cluster_train.py

import os
import sys
import time
import six
import numpy as np
import math
import argparse
import paddle.fluid as fluid
import paddle
import time
import utils
import net

SEED = 102


def parse_args():
    parser = argparse.ArgumentParser("gru4rec benchmark.")
    parser.add_argument(
        '--train_dir',
        type=str,
        default='train_data',
        help='train file address')
    parser.add_argument(
        '--vocab_path',
        type=str,
        default='vocab.txt',
        help='vocab file address')
    parser.add_argument('--is_local', type=int, default=1, help='whether local')
    parser.add_argument('--hid_size', type=int, default=100, help='hid size')
    parser.add_argument(
        '--model_dir', type=str, default='model_recall20', help='model dir')
    parser.add_argument(
        '--batch_size', type=int, default=5, help='num of batch size')
    parser.add_argument('--pass_num', type=int, default=10, help='num of epoch')
    parser.add_argument(
        '--print_batch', type=int, default=10, help='num of print batch')
    parser.add_argument(
        '--use_cuda', type=int, default=0, help='whether use gpu')
    parser.add_argument(
        '--base_lr', type=float, default=0.01, help='learning rate')
    parser.add_argument(
        '--num_devices', type=int, default=1, help='Number of GPU devices')
    parser.add_argument(
        '--role', type=str, default='pserver', help='trainer or pserver')
    parser.add_argument(
        '--endpoints',
        type=str,
        default='127.0.0.1:6000',
        help='The pserver endpoints, like: 127.0.0.1:6000, 127.0.0.1:6001')
    parser.add_argument(
        '--current_endpoint',
        type=str,
        default='127.0.0.1:6000',
        help='The current_endpoint')
    parser.add_argument(
        '--trainer_id',
        type=int,
        default=0,
        help='trainer id ,only trainer_id=0 save model')
    parser.add_argument(
        '--trainers',
        type=int,
        default=1,
        help='The num of trianers, (default: 1)')
    args = parser.parse_args()
    return args


def get_cards(args):
    return args.num_devices


def train():
    """ do training """
    args = parse_args()
    hid_size = args.hid_size
    train_dir = args.train_dir
    vocab_path = args.vocab_path
    use_cuda = True if args.use_cuda else False
    print("use_cuda:", use_cuda)
    batch_size = args.batch_size
    vocab_size, train_reader = utils.prepare_data(
        train_dir, vocab_path, batch_size=batch_size * get_cards(args),\
        buffer_size=1000, word_freq_threshold=0, is_train=True)

    # Train program
    src_wordseq, dst_wordseq, avg_cost, acc = net.all_vocab_network(
        vocab_size=vocab_size, hid_size=hid_size)

    # Optimization to minimize lost
    sgd_optimizer = fluid.optimizer.SGD(learning_rate=args.base_lr)
    sgd_optimizer.minimize(avg_cost)

    def train_loop(main_program):
        """ train network """
        pass_num = args.pass_num
        model_dir = args.model_dir
        fetch_list = [avg_cost.name]

        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())
        total_time = 0.0
        for pass_idx in six.moves.xrange(pass_num):
            epoch_idx = pass_idx + 1
            print("epoch_%d start" % epoch_idx)

            t0 = time.time()
            i = 0
            newest_ppl = 0
            for data in train_reader():
                i += 1
                lod_src_wordseq = utils.to_lodtensor([dat[0] for dat in data],
                                                     place)
                lod_dst_wordseq = utils.to_lodtensor([dat[1] for dat in data],
                                                     place)
                ret_avg_cost = exe.run(main_program,
                                       feed={
                                           "src_wordseq": lod_src_wordseq,
                                           "dst_wordseq": lod_dst_wordseq
                                       },
                                       fetch_list=fetch_list)
                avg_ppl = np.exp(ret_avg_cost[0])
                newest_ppl = np.mean(avg_ppl)
                if i % args.print_batch == 0:
                    print("step:%d ppl:%.3f" % (i, newest_ppl))

            t1 = time.time()
            total_time += t1 - t0
            print("epoch:%d num_steps:%d time_cost(s):%f" %
                  (epoch_idx, i, total_time / epoch_idx))
            save_dir = "%s/epoch_%d" % (model_dir, epoch_idx)
            feed_var_names = ["src_wordseq", "dst_wordseq"]
            fetch_vars = [avg_cost, acc]
            if args.trainer_id == 0:
                fluid.io.save_inference_model(save_dir, feed_var_names,
                                              fetch_vars, exe)
                print("model saved in %s" % save_dir)
        print("finish training")

    if args.is_local:
        print("run local training")
        train_loop(fluid.default_main_program())
    else:
        print("run distribute training")
        t = fluid.DistributeTranspiler()
        t.transpile(
            args.trainer_id, pservers=args.endpoints, trainers=args.trainers)
        if args.role == "pserver":
            print("run psever")
            pserver_prog = t.get_pserver_program(args.current_endpoint)
            pserver_startup = t.get_startup_program(args.current_endpoint,
                                                    pserver_prog)
            exe = fluid.Executor(fluid.CPUPlace())
            exe.run(pserver_startup)
            exe.run(pserver_prog)
        elif args.role == "trainer":
            print("run trainer")
            train_loop(t.get_trainer_program())


if __name__ == "__main__":
    train()