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dataset_utils.py
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dataset_utils.py
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# 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:https://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 math
import os
import time
import collections
import numpy as np
import torch
from megatron.training import (
get_args,
print_rank_0
)
from megatron.core import mpu
from megatron.core.datasets.indexed_dataset import IndexedDataset
DSET_TYPE_BERT = 'standard_bert'
DSET_TYPE_ICT = 'ict'
DSET_TYPE_T5 = 't5'
DSET_TYPE_MULTIMODAL = 'multimodal'
DSET_TYPES = [DSET_TYPE_BERT, DSET_TYPE_ICT, DSET_TYPE_T5, DSET_TYPE_MULTIMODAL]
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.
if isinstance(train_valid_test_num_samples, list):
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])
else:
# Used when separate dataset files are provided for train,
# valid and test
datasets_train_valid_test_num_samples = [
int(math.ceil(train_valid_test_num_samples * weight * 1.005))
for weight in weights]
return prefixes, weights, datasets_train_valid_test_num_samples
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,
geometric_dist=False,
masking_style="bert"):
"""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))))
ngrams = np.arange(1, max_ngrams + 1, dtype=np.int64)
if not geometric_dist:
# Note(mingdachen):
# By default, we set the probilities to favor shorter ngram sequences.
pvals = 1. / 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, masked_spans) = ([], [])
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
if not geometric_dist:
n = np_rng.choice(ngrams[:len(cand_index_set)],
p=pvals[:len(cand_index_set)] /
pvals[:len(cand_index_set)].sum(keepdims=True))
else:
# Sampling "n" from the geometric distribution and clipping it to
# the max_ngrams. Using p=0.2 default from the SpanBERT paper
# https://arxiv.org/pdf/1907.10529.pdf (Sec 3.1)
n = min(np_rng.geometric(0.2), max_ngrams)
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
if masking_style == "bert":
# 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))]
elif masking_style == "t5":
masked_token = mask_id
else:
raise ValueError("invalid value of masking style")
output_tokens[index] = masked_token
masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))
masked_spans.append(MaskedLmInstance(
index=index_set,
label=[tokens[index] for index in index_set]))
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)
# Sort the spans by the index of the first span
masked_spans = sorted(masked_spans, key=lambda x: x.index[0])
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, masked_spans)
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_with_prefixes(train_valid_test_num_samples,
max_seq_length,
seed,
train_data_prefix=None,
valid_data_prefix=None,
test_data_prefix=None,
binary_head=False,
max_seq_length_dec=None,
dataset_type='standard_bert'):
print_rank_0("Separate data paths provided for train, valid & test.")
train_dataset, valid_dataset, test_dataset = None, None, None
# Single dataset.
if train_data_prefix is not None:
train_dataset = build_dataset("train", train_data_prefix,
train_valid_test_num_samples[0],
max_seq_length, seed,
binary_head, max_seq_length_dec,
dataset_type=dataset_type)
if valid_data_prefix is not None:
valid_dataset = build_dataset("valid", valid_data_prefix,
train_valid_test_num_samples[1],
max_seq_length, seed, False,
binary_head, max_seq_length_dec,
dataset_type=dataset_type)
if test_data_prefix is not None:
test_dataset = build_dataset("test", test_data_prefix,
train_valid_test_num_samples[2],
max_seq_length, seed, False,
binary_head, max_seq_length_dec,
dataset_type=dataset_type)
return (train_dataset, valid_dataset, test_dataset)
def build_train_valid_test_datasets(data_prefix, splits_string,
train_valid_test_num_samples,
max_seq_length, seed,
binary_head=False,
max_seq_length_dec=None,
dataset_type='standard_bert'):
if len(data_prefix) == 1:
return _build_train_valid_test_datasets(data_prefix[0],
splits_string,
train_valid_test_num_samples,
max_seq_length, seed,
binary_head,
max_seq_length_dec,
dataset_type=dataset_type)
raise NotImplementedError("Blending currently unsupported for non-GPT dataset instances")
def _build_train_valid_test_datasets(data_prefix, splits_string,
train_valid_test_num_samples,
max_seq_length, seed,
binary_head,
max_seq_length_dec,
dataset_type='standard_bert'):
# Indexed dataset.
indexed_dataset = get_indexed_dataset_(data_prefix,
dataset_type)
# Get start and end indices of train/valid/train into doc-idx
# Note that doc-idx is desinged to be num-docs + 1 so we can
# easily iterate over it.
total_num_of_documents = indexed_dataset.document_indices.shape[0] - 1
splits = get_train_valid_test_split_(splits_string, total_num_of_documents)
# Print stats about the splits.
print_rank_0(' > dataset split:')
def print_split_stats(name, index):
print_rank_0(' {}:'.format(name))
print_rank_0(' document indices in [{}, {}) total of {} '
'documents'.format(splits[index], splits[index + 1],
splits[index + 1] - splits[index]))
start_index = indexed_dataset.document_indices[splits[index]]
end_index = indexed_dataset.document_indices[splits[index + 1]]
print_rank_0(' sentence indices in [{}, {}) total of {} '
'sentences'.format(start_index, end_index,
end_index - start_index))
print_split_stats('train', 0)
print_split_stats('validation', 1)
print_split_stats('test', 2)
def build_split_dataset(index, name):
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_document_indices()
# 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_document_indices(doc_idx_ptr[start_index:end_index])
dataset = build_dataset(
name, data_prefix,
train_valid_test_num_samples[index], max_seq_length,
seed, binary_head, max_seq_length_dec,
dataset_type, indexed_dataset)
# Set the original pointer so dataset remains the main dataset.
indexed_dataset.set_document_indices(doc_idx_ptr)
# Checks.
assert indexed_dataset.document_indices[0] == 0
assert indexed_dataset.document_indices.shape[0] == \
(total_num_of_documents + 1)
return dataset
train_dataset = build_split_dataset(0, 'train')
valid_dataset = build_split_dataset(1, 'valid')
test_dataset = build_split_dataset(2, 'test')
return (train_dataset, valid_dataset, test_dataset)
def build_dataset(name, data_prefix, max_num_samples,
max_seq_length, seed, binary_head,
max_seq_length_dec, dataset_type='standard_bert',
indexed_dataset=None):
from megatron.legacy.data.ict_dataset import ICTDataset
from megatron.legacy.data.multimodal_dataset import MultiModalDataset
if dataset_type == DSET_TYPE_BERT or dataset_type == DSET_TYPE_T5:
raise ValueError("The Megatron-LM BERT and T5 datasets are deprecated.")
