_embeddings):
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self.cls.predictions.decoder = new_embeddings
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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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encoder_hidden_states=None,
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encoder_attention_mask=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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):
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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__`
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for details.
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`What are input IDs? <../glossary.html#input-ids>`__
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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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`What are attention masks? <../glossary.html#attention-mask>`__
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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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`What are token type IDs? <../glossary.html#token-type-ids>`_
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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
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``[0, 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)`,
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`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)`,
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`optional`):
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Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded
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representation. This is useful if you want more control over how to convert :obj:`input_ids` indices
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into associated 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
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returned 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
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for 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, sequence_length)`, `optional`):
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Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
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config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
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(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
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Returns:
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Returns `modelscope.outputs.AttentionFillMaskModelOutput`
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Examples:
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>>> from modelscope.models import Model
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>>> from modelscope.preprocessors import Preprocessor, FillMaskTransformersPreprocessor
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>>> model = Model.from_pretrained('damo/nlp_structbert_fill-mask_chinese-large')
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>>> preprocessor = FillMaskTransformersPreprocessor('damo/nlp_structbert_fill-mask_chinese-large')
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>>> # Call the model, return some tensors
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>>> print(model(**preprocessor('你师父差得动你，你师父可[MASK]不动我。')))
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>>> # Call the pipeline
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>>> from modelscope.pipelines import pipeline
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>>> pipeline_ins = pipeline('fill-mask', model=model, preprocessor=preprocessor)
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>>> print(pipeline_ins('你师父差得动你，你师父可[MASK]不动我。'))
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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.bert(
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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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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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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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)
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sequence_output = outputs[0]
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prediction_scores = self.cls(sequence_output)
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masked_lm_loss = None
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if labels is not None:
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loss_fct = CrossEntropyLoss() # -100 index = padding token
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masked_lm_loss = loss_fct(
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prediction_scores.view(-1, self.config.vocab_size),
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labels.view(-1))
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if not return_dict:
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output = (prediction_scores, ) + outputs[2:-1]
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return ((masked_lm_loss, )
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+ output) if masked_lm_loss is not None else output
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return AttentionFillMaskModelOutput(
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loss=masked_lm_loss,
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logits=prediction_scores,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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input_ids=input_ids,
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)
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def prepare_inputs_for_generation(self,
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input_ids,
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attention_mask=None,
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**model_kwargs):
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input_shape = input_ids.shape
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effective_batch_size = input_shape[0]
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# add a dummy token
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assert self.config.pad_token_id is not None, 'The PAD token should be defined for generation'
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attention_mask_zero = attention_mask.new_zeros(
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(attention_mask.shape[0], 1))
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attention_mask = torch.cat([attention_mask, attention_mask_zero],
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dim=-1)
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dummy_token = torch.full((effective_batch_size, 1),
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self.config.pad_token_id,
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dtype=torch.long,
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device=input_ids.device)
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input_ids = torch.cat([input_ids, dummy_token], dim=1)
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return {'input_ids': input_ids, 'attention_mask': attention_mask}
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