# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, and NVIDIA.
#
# 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.
# Most of the code here has been copied from:
# https://github.com/google-research/albert/blob/master/create_pretraining_data.py
# with some modifications.
import collections
import math
import time
import numpy as np
from colossalai.logging import get_dist_logger
from .blendable_dataset import BlendableDataset
from .indexed_dataset import make_dataset as make_indexed_dataset
DSET_TYPE_STD = "standard_bert"
DSET_TYPE_ICT = "ict"
DSET_TYPES = [DSET_TYPE_ICT, DSET_TYPE_STD]
def get_datasets_weights_and_num_samples(data_prefix, train_valid_test_num_samples):
# The data prefix should be in the format of:
# weight-1, data-prefix-1, weight-2, data-prefix-2, ..
assert len(data_prefix) % 2 == 0
num_datasets = len(data_prefix) // 2
weights = [0] * num_datasets
prefixes = [0] * num_datasets
for i in range(num_datasets):
weights[i] = float(data_prefix[2 * i])
prefixes[i] = (data_prefix[2 * i + 1]).strip()
# Normalize weights
weight_sum = 0.0
for weight in weights:
weight_sum += weight
assert weight_sum > 0.0
weights = [weight / weight_sum for weight in weights]
# Add 0.5% (the 1.005 factor) so in case the bleding dataset does
# not uniformly distribute the number of samples, we still have
# samples left to feed to the network.
datasets_train_valid_test_num_samples = []
for weight in weights:
datasets_train_valid_test_num_samples.append(
[int(math.ceil(val * weight * 1.005)) for val in train_valid_test_num_samples]
)
return prefixes, weights, datasets_train_valid_test_num_samples
def compile_helper():
"""Compile helper function ar runtime. Make sure this
is invoked on a single process."""
import os
import subprocess
path = os.path.abspath(os.path.dirname(__file__))
ret = subprocess.run(["make", "-C", path])
if ret.returncode != 0:
print("Making C++ dataset helpers module failed, exiting.")
import sys
sys.exit(1)
def get_a_and_b_segments(sample, np_rng):
"""Divide sample into a and b segments."""
# Number of sentences in the sample.
n_sentences = len(sample)
# Make sure we always have two sentences.
assert n_sentences > 1, "make sure each sample has at least two sentences."
# First part:
# `a_end` is how many sentences go into the `A`.
a_end = 1
if n_sentences >= 3:
# Note that randin in numpy is exclusive.
a_end = np_rng.randint(1, n_sentences)
tokens_a = []
for j in range(a_end):
tokens_a.extend(sample[j])
# Second part:
tokens_b = []
for j in range(a_end, n_sentences):
tokens_b.extend(sample[j])
# Random next:
is_next_random = False
if np_rng.random() < 0.5:
is_next_random = True
tokens_a, tokens_b = tokens_b, tokens_a
return tokens_a, tokens_b, is_next_random
def truncate_segments(tokens_a, tokens_b, len_a, len_b, max_num_tokens, np_rng):
"""Truncates a pair of sequences to a maximum sequence length."""
# print(len_a, len_b, max_num_tokens)
assert len_a > 0
if len_a + len_b <= max_num_tokens:
return False
while len_a + len_b > max_num_tokens:
if len_a > len_b:
len_a -= 1
tokens = tokens_a
else:
len_b -= 1
tokens = tokens_b
if np_rng.random() < 0.5:
del tokens[0]
else:
tokens.pop()
return True
def create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id):
"""Merge segments A and B, add [CLS] and [SEP] and build tokentypes."""
tokens = []
tokentypes = []
# [CLS].
tokens.append(cls_id)
tokentypes.append(0)
# Segment A.
for token in tokens_a:
tokens.append(token)
tokentypes.append(0)
# [SEP].
tokens.append(sep_id)
tokentypes.append(0)
# Segment B.
for token in tokens_b:
tokens.append(token)
tokentypes.append(1)
if tokens_b:
# [SEP].
tokens.append(sep_id)
tokentypes.append(1)
return tokens, tokentypes
MaskedLmInstance = collections.namedtuple("MaskedLmInstance", ["index", "label"])
def is_start_piece(piece):
"""Check if the current word piece is the starting piece (BERT)."""
# When a word has been split into
# WordPieces, the first token does not have any marker and any subsequence
# tokens are prefixed with ##. So whenever we see the ## token, we
# append it to the previous set of word indexes.
return not piece.startswith("##")
def create_masked_lm_predictions(
tokens,
vocab_id_list,
vocab_id_to_token_dict,
masked_lm_prob,
cls_id,
sep_id,
mask_id,
max_predictions_per_seq,
np_rng,
max_ngrams=3,
do_whole_word_mask=True,
favor_longer_ngram=False,
do_permutation=False,
):
"""Creates the predictions for the masked LM objective.
