def pipeline(self, dataset: tf.data.Dataset) -> tf.data.Dataset:
"""Build a pipeline fetching, shuffling, and preprocessing the dataset.
Args:
dataset: A `tf.data.Dataset` that loads raw files.
Returns:
A TensorFlow dataset outputting batched images and labels.
"""
if self._num_gpus > 1:
dataset = dataset.shard(self._num_gpus, hvd.rank())
if self.is_training:
# Shuffle the input files.
dataset.shuffle(buffer_size=self._file_shuffle_buffer_size)
if self.is_training and not self._cache:
dataset = dataset.repeat()
# Read the data from disk in parallel
dataset = dataset.interleave(
tf.data.TFRecordDataset,
cycle_length=10,
block_length=1,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if self._cache:
dataset = dataset.cache()
if self.is_training:
dataset = dataset.shuffle(self._shuffle_buffer_size)
dataset = dataset.repeat()
# Parse, pre-process, and batch the data in parallel
preprocess = self.parse_record
dataset = dataset.map(preprocess,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if self._num_gpus > 1:
# The batch size of the dataset will be multiplied by the number of
# replicas automatically when strategy.distribute_datasets_from_function
# is called, so we use local batch size here.
dataset = dataset.batch(self.local_batch_size,
drop_remainder=self.is_training)
else:
dataset = dataset.batch(self.global_batch_size,
drop_remainder=self.is_training)
# Apply Mixup
mixup_alpha = self.mixup_alpha if self.is_training else 0.0
dataset = dataset.map(
functools.partial(self.mixup, self.local_batch_size, mixup_alpha),
num_parallel_calls=64)
# Prefetch overlaps in-feed with training
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
return dataset
def _maybe_apply_data_service(
self,
dataset: tf.data.Dataset,
input_context: Optional[tf.distribute.InputContext] = None
) -> tf.data.Dataset:
"""Potentially distributes a dataset."""
if self._enable_tf_data_service and input_context:
if self._enable_round_robin_tf_data_service:
replicas_per_input_pipeline = input_context.num_replicas_in_sync // (
input_context.num_input_pipelines)
base_consumer_index = input_context.input_pipeline_id * (
replicas_per_input_pipeline)
num_consumers = input_context.num_input_pipelines * (
replicas_per_input_pipeline)
range_dataset = tf.data.Dataset.range(
replicas_per_input_pipeline)
tfds_kwargs = {
'processing_mode': 'parallel_epochs',
'service': self._tf_data_service_address,
'job_name': self._tf_data_service_job_name,
'num_consumers': num_consumers
}
if self._enable_shared_tf_data_service_between_parallel_trainers:
raise ValueError(
'Shared tf.data service does not support round-robin'
' tf.data service.')
dataset = range_dataset.map(lambda i: dataset.apply( # pylint: disable=g-long-lambda
tf.data.experimental.service.
distribute(consumer_index=base_consumer_index + i,
**tfds_kwargs)))
# Use parallel interleave to read multiple batches from a tf.data
# service worker in parallel.
dataset = dataset.interleave(
lambda x: x,
cycle_length=replicas_per_input_pipeline,
num_parallel_calls=replicas_per_input_pipeline,
deterministic=True)
else:
tfds_kwargs = {
'processing_mode': 'parallel_epochs',
'service': self._tf_data_service_address,
'job_name': self._tf_data_service_job_name,
}
if self._enable_shared_tf_data_service_between_parallel_trainers:
tfds_kwargs.update({
'processing_mode':
tf.data.experimental.service.ShardingPolicy.OFF,
'cross_trainer_cache':
tf.data.experimental.service.CrossTrainerCache(
trainer_id=self._trainer_id)
})
dataset = dataset.apply(
tf.data.experimental.service.distribute(**tfds_kwargs))
return dataset
def episodes_to_timestep_batched_transitions(
episode_dataset: tf.data.Dataset,
return_horizon: int = 10,
drop_return_horizon: bool = False,
min_return_filter: Optional[float] = None) -> tf.data.Dataset:
"""Process an existing dataset converting it to episode to 3-transitions.
