140 lines
5.2 KiB
Python
140 lines
5.2 KiB
Python
# 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 class OneDeviceStrategy."""
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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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from tensorflow.python import tf2
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.distribute import combinations
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from tensorflow.python.distribute import strategy_combinations
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from tensorflow.python.distribute import strategy_test_lib
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from tensorflow.python.eager import context
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from tensorflow.python.eager import test
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.one_device_strategy,
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strategy_combinations.one_device_strategy_gpu
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],
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mode=["eager", "graph"]))
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class OneDeviceStrategyTest(
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strategy_test_lib.DistributionTestBase,
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strategy_test_lib.OneDeviceDistributionTestBase):
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def testMinimizeLoss(self, distribution):
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if context.executing_eagerly():
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self._test_minimize_loss_eager(distribution)
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else:
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self._test_minimize_loss_graph(distribution)
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def testReplicaId(self, distribution):
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self._test_replica_id(distribution)
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def testCallAndMergeExceptions(self, distribution):
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self._test_call_and_merge_exceptions(distribution)
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def testReplicateDataset(self, distribution):
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if tf2.enabled() and not context.executing_eagerly():
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self.skipTest("Skipping test since we do not support graph mode in TF 2")
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dataset_fn = lambda: dataset_ops.Dataset.range(10)
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expected_values = [[i] for i in range(10)]
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input_fn = self._input_fn_to_test_input_context(
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dataset_fn,
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expected_num_replicas_in_sync=1,
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expected_num_input_pipelines=1,
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expected_input_pipeline_id=0)
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self._test_input_fn_iterable(distribution, input_fn, expected_values)
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def testMakeInputFnIteratorWithDataset(self, distribution):
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dataset_fn = lambda: dataset_ops.Dataset.range(10)
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expected_values = [[i] for i in range(10)]
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input_fn = self._input_fn_to_test_input_context(
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dataset_fn,
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expected_num_replicas_in_sync=1,
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expected_num_input_pipelines=1,
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expected_input_pipeline_id=0)
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iterator = distribution.make_input_fn_iterator(input_fn)
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self._test_input_fn_iterator(
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iterator, distribution.extended.worker_devices, expected_values)
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def testMakeInputFnIteratorWithCallable(self, distribution):
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def fn():
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dataset = dataset_ops.Dataset.range(10)
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it = dataset_ops.make_one_shot_iterator(dataset)
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return it.get_next
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expected_values = [[i] for i in range(10)]
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input_fn = self._input_fn_to_test_input_context(
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fn,
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expected_num_replicas_in_sync=1,
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expected_num_input_pipelines=1,
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expected_input_pipeline_id=0)
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iterator = distribution.make_input_fn_iterator(input_fn)
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self._test_input_fn_iterator(
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iterator, distribution.extended.worker_devices, expected_values,
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test_reinitialize=False, ignore_order=True)
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def testNumpyDataset(self, distribution):
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self._test_numpy_dataset(distribution)
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def testRun(self, distribution):
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self._test_run(distribution)
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def testAllReduceSum(self, distribution):
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self._test_all_reduce_sum(distribution)
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def testAllReduceSumGradients(self, distribution):
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self._test_all_reduce_sum_gradients(distribution)
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def testAllReduceSumGradientTape(self, distribution):
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self._test_all_reduce_sum_gradient_tape(distribution)
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def testAllReduceMean(self, distribution):
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self._test_all_reduce_mean(distribution)
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def testAllReduceMeanGradients(self, distribution):
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self._test_all_reduce_mean_gradients(distribution)
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def testAllReduceMeanGradientTape(self, distribution):
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self._test_all_reduce_mean_gradient_tape(distribution)
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def testTrainableVariables(self, distribution):
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self._test_trainable_variable(distribution)
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.one_device_strategy_on_worker_1,
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strategy_combinations.one_device_strategy_gpu_on_worker_1
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],
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mode=["eager", "graph"]))
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class OneDeviceStrategyOnRemoteWorkerTest(
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strategy_test_lib.DistributionTestBase,
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strategy_test_lib.OneDeviceDistributionTestBase):
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def testDeviceAndInputDeviceAreColocated(self, distribution):
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self._test_device_and_input_device_are_colocated(distribution)
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def testDeviceAndInputDeviceAreColocatedWithFunction(self, distribution):
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self._test_device_and_input_device_are_colocated_with_function(distribution)
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if __name__ == "__main__":
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test.main()
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