Note: Tokens here are vocab ids and not text tokens."""
cand_indexes = []
# Note(mingdachen): We create a list for recording if the piece is
# the starting piece of current token, where 1 means true, so that
# on-the-fly whole word masking is possible.
token_boundary = [0] * len(tokens)
for i, token in enumerate(tokens):
if token == cls_id or token == sep_id:
token_boundary[i] = 1
continue
# Whole Word Masking means that if we mask all of the wordpieces
# corresponding to an original word.
#
# Note that Whole Word Masking does *not* change the training code
# at all -- we still predict each WordPiece independently, softmaxed
# over the entire vocabulary.
if do_whole_word_mask and len(cand_indexes) >= 1 and not is_start_piece(vocab_id_to_token_dict[token]):
cand_indexes[-1].append(i)
else:
cand_indexes.append([i])
if is_start_piece(vocab_id_to_token_dict[token]):
token_boundary[i] = 1
output_tokens = list(tokens)
masked_lm_positions = []
masked_lm_labels = []
if masked_lm_prob == 0:
return (output_tokens, masked_lm_positions, masked_lm_labels, token_boundary)
num_to_predict = min(max_predictions_per_seq, max(1, int(round(len(tokens) * masked_lm_prob))))
# Note(mingdachen):
# By default, we set the probabilities to favor shorter ngram sequences.
ngrams = np.arange(1, max_ngrams + 1, dtype=np.int64)
pvals = 1.0 / np.arange(1, max_ngrams + 1)
pvals /= pvals.sum(keepdims=True)
if favor_longer_ngram:
pvals = pvals[::-1]
ngram_indexes = []
for idx in range(len(cand_indexes)):
ngram_index = []
for n in ngrams:
ngram_index.append(cand_indexes[idx : idx + n])
ngram_indexes.append(ngram_index)
np_rng.shuffle(ngram_indexes)
masked_lms = []
covered_indexes = set()
for cand_index_set in ngram_indexes:
if len(masked_lms) >= num_to_predict:
break
if not cand_index_set:
continue
# Note(mingdachen):
# Skip current piece if they are covered in lm masking or previous ngrams.
for index_set in cand_index_set[0]:
for index in index_set:
if index in covered_indexes:
continue
n = np_rng.choice(
ngrams[: len(cand_index_set)],
p=pvals[: len(cand_index_set)] / pvals[: len(cand_index_set)].sum(keepdims=True),
)
index_set = sum(cand_index_set[n - 1], [])
n -= 1
# Note(mingdachen):
# Repeatedly looking for a candidate that does not exceed the
# maximum number of predictions by trying shorter ngrams.
while len(masked_lms) + len(index_set) > num_to_predict:
if n == 0:
break
index_set = sum(cand_index_set[n - 1], [])
n -= 1
# If adding a whole-word mask would exceed the maximum number of
# predictions, then just skip this candidate.
if len(masked_lms) + len(index_set) > num_to_predict:
continue
is_any_index_covered = False
for index in index_set:
if index in covered_indexes:
is_any_index_covered = True
break
if is_any_index_covered:
continue
for index in index_set:
covered_indexes.add(index)
masked_token = None
# 80% of the time, replace with [MASK]
if np_rng.random() < 0.8:
masked_token = mask_id
else:
# 10% of the time, keep original
if np_rng.random() < 0.5:
masked_token = tokens[index]
# 10% of the time, replace with random word
else:
masked_token = vocab_id_list[np_rng.randint(0, len(vocab_id_list))]
output_tokens[index] = masked_token
masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))
assert len(masked_lms) <= num_to_predict
np_rng.shuffle(ngram_indexes)
select_indexes = set()
if do_permutation:
for cand_index_set in ngram_indexes:
if len(select_indexes) >= num_to_predict:
break
if not cand_index_set:
continue
# Note(mingdachen):
# Skip current piece if they are covered in lm masking or previous ngrams.
for index_set in cand_index_set[0]:
for index in index_set:
if index in covered_indexes or index in select_indexes:
continue
n = np.random.choice(
ngrams[: len(cand_index_set)],
p=pvals[: len(cand_index_set)] / pvals[: len(cand_index_set)].sum(keepdims=True),
)
index_set = sum(cand_index_set[n - 1], [])
n -= 1
while len(select_indexes) + len(index_set) > num_to_predict:
if n == 0:
break
index_set = sum(cand_index_set[n - 1], [])