A 3-transition is an Transition with each attribute having an extra dimension
of size 3, representing 3 consecutive timesteps. Each 3-step object will be
in random order relative to each other. See `episode_to_timestep_batch` for
more information.
Args:
episode_dataset: An RLDS dataset to process.
return_horizon: The horizon we want calculate Monte-Carlo returns to.
drop_return_horizon: Whether we should drop the last `return_horizon` steps.
min_return_filter: Minimum episode return below which we drop an episode.
Returns:
A tf.data.Dataset of 3-transitions.
"""
dataset = episode_dataset.interleave(
functools.partial(episode_to_timestep_batch,
return_horizon=return_horizon,
drop_return_horizon=drop_return_horizon,
calculate_episode_return=min_return_filter
is not None),
num_parallel_calls=tf.data.experimental.AUTOTUNE,
deterministic=False)
if min_return_filter is not None:
def filter_on_return(step):
return step[EPISODE_RETURN][0][0] > min_return_filter
dataset = dataset.filter(filter_on_return)
dataset = dataset.map(_step_to_transition,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset
def _maybe_apply_data_service(
self,
dataset: tf.data.Dataset,
input_context: Optional[tf.distribute.InputContext] = None
) -> tf.data.Dataset:
"""Potentially distributes a dataset."""
if self._enable_tf_data_service and input_context:
if self._enable_round_robin_tf_data_service:
replicas_per_input_pipeline = input_context.num_replicas_in_sync // (
input_context.num_input_pipelines)
base_consumer_index = input_context.input_pipeline_id * (
replicas_per_input_pipeline)
num_consumers = input_context.num_input_pipelines * (
replicas_per_input_pipeline)
range_dataset = tf.data.Dataset.range(
replicas_per_input_pipeline)
dataset = range_dataset.map(lambda i: dataset.apply( # pylint: disable=g-long-lambda
tf.data.experimental.service.distribute(
processing_mode='parallel_epochs',
service=self._tf_data_service_address,
job_name=self._tf_data_service_job_name,
consumer_index=base_consumer_index + i,
num_consumers=num_consumers)))
# Use parallel interleave to read multiple batches from a tf.data
# service worker in parallel.
dataset = dataset.interleave(
lambda x: x,
cycle_length=replicas_per_input_pipeline,
num_parallel_calls=replicas_per_input_pipeline,
deterministic=True)
else:
dataset = dataset.apply(
tf.data.experimental.service.distribute(
processing_mode='parallel_epochs',
service=self._tf_data_service_address,
job_name=self._tf_data_service_job_name))
return dataset
def pipeline(self, dataset: tf.data.Dataset) -> tf.data.Dataset:
"""Build a pipeline fetching, shuffling, and preprocessing the dataset.
Args:
dataset: A `tf.data.Dataset` that loads raw files.
Returns:
A TensorFlow dataset outputting batched images and labels.
"""
if (self.config.builder != 'tfds' and self.input_context
and self.input_context.num_input_pipelines > 1):
dataset = dataset.shard(self.input_context.num_input_pipelines,
self.input_context.input_pipeline_id)
logging.info(
'Sharding the dataset: input_pipeline_id=%d '
'num_input_pipelines=%d',
self.input_context.num_input_pipelines,
self.input_context.input_pipeline_id)
if self.is_training and self.config.builder == 'records':
# Shuffle the input files.
dataset.shuffle(buffer_size=self.config.file_shuffle_buffer_size)
if self.is_training and not self.config.cache:
dataset = dataset.repeat()
if self.config.builder == 'records':
# Read the data from disk in parallel
dataset = dataset.interleave(
tf.data.TFRecordDataset,
cycle_length=10,
block_length=1,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if self.config.cache:
dataset = dataset.cache()
if self.is_training:
dataset = dataset.shuffle(self.config.shuffle_buffer_size)
dataset = dataset.repeat()
# Parse, pre-process, and batch the data in parallel
if self.config.builder == 'records':
preprocess = self.parse_record
else:
preprocess = self.preprocess
dataset = dataset.map(preprocess,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if self.input_context and self.config.num_devices > 1:
if not self.config.use_per_replica_batch_size:
raise ValueError(
'The builder does not support a global batch size with more than '
'one replica. Got {} replicas. Please set a '
'`per_replica_batch_size` and enable '
'`use_per_replica_batch_size=True`.'.format(
self.config.num_devices))
