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# 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.
"""Wikipedia dataset from DPR code for ORQA."""
from abc import ABC
import csv
import numpy as np
import random
import torch
from torch.utils.data import Dataset
from megatron import print_rank_0, get_args, get_tokenizer, mpu
from megatron.data.biencoder_dataset_utils import make_attention_mask
def get_open_retrieval_wiki_dataset():
args = get_args()
tokenizer = get_tokenizer()
dataset = OpenRetrievalEvidenceDataset('2018 Wikipedia from DPR codebase',
'evidence',
args.evidence_data_path,
tokenizer,
args.retriever_seq_length)
return dataset
def get_open_retrieval_batch(data_iterator):
# Items and their type.
keys = ['row_id', 'context', 'context_mask', 'context_types',
'context_pad_mask']
datatype = torch.int64
# Broadcast data.
data = None if data_iterator is None else next(data_iterator)
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
row_id = data_b['row_id'].long()
context = data_b['context'].long()
# TODO: make the context mask a binary one
context_mask = (data_b['context_mask'] < 0.5)
context_types = data_b['context_types'].long()
context_pad_mask = data_b['context_pad_mask'].long()
return row_id, context, context_mask, context_types, context_pad_mask
def build_tokens_types_paddings_from_text(row, tokenizer, max_seq_length):
"""Build token types and paddings, trim if needed, and pad if needed."""
title_ids = tokenizer.tokenize(row['title'])
context_ids = tokenizer.tokenize(row['text'])
# Appending the title of the context at front
extended_context_ids = title_ids + [tokenizer.sep_id] + context_ids
context_ids, context_types, context_pad_mask = \
build_tokens_types_paddings_from_ids(extended_context_ids,
max_seq_length, tokenizer.cls, tokenizer.sep, tokenizer.pad)
return context_ids, context_types, context_pad_mask
# noinspection DuplicatedCode
def build_tokens_types_paddings_from_ids(text_ids, max_seq_length,
cls_id, sep_id, pad_id):
"""Build token types and paddings, trim if needed, and pad if needed."""
enc_ids = []
tokentypes_enc = []
# [CLS].
enc_ids.append(cls_id)
tokentypes_enc.append(0)
# A.
len_src = len(text_ids)
enc_ids.extend(text_ids)
tokentypes_enc.extend([0] * len_src)
# Cap the size.
if len(enc_ids) > max_seq_length - 1:
enc_ids = enc_ids[0: max_seq_length - 1]
tokentypes_enc = tokentypes_enc[0: max_seq_length - 1]
# [SEP].
enc_ids.append(sep_id)
tokentypes_enc.append(0)
num_tokens_enc = len(enc_ids)
# Padding.
padding_length = max_seq_length - len(enc_ids)
if padding_length > 0:
enc_ids.extend([pad_id] * padding_length)
tokentypes_enc.extend([pad_id] * padding_length)
pad_mask = ([1] * num_tokens_enc) + ([0] * padding_length)
pad_mask = np.array(pad_mask, dtype=np.int64)
return enc_ids, tokentypes_enc, pad_mask
def build_sample(row_id, context_ids, context_types, context_pad_mask):
"""Convert to numpy and return a sample consumed by the batch producer."""
context_ids = np.array(context_ids, dtype=np.int64)
context_types = np.array(context_types, dtype=np.int64)
context_mask = make_attention_mask(context_ids, context_ids)
sample = ({
'row_id': row_id,
'context': context_ids,
'context_mask': context_mask,
'context_types': context_types,
'context_pad_mask': context_pad_mask
})
return sample
class OpenRetrievalEvidenceDataset(ABC, Dataset):
"""Open Retrieval Evidence dataset class."""
def __init__(self, task_name, dataset_name, datapath, 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.
print_rank_0(datapath)
self.samples, self.id2text = self.process_samples_from_single_path(
datapath)
args = get_args()
if args.sample_rate < 1: # subsample
k = int(len(self.samples) * args.sample_rate)
self.samples = random.sample(self.samples, k)
print_rank_0(' >> total number of samples: {}'.format(
len(self.samples)))
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
row = self.samples[idx]
context_ids, context_types, context_pad_mask = \
build_tokens_types_paddings_from_text(row, self.tokenizer,
self.max_seq_length)
sample = build_sample(row['doc_id'],
context_ids,
context_types,
context_pad_mask)
return sample
@staticmethod
def process_samples_from_single_path(filename):
print_rank_0(' > Processing {} ...'.format(filename))
total = 0
rows = []
id2text = {}
with open(filename) as tsvfile:
reader = csv.reader(tsvfile, delimiter='\t')
next(reader, None) # skip the headers
for row in reader:
# file format: doc_id, doc_text, title
doc_id = int(row[0])
text = row[1]
title = row[2]
rows.append({'doc_id': doc_id,
'text': text,
'title': title})
assert doc_id not in id2text
id2text[doc_id] = (text, title)
total += 1
if total % 100000 == 0:
print_rank_0(' > processed {} rows so far ...'.format(
total))
print_rank_0(' >> processed {} samples.'.format(len(rows)))
return rows, id2text
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