n -= 1
# If adding a whole-word mask would exceed the maximum number of
# predictions, then just skip this candidate.
if len(select_indexes) + len(index_set) > num_to_predict:
continue
is_any_index_covered = False
for index in index_set:
if index in covered_indexes or index in select_indexes:
is_any_index_covered = True
break
if is_any_index_covered:
continue
for index in index_set:
select_indexes.add(index)
assert len(select_indexes) <= num_to_predict
select_indexes = sorted(select_indexes)
permute_indexes = list(select_indexes)
np_rng.shuffle(permute_indexes)
orig_token = list(output_tokens)
for src_i, tgt_i in zip(select_indexes, permute_indexes):
output_tokens[src_i] = orig_token[tgt_i]
masked_lms.append(MaskedLmInstance(index=src_i, label=orig_token[src_i]))
masked_lms = sorted(masked_lms, key=lambda x: x.index)
for p in masked_lms:
masked_lm_positions.append(p.index)
masked_lm_labels.append(p.label)
return (output_tokens, masked_lm_positions, masked_lm_labels, token_boundary)
def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions, masked_labels, pad_id, max_seq_length):
"""Pad sequences and convert them to numpy."""
# Some checks.
num_tokens = len(tokens)
padding_length = max_seq_length - num_tokens
assert padding_length >= 0
assert len(tokentypes) == num_tokens
assert len(masked_positions) == len(masked_labels)
# Tokens and token types.
filler = [pad_id] * padding_length
tokens_np = np.array(tokens + filler, dtype=np.int64)
tokentypes_np = np.array(tokentypes + filler, dtype=np.int64)
# Padding mask.
padding_mask_np = np.array([1] * num_tokens + [0] * padding_length, dtype=np.int64)
# Lables and loss mask.
labels = [-1] * max_seq_length
loss_mask = [0] * max_seq_length
for i in range(len(masked_positions)):
assert masked_positions[i] < num_tokens
labels[masked_positions[i]] = masked_labels[i]
loss_mask[masked_positions[i]] = 1
labels_np = np.array(labels, dtype=np.int64)
loss_mask_np = np.array(loss_mask, dtype=np.int64)
return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np
def build_train_valid_test_datasets(
data_prefix,
data_impl,
splits_string,
train_valid_test_num_samples,
max_seq_length,
masked_lm_prob,
short_seq_prob,
seed,
skip_warmup,
binary_head,
dataset_type="standard_bert",
):
if len(data_prefix) == 1:
return _build_train_valid_test_datasets(
data_prefix[0],
data_impl,
splits_string,
train_valid_test_num_samples,
max_seq_length,
masked_lm_prob,
short_seq_prob,
seed,
skip_warmup,
binary_head,
dataset_type=dataset_type,
)
# Blending dataset.
# Parse the values.
output = get_datasets_weights_and_num_samples(data_prefix, train_valid_test_num_samples)
prefixes, weights, datasets_train_valid_test_num_samples = output
# Build individual datasets.
train_datasets = []
valid_datasets = []
test_datasets = []
for i in range(len(prefixes)):
train_ds, valid_ds, test_ds = _build_train_valid_test_datasets(
prefixes[i],
data_impl,
splits_string,
datasets_train_valid_test_num_samples[i],
max_seq_length,
masked_lm_prob,
short_seq_prob,
seed,
skip_warmup,
binary_head,
dataset_type=dataset_type,
)
if train_ds:
train_datasets.append(train_ds)
if valid_ds:
valid_datasets.append(valid_ds)
if test_ds:
test_datasets.append(test_ds)
# Blend.
blending_train_dataset = None
if train_datasets:
blending_train_dataset = BlendableDataset(train_datasets, weights)
blending_valid_dataset = None
if valid_datasets:
blending_valid_dataset = BlendableDataset(valid_datasets, weights)
blending_test_dataset = None
if test_datasets:
blending_test_dataset = BlendableDataset(test_datasets, weights)
return (blending_train_dataset, blending_valid_dataset, blending_test_dataset)
def _build_train_valid_test_datasets(
data_prefix,
data_impl,
splits_string,
train_valid_test_num_samples,
max_seq_length,
masked_lm_prob,
short_seq_prob,
seed,
skip_warmup,
binary_head,
dataset_type="standard_bert",
):
logger = get_dist_logger()
if dataset_type not in DSET_TYPES:
raise ValueError("Invalid dataset_type: ", dataset_type)
