File size: 2,556 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 |
# 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 dataset."""
from abc import ABC
from abc import abstractmethod
from torch.utils.data import Dataset
from megatron import print_rank_0
from tasks.data_utils import build_sample
from tasks.data_utils import build_tokens_types_paddings_from_text
class GLUEAbstractDataset(ABC, Dataset):
"""GLUE base dataset class."""
def __init__(self, task_name, dataset_name, datapaths,
tokenizer, max_seq_length):
# Store inputs.
self.task_name = task_name
self.dataset_name = dataset_name
self.tokenizer = tokenizer
self.max_seq_length = max_seq_length
print_rank_0(' > building {} dataset for {}:'.format(self.task_name,
self.dataset_name))
# Process the files.
string = ' > paths:'
for path in datapaths:
string += ' ' + path
print_rank_0(string)
self.samples = []
for datapath in datapaths:
self.samples.extend(self.process_samples_from_single_path(datapath))
print_rank_0(' >> total number of samples: {}'.format(
len(self.samples)))
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
raw_sample = self.samples[idx]
ids, types, paddings = build_tokens_types_paddings_from_text(
raw_sample['text_a'], raw_sample['text_b'],
self.tokenizer, self.max_seq_length)
sample = build_sample(ids, types, paddings,
raw_sample['label'], raw_sample['uid'])
return sample
@abstractmethod
def process_samples_from_single_path(self, datapath):
"""Abstract method that takes a single path / filename and
returns a list of dataset samples, each sample being a dict of
{'text_a': string, 'text_b': string, 'label': int, 'uid': int}
"""
pass
|