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dataset.py
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import torch
import json
import math
import numpy as np
from utils import print_rank_0
from tasks.data_utils import build_input_from_ids, num_special_tokens_to_add
from tasks.language_model.detokenizer import get_detokenizer
from bisect import bisect_right
from itertools import accumulate
class LMDataset(torch.utils.data.Dataset):
def __init__(self, args, documents, tokenizer, num_original_tokens, num_tokenized_tokens):
self.args = args
self.documents = documents
self.max_seq_len = args.seq_length - 1
self.tokenizer = tokenizer
self.overalapping_eval = args.overlapping_eval
if self.overalapping_eval is None:
self.overalapping_eval = self.max_seq_len
self.overalapping_eval = max(1, self.overalapping_eval)
self.num_original_tokens = num_original_tokens
self.num_tokenized_tokens = num_tokenized_tokens
# remove first sequence tokens
targets = [max(len(tokens) - self.max_seq_len, 0) for tokens in self.documents]
self.num_sequences = [max(math.ceil(target / self.overalapping_eval) + 1, 1) for target in targets]
self.weights = list(accumulate(self.num_sequences))
self.left_weights = [0] + self.weights[:-1]
self.unidirectional = args.unidirectional
self.block_lm = args.block_lm
mask_token = "gMASK" if args.task_mask else 'MASK'
self.mask_id = self.tokenizer.get_command(mask_token).Id
def __len__(self):
return sum(self.num_sequences)
def __getitem__(self, idx):
document_idx = bisect_right(self.weights, idx)
idx = idx - self.left_weights[document_idx]
start_idx = idx * self.overalapping_eval
end_idx = start_idx + self.max_seq_len
tokens = self.documents[document_idx][start_idx:end_idx]
if self.block_lm:
if idx == 0 or self.unidirectional:
prompt, text = tokens[:1], tokens[1:]
else:
prompt_length = self.max_seq_len - self.overalapping_eval
prompt, text = tokens[:prompt_length], tokens[prompt_length:]
prompt = prompt + [self.mask_id]
num_special_tokens = num_special_tokens_to_add(prompt, None, text, add_cls=True, add_sep=False,
add_piece=True,
add_eos=False)
data = build_input_from_ids(prompt, None, text, self.max_seq_len + num_special_tokens + 1, self.tokenizer,
args=self.args, add_cls=True, add_sep=False, add_piece=True, add_eos=False, mask_id=self.mask_id)
ids, types, paddings, position_ids, sep, target_ids, loss_masks = data
if idx != 0 and self.unidirectional:
loss_masks = np.array(loss_masks, dtype=np.int64)
loss_masks[:-self.overalapping_eval] = 0
return {'text': np.array(ids, dtype=np.int64), 'target': np.array(target_ids, dtype=np.int64),
'attention_mask': np.array(sep, dtype=np.int64), 'loss_mask': np.array(loss_masks, dtype=np.int64),
"position_id": np.array(position_ids, dtype=np.int64)}
else:
loss_masks = [1] * len(tokens)
if len(tokens) < self.max_seq_len:
tokens = tokens + [0] * (self.max_seq_len - len(tokens))
loss_masks = loss_masks + [0] * (self.max_seq_len - len(loss_masks))
if idx != 0:
loss_masks = np.array(loss_masks, dtype=np.int64)
loss_masks[:-self.overalapping_eval] = 0
return {'text': np.array(tokens, dtype=np.int64), 'loss_mask': np.array(loss_masks, dtype=np.int64)}
class LambadaDataset(torch.utils.data.Dataset):
def __init__(self, args, tokenizer, strict=True):
data_path = args.valid_data[0]
print_rank_0('> building lambada dataset from {} ...'.format(data_path))
self.args = args
self.max_seq_length = args.seq_length
self.tokenizer = tokenizer
self.pad_idx = tokenizer.get_command('pad').Id
self.strict = strict
self.block_lm = args.block_lm
self.unidirectional = args.unidirectional
mask_token = "gMASK" if args.task_mask else 'MASK'
