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data_utils.py
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data_utils.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
from functools import partial
from collections import Counter, OrderedDict
import pickle
import json
import multiprocessing as mp
import numpy as np
from absl import flags
import tensorflow as tf
from vocabulary import Vocab
from tensorflow.gfile import Exists as exists
from tensorflow.gfile import MakeDirs as makedirs
from tensorflow.gfile import Glob as glob
def _preprocess(shard, train, vocab, save_dir, cutoffs, bin_sizes, bsz, tgt_len,
num_core_per_host, use_tpu, num_shuffle):
file_names = []
num_batch = 0
path = train[shard]
data_shard = vocab.encode_file(path, ordered=False, add_double_eos=True)
for shuffle in range(num_shuffle):
basename = "train-{:03d}-{:02d}".format(shard, shuffle)
print("Processing shard {} shuffle {}".format(shard, shuffle))
np.random.shuffle(data_shard)
file_name, num_batch_shuffle = create_ordered_tfrecords(
save_dir, basename, np.concatenate(data_shard), bsz, tgt_len,
num_core_per_host, cutoffs, bin_sizes, use_tpu=use_tpu)
file_names.append(file_name)
num_batch += num_batch_shuffle
return file_names, num_batch
class Corpus(object):
def __init__(self, path, dataset, *args, **kwargs):
self.dataset = dataset
self.vocab = Vocab(*args, **kwargs)
if self.dataset in ["ptb", "wt2", "enwik8", "text8"]:
self.vocab.count_file(os.path.join(path, "train.txt"))
self.vocab.count_file(os.path.join(path, "valid.txt"))
self.vocab.count_file(os.path.join(path, "test.txt"))
elif self.dataset == "wt103":
self.vocab.count_file(os.path.join(path, "train.txt"))
elif self.dataset == "lm1b":
train_path_pattern = os.path.join(
path, "1-billion-word-language-modeling-benchmark-r13output",
"training-monolingual.tokenized.shuffled", "news.en-*")
train_paths = glob(train_path_pattern)
# the vocab will load from file when build_vocab() is called
# for train_path in sorted(train_paths):
# self.vocab.count_file(train_path, verbose=True)
self.vocab.build_vocab()
if self.dataset in ["ptb", "wt2", "wt103"]:
self.train = self.vocab.encode_file(
os.path.join(path, "train.txt"), ordered=True)
self.valid = self.vocab.encode_file(
os.path.join(path, "valid.txt"), ordered=True)
self.test = self.vocab.encode_file(
os.path.join(path, "test.txt"), ordered=True)
elif self.dataset in ["enwik8", "text8"]:
self.train = self.vocab.encode_file(
os.path.join(path, "train.txt"), ordered=True, add_eos=False)
self.valid = self.vocab.encode_file(
os.path.join(path, "valid.txt"), ordered=True, add_eos=False)
self.test = self.vocab.encode_file(
os.path.join(path, "test.txt"), ordered=True, add_eos=False)
elif self.dataset == "lm1b":
self.train = train_paths
valid_path = os.path.join(path, "valid.txt")
test_path = valid_path
self.valid = self.vocab.encode_file(
valid_path, ordered=True, add_double_eos=True)
self.test = self.vocab.encode_file(
test_path, ordered=True, add_double_eos=True)
if self.dataset == "wt103":
self.cutoffs = [0, 20000, 40000, 200000] + [len(self.vocab)]
elif self.dataset == "lm1b":
self.cutoffs = [0, 60000, 100000, 640000] + [len(self.vocab)]
else:
self.cutoffs = []
def convert_to_tfrecords(self, split, save_dir, bsz, tgt_len,
num_core_per_host, **kwargs):
FLAGS = kwargs.get('FLAGS')
file_names = []
use_tpu = FLAGS.use_tpu and not (split == "test" and num_core_per_host == 1)
if use_tpu:
record_name = "record_info-{}.bsz-{}.tlen-{}.core-{}.json".format(
split, bsz, tgt_len, num_core_per_host)
