test_boxps.py

#   Copyright (c) 2019 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 paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
import os
import shutil
import paddle.fluid.core as core
import unittest
from paddle.fluid.layers.nn import _pull_box_sparse
from paddle.fluid.transpiler import collective


class TestTranspile(unittest.TestCase):
    """  TestCases for BoxPS Preload """

    def get_transpile(self, mode, trainers="127.0.0.1:6174"):
        config = fluid.DistributeTranspilerConfig()
        config.mode = 'collective'
        config.collective_mode = mode
        t = fluid.DistributeTranspiler(config=config)
        return t

    def test_transpile(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()
        t = self.get_transpile("single_process_multi_thread")
        t.transpile(
            trainer_id=0,
            startup_program=startup_program,
            trainers="127.0.0.1:6174",
            program=main_program)
        t = self.get_transpile("grad_allreduce")
        try:
            t.transpile(
                trainer_id=0,
                startup_program=startup_program,
                trainers="127.0.0.1:6174",
                program=main_program)
        except ValueError as e:
            print(e)

    def test_single_trainers(self):
        transpiler = collective.GradAllReduce(0)
        try:
            transpiler.transpile(
                startup_program=fluid.Program(),
                main_program=fluid.Program(),
                rank=1,
                endpoints="127.0.0.1:6174",
                current_endpoint="127.0.0.1:6174",
                wait_port="6174")
        except ValueError as e:
            print(e)
        transpiler = collective.LocalSGD(0)
        try:
            transpiler.transpile(
                startup_program=fluid.Program(),
                main_program=fluid.Program(),
                rank=1,
                endpoints="127.0.0.1:6174",
                current_endpoint="127.0.0.1:6174",
                wait_port="6174")
        except ValueError as e:
            print(e)


class TestRunCmd(unittest.TestCase):
    """ TestCases for run_cmd"""

    def test_run_cmd(self):
        ret1 = int(core.run_cmd("ls; echo $?").strip().split('\n')[-1])
        ret2 = int(core.run_cmd("ls; echo $?", -1, -1).strip().split('\n')[-1])
        self.assertTrue(ret1 == 0)
        self.assertTrue(ret2 == 0)


class TestBoxPSPreload(unittest.TestCase):
    """  TestCases for BoxPS Preload """

    def test_boxps_cpu(self):
        self.run_boxps_preload(True, True)
        self.run_boxps_preload(True, False)

    def test_boxps_gpu(self):
        self.run_boxps_preload(False, True)
        self.run_boxps_preload(False, False)

    def run_boxps_preload(self, is_cpu=True, random_with_lineid=False):
        program = fluid.Program()
        with fluid.program_guard(program):
            x = fluid.layers.data(
                name='x', shape=[1], dtype='int64', lod_level=0)
            y = fluid.layers.data(
                name='y', shape=[1], dtype='int64', lod_level=0)
            emb_x, emb_y = _pull_box_sparse([x, y], size=2)
            emb_xp = _pull_box_sparse(x, size=2)
            concat = layers.concat([emb_x, emb_y], axis=1)
            fc = layers.fc(input=concat,
                           name="fc",
                           size=1,
                           num_flatten_dims=1,
                           bias_attr=False)
            loss = layers.reduce_mean(fc)
            layers.Print(loss)
            place = fluid.CPUPlace(
            ) if is_cpu or not core.is_compiled_with_cuda(
            ) else fluid.CUDAPlace(0)
            exe = fluid.Executor(place)
            batch_size = 100

            def binary_print(slot, fout):
                fout.write(str(len(slot)) + " ")
                for e in slot:
                    fout.write(str(e) + " ")

            batch1 = np.ones(
                (batch_size, 2, 1)).astype("int64").reshape(batch_size, 2, 1)
            filelist = []
            place_str = "cpu" if is_cpu else "gpu"
            for i in range(2):
                filelist.append("test_hdfs_" + place_str + "_" + str(i))
            for f in filelist:
                with open(f, "w") as fout:
                    for ins in batch1:
                        for slot in ins:
                            binary_print(slot, fout)
                    fout.write("\n")

            def create_dataset():
                dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
                dataset.set_date("20190930")
                dataset.set_use_var([x, y])
                dataset.set_batch_size(2)
                dataset.set_thread(1)
                dataset.set_filelist(filelist)
                return dataset

            datasets = []
            datasets.append(create_dataset())
            datasets.append(create_dataset())
            optimizer = fluid.optimizer.SGD(learning_rate=0.5)
            optimizer = fluid.optimizer.PipelineOptimizer(
                optimizer,
                cut_list=[],
                place_list=[place],
                concurrency_list=[1],
                queue_size=1,
                sync_steps=-1)
            optimizer.minimize(loss)

            program._pipeline_opt[
                "dump_fields"] = ["fc.tmp_0", "fc.tmp_0@GRAD", "hehe"]
            program._pipeline_opt["dump_fields_path"] = "./dump_log/"
            program._pipeline_opt["dump_param"] = ["fc.w_0"]
            program._pipeline_opt["enable_random_dump"] = True
            program._pipeline_opt["dump_interval"] = 10
            program._pipeline_opt["random_with_lineid"] = random_with_lineid

            exe.run(fluid.default_startup_program())
            datasets[0].load_into_memory()
            datasets[0].begin_pass()
            datasets[1].preload_into_memory()
            exe.train_from_dataset(
                program=fluid.default_main_program(),
                dataset=datasets[0],
                print_period=1)
            datasets[0].end_pass(True)
            datasets[1].wait_preload_done()
            datasets[1].begin_pass()
            exe.train_from_dataset(
                program=fluid.default_main_program(),
                dataset=datasets[1],
                print_period=1,
                debug=True)
            datasets[1].end_pass(False)
            for f in filelist:
                os.remove(f)
            if os.path.isdir("dump_log"):
                shutil.rmtree("dump_log")


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