main.py

#   Copyright (c) 2019 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.
"""
Deep Attention Matching Network
"""
import sys
import os
import six
import numpy as np
import time
import multiprocessing
import paddle
import paddle.fluid as fluid
import reader as reader
from util import mkdir
import evaluation as eva
import config

try:
    import cPickle as pickle  #python 2
except ImportError as e:
    import pickle  #python 3

from model_check import check_cuda
from net import Net


def evaluate(score_path, result_file_path):
    """
    Evaluate both douban and ubuntu dataset
    """
    if args.ext_eval:
        result = eva.evaluate_douban(score_path)
    else:
        result = eva.evaluate_ubuntu(score_path)
    #write evaluation result
    with open(result_file_path, 'w') as out_file:
        for p_at in result:
            out_file.write(p_at + '\t' + str(result[p_at]) + '\n')
    print('finish evaluation')
    print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))


def test_with_feed(exe, program, feed_names, fetch_list, score_path, batches,
                   batch_num, dev_count):
    """
    Test with feed
    """
    score_file = open(score_path, 'w')
    for it in six.moves.xrange(batch_num // dev_count):
        feed_list = []
        for dev in six.moves.xrange(dev_count):
            val_index = it * dev_count + dev
            batch_data = reader.make_one_batch_input(batches, val_index)
            feed_dict = dict(zip(feed_names, batch_data))
            feed_list.append(feed_dict)

            predicts = exe.run(feed=feed_list, fetch_list=fetch_list)

            scores = np.array(predicts[0])
            for dev in six.moves.xrange(dev_count):
                val_index = it * dev_count + dev
                for i in six.moves.xrange(args.batch_size):
                    score_file.write(
                        str(scores[args.batch_size * dev + i][0]) + '\t' + str(
                            batches["label"][val_index][i]) + '\n')
    score_file.close()


def test_with_pyreader(exe, program, pyreader, fetch_list, score_path, batches,
                       batch_num, dev_count):
    """
    Test with pyreader
    """

    def data_provider():
        """
        Data reader
        """
        for index in six.moves.xrange(batch_num):
            yield reader.make_one_batch_input(batches, index)

    score_file = open(score_path, 'w')
    pyreader.decorate_tensor_provider(data_provider)
    it = 0
    pyreader.start()
    while True:
        try:
            predicts = exe.run(fetch_list=fetch_list)

            scores = np.array(predicts[0])
            for dev in six.moves.xrange(dev_count):
                val_index = it * dev_count + dev
                for i in six.moves.xrange(args.batch_size):
                    score_file.write(
                        str(scores[args.batch_size * dev + i][0]) + '\t' + str(
                            batches["label"][val_index][i]) + '\n')
            it += 1
        except fluid.core.EOFException:
            pyreader.reset()
            break
    score_file.close()


def train(args):
    """
    Train Program
    """
    if not os.path.exists(args.save_path):
        os.makedirs(args.save_path)

    # data data_config
    data_conf = {
        "batch_size": args.batch_size,
        "max_turn_num": args.max_turn_num,
        "max_turn_len": args.max_turn_len,
        "_EOS_": args._EOS_,
    }

    dam = Net(args.max_turn_num, args.max_turn_len, args.vocab_size,
              args.emb_size, args.stack_num, args.channel1_num,
              args.channel2_num)

    train_program = fluid.Program()
    train_startup = fluid.Program()
    if "CE_MODE_X" in os.environ:
        train_program.random_seed = 110
        train_startup.random_seed = 110
    with fluid.program_guard(train_program, train_startup):
        with fluid.unique_name.guard():
            if args.use_pyreader:
                train_pyreader = dam.create_py_reader(
                    capacity=10, name='train_reader')
            else:
                dam.create_data_layers()
            loss, logits = dam.create_network()
            loss.persistable = True
            logits.persistable = True
            # gradient clipping
            fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue(
                max=1.0, min=-1.0))

            optimizer = fluid.optimizer.Adam(
                learning_rate=fluid.layers.exponential_decay(
                    learning_rate=args.learning_rate,
                    decay_steps=400,
                    decay_rate=0.9,
                    staircase=True))
            optimizer.minimize(loss)

    test_program = fluid.Program()
    test_startup = fluid.Program()
    if "CE_MODE_X" in os.environ:
        test_program.random_seed = 110
        test_startup.random_seed = 110
    with fluid.program_guard(test_program, test_startup):
        with fluid.unique_name.guard():
            if args.use_pyreader:
                test_pyreader = dam.create_py_reader(
                    capacity=10, name='test_reader')
            else:
                dam.create_data_layers()

            loss, logits = dam.create_network()
            loss.persistable = True
            logits.persistable = True

    test_program = test_program.clone(for_test=True)