# The batch size of the dataset will be multiplied by the number of
# replicas automatically when strategy.distribute_datasets_from_function
# is called, so we use local batch size here.
dataset = dataset.batch(self.local_batch_size,
drop_remainder=self.is_training)
else:
dataset = dataset.batch(self.global_batch_size,
drop_remainder=self.is_training)
# Prefetch overlaps in-feed with training
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
if self.config.tf_data_service:
if not hasattr(tf.data.experimental, 'service'):
raise ValueError(
'The tf_data_service flag requires Tensorflow version '
'>= 2.3.0, but the version is {}'.format(tf.__version__))
dataset = dataset.apply(
tf.data.experimental.service.distribute(
processing_mode='parallel_epochs',
service=self.config.tf_data_service,
job_name='resnet_train'))
dataset = dataset.prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
return dataset
def pipeline(
self,
dataset: tf.data.Dataset,
input_context: tf.distribute.InputContext = None
) -> tf.data.Dataset:
"""Build a pipeline fetching, shuffling, and preprocessing the dataset.
Args:
dataset: A `tf.data.Dataset` that loads raw files.
input_context: An optional context provided by `tf.distribute` for
cross-replica training. This isn't necessary if using Keras
compile/fit.
Returns:
A TensorFlow dataset outputting batched images and labels.
"""
if input_context and input_context.num_input_pipelines > 1:
dataset = dataset.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
if self.is_training and not self.config.cache:
dataset = dataset.repeat()
if self.config.builder == 'records':
# Read the data from disk in parallel
buffer_size = 8 * 1024 * 1024 # Use 8 MiB per file
dataset = dataset.interleave(
lambda name: tf.data.TFRecordDataset(name,
buffer_size=buffer_size),
cycle_length=16,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.prefetch(self.global_batch_size)
if self.config.cache:
dataset = dataset.cache()
if self.is_training:
dataset = dataset.shuffle(self.config.shuffle_buffer_size)
dataset = dataset.repeat()
# Parse, pre-process, and batch the data in parallel
if self.config.builder == 'records':
preprocess = self.parse_record
else:
preprocess = self.preprocess
dataset = dataset.map(preprocess,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(self.batch_size,
drop_remainder=self.is_training)
# Note: we could do image normalization here, but we defer it to the model
# which can perform it much faster on a GPU/TPU
# TODO(dankondratyuk): if we fix prefetching, we can do it here
if self.is_training and self.config.deterministic_train is not None:
options = tf.data.Options()
options.experimental_deterministic = self.config.deterministic_train
options.experimental_slack = self.config.use_slack
options.experimental_optimization.parallel_batch = True
options.experimental_optimization.map_fusion = True
options.experimental_optimization.map_vectorization.enabled = True
options.experimental_optimization.map_parallelization = True
dataset = dataset.with_options(options)
# Prefetch overlaps in-feed with training
# Note: autotune here is not recommended, as this can lead to memory leaks.
# Instead, use a constant prefetch size like the the number of devices.
dataset = dataset.prefetch(self.config.num_devices)
return dataset
def pipeline(
self,
dataset: tf.data.Dataset,
input_context: tf.distribute.InputContext = None
) -> tf.data.Dataset:
"""Build a pipeline fetching, shuffling, and preprocessing the dataset.
Args:
dataset: A `tf.data.Dataset` that loads raw files.
input_context: An optional context provided by `tf.distribute` for
cross-replica training. If set with more than one replica, this
function assumes `use_per_replica_batch_size=True`.
Returns:
A TensorFlow dataset outputting batched images and labels.