else:
record_name = "record_info-{}.bsz-{}.tlen-{}.json".format(
split, bsz, tgt_len)
record_info_path = os.path.join(save_dir, record_name)
if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]:
data = getattr(self, split)
bin_sizes = get_bin_sizes(
data, bsz // num_core_per_host, tgt_len, self.cutoffs)
file_name, num_batch = create_ordered_tfrecords(
save_dir, split, data, bsz, tgt_len, num_core_per_host,
self.cutoffs, bin_sizes,
num_passes=FLAGS.num_passes if split == 'train' and use_tpu else 1,
use_tpu=use_tpu)
file_names.append(file_name)
elif self.dataset == "lm1b":
bin_sizes = get_bin_sizes(
self.valid, bsz // num_core_per_host, tgt_len, self.cutoffs)
if split == "train":
np.random.seed(123456)
num_batch = 0
if FLAGS.num_procs > 1:
_preprocess_wrapper = partial(_preprocess,
train=self.train, vocab=self.vocab, save_dir=save_dir,
cutoffs=self.cutoffs, bin_sizes=bin_sizes, bsz=bsz,
tgt_len=tgt_len, num_core_per_host=num_core_per_host,
use_tpu=use_tpu, num_shuffle=FLAGS.num_shuffle)
pool = mp.Pool(processes=FLAGS.num_procs)
results = pool.map(_preprocess_wrapper, range(len(self.train)))
for res in results:
file_names.extend(res[0])
num_batch += res[1]
else:
for shard, path in enumerate(self.train):
data_shard = self.vocab.encode_file(path, ordered=False,
add_double_eos=True)
num_shuffle = FLAGS.num_shuffle
for shuffle in range(num_shuffle):
print("Processing shard {} shuffle {}".format(shard, shuffle))
basename = "train-{:03d}-{:02d}".format(shard, shuffle)
np.random.shuffle(data_shard)
file_name, num_batch_ = create_ordered_tfrecords(
save_dir, basename, np.concatenate(data_shard), bsz, tgt_len,
num_core_per_host,
self.cutoffs, bin_sizes, use_tpu=use_tpu)
file_names.append(file_name)
num_batch += num_batch_
else:
file_name, num_batch = create_ordered_tfrecords(
save_dir, split, getattr(self, split), bsz, tgt_len,
num_core_per_host,
self.cutoffs, bin_sizes, use_tpu=use_tpu)
file_names.append(file_name)
with open(record_info_path, "w") as fp:
record_info = {
"filenames": file_names,
"bin_sizes": bin_sizes,
"num_batch": num_batch
}
json.dump(record_info, fp)
def get_bin_sizes(data, batch_size, tgt_len, cutoffs, std_mult=[2.5, 2.5, 2.5]):
"""
Note: the `batch_size` here should be per-core batch size
"""
bin_sizes = []
def _nearest_to_eight(x): # so that it's faster on TPUs
y = x - x % 8
return y + 8 if x % 8 >= 4 else max(8, y)
if cutoffs:
num_batch = len(data) // batch_size // tgt_len
data = data[:batch_size * num_batch * tgt_len]
data = data.reshape(batch_size, num_batch, tgt_len)
tot = batch_size * tgt_len
for b, (left, right) in enumerate(zip(cutoffs[1:-1], cutoffs[2:])):
mask = (data >= left) * (data < right)
percents = mask.astype(np.float64).sum(2).sum(0) / tot
mean = np.mean(percents)
std = np.std(percents)
bin_size = int(math.ceil(tgt_len * batch_size * (mean + std_mult[b] * std)))
bin_size = _nearest_to_eight(bin_size)
bin_sizes.append(bin_size)
return bin_sizes
def _int64_feature(values):
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
def _float_feature(values):
return tf.train.Feature(float_list=tf.train.FloatList(value=values))
def batchify(data, batch_size, num_passes):
"""
if use_tpu = True: num_passes > 1
Since TPU training requires entire [bsz x tgt_len] chunks, it can discard
as many as `bsz * tgt_len` tokens in training. When `bsz` and `tgt_len` are
both large, as in the case of TPU training for Transformer-XL, the problem
may lead to detectable performance drop.
Here, we use multiple randomly shifted copies to deal with this problem.