# Indexed dataset.
indexed_dataset = get_indexed_dataset_(data_prefix, data_impl, skip_warmup)
if dataset_type == DSET_TYPE_ICT:
args = get_args()
title_dataset = get_indexed_dataset_(args.titles_data_path, data_impl, skip_warmup)
# Get start and end indices of train/valid/train into doc-idx
# Note that doc-idx is designed to be num-docs + 1 so we can
# easily iterate over it.
total_num_of_documents = indexed_dataset.doc_idx.shape[0] - 1
splits = get_train_valid_test_split_(splits_string, total_num_of_documents)
# Print stats about the splits.
logger.info("\n > dataset split:")
def print_split_stats(name, index):
start_index = indexed_dataset.doc_idx[splits[index]]
end_index = indexed_dataset.doc_idx[splits[index + 1]]
logger.info(
"\n {}:".format(name)
+ "\n document indices in [{}, {}) total of {} documents".format(
splits[index], splits[index + 1], splits[index + 1] - splits[index]
)
+ "\n sentence indices in [{}, {}) total of {} sentences".format(
start_index, end_index, end_index - start_index
),
ranks=[0],
)
print_split_stats("train", 0)
print_split_stats("validation", 1)
print_split_stats("test", 2)
def build_dataset(index, name):
from .bert_dataset import BertDataset
dataset = None
if splits[index + 1] > splits[index]:
# Get the pointer to the original doc-idx so we can set it later.
doc_idx_ptr = indexed_dataset.get_doc_idx()
# Slice the doc-idx
start_index = splits[index]
# Add +1 so we can index into the dataset to get the upper bound.
end_index = splits[index + 1] + 1
# New doc_idx view.
indexed_dataset.set_doc_idx(doc_idx_ptr[start_index:end_index])
# Build the dataset accordingly.
kwargs = dict(
name=name,
data_prefix=data_prefix,
num_epochs=None,
max_num_samples=train_valid_test_num_samples[index],
max_seq_length=max_seq_length,
seed=seed,
binary_head=binary_head,
)
if dataset_type == DSET_TYPE_ICT:
args = get_args()
dataset = ICTDataset(
block_dataset=indexed_dataset,
title_dataset=title_dataset,
query_in_block_prob=args.query_in_block_prob,
use_one_sent_docs=args.use_one_sent_docs,
**kwargs,
)
else:
dataset = BertDataset(
indexed_dataset=indexed_dataset,
masked_lm_prob=masked_lm_prob,
short_seq_prob=short_seq_prob,
**kwargs,
)
# Set the original pointer so dataset remains the main dataset.
indexed_dataset.set_doc_idx(doc_idx_ptr)
# Checks.
assert indexed_dataset.doc_idx[0] == 0
assert indexed_dataset.doc_idx.shape[0] == (total_num_of_documents + 1)
return dataset
train_dataset = build_dataset(0, "train")
valid_dataset = build_dataset(1, "valid")
test_dataset = build_dataset(2, "test")
return (train_dataset, valid_dataset, test_dataset)
def get_indexed_dataset_(data_prefix, data_impl, skip_warmup):
logger = get_dist_logger()
start_time = time.time()
indexed_dataset = make_indexed_dataset(data_prefix, data_impl, skip_warmup)
assert indexed_dataset.sizes.shape[0] == indexed_dataset.doc_idx[-1]
logger.info("\n > building dataset index ...", ranks=[0])
logger.info(
"\n > finished creating indexed dataset in {:4f} " "seconds".format(time.time() - start_time), ranks=[0]
)
logger.info(
"\n > indexed dataset stats:"
+ "\n number of documents: {}".format(indexed_dataset.doc_idx.shape[0] - 1)
+ "\n number of sentences: {}".format(indexed_dataset.sizes.shape[0]),
ranks=[0],
)
return indexed_dataset
def get_train_valid_test_split_(splits_string, size):
"""Get dataset splits from comma or '/' separated string list."""
splits = []
if splits_string.find(",") != -1:
splits = [float(s) for s in splits_string.split(",")]
elif splits_string.find("/") != -1:
splits = [float(s) for s in splits_string.split("/")]
else:
splits = [float(splits_string)]
while len(splits) < 3:
splits.append(0.0)
splits = splits[:3]
splits_sum = sum(splits)
assert splits_sum > 0.0
splits = [split / splits_sum for split in splits]
splits_index = [0]
for index, split in enumerate(splits):
splits_index.append(splits_index[index] + int(round(split * float(size))))
diff = splits_index[-1] - size
for index in range(1, len(splits_index)):
splits_index[index] -= diff
assert len(splits_index) == 4
assert splits_index[-1] == size
return splits_index