test_fleet_nocvm_1.py

#   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.
"""Test fleet."""

import os
import unittest


class TestFleet1(unittest.TestCase):
    """
    Test cases for fleet minimize.
    """

    def setUp(self):
        """Set up, set envs."""
        os.environ["PADDLE_TRAINERS_NUM"] = "2"
        os.environ[
            "PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001,127.0.0.2:36001"

    def test_pslib_1(self):
        """Test cases for pslib."""
        import paddle.fluid as fluid
        from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
        from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker

        os.environ["POD_IP"] = "127.0.0.1"
        os.environ["PADDLE_PORT"] = "36001"
        os.environ["TRAINING_ROLE"] = "TRAINER"
        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
        os.environ["PADDLE_TRAINER_ID"] = "0"
        role_maker = GeneralRoleMaker()
        #role_maker.generate_role()
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        #fleet.init(role_maker)
        train_program = fluid.Program()
        startup_program = fluid.Program()
        scope = fluid.Scope()
        with fluid.program_guard(train_program, startup_program):
            show = fluid.layers.data(name="show", shape=[-1, 1], \
                dtype="int64", lod_level=1, append_batch_size=False)
            emb = fluid.layers.embedding(input=show, size=[1, 1], \
                is_sparse=True, is_distributed=True, \
                param_attr=fluid.ParamAttr(name="embedding"))
            fc = fluid.layers.fc(input=emb, size=1, act=None)
            label = fluid.layers.data(name="click", shape=[-1, 1], \
                dtype="int64", lod_level=1, append_batch_size=False)
            label_cast = fluid.layers.cast(label, dtype='float32')
            cost = fluid.layers.log_loss(fc, label_cast)
        try:
            adam = fluid.optimizer.Adam(learning_rate=0.000005)
            adam = fleet.distributed_optimizer(adam,
                                               strategy={
                                                   "embedding": {
                                                       "sparse_accessor_class":
                                                       "DownpourCtrAccessor"
                                                   }
                                               })
            adam.minimize([cost], [scope])
            fleet.run_server()
        except:
            print("do not support pslib test, skip")
            return
        try:
            # worker should call these methods instead of server
            # the following is only for test when with_pslib=off
            def test_func():
                """
                it is only a test function
                """
                return True

            fleet._role_maker.is_first_worker = test_func
            fleet._role_maker._barrier_worker = test_func
            fleet.save_model("./model_000")
            fleet.save_one_table(0, "./model_001")
            fleet.save_one_table(0, "./model_002", prefix="hahaha")
            fleet.load_model("./model_0003")
            fleet.load_one_table(0, "./model_004")
            fleet.confirm()
            fleet.revert()
        except:
            print("do not support pslib test, skip")
            return


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