File size: 4,110 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
# 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 clue_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 == 'AFQMC':
num_classes = 2
from tasks.clue.afqmc import AFQMCDataset as Dataset
def name_from_datapath(datapath):
return "afqmc"
elif args.task == 'CSL':
num_classes = 2
from tasks.clue.csl import CSLDataset as Dataset
def name_from_datapath(datapath):
return "csl"
elif args.task == 'IFLYTEK':
num_classes = 119
from tasks.clue.iflytek import IFLYTEKDataset as Dataset
def name_from_datapath(datapath):
return "iflytek"
elif args.task == 'OCNLI':
num_classes = 3
from tasks.clue.ocnli import OCNLIDataset as Dataset
def name_from_datapath(datapath):
return "ocnli"
elif args.task == 'TNEWS':
num_classes = 15
from tasks.clue.tnews import TNEWSDataset as Dataset
def name_from_datapath(datapath):
return "tnews"
elif args.task == 'WSC':
num_classes = 2
from tasks.clue.wsc import WSCDataset as Dataset
def name_from_datapath(datapath):
return "wsc"
elif args.task == 'CMNLI':
num_classes = 3
from tasks.clue.cmnli import CMNLIDataset as Dataset
def name_from_datapath(datapath):
return "cmnli"
elif args.task == 'ZC':
num_classes = 2
from tasks.clue.zc import ZCDataset as Dataset
def name_from_datapath(datapath):
return "zc"
else:
raise NotImplementedError('GLUE task {} is not implemented.'.format(
args.task))
clue_classification(num_classes, Dataset, name_from_datapath)
|