| # Copyright 2020 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 distributed values library.""" |
| |
| from absl.testing import parameterized |
| |
| from tensorflow.python.distribute import combinations |
| from tensorflow.python.distribute import ps_values |
| from tensorflow.python.distribute import strategy_combinations |
| from tensorflow.python.eager import def_function |
| from tensorflow.python.eager import test |
| from tensorflow.python.ops import variable_v1 |
| from tensorflow.python.ops import variables as variables_lib |
| |
| |
| @combinations.generate( |
| combinations.combine( |
| distribution=[ |
| strategy_combinations.central_storage_strategy_with_two_gpus |
| ], |
| mode=["graph", "eager"])) |
| class AggregatingVariableTest(test.TestCase, parameterized.TestCase): |
| |
| def testAssignOutOfScope(self, distribution): |
| with distribution.scope(): |
| aggregating = variables_lib.Variable(1.) |
| self.assertIsInstance(aggregating, ps_values.AggregatingVariable) |
| self.evaluate(aggregating.assign(3.)) |
| self.assertEqual(self.evaluate(aggregating.read_value()), 3.) |
| self.assertEqual(self.evaluate(aggregating._v.read_value()), 3.) |
| |
| def testAssignAdd(self, distribution): |
| with distribution.scope(): |
| v = variable_v1.VariableV1( |
| 1, aggregation=variables_lib.VariableAggregation.MEAN) |
| self.evaluate(variables_lib.global_variables_initializer()) |
| |
| @def_function.function |
| def assign(): |
| return v.assign_add(2) |
| |
| per_replica_results = self.evaluate( |
| distribution.experimental_local_results( |
| distribution.run(assign))) |
| self.assertAllEqual([3], per_replica_results) |
| |
| |
| if __name__ == "__main__": |
| test.main() |