train.py

import os
import time
import argparse
import ast
import numpy as np

import paddle
import paddle.fluid as fluid

from model import transformer, position_encoding_init
from optim import LearningRateScheduler
from config import *
import reader


def parse_args():
    parser = argparse.ArgumentParser("Training for Transformer.")
    parser.add_argument(
        "--src_vocab_fpath",
        type=str,
        required=True,
        help="The path of vocabulary file of source language.")
    parser.add_argument(
        "--trg_vocab_fpath",
        type=str,
        required=True,
        help="The path of vocabulary file of target language.")
    parser.add_argument(
        "--train_file_pattern",
        type=str,
        required=True,
        help="The pattern to match training data files.")
    parser.add_argument(
        "--val_file_pattern",
        type=str,
        help="The pattern to match validation data files.")
    parser.add_argument(
        "--use_token_batch",
        type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to "
        "produce batch data according to token number.")
    parser.add_argument(
        "--batch_size",
        type=int,
        default=2048,
        help="The number of sequences contained in a mini-batch, or the maximum "
        "number of tokens (include paddings) contained in a mini-batch. Note "
        "that this represents the number on single device and the actual batch "
        "size for multi-devices will multiply the device number.")
    parser.add_argument(
        "--pool_size",
        type=int,
        default=10000,
        help="The buffer size to pool data.")
    parser.add_argument(
        "--sort_type",
        default="pool",
        choices=("global", "pool", "none"),
        help="The grain to sort by length: global for all instances; pool for "
        "instances in pool; none for no sort.")
    parser.add_argument(
        "--shuffle",
        type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to shuffle instances in each pass.")
    parser.add_argument(
        "--shuffle_batch",
        type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to shuffle the data batches.")
    parser.add_argument(
        "--special_token",
        type=str,
        default=["<s>", "<e>", "<unk>"],
        nargs=3,
        help="The <bos>, <eos> and <unk> tokens in the dictionary.")
    parser.add_argument(
        'opts',
        help='See config.py for all options',
        default=None,
        nargs=argparse.REMAINDER)
    args = parser.parse_args()
    # Append args related to dict
    src_dict = reader.DataReader.load_dict(args.src_vocab_fpath)
    trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
    dict_args = [
        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
        str(len(trg_dict)), "bos_idx", str(src_dict[args.special_token[0]]),
        "eos_idx", str(src_dict[args.special_token[1]]), "unk_idx",
        str(src_dict[args.special_token[2]])
    ]
    merge_cfg_from_list(args.opts + dict_args,
                        [TrainTaskConfig, ModelHyperParams])
    return args


def pad_batch_data(insts,
                   pad_idx,
                   n_head,
                   is_target=False,
                   is_label=False,
                   return_attn_bias=True,
                   return_max_len=True,
                   return_num_token=False):
    """
    Pad the instances to the max sequence length in batch, and generate the
    corresponding position data and attention bias.
    """
    return_list = []
    max_len = max(len(inst) for inst in insts)
    num_token = reduce(lambda x, y: x + y,
                       [len(inst) for inst in insts]) if return_num_token else 0
    # Any token included in dict can be used to pad, since the paddings' loss
    # will be masked out by weights and make no effect on parameter gradients.
    inst_data = np.array(
        [inst + [pad_idx] * (max_len - len(inst)) for inst in insts])
    return_list += [inst_data.astype("int64").reshape([-1, 1])]
    if is_label:  # label weight
        inst_weight = np.array(
            [[1.] * len(inst) + [0.] * (max_len - len(inst)) for inst in insts])
        return_list += [inst_weight.astype("float32").reshape([-1, 1])]
    else:  # position data
        inst_pos = np.array([
            range(1, len(inst) + 1) + [0] * (max_len - len(inst))
            for inst in insts
        ])
        return_list += [inst_pos.astype("int64").reshape([-1, 1])]
    if return_attn_bias:
        if is_target:
            # This is used to avoid attention on paddings and subsequent
            # words.
            slf_attn_bias_data = np.ones((inst_data.shape[0], max_len, max_len))
            slf_attn_bias_data = np.triu(slf_attn_bias_data,
                                         1).reshape([-1, 1, max_len, max_len])
            slf_attn_bias_data = np.tile(slf_attn_bias_data,
                                         [1, n_head, 1, 1]) * [-1e9]
        else:
            # This is used to avoid attention on paddings.
            slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
                                           (max_len - len(inst))
                                           for inst in insts])
            slf_attn_bias_data = np.tile(
                slf_attn_bias_data.reshape([-1, 1, 1, max_len]),
                [1, n_head, max_len, 1])
        return_list += [slf_attn_bias_data.astype("float32")]
    if return_max_len:
        return_list += [max_len]
    if return_num_token:
        return_list += [num_token]
    return return_list if len(return_list) > 1 else return_list[0]


def prepare_batch_input(insts, data_input_names, util_input_names, src_pad_idx,
                        trg_pad_idx, n_head, d_model):
    """
    Put all padded data needed by training into a dict.
    """
    src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
        [inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
    trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = pad_batch_data(
        [inst[1] for inst in insts], trg_pad_idx, n_head, is_target=True)
    trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
                                [1, 1, trg_max_len, 1]).astype("float32")