"""
if input_context and input_context.num_input_pipelines > 1:
dataset = dataset.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
if self.is_training and not self.config.cache:
dataset = dataset.repeat()
if self.config.builder == 'records':
# Read the data from disk in parallel
buffer_size = 8 * 1024 * 1024 # Use 8 MiB per file
dataset = dataset.interleave(
lambda name: tf.data.TFRecordDataset(name,
buffer_size=buffer_size),
cycle_length=16,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if self.config.cache:
dataset = dataset.cache()
if self.is_training:
dataset = dataset.shuffle(self.config.shuffle_buffer_size)
dataset = dataset.repeat()
# Parse, pre-process, and batch the data in parallel
if self.config.builder == 'records':
preprocess = self.parse_record
else:
preprocess = self.preprocess
dataset = dataset.map(preprocess,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if input_context and self.config.num_devices > 1:
if not self.config.use_per_replica_batch_size:
raise ValueError(
'The builder does not support a global batch size with more than '
'one replica. Got {} replicas. Please set a '
'`per_replica_batch_size` and enable '
'`use_per_replica_batch_size=True`.'.format(
self.config.num_devices))
# The batch size of the dataset will be multiplied by the number of
# replicas automatically when strategy.distribute_datasets_from_function
# is called, so we use local batch size here.
dataset = dataset.batch(self.local_batch_size,
drop_remainder=self.is_training)
else:
dataset = dataset.batch(self.global_batch_size,
drop_remainder=self.is_training)
if self.is_training:
options = tf.data.Options()
options.experimental_deterministic = self.config.deterministic_train
options.experimental_slack = self.config.use_slack
options.experimental_optimization.parallel_batch = True
options.experimental_optimization.map_fusion = True
options.experimental_optimization.map_vectorization.enabled = True
options.experimental_optimization.map_parallelization = True
dataset = dataset.with_options(options)
# Prefetch overlaps in-feed with training
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
return dataset
def transform(self, ds: tf.data.Dataset) -> tf.data.Dataset:
ds = ds.interleave(self.item_to_dataset,
cycle_length=self.cycle_length,
block_length=self.block_length,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
return ds
def pipeline(self, dataset: tf.data.Dataset) -> tf.data.Dataset:
"""Build a pipeline fetching, shuffling, and preprocessing the dataset.
Args:
dataset: A `tf.data.Dataset` that loads raw files.
Returns:
A TensorFlow dataset outputting batched images and labels.
"""
# This can help resolve OOM issues when using only 1 GPU for training
options = tf.data.Options()
options.experimental_optimization.map_parallelization = (
not self.disable_map_parallelization)
dataset = dataset.with_options(options)
if self._num_gpus > 1:
# For multi-host training, we want each hosts to always process the same
# subset of files. Each host only sees a subset of the entire dataset,
# allowing us to cache larger datasets in memory.
dataset = dataset.shard(self._num_gpus, hvd.rank())
if self.is_training:
# Shuffle the input files.
dataset.shuffle(buffer_size=self._file_shuffle_buffer_size)
if self.is_training and not self._cache:
dataset = dataset.repeat()
# Read the data from disk in parallel
dataset = dataset.interleave(
tf.data.TFRecordDataset,
cycle_length=10,
block_length=1,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if self._cache:
dataset = dataset.cache()
if self.is_training:
dataset = dataset.shuffle(self._shuffle_buffer_size)
dataset = dataset.repeat()
# Parse, pre-process, and batch the data in parallel
preprocess = self.parse_record
dataset = dataset.map(preprocess,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if self._num_gpus > 1:
# The batch size of the dataset will be multiplied by the number of
# replicas automatically when strategy.distribute_datasets_from_function
# is called, so we use local batch size here.
dataset = dataset.batch(self.local_batch_size,
drop_remainder=self.is_training)
else:
dataset = dataset.batch(self.global_batch_size,
drop_remainder=self.is_training)
# apply Mixup/CutMix only during training, if requested in the data pipeline,
# otherwise they will be applied in the model module on device
mixup_alpha = self.mixup_alpha if self.is_training else 0.0
cutmix_alpha = self.cutmix_alpha if self.is_training else 0.0
dataset = dataset.map(functools.partial(mixing, self.local_batch_size,
mixup_alpha, cutmix_alpha,
self.defer_img_mixing),
num_parallel_calls=64)
# Assign static batch size dimension
# dataset = dataset.map(
# functools.partial(self.set_shapes, batch_size),
# num_parallel_calls=tf.data.experimental.AUTOTUNE)
# Prefetch overlaps in-feed with training
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
return dataset
