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from datasets import load_dataset
import random
import hetu
import os
import numpy as np

''' Usage example:
In dir Hetu/examples/nlp/bert/: python processBertData.py
'''

# https://the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz


class TrainingInstance(object):
"""A single training instance (sentence pair)."""

def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels,
is_random_next):
self.tokens = tokens
self.segment_ids = segment_ids
self.is_random_next = is_random_next
self.masked_lm_positions = masked_lm_positions
self.masked_lm_labels = masked_lm_labels

def __str__(self):
s = ""
s += "tokens: %s\n" % (" ".join(
[str(x) for x in self.tokens]))
s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids]))
s += "is_random_next: %s\n" % self.is_random_next
s += "masked_lm_positions: %s\n" % (" ".join(
[str(x) for x in self.masked_lm_positions]))
s += "masked_lm_labels: %s\n" % (" ".join(
[str(x) for x in self.masked_lm_labels]))
s += "\n"
return s

def __repr__(self):
return self.__str__()


def create_masked_lm_predictions(tokens, masked_lm_prob,
max_predictions_per_seq, vocab_words, rng):
"""Creates the predictions for the masked LM objective."""
cand_indexes = []
for (i, token) in enumerate(tokens):
if token == "[CLS]" or token == "[SEP]":
continue
cand_indexes.append(i)
rng.shuffle(cand_indexes)
output_tokens = list(tokens)
num_to_predict = min(max_predictions_per_seq,
max(1, int(round(len(tokens) * masked_lm_prob))))
masked_lms = []
for index in cand_indexes:
if len(masked_lms) >= num_to_predict:
break
masked_token = None
# replace with [MASK] at 80%.
if rng.random() < 0.8:
masked_token = "[MASK]"
else:
# keep original at 10%.
if rng.random() < 0.5:
masked_token = tokens[index]
# replace with random word at 10%.
else:
masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]
output_tokens[index] = masked_token
masked_lms.append([index, tokens[index]])

masked_lms.sort(key = lambda x: x[0])
masked_lm_positions = []
masked_lm_labels = []

for p in masked_lms:
masked_lm_positions.append(p[0])
masked_lm_labels.append(p[1])

return (output_tokens, masked_lm_positions, masked_lm_labels)


def create_data_from_document(all_document, doc_id, max_seq_length, short_seq_prob, masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
""" Create Training example for input document """
document = all_document[doc_id]
max_num_tokens = max_seq_length - 3 # [CLS], [SEP], [SEP]
target_seq_length = max_num_tokens
# generate short sequence at the probility of short_seq_prob
# In order to minimize the mismatch between pre-training and fine-tuning.
if rng.random() < short_seq_prob:
target_seq_length = rng.randint(2, max_num_tokens)
instances = []
current_chunk = []
current_length = 0
i = 0
while i < len(document):
segment = document[i]
current_chunk.append(segment)
current_length += len(segment)
if i == len(document) - 1 or current_length >= target_seq_length:
if current_chunk:
# create sentence A
a_end = 1
if len(current_chunk) >= 2:
a_end = rng.randint(1, len(current_chunk) - 1)
tokens_a = []
for j in range(a_end):
tokens_a.extend([current_chunk[j]])
tokens_b = []
# Random next
is_random_next = False
if len(current_chunk) == 1 or rng.random() < 0.5:
is_random_next = True
target_b_length = target_seq_length - len(tokens_a)
for _ in range(10):
random_document_index = rng.randint(0, len(all_document) - 1)
if random_document_index != doc_id:
break
#If picked random document is the same as the current document
if random_document_index == doc_id:
is_random_next = False
random_document = all_document[random_document_index]
random_start = rng.randint(0, len(random_document) - 1)
for j in range(random_start, len(random_document)):
tokens_b.extend([random_document[j]])
if len(tokens_b) >= target_b_length:
break
# We didn't actually use these segments so we "put them back" so
# they don't go to waste.
num_unused_segments = len(current_chunk) - a_end
i -= num_unused_segments
# Actual next
else:
is_random_next = False
for j in range(a_end, len(current_chunk)):
tokens_b.extend([current_chunk[j]])
truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)
assert len(tokens_a) >= 1
assert len(tokens_b) >= 1

tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)

tokens.append("[SEP]")
segment_ids.append(0)

for token in tokens_b:
tokens.append(token)
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)