    # These shape tensors are used in reshape_op.
    src_data_shape = np.array([-1, src_max_len, d_model], dtype="int32")
    trg_data_shape = np.array([-1, trg_max_len, d_model], dtype="int32")
    src_slf_attn_pre_softmax_shape = np.array(
        [-1, src_slf_attn_bias.shape[-1]], dtype="int32")
    src_slf_attn_post_softmax_shape = np.array(
        [-1] + list(src_slf_attn_bias.shape[1:]), dtype="int32")
    trg_slf_attn_pre_softmax_shape = np.array(
        [-1, trg_slf_attn_bias.shape[-1]], dtype="int32")
    trg_slf_attn_post_softmax_shape = np.array(
        [-1] + list(trg_slf_attn_bias.shape[1:]), dtype="int32")
    trg_src_attn_pre_softmax_shape = np.array(
        [-1, trg_src_attn_bias.shape[-1]], dtype="int32")
    trg_src_attn_post_softmax_shape = np.array(
        [-1] + list(trg_src_attn_bias.shape[1:]), dtype="int32")

    lbl_word, lbl_weight, num_token = pad_batch_data(
        [inst[2] for inst in insts],
        trg_pad_idx,
        n_head,
        is_target=False,
        is_label=True,
        return_attn_bias=False,
        return_max_len=False,
        return_num_token=True)

    data_input_dict = dict(
        zip(data_input_names, [
            src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
            trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
        ]))
    util_input_dict = dict(
        zip(util_input_names, [
            src_data_shape, src_slf_attn_pre_softmax_shape,
            src_slf_attn_post_softmax_shape, trg_data_shape,
            trg_slf_attn_pre_softmax_shape, trg_slf_attn_post_softmax_shape,
            trg_src_attn_pre_softmax_shape, trg_src_attn_post_softmax_shape
        ]))
    return data_input_dict, util_input_dict, np.asarray(
        [num_token], dtype="float32")


def read_multiple(reader, count, clip_last=True):
    """
    Stack data from reader for multi-devices.
    """

    def __impl__():
        res = []
        for item in reader():
            res.append(item)
            if len(res) == count:
                yield res
                res = []
        if len(res) == count:
            yield res
        elif not clip_last:
            data = []
            for item in res:
                data += item
            if len(data) > count:
                inst_num_per_part = len(data) // count
                yield [
                    data[inst_num_per_part * i:inst_num_per_part * (i + 1)]
                    for i in range(count)
                ]

    return __impl__


def split_data(data, num_part):
    """
    Split data for each device.
    """
    if len(data) == num_part:
        return data
    data = data[0]
    inst_num_per_part = len(data) // num_part
    return [
        data[inst_num_per_part * i:inst_num_per_part * (i + 1)]
        for i in range(num_part)
    ]


def train(args):
    dev_count = fluid.core.get_cuda_device_count()

    sum_cost, avg_cost, predict, token_num = transformer(
        ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size,
        ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
        ModelHyperParams.n_head, ModelHyperParams.d_key,
        ModelHyperParams.d_value, ModelHyperParams.d_model,
        ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
        ModelHyperParams.weight_sharing, TrainTaskConfig.label_smooth_eps)

    lr_scheduler = LearningRateScheduler(ModelHyperParams.d_model,
                                         TrainTaskConfig.warmup_steps,
                                         TrainTaskConfig.learning_rate)
    optimizer = fluid.optimizer.Adam(
        learning_rate=lr_scheduler.learning_rate,
        beta1=TrainTaskConfig.beta1,
        beta2=TrainTaskConfig.beta2,
        epsilon=TrainTaskConfig.eps)
    optimizer.minimize(sum_cost)

    place = fluid.CUDAPlace(0) if TrainTaskConfig.use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)
    # Initialize the parameters.
    if TrainTaskConfig.ckpt_path:
        fluid.io.load_persistables(exe, TrainTaskConfig.ckpt_path)
        lr_scheduler.current_steps = TrainTaskConfig.start_step
    else:
        exe.run(fluid.framework.default_startup_program())

    train_data = reader.DataReader(
        src_vocab_fpath=args.src_vocab_fpath,
        trg_vocab_fpath=args.trg_vocab_fpath,
        fpattern=args.train_file_pattern,
        use_token_batch=args.use_token_batch,
        batch_size=args.batch_size * (1 if args.use_token_batch else dev_count),
        pool_size=args.pool_size,
        sort_type=args.sort_type,
        shuffle=args.shuffle,
        shuffle_batch=args.shuffle_batch,
        start_mark=args.special_token[0],
        end_mark=args.special_token[1],
        unk_mark=args.special_token[2],
        max_length=ModelHyperParams.max_length,
        clip_last_batch=False)
    train_data = read_multiple(
        reader=train_data.batch_generator,
        count=dev_count if args.use_token_batch else 1)

