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310e9c7dbf
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11549696 * add plug mental model code * add test pipeline and fix annotation format bugs
109 lines
3.9 KiB
Python
109 lines
3.9 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 typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
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import torch
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from transformers.tokenization_utils_base import PreTrainedTokenizerBase
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from modelscope.metainfo import Preprocessors, Trainers
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from modelscope.models import Model
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from modelscope.msdatasets import MsDataset
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from modelscope.pipelines import pipeline
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from modelscope.trainers import build_trainer
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from modelscope.utils.constant import ModelFile, Tasks
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from modelscope.utils.test_utils import test_level
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class TestFinetunePlugMental(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(
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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(self.epoch_num):
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self.assertIn(f'epoch_{i + 1}.pth', results_files)
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output_files = os.listdir(
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os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR))
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self.assertIn(ModelFile.CONFIGURATION, output_files)
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self.assertIn(ModelFile.TORCH_MODEL_BIN_FILE, output_files)
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copy_src_files = os.listdir(trainer.model_dir)
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print(f'copy_src_files are {copy_src_files}')
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print(f'output_files are {output_files}')
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for item in copy_src_files:
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if not item.startswith('.'):
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self.assertIn(item, output_files)
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def pipeline_sentence_similarity(self, model_dir):
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sentence1 = '今天气温比昨天高么？'
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sentence2 = '今天湿度比昨天高么？'
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model = Model.from_pretrained(model_dir)
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pipeline_ins = pipeline(task=Tasks.sentence_similarity, model=model)
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print(pipeline_ins(input=(sentence1, sentence2)))
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@unittest.skip
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def test_finetune_afqmc(self):
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"""This unittest is used to reproduce the clue:afqmc dataset + plug meantal 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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def cfg_modify_fn(cfg):
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cfg.task = Tasks.sentence_similarity
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cfg['preprocessor'] = {'type': Preprocessors.sen_sim_tokenizer}
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cfg.train.optimizer.lr = 2e-5
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cfg['dataset'] = {
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'train': {
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'labels': ['0', '1'],
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'first_sequence': 'sentence1',
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'second_sequence': 'sentence2',
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'label': 'label',
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}
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}
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cfg.train.lr_scheduler.total_iters = int(
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len(dataset['train']) / 32) * cfg.train.max_epochs
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return cfg
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dataset = MsDataset.load('clue', subset_name='afqmc')
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self.finetune(
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model_id='damo/nlp_plug-mental_backbone_base',
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train_dataset=dataset['train'],
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eval_dataset=dataset['validation'],
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cfg_modify_fn=cfg_modify_fn)
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output_dir = os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR)
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self.pipeline_sentence_similarity(output_dir)
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if __name__ == '__main__':
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unittest.main()
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