dist_fleet_ctr.py 6.1 KB
Newer Older
T
tangwei12 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149
#   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.

from __future__ import print_function

import shutil
import tempfile
import time

import paddle.fluid as fluid
import os

import ctr_dataset_reader
from test_dist_fleet_base import runtime_main, FleetDistRunnerBase

# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1


class TestDistCTR2x2(FleetDistRunnerBase):
    def net(self, batch_size=4, lr=0.01):
        dnn_input_dim, lr_input_dim, train_file_path = ctr_dataset_reader.prepare_data(
        )
        """ network definition """
        dnn_data = fluid.layers.data(
            name="dnn_data",
            shape=[-1, 1],
            dtype="int64",
            lod_level=1,
            append_batch_size=False)
        lr_data = fluid.layers.data(
            name="lr_data",
            shape=[-1, 1],
            dtype="int64",
            lod_level=1,
            append_batch_size=False)
        label = fluid.layers.data(
            name="click",
            shape=[-1, 1],
            dtype="int64",
            lod_level=0,
            append_batch_size=False)

        datas = [dnn_data, lr_data, label]

        # build dnn model
        dnn_layer_dims = [128, 64, 32, 1]
        dnn_embedding = fluid.layers.embedding(
            is_distributed=False,
            input=dnn_data,
            size=[dnn_input_dim, dnn_layer_dims[0]],
            param_attr=fluid.ParamAttr(
                name="deep_embedding",
                initializer=fluid.initializer.Constant(value=0.01)),
            is_sparse=True)
        dnn_pool = fluid.layers.sequence_pool(
            input=dnn_embedding, pool_type="sum")
        dnn_out = dnn_pool
        for i, dim in enumerate(dnn_layer_dims[1:]):
            fc = fluid.layers.fc(
                input=dnn_out,
                size=dim,
                act="relu",
                param_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.Constant(value=0.01)),
                name='dnn-fc-%d' % i)
            dnn_out = fc

        # build lr model
        lr_embbding = fluid.layers.embedding(
            is_distributed=False,
            input=lr_data,
            size=[lr_input_dim, 1],
            param_attr=fluid.ParamAttr(
                name="wide_embedding",
                initializer=fluid.initializer.Constant(value=0.01)),
            is_sparse=True)
        lr_pool = fluid.layers.sequence_pool(input=lr_embbding, pool_type="sum")

        merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)

        predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
        acc = fluid.layers.accuracy(input=predict, label=label)
        auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict,
                                                              label=label)
        cost = fluid.layers.cross_entropy(input=predict, label=label)
        avg_cost = fluid.layers.mean(x=cost)

        self.feeds = datas
        self.train_file_path = train_file_path
        self.avg_cost = avg_cost
        self.predict = predict

        return avg_cost

    def check_model_right(self, dirname):
        model_filename = os.path.join(dirname, "__model__")

        with open(model_filename, "rb") as f:
            program_desc_str = f.read()

        program = fluid.Program.parse_from_string(program_desc_str)
        with open(os.path.join(dirname, "__model__.proto"), "w") as wn:
            wn.write(str(program))

    def do_training(self, fleet):
        dnn_input_dim, lr_input_dim, train_file_path = ctr_dataset_reader.prepare_data(
        )

        exe = fluid.Executor(fluid.CPUPlace())

        fleet.init_worker()
        exe.run(fleet.startup_program)

        thread_num = 2
        filelist = []
        for _ in range(thread_num):
            filelist.append(train_file_path)

        # config dataset
        dataset = fluid.DatasetFactory().create_dataset()
        dataset.set_batch_size(128)
        dataset.set_use_var(self.feeds)
        pipe_command = 'python ctr_dataset_reader.py'
        dataset.set_pipe_command(pipe_command)

        dataset.set_filelist(filelist)
        dataset.set_thread(thread_num)

        for epoch_id in range(2):
            pass_start = time.time()
            dataset.set_filelist(filelist)
            exe.train_from_dataset(
                program=fleet.main_program,
                dataset=dataset,
                fetch_list=[self.avg_cost],
                fetch_info=["cost"],
150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168
                print_period=2,
                debug=False)
            pass_time = time.time() - pass_start

        class FH(fluid.executor.FetchHandler):
            def handler(self, fetch_target_vars):
                for i in range(len(fetch_target_vars)):
                    print("{}: \n {}\n".format(self.fetch_target_names[0],
                                               fetch_target_vars[0]))

        for epoch_id in range(2):
            pass_start = time.time()
            dataset.set_filelist(filelist)
            exe.train_from_dataset(
                program=fleet.main_program,
                dataset=dataset,
                fetch_handler=FH([self.avg_cost.name],
                                 period_secs=2,
                                 return_np=True),
T
tangwei12 已提交
169 170 171 172 173 174 175 176 177 178 179 180 181
                debug=False)
            pass_time = time.time() - pass_start

        model_dir = tempfile.mkdtemp()
        fleet.save_inference_model(
            exe, model_dir, [feed.name for feed in self.feeds], self.avg_cost)
        self.check_model_right(model_dir)
        shutil.rmtree(model_dir)
        fleet.stop_worker()


if __name__ == "__main__":
    runtime_main(TestDistCTR2x2)