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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""GLUE finetuning/evaluation."""
from megatron import get_args
from megatron import print_rank_0
from megatron import get_tokenizer
from megatron.model.classification import Classification
from tasks.eval_utils import accuracy_func_provider
from tasks.finetune_utils import finetune, mse_forward_step
from megatron.arguments import core_transformer_config_from_args
def glue_classification(num_classes, Dataset,
name_from_datapath_func):
def train_valid_datasets_provider():
"""Build train and validation dataset."""
args = get_args()
tokenizer = get_tokenizer()
train_dataset = Dataset('training', args.train_data,
tokenizer, args.seq_length)
valid_dataset = Dataset('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."""
args = get_args()
config = core_transformer_config_from_args()
print_rank_0('building classification model for {} ...'.format(
args.task))
model = Classification(config=config, num_classes=num_classes, num_tokentypes=2,
pre_process=pre_process, post_process=post_process)
return model
def metrics_func_provider():
"""Privde metrics callback function."""
def single_dataset_provider(datapath):
args = get_args()
tokenizer = get_tokenizer()
name = name_from_datapath_func(datapath)
return Dataset(name, [datapath], tokenizer, args.seq_length)
return accuracy_func_provider(single_dataset_provider)
args = get_args()
"""Finetune/evaluate."""
if args.task == 'STS-B':
finetune(train_valid_datasets_provider, model_provider,
forward_step=mse_forward_step,
end_of_epoch_callback_provider=metrics_func_provider)
else:
finetune(train_valid_datasets_provider, model_provider,
end_of_epoch_callback_provider=metrics_func_provider)
def main():
args = get_args()
if args.task == 'MNLI':
num_classes = 3
from tasks.glue.mnli import MNLIDataset as Dataset
def name_from_datapath(datapath):
return datapath.split('MNLI')[-1].strip(
'.tsv').strip('/').replace('_', '-')
elif args.task == 'QQP':
num_classes = 2
from tasks.glue.qqp import QQPDataset as Dataset
def name_from_datapath(datapath):
return datapath.split('QQP')[-1].strip(
'.tsv').strip('/').replace('_', '-')
elif args.task == 'QNLI':
num_classes = 2
from tasks.glue.qnli import QNLIDataset as Dataset
def name_from_datapath(datapath):
return datapath.split('QNLI')[-1].strip(
'.tsv').strip('/').replace('_', '-')
elif args.task == 'SST-2':
num_classes = 2
from tasks.glue.sst2 import SST2Dataset as Dataset
def name_from_datapath(datapath):
return datapath.split('SST-2')[-1].strip(
'.tsv').strip('/').replace('_', '-')
elif args.task == 'CoLA':
num_classes = 2
from tasks.glue.cola import CoLADataset as Dataset
def name_from_datapath(datapath):
return datapath.split('CoLA')[-1].strip(
'.tsv').strip('/').replace('_', '-')
elif args.task == 'STS-B':
num_classes = 1
from tasks.glue.stsb import STSBDataset as Dataset
def name_from_datapath(datapath):
return datapath.split('STS-B')[-1].strip(
'.tsv').strip('/').replace('_', '-')
elif args.task == 'MRPC':
num_classes = 2
from tasks.glue.mrpc import MRPCDataset as Dataset
def name_from_datapath(datapath):
return datapath.split('MRPC')[-1].strip(
'.tsv').strip('/').replace('_', '-')
elif args.task == 'RTE':
num_classes = 2
from tasks.glue.rte import RTEDataset as Dataset
def name_from_datapath(datapath):
return datapath.split('RTE')[-1].strip(
'.tsv').strip('/').replace('_', '-')
else:
raise NotImplementedError('GLUE task {} is not implemented.'.format(
args.task))
glue_classification(num_classes, Dataset, name_from_datapath)