self.mask_id = self.tokenizer.get_command(mask_token).Id
self.tokens = []
self.labels = []
with open(data_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.EncodeAsIds(text).tokenization
return tokens[:-1], [tokens[-1]]
last_token = text.split()[-1]
start_idx = text.rfind(last_token)
beginning_tokens = self.tokenizer.EncodeAsIds(text[:start_idx].strip()).tokenization
last_token = self.tokenizer.EncodeAsIds(' ' + last_token).tokenization
return beginning_tokens, last_token
def __len__(self):
return len(self.tokens)
def __getitem__(self, idx):
tokens, answer = self.tokens[idx], self.labels[idx]
if self.block_lm:
if self.unidirectional:
tokens, answer_tokens = tokens[:1], tokens[1:] + answer
else:
answer_tokens = answer
tokens = tokens + [self.mask_id]
num_special_tokens = num_special_tokens_to_add(tokens, None, answer_tokens, add_cls=True, add_sep=False,
add_piece=True)
left_shift = len(tokens) + len(answer_tokens) + num_special_tokens - self.max_seq_length
if left_shift > 0:
tokens = tokens[left_shift:]
data = build_input_from_ids(tokens, None, answer_tokens, self.max_seq_length, self.tokenizer,
args=self.args, add_cls=True, add_sep=False, add_piece=True,
mask_id=self.mask_id)
ids, types, paddings, position_ids, sep, target_ids, loss_masks = data
if self.unidirectional:
loss_masks = np.array(loss_masks, dtype=np.int64)
last_index = len(loss_masks)
while loss_masks[last_index - 1] == 0:
last_index -= 1
loss_masks[:last_index - len(answer)] = 0
return {'text': np.array(ids, dtype=np.int64), 'target': np.array(target_ids, dtype=np.int64),
'attention_mask': np.array(sep, dtype=np.int64), 'loss_mask': np.array(loss_masks, dtype=np.int64),
"position_id": np.array(position_ids, dtype=np.int64)}
else:
left_shift = len(tokens) - self.max_seq_length
if left_shift > 0:
tokens = tokens[left_shift:]
ids = tokens + answer
if len(ids) < self.max_seq_length:
ids = ids + [0] * (self.max_seq_length - len(ids))
loss_masks = [0] * len(tokens) + [1] * len(answer)
if len(loss_masks) < self.max_seq_length:
loss_masks = loss_masks + [0] * (self.max_seq_length - len(loss_masks))
return {'text': np.array(ids, dtype=np.int64), 'loss_mask': np.array(loss_masks, dtype=np.int64)}
def build_lambada_dataset(tokenizer, args):
"""Build lambada dataset."""
assert len(args.valid_data) == 1
val_dataset = LambadaDataset(args, tokenizer, strict=True)
print_rank_0(' > found {} samples, {} label tokens.'.format(len(val_dataset), sum(map(len, val_dataset.labels))))
return val_dataset
def build_lm_dataset(tokenizer, args):
documents = []
num_tokens, num_original_tokens = 0, 0
with open(args.valid_data[0], encoding='utf-8') as file:
for line in file:
tokens = tokenizer.EncodeAsIds(line.strip()).tokenization
num_tokens += len(tokens)
num_original_tokens += len(line.strip().split(" "))
documents.append(tokens)
val_dataset = LMDataset(args, documents, tokenizer, num_original_tokens, num_tokens)
print_rank_0(
' > number of document: {}, number of original tokens {}, number of detokenized tokens: {}'.format(
len(documents), num_original_tokens, num_tokens))
return val_dataset
def build_wikitext103_dataset(tokenizer, args):
""""""
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('wikitext')(entire_data)
print_rank_0(entire_data[:1024])
tokenized_data = tokenizer.EncodeAsIds(entire_data).tokenization
num_tokenized_tokens = len(tokenized_data)
val_dataset = LMDataset(args, [tokenized_data], tokenizer, num_original_tokens, num_tokenized_tokens)
print_rank_0(' > number of original tokens: {}, number of detokenized '
'tokens: {}'.format(num_original_tokens, num_tokenized_tokens))
return val_dataset