"""
if num_passes > 1:
data_len = len(data)
double_data = np.concatenate([data, data])
data_list = []
for i in range(num_passes):
start = np.random.randint(0, data_len)
data_list.append(double_data[start:start+data_len])
data = np.concatenate(data_list)
num_step = len(data) // batch_size
data = data[:batch_size * num_step]
data = data.reshape(batch_size, num_step)
return data
def create_ordered_tfrecords(save_dir, basename, data, batch_size, tgt_len,
num_core_per_host, cutoffs=[], bin_sizes=[],
num_passes=1, use_tpu=False):
if use_tpu:
file_name = "{}.bsz-{}.tlen-{}.core-{}.tfrecords".format(
basename, batch_size, tgt_len, num_core_per_host)
else:
file_name = "{}.bsz-{}.tlen-{}.tfrecords".format(
basename, batch_size, tgt_len)
save_path = os.path.join(save_dir, file_name)
record_writer = tf.python_io.TFRecordWriter(save_path)
batched_data = batchify(data, batch_size, num_passes)
num_batch = 0
# for t in range(0, batched_data.shape[1] - tgt_len - 1, tgt_len):
for t in range(0, batched_data.shape[1] - 1, tgt_len):
cur_tgt_len = min(batched_data.shape[1] - 1 - t, tgt_len)
# drop the remainder if use tpu
if use_tpu and cur_tgt_len < tgt_len:
break
if num_batch % 500 == 0:
print(" processing batch {}".format(num_batch))
for idx in range(batch_size):
inputs = batched_data[idx, t:t + cur_tgt_len]
labels = batched_data[idx, t + 1:t + cur_tgt_len + 1]
# features dict
feature = {
"inputs": _int64_feature(inputs),
"labels": _int64_feature(labels),
}
if len(cutoffs) > 0 and use_tpu:
# validate `bin_sizes` and `cutoffs`
assert len(cutoffs) - len(bin_sizes) == 2, \
"len(cutoffs) - len(bin_sizes) != 2"
# mask for bin 0
left, right = cutoffs[:2]
inp_mask = ((inputs >= left) * (inputs < right)).astype(np.float32)
tgt_mask = ((labels >= left) * (labels < right)).astype(np.float32)
feature["inp_mask"] = _float_feature(inp_mask)
feature["tgt_mask"] = _float_feature(tgt_mask)
# refresh `inp_cnts` and `tgt_cnts` for each TPU core
if idx % (batch_size // num_core_per_host) == 0:
inp_cnts = [0] * len(bin_sizes)
tgt_cnts = [0] * len(bin_sizes)
head_labels = np.copy(labels)
inp_pos_per_bin, tgt_pos_per_bin = [], []
for b, (left, right) in enumerate(zip(cutoffs[1:-1], cutoffs[2:])):
inp_pos = np.where((inputs >= left) * (inputs < right))[0]
tgt_pos = np.where((labels >= left) * (labels < right))[0]
inp_pos_per_bin.append(inp_pos)
tgt_pos_per_bin.append(tgt_pos)
head_labels[tgt_pos] = cutoffs[1] + b
feature["head_labels"] = _int64_feature(head_labels)
# permutation feature
def _add_perm_feature(feature, pos_per_bin, cnts, prefix):
for b, pos in enumerate(pos_per_bin):
idx_tuple = []
for p in pos:
if cnts[b] < bin_sizes[b]:
idx_tuple.append([p, cnts[b]])
cnts[b] += 1
else:
break
n_tup = len(idx_tuple)
tup = np.array(idx_tuple).reshape(n_tup * 2)
feature["{}_cnt_{}".format(prefix, b)] = _int64_feature([n_tup])
feature["{}_tup_{}".format(prefix, b)] = _int64_feature(tup)
_add_perm_feature(feature, inp_pos_per_bin, inp_cnts, "inp")
_add_perm_feature(feature, tgt_pos_per_bin, tgt_cnts, "tgt")
example = tf.train.Example(features=tf.train.Features(feature=feature))
record_writer.write(example.SerializeToString())
num_batch += 1
record_writer.close()
print("Done writing {}. batches: {}".format(file_name, num_batch))
return file_name, num_batch
def get_lm_corpus(data_dir, dataset):
fn = os.path.join(data_dir, "cache.pkl")
if exists(fn):
print("Loading cached dataset...")
with open(fn, "rb") as fp:
corpus = pickle.load(fp)
else:
print("Producing dataset...")
