dist_fleet_ctr.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.
"""
Distribute CTR model for test fleet api
"""

from __future__ import print_function

import shutil
import tempfile
import time

import paddle
import paddle.fluid as fluid
import os
import numpy as np

import ctr_dataset_reader
from test_dist_fleet_base import runtime_main, FleetDistRunnerBase
from paddle.distributed.fleet.utils.ps_util import Distributed
import paddle.distributed.fleet as fleet

paddle.enable_static()

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


def fake_ctr_reader():
    def reader():
        for _ in range(1000):
            deep = np.random.random_integers(0, 1e5 - 1, size=16).tolist()
            wide = np.random.random_integers(0, 1e5 - 1, size=8).tolist()
            label = np.random.random_integers(0, 1, size=1).tolist()
            yield [deep, wide, label]

    return reader


class TestDistCTR2x2(FleetDistRunnerBase):
    """
    For test CTR model, using Fleet api
    """

    def net(self, args, is_train=True, batch_size=4, lr=0.01):
        """
        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.
        """
        dnn_input_dim, lr_input_dim = int(1e5), int(1e5)

        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]

        if args.reader == "pyreader":
            if is_train:
                self.reader = fluid.io.PyReader(
                    feed_list=datas,
                    capacity=64,
                    iterable=False,
                    use_double_buffer=False)
            else:
                self.test_reader = fluid.io.PyReader(
                    feed_list=datas,
                    capacity=64,
                    iterable=False,
                    use_double_buffer=False)

# build dnn model
        dnn_layer_dims = [128, 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,
            padding_idx=0)
        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,
            padding_idx=0)
        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 = ["fake1", "fake2"]
        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_distributed_testing(self, args, test_main_program,
                               test_startup_program):
        """
        do distributed
        """
        device_env = os.getenv("DEVICE", 'cpu')
        if device_env == 'cpu':
            device = fluid.CPUPlace()
        elif device_env == 'gpu':
            device = fluid.CUDAPlace(0)
        exe = fluid.Executor(device)

        batch_size = 4
        test_reader = paddle.batch(fake_ctr_reader(), batch_size=batch_size)
        self.test_reader.decorate_sample_list_generator(test_reader)

        pass_start = time.time()
        batch_idx = 0

        self.test_reader.start()
        try:
            while True:
                batch_idx += 1
                loss_val = exe.run(program=test_main_program,
                                   fetch_list=[self.avg_cost.name])
                loss_val = np.mean(loss_val)
                message = "TEST ---> batch_idx: {} loss: {}\n".format(batch_idx,
                                                                      loss_val)
                fleet.util.print_on_rank(message, 0)
        except fluid.core.EOFException:
            self.test_reader.reset()

        pass_time = time.time() - pass_start
        message = "Distributed Test Succeed, Using Time {}\n".format(pass_time)
        fleet.util.print_on_rank(message, 0)

    def do_pyreader_training(self, fleet):
        """
        do training using dataset, using fetch handler to catch variable
        Args:
            fleet(Fleet api): the fleet object of Parameter Server, define distribute training role
        """
        device_env = os.getenv("DEVICE", 'cpu')
        if device_env == 'cpu':
            device = fluid.CPUPlace()
        elif device_env == 'gpu':
            device = fluid.CUDAPlace(0)
        exe = fluid.Executor(device)
        exe.run(fluid.default_startup_program())
        fleet.init_worker()

        batch_size = 4
        train_reader = paddle.batch(fake_ctr_reader(), batch_size=batch_size)
        self.reader.decorate_sample_list_generator(train_reader)

        for epoch_id in range(1):
            self.reader.start()
            try:
                pass_start = time.time()
                while True:
                    loss_val = exe.run(program=fluid.default_main_program(),
                                       fetch_list=[self.avg_cost.name])
                    loss_val = np.mean(loss_val)
                    # TODO(randomly fail)
                    #   reduce_output = fleet.util.all_reduce(
                    #       np.array(loss_val), mode="sum")
                    #   loss_all_trainer = fleet.util.all_gather(float(loss_val))
                    #   loss_val = float(reduce_output) / len(loss_all_trainer)
                    message = "TRAIN ---> pass: {} loss: {}\n".format(epoch_id,
                                                                      loss_val)
                    fleet.util.print_on_rank(message, 0)

                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)

    def do_dataset_training(self, fleet):
        train_file_list = ctr_dataset_reader.prepare_fake_data()

        device_env = os.getenv("DEVICE", 'cpu')
        if device_env == 'cpu':
            device = fluid.CPUPlace()
        elif device_env == 'gpu':
            device = fluid.CUDAPlace(0)
        exe = fluid.Executor(device)

        exe.run(fluid.default_startup_program())
        fleet.init_worker()

        thread_num = 2
        batch_size = 128
        filelist = train_file_list

        # config dataset
        dataset = paddle.distributed.QueueDataset()
        pipe_command = 'python ctr_dataset_reader.py'

        dataset.init(
            batch_size=batch_size,
            use_var=self.feeds,
            pipe_command=pipe_command,
            thread_num=thread_num)

        dataset.set_filelist(filelist)

        for epoch_id in range(1):
            pass_start = time.time()
            dataset.set_filelist(filelist)
            exe.train_from_dataset(
                program=fluid.default_main_program(),
                dataset=dataset,
                fetch_list=[self.avg_cost],
                fetch_info=["cost"],
                print_period=2,
                debug=int(os.getenv("Debug", "0")))
            pass_time = time.time() - pass_start

        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)

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