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132 lines
4.5 KiB
Python
132 lines
4.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import shutil
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import tempfile
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import unittest
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from functools import reduce
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from modelscope.metainfo import Trainers
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from modelscope.trainers import build_trainer
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from modelscope.utils.test_utils import test_level
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class TestFinetuneTokenClassification(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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def tearDown(self):
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shutil.rmtree(self.tmp_dir)
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super().tearDown()
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def finetune(self,
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model_id,
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train_dataset,
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eval_dataset,
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name=Trainers.nlp_base_trainer,
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cfg_modify_fn=None,
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**kwargs):
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kwargs = dict(model=model_id,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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work_dir=self.tmp_dir,
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cfg_modify_fn=cfg_modify_fn,
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**kwargs)
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os.environ['LOCAL_RANK'] = '0'
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trainer = build_trainer(name=name, default_args=kwargs)
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trainer.train()
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results_files = os.listdir(self.tmp_dir)
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self.assertIn(f'{trainer.timestamp}.log.json', results_files)
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for i in range(10):
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self.assertIn(f'epoch_{i+1}.pth', results_files)
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@unittest.skip
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def test_word_segmentation(self):
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"""This unittest is used to reproduce the icwb2:pku dataset + structbert model training results.
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User can train a custom dataset by modifying this piece of code and comment the @unittest.skip.
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"""
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os.system(
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f'curl http://sighan.cs.uchicago.edu/bakeoff2005/data/icwb2-data.zip > {self.tmp_dir}/icwb2-data.zip'
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)
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shutil.unpack_archive(f'{self.tmp_dir}/icwb2-data.zip', self.tmp_dir)
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from datasets import load_dataset
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from modelscope.preprocessors.nlp import WordSegmentationBlankSetToLabelPreprocessor
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preprocessor = WordSegmentationBlankSetToLabelPreprocessor()
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dataset = load_dataset(
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'text',
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data_files=f'{self.tmp_dir}/icwb2-data/training/pku_training.utf8')
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def split_to_dict(examples):
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return preprocessor(examples['text'])
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dataset = dataset.map(split_to_dict, batched=False)
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def reducer(x, y):
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x = x.split(' ') if isinstance(x, str) else x
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y = y.split(' ') if isinstance(y, str) else y
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return x + y
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label_enumerate_values = list(
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set(reduce(reducer, dataset['train'][:1000]['labels'])))
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label_enumerate_values.sort()
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train_len = int(len(dataset['train']) * 0.7)
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train_dataset = dataset['train'].select(range(train_len))
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dev_dataset = dataset['train'].select(
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range(train_len, len(dataset['train'])))
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def cfg_modify_fn(cfg):
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cfg.task = 'token-classification'
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cfg['dataset'] = {
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'train': {
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'labels': label_enumerate_values,
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'first_sequence': 'tokens',
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'label': 'labels',
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}
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}
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cfg['preprocessor'] = {
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'type': 'token-cls-tokenizer',
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'padding': 'max_length'
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}
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cfg.train.max_epochs = 2
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cfg.train.dataloader.workers_per_gpu = 0
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cfg.evaluation.dataloader.workers_per_gpu = 0
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cfg.train.lr_scheduler = {
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'type': 'LinearLR',
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'start_factor': 1.0,
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'end_factor': 0.0,
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'total_iters':
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int(len(train_dataset) / 32) * cfg.train.max_epochs,
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'options': {
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'by_epoch': False
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}
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}
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cfg.train.hooks = [{
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'type': 'CheckpointHook',
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'interval': 1
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}, {
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'type': 'TextLoggerHook',
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'interval': 1
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}, {
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'type': 'IterTimerHook'
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}, {
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'type': 'EvaluationHook',
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'by_epoch': False,
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'interval': 50
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}]
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return cfg
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self.finetune('damo/nlp_structbert_backbone_base_std',
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train_dataset,
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dev_dataset,
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cfg_modify_fn=cfg_modify_fn)
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if __name__ == '__main__':
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unittest.main()
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