peacock-data-public-datasets-idc-mint
/
docker
/intel_code
/llama13b
/Megatron-DeepSpeed
/tasks
/glue
/finetune.py
# 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) | |