    if args.use_cuda:
        place = fluid.CUDAPlace(0)
        dev_count = fluid.core.get_cuda_device_count()
    else:
        place = fluid.CPUPlace()
        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))

    print("device count %d" % dev_count)
    print("theoretical memory usage: ")
    print(fluid.contrib.memory_usage(
        program=train_program, batch_size=args.batch_size))

    exe = fluid.Executor(place)
    exe.run(train_startup)
    exe.run(test_startup)

    train_exe = fluid.ParallelExecutor(
        use_cuda=args.use_cuda, loss_name=loss.name, main_program=train_program)

    test_exe = fluid.ParallelExecutor(
        use_cuda=args.use_cuda,
        main_program=test_program,
        share_vars_from=train_exe)

    if args.word_emb_init is not None:
        print("start loading word embedding init ...")
        if six.PY2:
            word_emb = np.array(pickle.load(open(args.word_emb_init,
                                                 'rb'))).astype('float32')
        else:
            word_emb = np.array(
                pickle.load(
                    open(args.word_emb_init, 'rb'), encoding="bytes")).astype(
                        'float32')
        dam.set_word_embedding(word_emb, place)
        print("finish init word embedding  ...")

    print("start loading data ...")
    with open(args.data_path, 'rb') as f:
        if six.PY2:
            train_data, val_data, test_data = pickle.load(f)
        else:
            train_data, val_data, test_data = pickle.load(f, encoding="bytes")
    print("finish loading data ...")

    val_batches = reader.build_batches(val_data, data_conf)

    batch_num = len(train_data[six.b('y')]) // args.batch_size
    val_batch_num = len(val_batches["response"])

    print_step = max(1, batch_num // (dev_count * 100))
    save_step = max(1, batch_num // (dev_count * 10))

    print("begin model training ...")
    print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))

    def train_with_feed(step):
        """
        Train on one epoch data by feeding
        """
        ave_cost = 0.0
        for it in six.moves.xrange(batch_num // dev_count):
            feed_list = []
            for dev in six.moves.xrange(dev_count):
                index = it * dev_count + dev
                batch_data = reader.make_one_batch_input(train_batches, index)
                feed_dict = dict(zip(dam.get_feed_names(), batch_data))
                feed_list.append(feed_dict)

            cost = train_exe.run(feed=feed_list, fetch_list=[loss.name])

            ave_cost += np.array(cost[0]).mean()
            step = step + 1
            if step % print_step == 0:
                print("processed: [" + str(step * dev_count * 1.0 / batch_num) +
                      "] ave loss: [" + str(ave_cost / print_step) + "]")
                ave_cost = 0.0

            if (args.save_path is not None) and (step % save_step == 0):
                save_path = os.path.join(args.save_path, "step_" + str(step))
                print("Save model at step %d ... " % step)
                print(time.strftime('%Y-%m-%d %H:%M:%S',
                                    time.localtime(time.time())))
                fluid.io.save_persistables(exe, save_path, train_program)

                score_path = os.path.join(args.save_path, 'score.' + str(step))
                test_with_feed(test_exe, test_program,
                               dam.get_feed_names(), [logits.name], score_path,
                               val_batches, val_batch_num, dev_count)

                result_file_path = os.path.join(args.save_path,
                                                'result.' + str(step))
                evaluate(score_path, result_file_path)
        return step, np.array(cost[0]).mean()

    def train_with_pyreader(step):
        """
        Train on one epoch with pyreader
        """

        def data_provider():
            """
            Data reader
            """
            for index in six.moves.xrange(batch_num):
                yield reader.make_one_batch_input(train_batches, index)

        train_pyreader.decorate_tensor_provider(data_provider)

        ave_cost = 0.0
        train_pyreader.start()
        while True:
            try:
                cost = train_exe.run(fetch_list=[loss.name])

                ave_cost += np.array(cost[0]).mean()
                step = step + 1
                if step % print_step == 0:
                    print("processed: [" + str(step * dev_count * 1.0 /
                                               batch_num) + "] ave loss: [" +
                          str(ave_cost / print_step) + "]")
                    ave_cost = 0.0

                if (args.save_path is not None) and (step % save_step == 0):
                    save_path = os.path.join(args.save_path,
                                             "step_" + str(step))
                    print("Save model at step %d ... " % step)
                    print(time.strftime('%Y-%m-%d %H:%M:%S',
                                        time.localtime(time.time())))
                    fluid.io.save_persistables(exe, save_path, train_program)

                    score_path = os.path.join(args.save_path,
                                              'score.' + str(step))
                    test_with_pyreader(test_exe, test_program, test_pyreader,
                                       [logits.name], score_path, val_batches,
                                       val_batch_num, dev_count)

                    result_file_path = os.path.join(args.save_path,
                                                    'result.' + str(step))
                    evaluate(score_path, result_file_path)

            except fluid.core.EOFException:
                train_pyreader.reset()
                break
        return step, np.array(cost[0]).mean()

