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"""Race.""" |
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from megatron import get_args |
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from megatron import print_rank_0 |
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from megatron import get_tokenizer |
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from megatron.model.multiple_choice import MultipleChoice |
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import tasks.eval_utils |
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import tasks.finetune_utils |
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from tasks.race.data import RaceDataset |
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from megatron.model import ModelType |
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def train_valid_datasets_provider(): |
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"""Provide train and validation datasets.""" |
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args = get_args() |
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tokenizer = get_tokenizer() |
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train_dataset = RaceDataset('training', args.train_data, |
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tokenizer, args.seq_length) |
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valid_dataset = RaceDataset('validation', args.valid_data, |
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tokenizer, args.seq_length) |
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return train_dataset, valid_dataset |
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def model_provider(pre_process=True, |
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post_process=True): |
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"""Build the model.""" |
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model_type = ModelType.encoder_or_decoder |
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print_rank_0('building multichoice model for RACE ...') |
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model = MultipleChoice(num_tokentypes=2, |
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pre_process=pre_process, |
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post_process=post_process, |
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model_type=model_type) |
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return model |
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def metrics_func_provider(): |
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"""Privde metrics callback function.""" |
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args = get_args() |
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tokenizer = get_tokenizer() |
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def single_dataset_provider(datapath): |
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name = datapath.split('RACE')[-1].strip('/').replace('/', '-') |
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return RaceDataset(name, [datapath], tokenizer, args.seq_length) |
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return tasks.eval_utils.accuracy_func_provider(single_dataset_provider) |
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def main(): |
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model_type = ModelType.encoder_or_decoder |
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tasks.finetune_utils.finetune(train_valid_datasets_provider, |
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model_provider, |
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model_type, |
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end_of_epoch_callback_provider=metrics_func_provider) |
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