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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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://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.
# ==============================================================================
"""Experimental API for controlling threading in `tf.data` pipelines."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import threading
from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import
from tensorflow.contrib.data.python.ops import gen_dataset_ops
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.util import nest
from tensorflow.python.data.util import sparse
from tensorflow.python.eager import context
from tensorflow.python.ops import resource_variable_ops
_uid_counter = 0
_uid_lock = threading.Lock()
def _generate_shared_name(prefix):
with _uid_lock:
global _uid_counter
uid = _uid_counter
_uid_counter += 1
return "{}{}".format(prefix, uid)
class PrivateThreadPool(object):
"""A stateful resource that represents a private thread pool."""
def __init__(self, num_threads, display_name=None):
"""Creates a `PrivateThreadPool` with the given number of threads."""
if context.executing_eagerly():
shared_name = _generate_shared_name("privatethreadpool")
self._resource = gen_dataset_ops.thread_pool_handle(
num_threads=num_threads,
display_name=display_name,
shared_name=shared_name)
self._resource_deleter = resource_variable_ops.EagerResourceDeleter(
handle=self._resource, handle_device=context.context().device_name)
else:
self._resource = gen_dataset_ops.thread_pool_handle(
num_threads=num_threads, display_name=display_name)
class _ThreadPoolDataset(dataset_ops.Dataset):
"""A `Dataset` that acts as an identity, and sets a custom threadpool."""
def __init__(self, input_dataset, thread_pool):
super(_ThreadPoolDataset, self).__init__()
self._input_dataset = input_dataset
self._thread_pool = thread_pool
def _as_variant_tensor(self):
return gen_dataset_ops.thread_pool_dataset(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
self._thread_pool._resource, # pylint: disable=protected-access
output_shapes=nest.flatten(
sparse.as_dense_shapes(self.output_shapes, self.output_classes)),
output_types=nest.flatten(
sparse.as_dense_types(self.output_types, self.output_classes)))
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
@property
def output_classes(self):
return self._input_dataset.output_classes
def override_threadpool(dataset, thread_pool):
"""Returns a new dataset that uses the given thread pool for its operations.
Args:
dataset: A `tf.data.Dataset` object.
thread_pool: A `PrivateThreadPool` object.
Returns:
A dataset containing the same values as `dataset`, but which uses
`thread_pool` to compute any of its parallel operations (such as
@{tf.data.Dataset.map}).
"""
return _ThreadPoolDataset(dataset, thread_pool)
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