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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 HostState 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 constant_op
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 HoistRandomUniformTest(test_base.DatasetTestBase, parameterized.TestCase):
@staticmethod
def map_functions():
plus_one = lambda x: x + 1
def random(_):
return random_ops.random_uniform([],
minval=1,
maxval=10,
dtype=dtypes.float32,
seed=42)
def random_with_assert(x):
y = random(x)
assert_op = control_flow_ops.Assert(math_ops.greater_equal(y, 1), [y])
with ops.control_dependencies([assert_op]):
return y
twice_random = lambda x: (random(x) + random(x)) / 2.
tests = [("PlusOne", plus_one, False), ("RandomUniform", random, True),
("RandomWithAssert", random_with_assert, True),
("TwiceRandom", twice_random, False)]
return tuple(tests)
@parameterized.named_parameters(*map_functions.__func__())
def testHoisting(self, function, will_optimize):
dataset = dataset_ops.Dataset.range(5).apply(
optimization.assert_next(
["Zip[0]", "Map"] if will_optimize else ["Map"])).map(function)
options = dataset_ops.Options()
options.experimental_hoist_random_uniform = True
dataset = dataset.with_options(options)
self._testDataset(dataset)
def testAdditionalInputs(self):
a = constant_op.constant(1, dtype=dtypes.float32)
b = constant_op.constant(0, dtype=dtypes.float32)
some_tensor = math_ops.mul(a, b)
def random_with_capture(_):
return some_tensor + random_ops.random_uniform(
[], minval=1, maxval=10, dtype=dtypes.float32, seed=42)
dataset = dataset_ops.Dataset.range(5).apply(
optimization.assert_next(["Zip[0]", "Map"])).map(random_with_capture)
options = dataset_ops.Options()
options.experimental_hoist_random_uniform = True
dataset = dataset.with_options(options)
self._testDataset(dataset)
def _testDataset(self, dataset):
iterator = dataset.make_one_shot_iterator()
get_next = iterator.get_next()
previous_result = 0
with self.cached_session() as sess:
for _ in range(5):
result = sess.run(get_next)
self.assertLessEqual(1, result)
self.assertLessEqual(result, 10)
# This checks if the result is somehow random by checking if we are not
# generating the same values.
self.assertNotEqual(previous_result, result)
previous_result = result
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
if __name__ == "__main__":
test.main()
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