kwargs = {}
if dataset in ["wt103", "wt2"]:
kwargs["special"] = ["<eos>"]
kwargs["lower_case"] = False
elif dataset == "ptb":
kwargs["special"] = ["<eos>"]
kwargs["lower_case"] = True
elif dataset == "lm1b":
kwargs["special"] = []
kwargs["lower_case"] = False
kwargs["vocab_file"] = os.path.join(data_dir, "1b_word_vocab.txt")
elif dataset in ["enwik8", "text8"]:
pass
corpus = Corpus(data_dir, dataset, **kwargs)
print("Saving dataset...")
with open(fn, "wb") as fp:
pickle.dump(corpus, fp, protocol=2)
corpus_info = {
"vocab_size" : len(corpus.vocab),
"cutoffs" : corpus.cutoffs,
"dataset" : corpus.dataset
}
with open(os.path.join(data_dir, "corpus-info.json"), "w") as fp:
json.dump(corpus_info, fp)
return corpus
def main(unused_argv):
del unused_argv # Unused
corpus = get_lm_corpus(FLAGS.data_dir, FLAGS.dataset)
save_dir = os.path.join(FLAGS.data_dir, "tfrecords")
if not exists(save_dir):
makedirs(save_dir)
# test mode
if FLAGS.per_host_test_bsz > 0:
corpus.convert_to_tfrecords("test", save_dir, FLAGS.per_host_test_bsz,
FLAGS.tgt_len, FLAGS.num_core_per_host,
FLAGS=FLAGS)
return
for split, batch_size in zip(
["train", "valid"],
[FLAGS.per_host_train_bsz, FLAGS.per_host_valid_bsz]):
if batch_size <= 0: continue
print("Converting {} set...".format(split))
corpus.convert_to_tfrecords(split, save_dir, batch_size, FLAGS.tgt_len,
FLAGS.num_core_per_host, FLAGS=FLAGS)
def load_record_info(record_info_dir, split, per_host_bsz, tgt_len,
num_core_per_host, use_tpu):
if use_tpu:
record_name = "record_info-{}.bsz-{}.tlen-{}.core-{}.json".format(
split, per_host_bsz, tgt_len, num_core_per_host)
else:
record_name = "record_info-{}.bsz-{}.tlen-{}.json".format(
split, per_host_bsz, tgt_len)
record_info_path = os.path.join(record_info_dir, record_name)
with open(record_info_path, "r") as fp:
record_info = json.load(fp)
return record_info
def get_input_fn(record_info_dir, split, per_host_bsz, tgt_len,
num_core_per_host, num_hosts=1, use_tpu=False):
"""Creates input function."""
record_info = load_record_info(record_info_dir, split, per_host_bsz, tgt_len,
num_core_per_host, use_tpu=use_tpu)
file_names = record_info["filenames"]
bin_sizes = record_info["bin_sizes"]
num_batch = record_info["num_batch"]
tf.logging.info("[{}] File names {}".format(split, file_names))
def input_fn(params):
# per-core batch size
per_core_bsz = params["batch_size"]
# data_dir could be a remote path, e.g., a google storage url
data_dir = params["data_dir"]
def parser(record):
# preprocess "inp_perm" and "tgt_perm"
def _process_perm_feature(example, prefix):
for b in range(len(bin_sizes)):
cnt = example.pop("{}_cnt_{}".format(prefix, b))[0]
tup = example.pop("{}_tup_{}".format(prefix, b))
tup = tf.reshape(
tf.sparse_tensor_to_dense(tup),
shape=[cnt, 2])
# tf.float32
perm = tf.sparse_to_dense(
sparse_indices=tup,
output_shape=[tgt_len, bin_sizes[b]],
sparse_values=1.0,
default_value=0.0)
example["{}_perm_{}".format(prefix, b)] = perm
# whether allow the last batch with a potentially shorter length
if use_tpu:
record_spec = {
"inputs": tf.FixedLenFeature([tgt_len], tf.int64),
"labels": tf.FixedLenFeature([tgt_len], tf.int64),
}
else:
record_spec = {
"inputs": tf.VarLenFeature(tf.int64),
"labels": tf.VarLenFeature(tf.int64),
}
# permutation related features
if bin_sizes and use_tpu:
# tf.float32
record_spec["inp_mask"] = tf.FixedLenFeature([tgt_len], tf.float32)
record_spec["tgt_mask"] = tf.FixedLenFeature([tgt_len], tf.float32)
