dist_fleet_ctr.py 8.6 KB
Newer Older
T
tangwei12 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13
#   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.
14 15 16
"""
Distribute CTR model for test fleet api
"""
T
tangwei12 已提交
17 18 19 20 21 22 23

from __future__ import print_function

import shutil
import tempfile
import time

1
123malin 已提交
24
import paddle
T
tangwei12 已提交
25 26
import paddle.fluid as fluid
import os
1
123malin 已提交
27
import numpy as np
T
tangwei12 已提交
28 29 30 31 32 33 34 35 36 37

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):
38 39 40 41
    """
    For test CTR model, using Fleet api
    """

T
tangwei12 已提交
42
    def net(self, batch_size=4, lr=0.01):
43 44 45 46 47 48 49 50 51
        """
        network definition

        Args:
            batch_size(int): the size of mini-batch for training
            lr(float): learning rate of training
        Returns:
            avg_cost: LoDTensor of cost.
        """
T
tangwei12 已提交
52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
        dnn_input_dim, lr_input_dim, train_file_path = ctr_dataset_reader.prepare_data(
        )
        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
C
Chengmo 已提交
76
        dnn_layer_dims = [128, 128, 64, 32, 1]
T
tangwei12 已提交
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
        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)
113

T
tangwei12 已提交
114 115
        auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict,
                                                              label=label)
116

T
tangwei12 已提交
117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136
        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))

1
123malin 已提交
137
    def do_pyreader_training(self, fleet):
138 139 140 141 142
        """
        do training using dataset, using fetch handler to catch variable
        Args:
            fleet(Fleet api): the fleet object of Parameter Server, define distribute training role
        """
T
tangwei12 已提交
143 144 145 146 147 148 149 150 151
        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
1
123malin 已提交
152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201
        batch_size = 128
        filelist = []
        for _ in range(thread_num):
            filelist.append(train_file_path)

        train_reader = paddle.batch(
            paddle.reader.shuffle(
                ctr_dataset_reader.CtrReader()._reader_creator(filelist),
                buf_size=batch_size * 100),
            batch_size=batch_size)
        self.reader.decorate_sample_list_generator(train_reader)

        compiled_prog = fluid.compiler.CompiledProgram(
            fleet.main_program).with_data_parallel(
                loss_name=self.avg_cost.name,
                build_strategy=self.strategy.get_build_strategy(),
                exec_strategy=self.strategy.get_execute_strategy())

        for epoch_id in range(1):
            self.reader.start()
            try:
                pass_start = time.time()
                while True:
                    loss_val = exe.run(program=compiled_prog,
                                       fetch_list=[self.avg_cost.name])
                    loss_val = np.mean(loss_val)
                    print("TRAIN ---> pass: {} loss: {}\n".format(epoch_id,
                                                                  loss_val))
                pass_time = time.time() - pass_start
            except fluid.core.EOFException:
                self.reader.reset()

        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()

    def do_dataset_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
        batch_size = 128
T
tangwei12 已提交
202 203 204 205 206 207
        filelist = []
        for _ in range(thread_num):
            filelist.append(train_file_path)

        # config dataset
        dataset = fluid.DatasetFactory().create_dataset()
1
123malin 已提交
208
        dataset.set_batch_size(batch_size)
T
tangwei12 已提交
209 210 211 212 213 214 215
        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)

216
        for epoch_id in range(1):
T
tangwei12 已提交
217 218 219 220 221 222 223
            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"],
224 225 226 227
                print_period=2,
                debug=False)
            pass_time = time.time() - pass_start

1
123malin 已提交
228 229 230
        res_dict = dict()
        res_dict['loss'] = self.avg_cost

231
        class FH(fluid.executor.FetchHandler):
1
123malin 已提交
232 233 234 235
            def handle(self, res_dict):
                for key in res_dict:
                    v = res_dict[key]
                    print("{}: \n {}\n".format(key, v))
236

237
        for epoch_id in range(1):
238 239 240 241 242
            pass_start = time.time()
            dataset.set_filelist(filelist)
            exe.train_from_dataset(
                program=fleet.main_program,
                dataset=dataset,
1
123malin 已提交
243
                fetch_handler=FH(var_dict=res_dict, period_secs=2),
T
tangwei12 已提交
244 245 246
                debug=False)
            pass_time = time.time() - pass_start

247 248 249 250 251 252 253
        if os.getenv("SAVE_MODEL") == "1":
            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)
T
tangwei12 已提交
254 255 256 257 258
        fleet.stop_worker()


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