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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Race."""
from megatron import get_args
from megatron import print_rank_0
from megatron import get_tokenizer
from megatron.model.multiple_choice import MultipleChoice
import tasks.eval_utils
import tasks.finetune_utils
from tasks.race.data import RaceDataset
from megatron.model import ModelType
def train_valid_datasets_provider():
"""Provide train and validation datasets."""
args = get_args()
tokenizer = get_tokenizer()
train_dataset = RaceDataset('training', args.train_data,
tokenizer, args.seq_length)
valid_dataset = RaceDataset('validation', args.valid_data,
tokenizer, args.seq_length)
return train_dataset, valid_dataset
def model_provider(pre_process=True,
post_process=True):
"""Build the model."""
model_type = ModelType.encoder_or_decoder
print_rank_0('building multichoice model for RACE ...')
model = MultipleChoice(num_tokentypes=2,
pre_process=pre_process,
post_process=post_process,
model_type=model_type)
return model
def metrics_func_provider():
"""Privde metrics callback function."""
args = get_args()
tokenizer = get_tokenizer()
def single_dataset_provider(datapath):
name = datapath.split('RACE')[-1].strip('/').replace('/', '-')
return RaceDataset(name, [datapath], tokenizer, args.seq_length)
return tasks.eval_utils.accuracy_func_provider(single_dataset_provider)
def main():
model_type = ModelType.encoder_or_decoder
tasks.finetune_utils.finetune(train_valid_datasets_provider,
model_provider,
model_type,
end_of_epoch_callback_provider=metrics_func_provider)
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