train.py 22.9 KB
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#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import six
import numpy as np
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import random
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import time
import os
import math
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.framework as framework
from paddle.fluid.executor import Executor
import data
from args import *
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from utils.cards import get_cards
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import lm_model
import logging

logging.basicConfig()
import pickle


def prepare_batch_input(batch, args):
    x = batch['token_ids']
    x_r = batch['token_ids_reverse']
    y = batch['next_token_id']
    y_r = batch['next_token_id_reverse']
    inst = []
    for i in range(len(x)):
        if args.use_custom_samples:
            custom_samples_array = np.zeros(
                (args.num_steps, args.n_negative_samples_batch + 1),
                dtype='int64')
            custom_samples_array_r = np.zeros(
                (args.num_steps, args.n_negative_samples_batch + 1),
                dtype='int64')
            custom_probabilities_array = np.zeros(
                (args.num_steps, args.n_negative_samples_batch + 1),
                dtype='float32')
            for j in range(args.num_steps):
                for k in range(args.n_negative_samples_batch + 1):
                    custom_samples_array[j][k] = k
                    custom_samples_array_r[j][k] = k
                    custom_probabilities_array[j][k] = 1.0
                custom_samples_array[j][0] = y[i][j]
                custom_samples_array_r[j][0] = y_r[i][j]
            inst.append([
                x[i], y[i], x_r[i], y_r[i], custom_samples_array,
                custom_samples_array_r, custom_probabilities_array
            ])
        else:
            inst.append([x[i], y[i], x_r[i], y_r[i]])
    return inst


def batch_reader(batch_list, args):
    res = []
    for batch in batch_list:
        res.append(prepare_batch_input(batch, args))
    return res


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

    def __impl__():
        # one time read batch_size * count data for rnn
        for data in reader():
            inst_num_per_part = batch_size
            split_data = {}
            len_check = True
            for k in data.keys():
                if data[k] is not None:
                    if len(data[k]) != batch_size * count:
                        len_check = False
                        print("data check error!!, data=" + data[k] + ", k=" +
                              k)
                        break
            if len_check:
                res = []
                for i in range(count):
                    split_data = {}
                    for k in data.keys():
                        if data[k] is not None:
                            split_data[k] = data[k][inst_num_per_part * i:
                                                    inst_num_per_part * (i + 1)]
                    res.append(split_data)
                yield res

    return __impl__


def LodTensor_Array(lod_tensor):
    lod = lod_tensor.lod()
    array = np.array(lod_tensor)
    new_array = []
    for i in range(len(lod[0]) - 1):
        new_array.append(array[lod[0][i]:lod[0][i + 1]])
    return new_array


def get_current_model_para(train_prog, train_exe):
    param_list = train_prog.block(0).all_parameters()
    param_name_list = [p.name for p in param_list]

    vals = {}
    for p_name in param_name_list:
        p_array = np.array(fluid.global_scope().find_var(p_name).get_tensor())
        vals[p_name] = p_array

    return vals


def save_para_npz(train_prog, train_exe):
    logger.info("begin to save model to model_base")
    param_list = train_prog.block(0).all_parameters()
    param_name_list = [p.name for p in param_list]

    vals = {}
    for p_name in param_name_list:
        p_array = np.array(fluid.global_scope().find_var(p_name).get_tensor())
        vals[p_name] = p_array

    emb = vals["embedding_para"]
    logger.info("begin to save model to model_base")
    np.savez("mode_base", **vals)


def prepare_input(batch, epoch_id=0, with_lr=True):
    x, y = batch
    inst = []
    for i in range(len(x)):
        inst.append([x[i], y[i]])
    return inst


def eval(vocab, infer_progs, dev_count, logger, args):
    infer_prog, infer_startup_prog, infer_model = infer_progs
    feed_order = infer_model.feed_order
    loss = infer_model.loss

    # prepare device
    place = core.CUDAPlace(0) if args.use_gpu else core.CPUPlace()
    exe = Executor(place)
    if not args.use_gpu:
        place = fluid.CPUPlace()
        import multiprocessing
        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
    else:
        place = fluid.CUDAPlace(0)
        dev_count = fluid.core.get_cuda_device_count()

    total_loss = 0.0
    total_cnt = 0
    n_batch_cnt = 0
    n_batch_loss = 0.0
    val_feed_list = [
        infer_prog.global_block().var(var_name) for var_name in feed_order
    ]
    val_feeder = fluid.DataFeeder(val_feed_list, place)
    dev_data = data.BidirectionalLMDataset(
        args.test_path, vocab, test=True, shuffle_on_load=False)
    dev_data_iter = lambda: dev_data.iter_batches(args.batch_size * dev_count, args.num_steps)
    dev_reader = read_multiple(dev_data_iter, args.batch_size, dev_count)

    last_hidden_values = np.zeros(
        (dev_count, args.num_layers * 2 * args.batch_size * args.embed_size),
        dtype='float32')
    last_cell_values = np.zeros(
        (dev_count, args.num_layers * 2 * args.batch_size * args.hidden_size),
        dtype='float32')
    for batch_id, batch_list in enumerate(dev_reader(), 1):
        feed_data = batch_reader(batch_list, args)
        feed = list(val_feeder.feed_parallel(feed_data, dev_count))
        for i in range(dev_count):
            init_hidden_tensor = fluid.core.LoDTensor()
            if args.use_gpu:
                placex = fluid.CUDAPlace(i)
            else:
                placex = fluid.CPUPlace()
            init_hidden_tensor.set(last_hidden_values[i], placex)
            init_cell_tensor = fluid.core.LoDTensor()
            init_cell_tensor.set(last_cell_values[i], placex)