    # train over different epoches
    global_step, train_time = 0, 0.0
    for epoch in six.moves.xrange(args.num_scan_data):
        shuffle_train = reader.unison_shuffle(
            train_data, seed=110 if ("CE_MODE_X" in os.environ) else None)
        train_batches = reader.build_batches(shuffle_train, data_conf)

        begin_time = time.time()
        if args.use_pyreader:
            global_step, last_cost = train_with_pyreader(global_step)
        else:
            global_step, last_cost = train_with_feed(global_step)

        pass_time_cost = time.time() - begin_time
        train_time += pass_time_cost
        print("Pass {0}, pass_time_cost {1}"
              .format(epoch, "%2.2f sec" % pass_time_cost))
    # For internal continuous evaluation
    if "CE_MODE_X" in os.environ:
        card_num = get_cards()
        print("kpis\ttrain_cost_card%d\t%f" % (card_num, last_cost))
        print("kpis\ttrain_duration_card%d\t%f" % (card_num, train_time))


def test(args):
    """
    Test
    """
    if not os.path.exists(args.save_path):
        mkdir(args.save_path)
    if not os.path.exists(args.model_path):
        raise ValueError("Invalid model init path %s" % args.model_path)
    # data data_config
    data_conf = {
        "batch_size": args.batch_size,
        "max_turn_num": args.max_turn_num,
        "max_turn_len": args.max_turn_len,
        "_EOS_": args._EOS_,
    }

    dam = Net(args.max_turn_num, args.max_turn_len, args.vocab_size,
              args.emb_size, args.stack_num, args.channel1_num,
              args.channel2_num)
    dam.create_data_layers()
    loss, logits = dam.create_network()

    loss.persistable = True
    logits.persistable = True

    # gradient clipping
    fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue(
        max=1.0, min=-1.0))

    test_program = fluid.default_main_program().clone(for_test=True)
    optimizer = fluid.optimizer.Adam(
        learning_rate=fluid.layers.exponential_decay(
            learning_rate=args.learning_rate,
            decay_steps=400,
            decay_rate=0.9,
            staircase=True))
    optimizer.minimize(loss)

    if args.use_cuda:
        place = fluid.CUDAPlace(0)
        dev_count = fluid.core.get_cuda_device_count()
    else:
        place = fluid.CPUPlace()
        #dev_count = multiprocessing.cpu_count()
        dev_count = 1

    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())

    fluid.io.load_persistables(exe, args.model_path)

    test_exe = fluid.ParallelExecutor(
        use_cuda=args.use_cuda, main_program=test_program)

    print("start loading data ...")
    with open(args.data_path, 'rb') as f:
        if six.PY2:
            train_data, val_data, test_data = pickle.load(f)
        else:
            train_data, val_data, test_data = pickle.load(f, encoding="bytes")
    print("finish loading data ...")

    test_batches = reader.build_batches(test_data, data_conf)

    test_batch_num = len(test_batches["response"])

    print("test batch num: %d" % test_batch_num)

    print("begin inference ...")
    print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))

    score_path = os.path.join(args.save_path, 'score.txt')
    score_file = open(score_path, 'w')

    for it in six.moves.xrange(test_batch_num // dev_count):
        feed_list = []
        for dev in six.moves.xrange(dev_count):
            index = it * dev_count + dev
            batch_data = reader.make_one_batch_input(test_batches, index)
            feed_dict = dict(zip(dam.get_feed_names(), batch_data))
            feed_list.append(feed_dict)

        predicts = test_exe.run(feed=feed_list, fetch_list=[logits.name])

        scores = np.array(predicts[0])
        print("step = %d" % it)

        for dev in six.moves.xrange(dev_count):
            index = it * dev_count + dev
            for i in six.moves.xrange(args.batch_size):
                score_file.write(
                    str(scores[args.batch_size * dev + i][0]) + '\t' + str(
                        test_batches["label"][index][i]) + '\n')

    score_file.close()

    #write evaluation result
    if args.ext_eval:
        result = eva.evaluate_douban(score_path)
    else:
        result = eva.evaluate_ubuntu(score_path)
    result_file_path = os.path.join(args.save_path, 'result.txt')
    with open(result_file_path, 'w') as out_file:
        for metric in result:
            out_file.write(metric + '\t' + str(result[metric]) + '\n')
    print('finish test')
    print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))


def get_cards():
    num = 0
    cards = os.environ.get('CUDA_VISIBLE_DEVICES', '')
    if cards != '':
        num = len(cards.split(","))
    return num


if __name__ == '__main__':
    args = config.parse_args()
    config.print_arguments(args)

    check_cuda(args.use_cuda)

    if args.do_train:
        train(args)

    if args.do_test:
        test(args)