test_dist_transpiler.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
import paddle.fluid.layers as layers
from paddle.fluid.transpiler.distribute_transpiler import delete_ops
import numpy


class TestDistTranspiler(unittest.TestCase):
    def setUp(self):
        self.trainer_id = 0
        self.trainers = 2
        self.pservers = 2
        self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
        self.current_pserver_ep = "127.0.0.1:6174"

    def net_conf(self):
        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')

        y_predict = fluid.layers.fc(input=x,
                                    size=1000,
                                    act=None,
                                    param_attr=fluid.ParamAttr(name='fc_w'))

        y = fluid.layers.data(name='y', shape=[1], dtype='float32')

        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
        avg_cost = fluid.layers.mean(cost)
        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)

        optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
        return optimize_ops, params_grads

    def test_transpiler(self):
        trainer = self.get_trainer()
        pserver, startup = self.get_pserver(self.current_pserver_ep)
        self.assertEqual([op.type for op in trainer.global_block().ops],
                         self.get_expect_trainer_ops())

        self.assertEqual(len(pserver.blocks), 3)
        # block0: listen_and_serv
        self.assertEqual([op.type for op in pserver.blocks[0].ops],
                         ["listen_and_serv"])
        # block2: optimize pass
        self.assertEqual([op.type for op in pserver.blocks[1].ops],
                         ["sum", "scale", "sgd"])

        # confirm startup program

        self.assertEqual([op.type for op in startup.global_block().ops], [
            "fill_constant", "fill_constant", "uniform_random", "uniform_random"
        ])

        # the variable #fc_w will be split into two blocks
        fc_w_var = startup.global_block().var("fc_w.block1")
        self.assertEqual(fc_w_var.shape, (500, 1000))

    def get_main_program(self):
        main = fluid.Program()

        with fluid.program_guard(main):
            self.net_conf()

        return main

    def get_expect_trainer_ops(self):
        trainer = fluid.Program()

        with fluid.program_guard(trainer):
            optimize_ops, params_grads = self.net_conf()

        delete_ops(trainer.global_block(), optimize_ops)
        ops = [op.type for op in trainer.global_block().ops] + [
            "split_byref", "send_vars", "send_barrier", "recv", "recv",
            "fetch_barrier", "concat"
        ]
        ops.insert(ops.index("elementwise_add_grad") + 1, "send_vars")
        return ops

    def get_trainer(self):
        return self._transpiler_instance().get_trainer_program()

    def get_pserver(self, ep):
        t = self._transpiler_instance()
        pserver = t.get_pserver_program(ep)
        startup = t.get_startup_program(ep, pserver)
        return pserver, startup

    def _transpiler_instance(self):
        main = self.get_main_program()
        t = fluid.DistributeTranspiler()
        t.transpile(
            self.trainer_id,
            program=main,
            pservers=self.pserver_eps,
            trainers=self.trainers)
        return t


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