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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 MapParallelization optimization."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
from tensorflow.python.data.experimental.ops import optimization
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.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test
class MapParallelizationTest(test_base.DatasetTestBase, parameterized.TestCase):
@staticmethod
def map_functions():
identity = lambda x: x
increment = lambda x: x + 1
def assert_greater(x):
assert_op = control_flow_ops.Assert(math_ops.greater(x, -1), [x])
with ops.control_dependencies([assert_op]):
return x
def random(_):
return random_ops.random_uniform([],
minval=0,
maxval=10,
dtype=dtypes.int64,
seed=42)
def assert_with_random(x):
x = assert_greater(x)
return random(x)
return (("Identity", identity, True), ("Increment", increment, True),
("AssertGreater", assert_greater, True), ("Random", random, False),
("AssertWithRandom", assert_with_random, False))
@parameterized.named_parameters(*map_functions.__func__())
def testMapParallelization(self, function, should_optimize):
next_nodes = ["ParallelMap"] if should_optimize else ["Map"]
dataset = dataset_ops.Dataset.range(5).apply(
optimization.assert_next(next_nodes)).map(function)
options = dataset_ops.Options()
options.experimental_map_parallelization = True
dataset = dataset.with_options(options)
iterator = dataset.make_one_shot_iterator()
get_next = iterator.get_next()
with self.test_session() as sess:
for x in range(5):
result = sess.run(get_next)
# No need to run the pipeline if it was not optimized. Also the results
# might be hard to check because of random.
if not should_optimize:
return
r = function(x)
self.assertAllEqual(r, result)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
if __name__ == "__main__":
test.main()
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