record_spec["head_labels"] = tf.FixedLenFeature([tgt_len], tf.int64)
for b in range(len(bin_sizes)):
record_spec["inp_cnt_{}".format(b)] = tf.FixedLenFeature([1], tf.int64)
record_spec["inp_tup_{}".format(b)] = tf.VarLenFeature(tf.int64)
record_spec["tgt_cnt_{}".format(b)] = tf.FixedLenFeature([1], tf.int64)
record_spec["tgt_tup_{}".format(b)] = tf.VarLenFeature(tf.int64)
# retrieve serialized example
example = tf.parse_single_example(
serialized=record,
features=record_spec)
# transform permutation tuples to permutation matrices
if bin_sizes and use_tpu:
_process_perm_feature(example, "inp")
_process_perm_feature(example, "tgt")
# cast int64 into int32
# cast sparse to dense
for key in list(example.keys()):
val = example[key]
if tf.keras.backend.is_sparse(val):
val = tf.sparse.to_dense(val)
if val.dtype == tf.int64:
val = tf.to_int32(val)
example[key] = val
if use_tpu:
return example
else:
return example["inputs"], example["labels"]
file_paths = []
for file_name in file_names:
file_path = os.path.join(data_dir, file_name)
file_paths.append(file_path)
if split == "train":
dataset = tf.data.Dataset.from_tensor_slices(file_paths)
if len(file_paths) > 1:
dataset = dataset.shuffle(len(file_paths)).repeat()
dataset = tf.data.TFRecordDataset(dataset)
elif num_hosts > 1:
host_id = params["context"].current_host
# drop the remaining batches
num_batch_per_host = num_batch // num_hosts
my_start_sample_id = (host_id * num_batch_per_host * num_core_per_host *
per_core_bsz)
my_sample_num = num_batch_per_host * num_core_per_host * per_core_bsz
dataset = tf.data.TFRecordDataset(dataset).skip(
my_start_sample_id).take(my_sample_num)
else:
dataset = tf.data.TFRecordDataset(dataset)
dataset = dataset.map(parser).cache().repeat()
dataset = dataset.batch(per_core_bsz, drop_remainder=True)
dataset = dataset.prefetch(num_core_per_host * per_core_bsz)
else:
# do not shuffle, repeat or cache in evaluation
dataset = tf.data.Dataset.from_tensor_slices(file_paths)
dataset = tf.data.TFRecordDataset(dataset)
dataset = dataset.map(parser)
dataset = dataset.batch(per_core_bsz, drop_remainder=True)
return dataset
if split == "train" and num_hosts > 1:
record_info["num_batch"] = num_batch // num_hosts
return input_fn, record_info
def get_corpus_info(corpus_info_path):
with open(corpus_info_path, "r") as fp:
corpus_info = json.load(fp)
return corpus_info
if __name__ == "__main__":
FLAGS = flags.FLAGS
flags.DEFINE_string("data_dir", None,
help="Location of the data corpus")
flags.DEFINE_enum("dataset", "wt103",
["ptb", "wt2", "wt103", "lm1b", "enwik8", "text8"],
help="Dataset name.")
flags.DEFINE_integer("per_host_train_bsz", 60,
help="train batch size each host")
flags.DEFINE_integer("per_host_valid_bsz", 60,
help="valid batch size each host")
flags.DEFINE_integer("per_host_test_bsz", 0,
help="If > 0, enter test mode and process test set only."
"Otherwise, process train and dev sets only.")
flags.DEFINE_integer("tgt_len", 70,
help="number of tokens to predict")
flags.DEFINE_integer("max_batch", -1,
help="run in debug mode")
flags.DEFINE_integer("num_core_per_host", 8,
help="8 for TPU v2.")
flags.DEFINE_bool("debug", default=False,
help="Process only the first batch without shuffle for lm1b.")
flags.DEFINE_integer("num_procs", 1,
help="number of processes")
flags.DEFINE_integer("num_passes", 10,
help="number of passes when use_tpu=True")
flags.DEFINE_integer("num_shuffle", 4,
help="number of shuffles for lm1b")
flags.DEFINE_bool("use_tpu", True,
help="use tpu")
tf.app.run(main)