    build_strategy = fluid.BuildStrategy()
    # Since the token number differs among devices, customize gradient scale to
    # use token average cost among multi-devices. and the gradient scale is
    # `1 / token_number` for average cost.
    build_strategy.gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.Customized
    train_exe = fluid.ParallelExecutor(
        use_cuda=TrainTaskConfig.use_gpu,
        loss_name=sum_cost.name,
        build_strategy=build_strategy)

    def test_context():
        # Context to do validation.
        test_program = fluid.default_main_program().clone(for_test=True)
        test_exe = fluid.ParallelExecutor(
            use_cuda=TrainTaskConfig.use_gpu,
            main_program=test_program,
            share_vars_from=train_exe)

        val_data = reader.DataReader(
            src_vocab_fpath=args.src_vocab_fpath,
            trg_vocab_fpath=args.trg_vocab_fpath,
            fpattern=args.val_file_pattern,
            use_token_batch=args.use_token_batch,
            batch_size=args.batch_size *
            (1 if args.use_token_batch else dev_count),
            pool_size=args.pool_size,
            sort_type=args.sort_type,
            start_mark=args.special_token[0],
            end_mark=args.special_token[1],
            unk_mark=args.special_token[2],
            max_length=ModelHyperParams.max_length,
            clip_last_batch=False,
            shuffle=False,
            shuffle_batch=False)

        def test(exe=test_exe):
            test_total_cost = 0
            test_total_token = 0
            test_data = read_multiple(
                reader=val_data.batch_generator,
                count=dev_count if args.use_token_batch else 1)
            for batch_id, data in enumerate(test_data()):
                feed_list = []
                for place_id, data_buffer in enumerate(
                        split_data(
                            data, num_part=dev_count)):
                    data_input_dict, util_input_dict, _ = prepare_batch_input(
                        data_buffer, data_input_names, util_input_names,
                        ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
                        ModelHyperParams.n_head, ModelHyperParams.d_model)
                    feed_list.append(
                        dict(data_input_dict.items() + util_input_dict.items()))

                outs = exe.run(feed=feed_list,
                               fetch_list=[sum_cost.name, token_num.name])
                sum_cost_val, token_num_val = np.array(outs[0]), np.array(outs[
                    1])
                test_total_cost += sum_cost_val.sum()
                test_total_token += token_num_val.sum()
            test_avg_cost = test_total_cost / test_total_token
            test_ppl = np.exp([min(test_avg_cost, 100)])
            return test_avg_cost, test_ppl

        return test

    if args.val_file_pattern is not None:
        test = test_context()

    data_input_names = encoder_data_input_fields + decoder_data_input_fields[:
                                                                             -1] + label_data_input_fields
    util_input_names = encoder_util_input_fields + decoder_util_input_fields
    init = False
    for pass_id in xrange(TrainTaskConfig.pass_num):
        pass_start_time = time.time()
        for batch_id, data in enumerate(train_data()):
            feed_list = []
            total_num_token = 0
            lr_rate = lr_scheduler.update_learning_rate()
            for place_id, data_buffer in enumerate(
                    split_data(
                        data, num_part=dev_count)):
                data_input_dict, util_input_dict, num_token = prepare_batch_input(
                    data_buffer, data_input_names, util_input_names,
                    ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
                    ModelHyperParams.n_head, ModelHyperParams.d_model)
                total_num_token += num_token
                feed_list.append(
                    dict(data_input_dict.items() + util_input_dict.items() +
                         {lr_scheduler.learning_rate.name: lr_rate}.items()))

                if not init:  # init the position encoding table
                    for pos_enc_param_name in pos_enc_param_names:
                        pos_enc = position_encoding_init(
                            ModelHyperParams.max_length + 1,
                            ModelHyperParams.d_model)
                        feed_list[place_id][pos_enc_param_name] = pos_enc
            for feed_dict in feed_list:
                feed_dict[sum_cost.name + "@GRAD"] = 1. / total_num_token
            outs = train_exe.run(fetch_list=[sum_cost.name, token_num.name],
                                 feed=feed_list)
            sum_cost_val, token_num_val = np.array(outs[0]), np.array(outs[1])
            total_sum_cost = sum_cost_val.sum(
            )  # sum the cost from multi-devices
            total_token_num = token_num_val.sum()
            total_avg_cost = total_sum_cost / total_token_num
            print("epoch: %d, batch: %d, sum loss: %f, avg loss: %f, ppl: %f" %
                  (pass_id, batch_id, total_sum_cost, total_avg_cost,
                   np.exp([min(total_avg_cost, 100)])))
            init = True
        # Validate and save the model for inference.
        print("epoch: %d, " % pass_id + (
            "val avg loss: %f, val ppl: %f, " % test()
            if args.val_file_pattern is not None else "") + "consumed %fs" % (
                time.time() - pass_start_time))
        fluid.io.save_persistables(
            exe,
            os.path.join(TrainTaskConfig.ckpt_dir,
                         "pass_" + str(pass_id) + ".checkpoint"))
        fluid.io.save_inference_model(
            os.path.join(TrainTaskConfig.model_dir,
                         "pass_" + str(pass_id) + ".infer.model"),
            data_input_names[:-2] + util_input_names, [predict], exe)


if __name__ == "__main__":
    args = parse_args()
    train(args)