File size: 3,156 Bytes
c2c125c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""GLUE finetuning/evaluation."""
from megatron import get_args
from megatron import print_rank_0
from megatron import get_tokenizer
from megatron import mpu
from megatron.model.classification import Classification
from tasks.eval_utils import accuracy_func_provider
from tasks.finetune_utils import finetune
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()
print_rank_0('building classification model for {} ...'.format(
args.task))
model = Classification(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)
"""Finetune/evaluate."""
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('_', '-')
else:
raise NotImplementedError('GLUE task {} is not implemented.'.format(
args.task))
glue_classification(num_classes, Dataset, name_from_datapath)
|