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import glob
import json
import os
import time
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_ids
from tasks.data_utils import clean_text
NUM_CHOICES = 4
MAX_QA_LENGTH = 128
class RaceDataset(Dataset):
def __init__(self, dataset_name, datapaths, tokenizer, max_seq_length,
max_qa_length=MAX_QA_LENGTH):
self.dataset_name = dataset_name
print_rank_0(' > building RACE dataset for {}:'.format(
self.dataset_name))
string = ' > paths:'
for path in datapaths:
string += ' ' + path
print_rank_0(string)
self.samples = []
for datapath in datapaths:
self.samples.extend(process_single_datapath(datapath, tokenizer,
max_qa_length,
max_seq_length))
print_rank_0(' >> total number of samples: {}'.format(
len(self.samples)))
# This indicates that each "sample" has multiple samples that
# will collapse into batch dimension
self.sample_multiplier = NUM_CHOICES
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
return self.samples[idx]
def process_single_datapath(datapath, tokenizer, max_qa_length, max_seq_length):
"""Read in RACE files, combine, clean-up, tokenize, and convert to
samples."""
print_rank_0(' > working on {}'.format(datapath))
start_time = time.time()
# Get list of files.
filenames = glob.glob(os.path.join(datapath, '*.txt'))
samples = []
num_docs = 0
num_questions = 0
num_samples = 0
# Load all the files
for filename in filenames:
with open(filename, 'r') as f:
for line in f:
data = json.loads(line)
num_docs += 1
context = data["article"]
questions = data["questions"]
choices = data["options"]
answers = data["answers"]
# Check the length.
assert len(questions) == len(answers)
assert len(questions) == len(choices)
# Context: clean up and convert to ids.
context = clean_text(context)
context_ids = tokenizer.tokenize(context)
# Loop over questions.
for qi, question in enumerate(questions):
num_questions += 1
# Label.
label = ord(answers[qi]) - ord("A")
assert label >= 0
assert label < NUM_CHOICES
assert len(choices[qi]) == NUM_CHOICES
# For each question, build num-choices samples.
ids_list = []
types_list = []
paddings_list = []
for ci in range(NUM_CHOICES):
choice = choices[qi][ci]
# Merge with choice.
if "_" in question:
qa = question.replace("_", choice)
else:
qa = " ".join([question, choice])
# Clean QA.
qa = clean_text(qa)
# Tokenize.
qa_ids = tokenizer.tokenize(qa)
# Trim if needed.
if len(qa_ids) > max_qa_length:
qa_ids = qa_ids[0:max_qa_length]
# Build the sample.
ids, types, paddings \
= build_tokens_types_paddings_from_ids(
qa_ids, context_ids, max_seq_length,
tokenizer.cls, tokenizer.sep, tokenizer.pad)
ids_list.append(ids)
types_list.append(types)
paddings_list.append(paddings)
# Convert to numpy and add to samples
samples.append(build_sample(ids_list, types_list,
paddings_list, label,
num_samples))
num_samples += 1
elapsed_time = time.time() - start_time
print_rank_0(' > processed {} document, {} questions, and {} samples'
' in {:.2f} seconds'.format(num_docs, num_questions,
num_samples, elapsed_time))
return samples
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