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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Zero-shot datasets."""
import json
import math
import numpy as np
import torch
from megatron import get_args
from megatron import print_rank_0
from megatron import get_tokenizer
from .detokenizer import get_detokenizer
def build_dataset(task):
"""Helper function to select and build dataset."""
if task == 'LAMBADA':
return _build_lambada_dataset()
if task == 'WIKITEXT103':
return _build_wikitext103_dataset()
raise NotImplementedError('dataset for {} task is not '
'implemented.'.format(task))
class _LMDataset(torch.utils.data.Dataset):
def __init__(self, tokens, seq_len, pad_idx, num_original_tokens,
num_tokenized_tokens, overalapping_eval=None):
self.tokens = tokens
self.seq_len = seq_len
self.pad_idx = pad_idx
self.overalapping_eval = overalapping_eval
if self.overalapping_eval is None:
self.overalapping_eval = self.seq_len
self.overalapping_eval = max(1, self.overalapping_eval)
self.num_original_tokens = num_original_tokens
self.num_tokenized_tokens = num_tokenized_tokens
self.total_targets = len(self.tokens) - 1
# remove first sequence tokens
targets = max(self.total_targets - self.overalapping_eval, 0)
self.total_sequences = max(
math.ceil(targets / self.overalapping_eval) + 1, 1)
def __len__(self):
return self.total_sequences
def __getitem__(self, idx):
start_idx = idx * self.overalapping_eval
end_idx = start_idx + self.seq_len
tokens = self.tokens[start_idx:end_idx + 1]
num_tokens = len(tokens)
pad_mask = [1] * num_tokens
if num_tokens < self.seq_len + 1:
num_pad = (self.seq_len + 1 - num_tokens)
pad_mask += [0] * (num_pad)
tokens += [self.pad_idx] * num_pad
pad_mask = np.array(pad_mask[1:])
if self.overalapping_eval != self.seq_len and idx != 0:
pad_mask[:-self.overalapping_eval] *= 0
return {'text': np.array(tokens), 'pad_mask': pad_mask}
class _LambadaDataset(torch.utils.data.Dataset):
def __init__(self, path, pad_idx, tokenizer, seq_len, strict=False):
print_rank_0('> building lambada dataset from {} ...'.format(path))
self.seq_len = seq_len
self.pad_idx = pad_idx
self.tokenizer = tokenizer
self.strict = strict
self.tokens = []
self.labels = []
with open(path, 'r') as f:
for line in f.readlines():
text = json.loads(line)['text']
tokens, labels = self.get_tokens(text)
self.tokens.append(tokens)
self.labels.append(labels)
def get_tokens(self, text):
if not self.strict:
tokens = self.tokenizer.tokenize(text)
return tokens[:-1], [tokens[-1]]
last_token = text.split()[-1]
start_idx = text.rfind(last_token)
beginning_tokens = self.tokenizer.tokenize(text[:start_idx].strip())
last_token = self.tokenizer.tokenize(' ' + last_token)
return beginning_tokens, last_token
def __len__(self):
return len(self.tokens)
def __getitem__(self, idx):
tokens = self.tokens[idx]
num_tokens = len(tokens)
pad_mask = [0] * num_tokens
labels = self.labels[idx]
pad_mask += [1] * len(labels)
tokens = tokens + labels
num_tokens = len(tokens)
if num_tokens < self.seq_len + 1:
num_pad = (self.seq_len + 1 - num_tokens)
pad_mask += [0] * (num_pad)
tokens += [self.pad_idx] * num_pad
pad_mask = np.array(pad_mask[1:])
return {'text': np.array(tokens), 'pad_mask': pad_mask}
def _build_lambada_dataset():
"""Build lambada dataset."""
args = get_args()
tokenizer = get_tokenizer()
assert len(args.valid_data) == 1
val_dataset = _LambadaDataset(args.valid_data[0], tokenizer.eod, tokenizer,
args.seq_length, args.strict_lambada)
print_rank_0(' > found {} samples.'.format(len(val_dataset)))
return val_dataset
def _build_wikitext103_dataset():
""""""
args = get_args()
tokenizer = get_tokenizer()
assert len(args.valid_data) == 1
with open(args.valid_data[0], "rb") as reader:
entire_data = reader.read().decode('utf-8')
num_original_tokens = len(entire_data.strip().split(" "))
entire_data = get_detokenizer(args.valid_data[0])(entire_data)
tokenized_data = tokenizer.tokenize(entire_data)
num_tokenized_tokens = len(tokenized_data)
val_dataset = _LMDataset(tokenized_data, args.seq_length, tokenizer.eod,
num_original_tokens, num_tokenized_tokens,
args.overlapping_eval)
print_rank_0(' > number of original tokens: {}, number of detokenized '
'tokens: {}'.format(num_original_tokens, num_tokenized_tokens))
return val_dataset
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