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fix isolated var fetch bug, test=release/2.0 (#24086)
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106 changes: 106 additions & 0 deletions
106
python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_isolated_var.py
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| Original file line number | Diff line number | Diff line change |
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| # Copyright (c) 2020 PaddlePaddle 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. | ||
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| import unittest | ||
| import numpy as np | ||
| import six | ||
| import paddle.fluid as fluid | ||
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| def enable_parallel_ssa_executor(enabled=True): | ||
| if fluid.is_compiled_with_cuda(): | ||
| fluid.core.globals()['FLAGS_enable_parallel_graph'] = enabled | ||
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| class TestParallelExecutorFetchIsolatedVarBase(unittest.TestCase): | ||
| def build_network(self, is_training): | ||
| x = fluid.data(name='x', shape=[-1, 10], dtype='float32') | ||
| y = fluid.data(name='y', shape=[-1, 10], dtype='float32') | ||
| fc = fluid.layers.fc(x, size=30) | ||
| loss = fluid.layers.reduce_mean(fc) | ||
| if is_training: | ||
| adam = fluid.optimizer.Adam(learning_rate=1e-3) | ||
| adam.minimize(loss) | ||
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| return loss, y | ||
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| def exec_strategy(self, use_experimental_executor): | ||
| strategy = fluid.ExecutionStrategy() | ||
| strategy.use_experimental_executor = use_experimental_executor | ||
| return strategy | ||
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| def places(self, use_gpu, dev_cnt): | ||
| if use_gpu: | ||
| return fluid.cuda_places(list(range(dev_cnt))) | ||
| else: | ||
| return fluid.cpu_places(dev_cnt) | ||
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| def test_main(self): | ||
| for use_gpu in [False, True]: | ||
| for dev_cnt in [1, 2]: | ||
| for is_training in [False, True]: | ||
| for use_experimental_executor in [False, True]: | ||
| for use_parallel_ssa_executor in [False, True]: | ||
| func = lambda: self.run_impl(use_gpu, dev_cnt, is_training, use_experimental_executor, use_parallel_ssa_executor) | ||
| self.run_func_with_guard(func) | ||
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| def run_impl(self, use_gpu, dev_cnt, is_training, use_experimental_executor, | ||
| use_parallel_ssa_executor): | ||
| enable_parallel_ssa_executor(use_parallel_ssa_executor) | ||
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| if fluid.is_compiled_with_cuda(): | ||
| if fluid.core.globals()[ | ||
| 'FLAGS_enable_parallel_graph'] and not use_gpu: | ||
| return | ||
| else: | ||
| if use_gpu: | ||
| return | ||
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| loss, isolated_var = self.build_network(is_training) | ||
| loss_name = loss.name if is_training else None | ||
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| places = self.places(use_gpu, dev_cnt) | ||
| exe = fluid.Executor(places[0]) | ||
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| exe.run(fluid.default_startup_program()) | ||
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| prog = fluid.CompiledProgram(fluid.default_main_program( | ||
| )).with_data_parallel( | ||
| loss_name=loss_name, | ||
| exec_strategy=self.exec_strategy(use_experimental_executor), | ||
| places=places) | ||
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| BATCH_SIZE = 8 * dev_cnt | ||
| for _ in six.moves.range(10): | ||
| x_np = np.random.random(size=[BATCH_SIZE, 10]).astype('float32') | ||
| y_np = np.random.random(size=[BATCH_SIZE, 10]).astype('float32') | ||
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| _, y_np_fetch = exe.run(prog, | ||
| feed={'x': x_np, | ||
| 'y': y_np}, | ||
| fetch_list=[loss, isolated_var]) | ||
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| self.assertTrue(np.array_equal(y_np, y_np_fetch)) | ||
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| enable_parallel_ssa_executor(False) | ||
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| def run_func_with_guard(self, func): | ||
| with fluid.program_guard(fluid.Program(), fluid.Program()): | ||
| with fluid.unique_name.guard(): | ||
| with fluid.scope_guard(fluid.Scope()): | ||
| func() | ||
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| if __name__ == '__main__': | ||
| unittest.main() |