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https://github.com/modelscope/modelscope.git
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132 lines
4.2 KiB
Python
132 lines
4.2 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 modelscope.metainfo import Trainers
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from modelscope.msdatasets import MsDataset
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from modelscope.trainers import build_trainer
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class TestFinetuneTextGeneration(unittest.TestCase):
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test_model_id = 'PAI/nlp_gpt3_text-generation_0.35B_MoE-64'
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def setUp(self):
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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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@unittest.skip(
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'skip since the test requires multiple GPU and takes a long time to run'
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)
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def test_finetune_poetry(self):
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dataset_dict = MsDataset.load('chinese-poetry-collection')
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train_dataset = dataset_dict['train'].remap_columns(
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{'text1': 'src_txt'})
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eval_dataset = dataset_dict['test'].remap_columns({'text1': 'src_txt'})
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max_epochs = 10
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tmp_dir = './gpt_moe_poetry'
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num_warmup_steps = 100
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def noam_lambda(current_step: int):
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current_step += 1
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return min(current_step**(-0.5),
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current_step * num_warmup_steps**(-1.5))
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def cfg_modify_fn(cfg):
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cfg.train.lr_scheduler = {
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'type': 'LambdaLR',
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'lr_lambda': noam_lambda,
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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.optimizer = {'type': 'AdamW', 'lr': 3e-4}
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cfg.train.dataloader = {
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'batch_size_per_gpu': 1,
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'workers_per_gpu': 1
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}
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return cfg
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kwargs = dict(
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model=self.test_model_id,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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max_epochs=max_epochs,
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work_dir=tmp_dir,
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cfg_modify_fn=cfg_modify_fn)
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# Construct trainer and train
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trainer = build_trainer(
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name=Trainers.gpt_moe_trainer, default_args=kwargs)
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trainer.train()
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@unittest.skip(
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'skip since the test requires multiple GPU and takes a long time to run'
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)
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def test_finetune_dureader(self):
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# DuReader_robust-QG is an example data set,
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# users can also use their own data set for training
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dataset_dict = MsDataset.load('DuReader_robust-QG')
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train_dataset = dataset_dict['train'].remap_columns({'text1': 'src_txt', 'text2': 'tgt_txt'}) \
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.map(lambda example: {'src_txt': example['src_txt'].replace('[SEP]', '<sep>') + '\n'})
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eval_dataset = dataset_dict['validation'].remap_columns({'text1': 'src_txt', 'text2': 'tgt_txt'}) \
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.map(lambda example: {'src_txt': example['src_txt'].replace('[SEP]', '<sep>') + '\n'})
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max_epochs = 10
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tmp_dir = './gpt_moe_dureader'
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num_warmup_steps = 200
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def noam_lambda(current_step: int):
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current_step += 1
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return min(current_step**(-0.5),
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current_step * num_warmup_steps**(-1.5))
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def cfg_modify_fn(cfg):
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cfg.train.lr_scheduler = {
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'type': 'LambdaLR',
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'lr_lambda': noam_lambda,
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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.optimizer = {'type': 'AdamW', 'lr': 3e-4}
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cfg.train.dataloader = {
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'batch_size_per_gpu': 16,
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'workers_per_gpu': 1
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}
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cfg.train.hooks.append({
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'type': 'EvaluationHook',
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'by_epoch': True,
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'interval': 1
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})
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cfg.preprocessor.sequence_length = 512
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cfg.model.checkpoint_model_parallel_size = 1
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return cfg
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kwargs = dict(
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model=self.test_model_id,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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max_epochs=max_epochs,
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work_dir=tmp_dir,
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cfg_modify_fn=cfg_modify_fn)
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# Construct trainer and train
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trainer = build_trainer(
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name=Trainers.gpt_moe_trainer, default_args=kwargs)
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trainer.train()
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if __name__ == '__main__':
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unittest.main()
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