test_concurrency.py

#   Copyright (c) 2018 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.

import unittest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import framework, unique_name
from paddle.fluid.executor import Executor
from paddle.fluid.layers import fill_constant


class TestRoutineOp(unittest.TestCase):
    def test_simple_routine(self):
        ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)

        # Create LOD_TENSOR<INT64> and put it into the scope.  This placeholder
        # variable will be filled in and returned by fluid.channel_recv
        result = self._create_tensor('return_value',
                                     core.VarDesc.VarType.LOD_TENSOR,
                                     core.VarDesc.VarType.INT64)

        with fluid.Go():
            input_value = fill_constant(
                shape=[1], dtype=core.VarDesc.VarType.FP64, value=1234)
            fluid.channel_send(ch, input_value)

        result, status = fluid.channel_recv(ch, result)
        fluid.channel_close(ch)

        cpu = core.CPUPlace()
        exe = Executor(cpu)

        outs = exe.run(fetch_list=[result])
        self.assertEqual(outs[0], 1234)

    def test_daisy_chain(self):
        '''
        Mimics classic Daisy-chain test:  https://talks.golang.org/2012/concurrency.slide#39
        '''
        n = 100

        leftmost = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
        left = leftmost

        # TODO(thuan): Use fluid.While() after scope capture is implemented.
        # https://github.com/PaddlePaddle/Paddle/issues/8502
        for i in range(n):
            right = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
            with fluid.Go():
                one_tensor = self._create_one_dim_tensor(1)
                result = self._create_tensor('return_value',
                                             core.VarDesc.VarType.LOD_TENSOR,
                                             core.VarDesc.VarType.INT64)

                result, status = fluid.channel_recv(right, result)
                one_added = fluid.layers.elementwise_add(x=one_tensor, y=result)
                fluid.channel_send(left, one_added)
            left = right

        # Trigger the channel propagation by sending a "1" to rightmost channel
        with fluid.Go():
            one_tensor = self._create_one_dim_tensor(1)
            fluid.channel_send(right, one_tensor)

        leftmost_result = self._create_tensor('return_value',
                                              core.VarDesc.VarType.LOD_TENSOR,
                                              core.VarDesc.VarType.INT64)
        leftmost_result, status = fluid.channel_recv(leftmost, leftmost_result)

        cpu = core.CPUPlace()
        exe = Executor(cpu)
        leftmost_data = exe.run(fetch_list=[leftmost_result])

        # The leftmost_data should be equal to the number of channels + 1
        self.assertEqual(leftmost_data[0][0], n + 1)

    def _create_one_dim_tensor(self, value):
        one_dim_tensor = fill_constant(
            shape=[1], dtype=core.VarDesc.VarType.INT64, value=value)
        one_dim_tensor.stop_gradient = True
        return one_dim_tensor

    def _create_tensor(self, name, type, dtype):
        return framework.default_main_program().current_block().create_var(
            name=unique_name.generate(name), type=type, dtype=dtype)


if __name__ == '__main__':
    unittest.main()