if dataset_type not in DSET_TYPES:
raise ValueError("Invalid dataset_type: ", dataset_type)
if indexed_dataset is None:
indexed_dataset = get_indexed_dataset_(data_prefix,
dataset_type)
kwargs = dict(
name=name,
data_prefix=data_prefix,
num_epochs=None,
max_num_samples=max_num_samples,
max_seq_length=max_seq_length,
seed=seed,
)
if dataset_type == DSET_TYPE_ICT:
args = get_args()
title_dataset = get_indexed_dataset_(
args.titles_data_path,
dataset_type)
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,
binary_head=binary_head,
**kwargs
)
elif dataset_type == DSET_TYPE_MULTIMODAL:
args = get_args()
dataset = MultiModalDataset(
name=name,
data_prefix=data_prefix,
indexed_dataset=indexed_dataset,
num_samples=max_num_samples,
seq_length=max_seq_length,
seed=seed,
img_h=args.img_h,
img_w=args.img_w,
)
else:
raise NotImplementedError("Dataset type not fully implemented.")
return dataset
def get_indexed_dataset_(data_prefix, dataset_type):
print_rank_0(' > building dataset index ...')
start_time = time.time()
multimodal = dataset_type == DSET_TYPE_MULTIMODAL
indexed_dataset = IndexedDataset(data_prefix, multimodal)
assert indexed_dataset.sequence_lengths.shape[0] == indexed_dataset.document_indices[-1]
print_rank_0(' > finished creating indexed dataset in {:4f} '
'seconds'.format(time.time() - start_time))
print_rank_0(' > indexed dataset stats:')
print_rank_0(' number of documents: {}'.format(
indexed_dataset.document_indices.shape[0] - 1))
print_rank_0(' number of sentences: {}'.format(
indexed_dataset.sequence_lengths.shape[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.)
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
def get_samples_mapping(indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
max_seq_length,
short_seq_prob,
seed,
name,
binary_head):
"""Get a list that maps a sample index to a starting sentence index, end sentence index, and length"""
if not num_epochs:
if not max_num_samples:
raise ValueError("Need to specify either max_num_samples "
"or num_epochs")
num_epochs = np.iinfo(np.int32).max - 1
if not max_num_samples:
max_num_samples = np.iinfo(np.int64).max - 1
# Filename of the index mapping
indexmap_filename = data_prefix
indexmap_filename += '_{}_indexmap'.format(name)
if num_epochs != (np.iinfo(np.int32).max - 1):
indexmap_filename += '_{}ep'.format(num_epochs)
if max_num_samples != (np.iinfo(np.int64).max - 1):
indexmap_filename += '_{}mns'.format(max_num_samples)
indexmap_filename += '_{}msl'.format(max_seq_length)
indexmap_filename += '_{:0.2f}ssp'.format(short_seq_prob)
indexmap_filename += '_{}s'.format(seed)
indexmap_filename += '.npy'
# Build the indexed mapping if not exist.
if torch.distributed.get_rank() == 0 and \
not os.path.isfile(indexmap_filename):
print(' > WARNING: could not find index map file {}, building '
'the indices on rank 0 ...'.format(indexmap_filename))
# Make sure the types match the helpers input types.
assert indexed_dataset.document_indices.dtype == np.int64
assert indexed_dataset.sequence_lengths.dtype == np.int32
# Build samples mapping
verbose = torch.distributed.get_rank() == 0
start_time = time.time()
print_rank_0(' > building samples index mapping for {} ...'.format(
name))
# First compile and then import.
from megatron.core.datasets import helpers
samples_mapping = helpers.build_mapping(
indexed_dataset.document_indices,
indexed_dataset.sequence_lengths,
num_epochs,
max_num_samples,
max_seq_length,
short_seq_prob,
seed,
verbose,
2 if binary_head else 1)
print_rank_0(' > done building samples index maping')
np.save(indexmap_filename, samples_mapping, allow_pickle=True)
print_rank_0(' > saved the index mapping in {}'.format(
indexmap_filename))
# Make sure all the ranks have built the mapping
print_rank_0(' > elasped time to build and save samples mapping '
'(seconds): {:4f}'.format(
time.time() - start_time))
# This should be a barrier but nccl barrier assumes
# device_index=rank which is not the case for model
# parallel case
counts = torch.tensor([1], dtype=torch.long, device='cuda')
torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group())
torch.distributed.all_reduce(counts, group=mpu.get_pipeline_model_parallel_group())
assert counts[0].item() == (
torch.distributed.get_world_size() //
torch.distributed.get_world_size(group=mpu.get_tensor_model_parallel_group()))
# Load indexed dataset.
print_rank_0(' > loading indexed mapping from {}'.format(
indexmap_filename))
start_time = time.time()
samples_mapping = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r')
print_rank_0(' loaded indexed file in {:3.3f} seconds'.format(
time.time() - start_time))
print_rank_0(' total number of samples: {}'.format(
samples_mapping.shape[0]))
return samples_mapping