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module.py
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module.py
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# Copyright (c) 2020 PaddlePaddle Authors. 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.
from typing import Dict
import os
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddlenlp.transformers.bert.modeling import BertForSequenceClassification, BertModel, BertForTokenClassification
from paddlenlp.transformers.bert.tokenizer import BertTokenizer
from paddlenlp.metrics import ChunkEvaluator
from paddlehub.module.module import moduleinfo
from paddlehub.module.nlp_module import TransformerModule
from paddlehub.utils.log import logger
@moduleinfo(
name="bert-base-multilingual-uncased",
version="2.0.2",
summary=
"bert_multi_uncased_L-12_H-768_A-12, 12-layer, 768-hidden, 12-heads, 110M parameters. The module is executed as paddle.dygraph.",
author="paddlepaddle",
author_email="",
type="nlp/semantic_model",
meta=TransformerModule)
class Bert(nn.Layer):
"""
BERT model
"""
def __init__(
self,
task: str = None,
load_checkpoint: str = None,
label_map: Dict = None,
num_classes: int = 2,
suffix: bool = False,
**kwargs,
):
super(Bert, self).__init__()
if label_map:
self.label_map = label_map
self.num_classes = len(label_map)
else:
self.num_classes = num_classes
if task == 'sequence_classification':
task = 'seq-cls'
logger.warning(
"current task name 'sequence_classification' was renamed to 'seq-cls', "
"'sequence_classification' has been deprecated and will be removed in the future.", )
if task == 'seq-cls':
self.model = BertForSequenceClassification.from_pretrained(
pretrained_model_name_or_path='bert-base-multilingual-uncased', num_classes=self.num_classes, **kwargs)
self.criterion = paddle.nn.loss.CrossEntropyLoss()
self.metric = paddle.metric.Accuracy()
elif task == 'token-cls':
self.model = BertForTokenClassification.from_pretrained(
pretrained_model_name_or_path='bert-base-multilingual-uncased', num_classes=self.num_classes, **kwargs)
self.criterion = paddle.nn.loss.CrossEntropyLoss()
self.metric = ChunkEvaluator(label_list=[self.label_map[i] for i in sorted(self.label_map.keys())], suffix=suffix)
elif task == 'text-matching':
self.model = BertModel.from_pretrained(
pretrained_model_name_or_path='bert-base-multilingual-uncased', **kwargs)
self.dropout = paddle.nn.Dropout(0.1)
self.classifier = paddle.nn.Linear(self.model.config['hidden_size'] * 3, 2)
self.criterion = paddle.nn.loss.CrossEntropyLoss()
self.metric = paddle.metric.Accuracy()
elif task is None:
self.model = BertModel.from_pretrained(
pretrained_model_name_or_path='bert-base-multilingual-uncased', **kwargs)
else:
raise RuntimeError("Unknown task {}, task should be one in {}".format(task, self._tasks_supported))
self.task = task
if load_checkpoint is not None and os.path.isfile(load_checkpoint):
state_dict = paddle.load(load_checkpoint)
self.set_state_dict(state_dict)
logger.info('Loaded parameters from %s' % os.path.abspath(load_checkpoint))
def forward(self,
input_ids=None,
token_type_ids=None,
position_ids=None,
attention_mask=None,
query_input_ids=None,
query_token_type_ids=None,
query_position_ids=None,
query_attention_mask=None,
title_input_ids=None,
title_token_type_ids=None,
title_position_ids=None,
title_attention_mask=None,
seq_lengths=None,
labels=None):
if self.task != 'text-matching':
result = self.model(input_ids, token_type_ids, position_ids, attention_mask)
else:
query_result = self.model(query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask)
title_result = self.model(title_input_ids, title_token_type_ids, title_position_ids, title_attention_mask)
if self.task == 'seq-cls':
logits = result
probs = F.softmax(logits, axis=1)
if labels is not None:
loss = self.criterion(logits, labels)
correct = self.metric.compute(probs, labels)
acc = self.metric.update(correct)
return probs, loss, {'acc': acc}
return probs
elif self.task == 'token-cls':
logits = result
token_level_probs = F.softmax(logits, axis=-1)
preds = token_level_probs.argmax(axis=-1)
if labels is not None:
loss = self.criterion(logits, labels.unsqueeze(-1))
num_infer_chunks, num_label_chunks, num_correct_chunks = \
self.metric.compute(None, seq_lengths, preds, labels)
self.metric.update(num_infer_chunks.numpy(), num_label_chunks.numpy(), num_correct_chunks.numpy())
_, _, f1_score = map(float, self.metric.accumulate())
return token_level_probs, loss, {'f1_score': f1_score}
return token_level_probs
elif self.task == 'text-matching':
query_token_embedding, _ = query_result
query_token_embedding = self.dropout(query_token_embedding)
query_attention_mask = paddle.unsqueeze(
(query_input_ids != self.model.pad_token_id).astype(self.model.pooler.dense.weight.dtype), axis=2)
query_token_embedding = query_token_embedding * query_attention_mask
query_sum_embedding = paddle.sum(query_token_embedding, axis=1)
query_sum_mask = paddle.sum(query_attention_mask, axis=1)
query_mean = query_sum_embedding / query_sum_mask
title_token_embedding, _ = title_result
title_token_embedding = self.dropout(title_token_embedding)
title_attention_mask = paddle.unsqueeze(
(title_input_ids != self.model.pad_token_id).astype(self.model.pooler.dense.weight.dtype), axis=2)
title_token_embedding = title_token_embedding * title_attention_mask
title_sum_embedding = paddle.sum(title_token_embedding, axis=1)
title_sum_mask = paddle.sum(title_attention_mask, axis=1)
title_mean = title_sum_embedding / title_sum_mask
sub = paddle.abs(paddle.subtract(query_mean, title_mean))
projection = paddle.concat([query_mean, title_mean, sub], axis=-1)
logits = self.classifier(projection)
probs = F.softmax(logits)
if labels is not None:
loss = self.criterion(logits, labels)
correct = self.metric.compute(probs, labels)
acc = self.metric.update(correct)
return probs, loss, {'acc': acc}
return probs
else:
sequence_output, pooled_output = result
return sequence_output, pooled_output
@staticmethod
def get_tokenizer(*args, **kwargs):
"""
Gets the tokenizer that is customized for this module.
"""
return BertTokenizer.from_pretrained(
pretrained_model_name_or_path='bert-base-multilingual-uncased', *args, **kwargs)