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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.
# ==============================================================================
"""Tests for the private `override_threadpool()` transformation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import threading
from absl.testing import parameterized
import numpy as np
from tensorflow.python.data.experimental.ops import threadpool
from tensorflow.python.data.experimental.ops import unique
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.ops import script_ops
from tensorflow.python.platform import test
class OverrideThreadpoolTest(test_base.DatasetTestBase,
parameterized.TestCase):
@parameterized.named_parameters(
("1", 1, None),
("2", 2, None),
("3", 4, None),
("4", 8, None),
("5", 16, None),
("6", 4, -1),
("7", 4, 0),
("8", 4, 1),
("9", 4, 4),
)
def testNumThreads(self, num_threads, max_intra_op_parallelism):
def get_thread_id(_):
# Python creates a dummy thread object to represent the current
# thread when called from an "alien" thread (such as a
# `PrivateThreadPool` thread in this case). It does not include
# the TensorFlow-given display name, but it has a unique
# identifier that maps one-to-one with the underlying OS thread.
return np.array(threading.current_thread().ident).astype(np.int64)
dataset = (
dataset_ops.Dataset.range(1000).map(
lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64),
num_parallel_calls=32).apply(unique.unique()))
dataset = threadpool.override_threadpool(
dataset,
threadpool.PrivateThreadPool(
num_threads,
max_intra_op_parallelism=max_intra_op_parallelism,
display_name="private_thread_pool_%d" % num_threads))
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
sess.run(iterator.initializer)
thread_ids = []
try:
while True:
thread_ids.append(sess.run(next_element))
except errors.OutOfRangeError:
pass
self.assertEqual(len(thread_ids), len(set(thread_ids)))
self.assertGreater(len(thread_ids), 0)
# NOTE(mrry): We don't control the thread pool scheduling, and
# so cannot guarantee that all of the threads in the pool will
# perform work.
self.assertLessEqual(len(thread_ids), num_threads)
if __name__ == "__main__":
test.main()
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