Skip to content

internal #866

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
copybara-service wants to merge 1 commit into
base: master
Choose a base branch
from
+224 −5
Open

internal #866

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
229 changes: 224 additions & 5 deletions init2winit/dataset_lib/criteo_terabyte_dataset.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -34,9 +34,9 @@
import tensorflow as tf
import tensorflow_datasets as tfds


# Change to the path to raw dataset files.
RAW_CRITEO1TB_FILE_PATH = ''
PREPROCESSED_CRITEO1TB_FILE_PATH = '' # pylint: disable=invalid-name
CRITEO1TB_DEFAULT_HPARAMS = config_dict.ConfigDict(
dict(
input_shape=(13 + 26,),
Expand Down Expand Up @@ -157,6 +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 _eval_numpy_iterator(
num_batches, per_host_eval_batch_size, tf_dataset, split_size
):
Expand Down Expand Up @@ -222,14 +310,145 @@ 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(
_eval_numpy_iterator,
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(
_eval_numpy_iterator,
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(
_eval_numpy_iterator,
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.CachedIteratorFactory(
eval_train_iterator_fn(None), 'eval_train'
)
validation_iterator_fn = data_utils.CachedIteratorFactory(
validation_iterator_fn(None), 'validation'
)
test_iterator_fn = data_utils.CachedIteratorFactory(
test_iterator_fn(None), '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)

num_device_prefetches = hps.get('num_device_prefetches', 0)

train_dataset = criteo_tsv_reader(
split='train',
Expand Down