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272 changes: 258 additions & 14 deletions init2winit/dataset_lib/criteo_terabyte_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,17 +34,19 @@
import tensorflow as tf
import tensorflow_datasets as tfds


# Change to the path to raw dataset files.
RAW_CRITEO1TB_FILE_PATH = ''
CRITEO1TB_DEFAULT_HPARAMS = config_dict.ConfigDict(dict(
input_shape=(13 + 26,),
train_size=4_195_197_692,
# We assume the tie breaking example went to the validation set, because
# the test set in the mlperf version has 89_137_318 examples.
valid_size=89_137_319,
test_size=89_137_318,
))
PREPROCESSED_CRITEO1TB_FILE_PATH = '' # pylint: disable=invalid-name
CRITEO1TB_DEFAULT_HPARAMS = config_dict.ConfigDict(
dict(
input_shape=(13 + 26,),
train_size=4_195_197_692,
# We assume the tie breaking example went to the validation set, because
# the test set in the mlperf version has 89_137_318 examples.
valid_size=89_137_319,
test_size=89_137_318,
)
)
CRITEO1TB_METADATA = {
'apply_one_hot_in_loss': True,
}
Expand Down Expand Up @@ -142,9 +144,9 @@ def criteo_tsv_reader(
ds = ds.repeat()
ds = ds.interleave(
tf.data.TextLineDataset,
cycle_length=128,
cycle_length=64,
block_length=batch_size // 8,
num_parallel_calls=128,
num_parallel_calls=64,
deterministic=False)
if is_training:
ds = ds.shuffle(buffer_size=524_288 * 100, seed=data_shuffle_seed)
Expand All @@ -155,6 +157,94 @@ def criteo_tsv_reader(
return ds


_ARRAYRECORD_FEATURE_SPEC = {
'inputs': tf.io.FixedLenFeature([13 + 26], tf.float32),
'targets': tf.io.FixedLenFeature([1], tf.float32),
}


@tf.function
def _parse_arrayrecord_example_fn(serialized_examples):
"""Parse a batch of serialized tf.train.Examples from ArrayRecord."""
parsed = tf.io.parse_example(serialized_examples, _ARRAYRECORD_FEATURE_SPEC)
return {
'inputs': parsed['inputs'],
'targets': tf.squeeze(parsed['targets'], axis=-1),
}


def criteo_arrayrecord_reader(
split, shuffle_rng, file_path, batch_size, num_batches_to_prefetch
):
"""Input reader for preprocessed Criteo ArrayRecord data.

Args:
split: one of {'train', 'eval_train', 'validation', 'test'}.
shuffle_rng: jax.random.PRNGKey for shuffling (train).
file_path: glob pattern for .array_record files.
batch_size: per-host batch size.
num_batches_to_prefetch: number of batches to prefetch.

Returns:
A tf.data.Dataset object.
"""
# Import here to avoid hard dependency for TSV-only users.
if split not in ['train', 'eval_train', 'validation', 'test']:
raise ValueError(f'Invalid split name {split}.')
data_shuffle_seed = None

is_training = split == 'train'
if is_training:
_, data_shuffle_seed = jax.random.split(shuffle_rng, 2)
data_shuffle_seed = data_utils.convert_jax_to_tf_random_seed(
data_shuffle_seed
)

# Discover all matching files.
all_files = sorted(tf.io.gfile.glob(file_path))
if not all_files:
raise ValueError(f'No ArrayRecord files found matching: {file_path}')

# Shard files across hosts.
index = jax.process_index()
num_hosts = jax.process_count()
host_files = all_files[index::num_hosts]

