3b5bb5a706
Instead of using def_function, in test we can parameterize with these two objects to test both tf.function and eager execution. PiperOrigin-RevId: 351420316 Change-Id: I037d1678ca843f6df88694981efd4519c2947cd3
202 lines
7.8 KiB
Python
202 lines
7.8 KiB
Python
# Lint as: python3
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for tensorflow.python.distribute.combinations."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import unittest
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from absl.testing import parameterized
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from tensorflow.python.distribute import combinations
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from tensorflow.python.distribute import test_util
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from tensorflow.python.distribute.cluster_resolver import tfconfig_cluster_resolver
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from tensorflow.python.eager import context
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from tensorflow.python.framework import combinations as framework_combinations
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from tensorflow.python.platform import test
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class ClusterCombinationTest(test.TestCase, parameterized.TestCase):
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# For this test we need to use `framework.test_combinations` because our
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# `generate` eats the cluster parameters.
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#
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# Note that we don't have a standalone combination for ClusterParameters, so
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# we should use GPUCombination which contains it.
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@framework_combinations.generate(
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framework_combinations.combine(distribution=[
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combinations.NamedDistribution(
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"HasClusterParams", lambda: None, has_chief=True, num_workers=2),
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]),
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test_combinations=(combinations.ClusterCombination(),))
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def testClusterParams(self, distribution, has_chief, num_workers):
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self.assertTrue(has_chief)
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self.assertEqual(num_workers, 2)
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@framework_combinations.generate(
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framework_combinations.combine(distribution=[
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combinations.NamedDistribution("NoClusterParams", lambda: None),
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]),
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test_combinations=(combinations.ClusterCombination(),))
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def testClusterParamsHasDefault(self, distribution, has_chief, num_workers):
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self.assertFalse(has_chief)
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self.assertEqual(num_workers, 1)
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@framework_combinations.generate(
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framework_combinations.combine(v=1),
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test_combinations=(combinations.ClusterCombination(),))
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def testClusterParamsNoStrategy(self, v, has_chief, num_workers):
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self.assertFalse(has_chief)
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self.assertEqual(num_workers, 1)
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@framework_combinations.generate(
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framework_combinations.combine(distribution=[
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combinations.NamedDistribution(
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"WithClusterParams", lambda: None, has_chief=True, num_workers=2),
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combinations.NamedDistribution("WithoutClusterParams", lambda: None),
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]),
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test_combinations=(combinations.ClusterCombination(),))
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def testClusterParamsAreOptional(self, distribution):
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# If combinations library doesn't raise an exception, the test is passed.
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pass
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@framework_combinations.generate(
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framework_combinations.combine(
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ds1=combinations.NamedDistribution(
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"Strategy1", lambda: None, has_chief=True, num_workers=0),
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ds2=combinations.NamedDistribution(
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"Strategy2", lambda: None, has_chief=False, num_workers=1),
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ds3=combinations.NamedDistribution(
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"Strategy3", lambda: None, has_chief=True, num_workers=0),
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),
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test_combinations=(combinations.ClusterCombination(),))
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def testMultipleDistributionSingleWorker(self, ds1, ds2, ds3):
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# If combinations library doesn't raise an exception, the test is passed.
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pass
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@combinations.generate(combinations.combine(num_workers=2,))
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def testUseWithoutStrategy(self):
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# There's no perfect way to check if the test runs in a subprocess. We
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# approximate by checking the presence of TF_CONFIG, which is normally not
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# set to the main process.
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self.assertNotEqual(os.getenv("TF_CONFIG"), "")
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@combinations.generate(combinations.combine(num_workers=2))
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class ClusterCombinationTestEnvTest(test.TestCase, parameterized.TestCase):
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def setUp(self):
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# Note that test case fixtures are executed in both the main process and
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# worker processes.
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super().setUp()
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if combinations.in_main_process():
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combinations.env().tf_data_service_dispatcher = "localhost"
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def testTfDataServiceDispatcher(self):
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self.assertEqual(combinations.env().tf_data_service_dispatcher, "localhost")
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def testUpdateEnvInWorker(self):
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with self.assertRaises(ValueError):
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combinations.env().tf_data_service_dispatcher = "localhost"
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# unittest.expectedFailure doesn't work with parameterized test methods, so we
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# have to decorate the class instead.
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@unittest.expectedFailure
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class ClusterParametersShouldFailTest(test.TestCase, parameterized.TestCase):
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@framework_combinations.generate(
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framework_combinations.combine(
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ds1=combinations.NamedDistribution(
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"Strategy1", lambda: None, has_chief=True, num_workers=2),
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ds2=combinations.NamedDistribution(
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"Strategy2", lambda: None, has_chief=True, num_workers=2),
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),
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test_combinations=(combinations.ClusterCombination(),))
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def testMultipleDistributionMultiWorker(self, ds1, ds2):
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# combinations library should raise an exception.
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pass
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# Tests that we *actually* run the test method in multiple workers instead of
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# just passing silently. More importantly, it verifies that the test can fail.
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# Note that unittest.expectedFailure doesn't work with parameterized test
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# methods, so we have to decorate the class instead.
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@unittest.expectedFailure
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class CombinationsExpectedFailureTest(test.TestCase, parameterized.TestCase):
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@combinations.generate(
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combinations.combine(distribution=[
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combinations.NamedDistribution(
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"OneChiefOneWorker", lambda: None, has_chief=True, num_workers=1),
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combinations.NamedDistribution(
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"TwoWorkers", lambda: None, has_chief=False, num_workers=2),
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]))
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def testMultiWorkerCanFail(self, distribution):
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resolver = tfconfig_cluster_resolver.TFConfigClusterResolver()
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# This should fail.
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self.assertIsNone(resolver.task_id)
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# Tests that we *actually* run the test method in multiple workers instead of
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# just passing silently. More importantly, it verifies that the test can fail.
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# Note that unittest.expectedFailure doesn't work with parameterized test
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# methods, so we have to decorate the class instead.
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@unittest.expectedFailure
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@combinations.generate(
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combinations.combine(distribution=[
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combinations.NamedDistribution(
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"OneChiefOneWorker", lambda: None, has_chief=True, num_workers=1),
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combinations.NamedDistribution(
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"TwoWorkers", lambda: None, has_chief=False, num_workers=2),
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]))
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class CombinationsOnClassMultiWorkerExpectedFailureTest(test.TestCase,
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parameterized.TestCase):
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def test(self, distribution):
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resolver = tfconfig_cluster_resolver.TFConfigClusterResolver()
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# This should fail.
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self.assertIsNone(resolver.task_id)
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class TfFunctionTest(test.TestCase, parameterized.TestCase):
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@combinations.generate(
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combinations.combine(
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tf_function_1=combinations.tf_function,
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tf_function_2=combinations.no_tf_function,
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mode="eager",
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))
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def testFunc(self, tf_function_1, tf_function_2):
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@tf_function_1
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def foo():
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self.assertFalse(context.executing_eagerly())
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@tf_function_2
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def bar():
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self.assertTrue(context.executing_eagerly())
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foo()
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bar()
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if __name__ == "__main__":
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test_util.main()
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