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1. Merge(add) daily regression from github PR (daily_regression.yaml) 2. Add lora stable diffusion from github PR Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/13010802 * fix: device arg not work, rename device to ngpu (#272) * Correcting the lora stable diffusion example script (#300) * add vad model and punc model in README.md add vad model and punc model * Merge pull request #302 from modelscope/langgz-patch-1 add vad model and punc model in README.md * add 1.6 * modify ignore * Merge pull request #307 from modelscope/dev_rs_16 Merge release 1.6 * undo datetime to 2099 * Merge pull request #311 from modelscope/fix_master_version undo datetime to 2099 * add daily regression workflow * modify workflow name * fix cron format issue * lora trainer * Merge pull request #315 from liuyhwangyh/add_regression_workflow add daily regression workflow
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
from modelscope.metainfo import Trainers
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from modelscope.msdatasets import MsDataset
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from modelscope.trainers import EpochBasedTrainer, build_trainer
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from modelscope.trainers.training_args import TrainingArgs
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from modelscope.utils.constant import DownloadMode
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training_args = TrainingArgs(task='text-to-image-synthesis').parse_cli()
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config, args = training_args.to_config()
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print(args)
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train_dataset = MsDataset.load(
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args.train_dataset_name,
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split='train',
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download_mode=DownloadMode.FORCE_REDOWNLOAD)
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validation_dataset = MsDataset.load(
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args.train_dataset_name,
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split='validation',
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download_mode=DownloadMode.FORCE_REDOWNLOAD)
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def cfg_modify_fn(cfg):
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if args.use_model_config:
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cfg.merge_from_dict(config)
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else:
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cfg = config
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cfg.train.lr_scheduler = {
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'type': 'LambdaLR',
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'lr_lambda': lambda _: 1,
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'last_epoch': -1
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}
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cfg.train.optimizer.lr = 1e-4
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return cfg
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kwargs = dict(
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model=training_args.model,
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model_revision='v1.0.6',
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work_dir=training_args.work_dir,
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train_dataset=train_dataset,
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eval_dataset=validation_dataset,
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cfg_modify_fn=cfg_modify_fn)
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trainer = build_trainer(name=Trainers.lora_diffusion, default_args=kwargs)
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trainer.train()
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