# Interleave per-file datasets, with batch+parse inside each file's
# sub-pipeline. This is critical for performance: interleaving dense float
# tensors (post-parse) is much faster than interleaving raw byte strings
# and batching them later.
file_ds = tf.data.Dataset.from_tensor_slices(host_files)
if is_training:
file_ds = file_ds.repeat()
file_ds = file_ds.shuffle(
buffer_size=2 * len(host_files), seed=data_shuffle_seed
)

ds = file_ds.interleave(
lambda f: (
ar_dataset.ArrayRecordDataset([f])
.batch(
batch_size,
drop_remainder=is_training,
num_parallel_calls=tf.data.AUTOTUNE,
deterministic=False,
)
.map(
_parse_arrayrecord_example_fn,
num_parallel_calls=tf.data.AUTOTUNE,
deterministic=False,
)
),
cycle_length=64,
block_length=batch_size // 8,
num_parallel_calls=64,
deterministic=False,
)
ds = ds.prefetch(num_batches_to_prefetch)
return ds


def _convert_to_numpy_iterator_fn(
num_batches, per_host_eval_batch_size, tf_dataset, split_size):
"""Make eval iterator. This function is called at the start of each eval."""
Expand Down Expand Up @@ -211,12 +301,143 @@ def get_criteo1tb(shuffle_rng,
per_host_eval_batch_size = eval_batch_size // process_count
per_host_batch_size = batch_size // process_count

use_raw_tsv = hps.get('use_raw_tsv', False)
num_batches_to_prefetch = (
hps.num_tf_data_prefetches
if hps.num_tf_data_prefetches > 0
else tf.data.AUTOTUNE
)
num_device_prefetches = hps.get('num_device_prefetches', 0)

if use_raw_tsv:
return _get_criteo1tb_tsv(
shuffle_rng,
per_host_batch_size,
per_host_eval_batch_size,
hps,
num_batches_to_prefetch,
num_device_prefetches,
)
else:
return _get_criteo1tb_arrayrecord(
shuffle_rng,
per_host_batch_size,
per_host_eval_batch_size,
hps,
num_batches_to_prefetch,
num_device_prefetches,
)


def _get_criteo1tb_arrayrecord(
shuffle_rng,
per_host_batch_size,
per_host_eval_batch_size,
hps,
num_batches_to_prefetch,
num_device_prefetches,
):
"""Load Criteo 1TB from preprocessed ArrayRecord files."""
base = hps.get('preprocessed_data_path', PREPROCESSED_CRITEO1TB_FILE_PATH)
train_file_path = os.path.join(base, 'train', '*')
validation_file_path = os.path.join(
base, 'val_set_second_half_of_day23_not_used', '*'
)
test_file_path = os.path.join(base, 'eval', '*')

train_dataset = criteo_arrayrecord_reader(
split='train',
shuffle_rng=shuffle_rng,
file_path=train_file_path,
batch_size=per_host_batch_size,
num_batches_to_prefetch=num_batches_to_prefetch,
)
data_utils.log_rss('train arrayrecord dataset created')

if num_device_prefetches > 0:
train_iterator_fn = lambda: data_utils.prefetch_iterator(
tfds.as_numpy(train_dataset), num_device_prefetches
)
data_utils.log_rss(
f'using prefetching with {num_device_prefetches} in the train dataset'
)
else:
train_iterator_fn = lambda: tfds.as_numpy(train_dataset)

eval_train_dataset = criteo_arrayrecord_reader(
split='eval_train',
shuffle_rng=None,
file_path=train_file_path,
batch_size=per_host_eval_batch_size,
num_batches_to_prefetch=num_batches_to_prefetch,
)
eval_train_iterator_fn = functools.partial(
_convert_to_numpy_iterator_fn,
per_host_eval_batch_size=per_host_eval_batch_size,
tf_dataset=eval_train_dataset,
split_size=hps.train_size,
)
data_utils.log_rss('eval_train arrayrecord dataset created')

validation_dataset = criteo_arrayrecord_reader(
split='validation',
shuffle_rng=None,
file_path=validation_file_path,
batch_size=per_host_eval_batch_size,
num_batches_to_prefetch=num_batches_to_prefetch,
)
validation_iterator_fn = functools.partial(
_convert_to_numpy_iterator_fn,
per_host_eval_batch_size=per_host_eval_batch_size,
tf_dataset=validation_dataset,
split_size=hps.valid_size,
)
data_utils.log_rss('validation arrayrecord dataset created')

