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82482b3e96
Based on feat/0131/nlp_args branch, the original code review: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11408570 Support for running finetuning from the command line with training args, Compatible with the configuration optimization.
91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
import os
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from dataclasses import dataclass, field
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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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def get_labels(cfg, metadata):
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label2id = cfg.safe_get(metadata['cfg_node'])
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if label2id is not None:
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return ','.join(label2id.keys())
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def set_labels(cfg, labels, metadata):
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if isinstance(labels, str):
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labels = labels.split(',')
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cfg.merge_from_dict(
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{metadata['cfg_node']: {label: id
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for id, label in enumerate(labels)}})
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@dataclass
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class TextClassificationArguments(TrainingArgs):
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first_sequence: str = field(
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default=None,
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metadata={
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'help': 'The first sequence key of preprocessor',
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'cfg_node': 'preprocessor.first_sequence'
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})
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second_sequence: str = field(
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default=None,
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metadata={
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'help': 'The second sequence key of preprocessor',
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'cfg_node': 'preprocessor.second_sequence'
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})
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label: str = field(
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default=None,
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metadata={
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'help': 'The label key of preprocessor',
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'cfg_node': 'preprocessor.label'
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})
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labels: str = field(
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default=None,
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metadata={
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'help': 'The labels of the dataset',
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'cfg_node': 'preprocessor.label2id',
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'cfg_getter': get_labels,
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'cfg_setter': set_labels,
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})
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preprocessor: str = field(
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default=None,
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metadata={
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'help': 'The preprocessor type',
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'cfg_node': 'preprocessor.type'
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})
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def __call__(self, config):
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config = super().__call__(config)
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config.model['num_labels'] = len(self.labels)
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if config.train.lr_scheduler.type == 'LinearLR':
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config.train.lr_scheduler['total_iters'] = \
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int(len(train_dataset) / self.per_device_train_batch_size) * self.max_epochs
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return config
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args = TextClassificationArguments.from_cli(
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task='text-classification', eval_metrics='seq-cls-metric')
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print(args)
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dataset = MsDataset.load(args.dataset_name, subset_name=args.subset_name)
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train_dataset = dataset['train']
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validation_dataset = dataset['validation']
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kwargs = dict(
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model=args.model,
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train_dataset=train_dataset,
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eval_dataset=validation_dataset,
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seed=args.seed,
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cfg_modify_fn=args)
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os.environ['LOCAL_RANK'] = str(args.local_rank)
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trainer: EpochBasedTrainer = build_trainer(name='trainer', default_args=kwargs)
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trainer.train()
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