(tokens, masked_lm_positions, masked_lm_labels) = create_masked_lm_predictions(
tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng)
instance = TrainingInstance(
tokens=tokens,
segment_ids=segment_ids,
is_random_next=is_random_next,
masked_lm_positions=masked_lm_positions,
masked_lm_labels=masked_lm_labels)
instances.append(instance)
current_chunk = []
current_length = 0
i += 1

return instances
def convert_instances_to_data(instances, tokenizer, max_seq_length):
num_instances = len(instances)
input_ids_list = np.zeros([num_instances, max_seq_length], dtype="int32")
input_mask_list = np.zeros([num_instances, max_seq_length], dtype="int32")
segment_ids_list = np.zeros([num_instances, max_seq_length], dtype="int32")
masked_lm_labels = np.full([num_instances, max_seq_length],-1, dtype="int32")
next_sentence_labels_list = np.zeros(num_instances, dtype="int32")

for (idx, instance) in enumerate(instances):
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
input_mask = [1] * len(input_ids)
segment_ids = list(instance.segment_ids)
assert len(input_ids) <= max_seq_length

padding_zero_list = [0]*int(max_seq_length - len(input_ids))
input_ids += padding_zero_list
input_mask += padding_zero_list
segment_ids += padding_zero_list

assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length

masked_lm_positions = list(instance.masked_lm_positions)
masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)

input_ids_list[idx][:] = input_ids
input_mask_list[idx][:] = input_mask
segment_ids_list[idx][:] = segment_ids
masked_lm_labels[idx][masked_lm_positions] = masked_lm_ids
next_sentence_labels_list[idx] = 1 if instance.is_random_next else 0

return input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list

def create_pretrain_data(dataset, tokenizer, max_seq_length, short_seq_prob, masked_lm_prob, max_predictions_per_seq, rng):
documents, all_data = [], [[],[],[],[],[]]
vocab_words = list(tokenizer.vocab.keys())

save_path='./preprocessed_data/bookcorpus/'
if not os.path.exists(save_path):
os.makedirs(save_path)

for i in range(dataset['train'].shape[0]):
tokens = tokenizer.tokenize(dataset['train'][i]['text'])
documents.append(tokens)
instance = create_data_from_document(documents, i,\
max_seq_length, short_seq_prob, masked_lm_prob,
max_predictions_per_seq, vocab_words, rng)
data = convert_instances_to_data(instance, tokenizer, max_seq_length)
print(i, len(tokens), len(instance))
for j in range(5):
all_data[j].append(data[j])

save_gap=200
if (i+1)%save_gap==0 and i:
input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list = [np.concatenate(all_data[j],axis=0) for j in range(5)]
print('Saving data from %d to %d: doc_num = %d, input_ids_shape ='%(i+1-save_gap,i, i+1), input_ids_list.shape)
save_data(input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list, name='_%d_%d'%(i+1-save_gap,i))
all_data = [[],[],[],[],[]]
if i == dataset['train'].shape[0]-1:
input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list = [np.concatenate(all_data[j],axis=0) for j in range(5)]
print('Saving data from %d to %d: doc_num = %d, input_ids_shape ='%(save_gap*int(i/save_gap),i, i+1), input_ids_list.shape)
save_data(input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list, name='_%d_%d'%(save_gap*int(i/save_gap),i))

def save_data(input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list,name=''):
save_path='./preprocessed_data/bookcorpus/'
np.save(save_path+'input_ids'+name,np.array(input_ids_list))
np.save(save_path+'token_type_ids'+name,np.array(segment_ids_list))
np.save(save_path+'attention_mask'+name,np.array(input_mask_list))
np.save(save_path+'masked_lm_labels'+name,np.array(masked_lm_labels))
np.save(save_path+'next_sentence_label'+name,np.array(next_sentence_labels_list))

def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):
"""Truncates a pair of sequences to a maximum sequence length."""
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_num_tokens:
break

trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
assert len(trunc_tokens) >= 1

#add more randomness and avoid biases.
if rng.random() < 0.5:
del trunc_tokens[0]
else:
trunc_tokens.pop()

def show_dataset_detail(dataset):
print(dataset.shape)
print(dataset.column_names)
print(dataset['train'].features)
print(dataset['train'][0]['text'])

if __name__ == "__main__":
max_seq_length = 512
do_lower_case = True
short_seq_prob = 0.1
masked_lm_prob = 0.15
max_predictions_per_seq = 20
vocab_path = "./datasets/bert-base-uncased-vocab.txt"
dataset = load_dataset('../bookcorpus', cache_dir = "./cached_data")
print("total number of documents {} ".format(dataset['train'].shape[0]))
random_seed = 123
rng = random.Random(random_seed)
tokenizer = hetu.BertTokenizer(vocab_file=vocab_path, do_lower_case = do_lower_case)

print("vocab_size =",len(tokenizer.vocab))
print("max_seq_len =", max_seq_length)
create_pretrain_data(dataset, tokenizer, max_seq_length, short_seq_prob, masked_lm_prob, max_predictions_per_seq, rng)






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