test_dataset = criteo_arrayrecord_reader(
split='test',
shuffle_rng=None,
file_path=test_file_path,
batch_size=per_host_eval_batch_size,
num_batches_to_prefetch=num_batches_to_prefetch,
)
test_iterator_fn = functools.partial(
_convert_to_numpy_iterator_fn,
per_host_eval_batch_size=per_host_eval_batch_size,
tf_dataset=test_dataset,
split_size=hps.test_size,
)
data_utils.log_rss('test arrayrecord dataset created')

eval_train_iterator_fn = data_utils.CachedEvalIterator(
eval_train_iterator_fn, 'eval_train'
)
validation_iterator_fn = data_utils.CachedEvalIterator(
validation_iterator_fn, 'validation'
)
test_iterator_fn = data_utils.CachedEvalIterator(test_iterator_fn, 'test')

return data_utils.Dataset(
train_iterator_fn,
eval_train_iterator_fn,
validation_iterator_fn,
test_iterator_fn,
)


def _get_criteo1tb_tsv(
shuffle_rng,
per_host_batch_size,
per_host_eval_batch_size,
hps,
num_batches_to_prefetch,
num_device_prefetches,
):
"""Load Criteo 1TB from raw TSV files (legacy path)."""
train_file_path = os.path.join(RAW_CRITEO1TB_FILE_PATH, 'train/*/*')
validation_file_path = os.path.join(
RAW_CRITEO1TB_FILE_PATH, 'val_set_second_half_of_day23_not_used/*')
test_file_path = os.path.join(RAW_CRITEO1TB_FILE_PATH, 'eval/day_23/*')
num_batches_to_prefetch = (hps.num_tf_data_prefetches
if hps.num_tf_data_prefetches > 0 else tf.data.AUTOTUNE)

train_dataset = criteo_tsv_reader(
split='train',
Expand All @@ -225,7 +446,16 @@ def get_criteo1tb(shuffle_rng,
num_dense_features=hps.num_dense_features,
batch_size=per_host_batch_size,
num_batches_to_prefetch=num_batches_to_prefetch)
train_iterator_fn = lambda: tfds.as_numpy(train_dataset)
data_utils.log_rss('train dataset created')
if num_device_prefetches > 0:
train_iterator_fn = lambda: data_utils.prefetch_iterator(
tfds.as_numpy(train_dataset), num_device_prefetches
)
data_utils.log_rss(
f'using prefetching with {num_device_prefetches} in the train dataset'
)
else:
train_iterator_fn = lambda: tfds.as_numpy(train_dataset)
eval_train_dataset = criteo_tsv_reader(
split='eval_train',
shuffle_rng=None,
Expand All @@ -238,6 +468,7 @@ def get_criteo1tb(shuffle_rng,
per_host_eval_batch_size=per_host_eval_batch_size,
tf_dataset=eval_train_dataset,
split_size=hps.train_size)
data_utils.log_rss('eval_train dataset created')
validation_dataset = criteo_tsv_reader(
split='validation',
shuffle_rng=None,
Expand All @@ -250,6 +481,7 @@ def get_criteo1tb(shuffle_rng,
per_host_eval_batch_size=per_host_eval_batch_size,
tf_dataset=validation_dataset,
split_size=hps.valid_size)
data_utils.log_rss('validation dataset created')
test_dataset = criteo_tsv_reader(
split='test',
shuffle_rng=None,
Expand All @@ -262,6 +494,18 @@ def get_criteo1tb(shuffle_rng,
per_host_eval_batch_size=per_host_eval_batch_size,
tf_dataset=test_dataset,
split_size=hps.test_size)
data_utils.log_rss('test dataset created')

# Cache all the eval_train/validation/test iterators to avoid re-processing the
# same data files.
eval_train_iterator_fn = data_utils.CachedEvalIterator(
eval_train_iterator_fn, 'eval_train'
)
validation_iterator_fn = data_utils.CachedEvalIterator(
validation_iterator_fn, 'validation'
)
test_iterator_fn = data_utils.CachedEvalIterator(test_iterator_fn, 'test')

return data_utils.Dataset(
train_iterator_fn,
eval_train_iterator_fn,
Expand Down
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