Unittests concurrency (#8666)
Python Unit Tests for CSP * Simple Channel Send and Receive test * Daisy Chain test with 100 channels/Go opsoptimizer
Thuan Nguyen
8 years ago
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# Copyright (c) 2018 PaddlePaddle 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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import unittest
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import paddle.fluid as fluid
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class TestCSPFramework(unittest.TestCase):
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def daisy_chain(self):
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n = 10000
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leftmost = fluid.make_channel(dtype=int)
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right = leftmost
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left = leftmost
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with fluid.While(steps=n):
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right = fluid.make_channel(dtype=int)
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with fluid.go():
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fluid.send(left, 1 + fluid.recv(right))
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left = right
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with fluid.go():
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fluid.send(right, 1)
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fluid.Print(fluid.recv(leftmost))
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,100 @@
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# Copyright (c) 2018 PaddlePaddle 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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import unittest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid import framework, unique_name
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from paddle.fluid.executor import Executor
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from paddle.fluid.layers import fill_constant
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class TestRoutineOp(unittest.TestCase):
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def test_simple_routine(self):
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ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
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# Create LOD_TENSOR<INT64> and put it into the scope. This placeholder
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# variable will be filled in and returned by fluid.channel_recv
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result = self._create_tensor('return_value',
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core.VarDesc.VarType.LOD_TENSOR,
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core.VarDesc.VarType.INT64)
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with fluid.Go():
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input_value = fill_constant(
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shape=[1], dtype=core.VarDesc.VarType.FP64, value=1234)
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fluid.channel_send(ch, input_value)
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result, status = fluid.channel_recv(ch, result)
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fluid.channel_close(ch)
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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outs = exe.run(fetch_list=[result])
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self.assertEqual(outs[0], 1234)
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def test_daisy_chain(self):
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'''
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Mimics classic Daisy-chain test: https://talks.golang.org/2012/concurrency.slide#39
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'''
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n = 100
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leftmost = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
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left = leftmost
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# TODO(thuan): Use fluid.While() after scope capture is implemented.
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# https://github.com/PaddlePaddle/Paddle/issues/8502
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for i in range(n):
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right = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
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with fluid.Go():
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one_tensor = self._create_one_dim_tensor(1)
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result = self._create_tensor('return_value',
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core.VarDesc.VarType.LOD_TENSOR,
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core.VarDesc.VarType.INT64)
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result, status = fluid.channel_recv(right, result)
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one_added = fluid.layers.elementwise_add(x=one_tensor, y=result)
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fluid.channel_send(left, one_added)
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left = right
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# Trigger the channel propagation by sending a "1" to rightmost channel
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with fluid.Go():
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one_tensor = self._create_one_dim_tensor(1)
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fluid.channel_send(right, one_tensor)
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leftmost_result = self._create_tensor('return_value',
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core.VarDesc.VarType.LOD_TENSOR,
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core.VarDesc.VarType.INT64)
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leftmost_result, status = fluid.channel_recv(leftmost, leftmost_result)
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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leftmost_data = exe.run(fetch_list=[leftmost_result])
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# The leftmost_data should be equal to the number of channels + 1
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self.assertEqual(leftmost_data[0][0], n + 1)
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def _create_one_dim_tensor(self, value):
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one_dim_tensor = fill_constant(
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shape=[1], dtype=core.VarDesc.VarType.INT64, value=value)
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one_dim_tensor.stop_gradient = True
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return one_dim_tensor
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def _create_tensor(self, name, type, dtype):
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return framework.default_main_program().current_block().create_var(
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name=unique_name.generate(name), type=type, dtype=dtype)
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if __name__ == '__main__':
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unittest.main()
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