            feed[i]['init_hiddens'] = init_hidden_tensor
            feed[i]['init_cells'] = init_cell_tensor
        last_hidden_values = []
        last_cell_values = []
        for i in range(dev_count):
            val_fetch_outs = exe.run(program=infer_prog,
                                     feed=feed[i],
                                     fetch_list=[
                                         infer_model.loss.name,
                                         infer_model.last_hidden.name,
                                         infer_model.last_cell.name
                                     ],
                                     return_numpy=False)
            last_hidden_values.append(np.array(val_fetch_outs[1]))
            last_cell_values.append(np.array(val_fetch_outs[2]))
            total_loss += np.array(val_fetch_outs[0]).sum()

            n_batch_cnt += len(np.array(val_fetch_outs[0]))
            total_cnt += len(np.array(val_fetch_outs[0]))
            n_batch_loss += np.array(val_fetch_outs[0]).sum()

        last_hidden_values = np.array(last_hidden_values).reshape((
            dev_count, args.num_layers * 2 * args.batch_size * args.embed_size))
        last_cell_values = np.array(last_cell_values).reshape(
            (dev_count,
             args.num_layers * 2 * args.batch_size * args.hidden_size))

        log_every_n_batch = args.log_interval
        if log_every_n_batch > 0 and batch_id % log_every_n_batch == 0:
            logger.info('Average dev loss from batch {} to {} is {}'.format(
                batch_id - log_every_n_batch + 1, batch_id, "%.10f" % (
                    n_batch_loss / n_batch_cnt)))
            n_batch_loss = 0.0
            n_batch_cnt = 0
        batch_offset = 0

    ppl = np.exp(total_loss / total_cnt)
    return ppl


def train():
    args = parse_args()
    if args.random_seed == 0:
        args.random_seed = None
        print("random seed is None")
    if args.enable_ce:
        random.seed(args.random_seed)
        np.random.seed(args.random_seed)
    logger = logging.getLogger("lm")
    logger.setLevel(logging.INFO)
    formatter = logging.Formatter(
        '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    console_handler = logging.StreamHandler()
    console_handler.setLevel(logging.INFO)
    console_handler.setFormatter(formatter)

    logger.info('Running with args : {}'.format(args))
    logger.info('Running paddle : {}'.format(paddle.version.commit))

    hidden_size = args.hidden_size
    batch_size = args.batch_size
    data_path = args.data_path
    logger.info("begin to load vocab")
    vocab = data.Vocabulary(args.vocab_path, validate_file=True)
    vocab_size = vocab.size
    logger.info("finished load vocab")

    logger.info('build the model...')
    # build model
    train_prog = fluid.Program()
    train_startup_prog = fluid.Program()
    if args.enable_ce:
        train_prog.random_seed = args.random_seed
        train_startup_prog.random_seed = args.random_seed

    # build infer model
    infer_prog = fluid.Program()
    infer_startup_prog = fluid.Program()
    with fluid.program_guard(infer_prog, infer_startup_prog):
        with fluid.unique_name.guard():
            # Infer process
            infer_model = lm_model.LanguageModel(
                args, vocab_size, test_mode=True)
            infer_model.build()
    infer_progs = infer_prog, infer_startup_prog, infer_model

    with fluid.program_guard(train_prog, train_startup_prog):
        with fluid.unique_name.guard():
            # Training process
            train_model = lm_model.LanguageModel(
                args, vocab_size, test_mode=False)
            train_model.build()
            fluid.clip.set_gradient_clip(
                clip=fluid.clip.GradientClipByGlobalNorm(
                    clip_norm=args.max_grad_norm))

            # build optimizer
            if args.optim == 'adagrad':
                optimizer = fluid.optimizer.Adagrad(
                    learning_rate=args.learning_rate,
                    epsilon=0.0,
                    initial_accumulator_value=1.0)
            elif args.optim == 'sgd':
                optimizer = fluid.optimizer.SGD(
                    learning_rate=args.learning_rate)
            elif args.optim == 'adam':
                optimizer = fluid.optimizer.Adam(
                    learning_rate=args.learning_rate)
            elif args.optim == 'rprop':
                optimizer = fluid.optimizer.RMSPropOptimizer(
                    learning_rate=args.learning_rate)
            else:
                logger.error('Unsupported optimizer: {}'.format(args.optim))
                exit(-1)
            optimizer.minimize(train_model.loss * args.num_steps)

            # initialize parameters
            place = core.CUDAPlace(0) if args.use_gpu else core.CPUPlace()
            exe = Executor(place)
    train_progs = train_prog, train_startup_prog, train_model

    if args.local:
        logger.info("local start_up:")
        train_loop(args, logger, vocab, train_progs, infer_progs, optimizer)
    else:
        if args.update_method == "nccl2":
            trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
            if args.test_nccl:
                worker_endpoints_env = os.getenv("PADDLE_WORK_ENDPOINTS")
                worker_endpoints = worker_endpoints_env.split(',')
                trainers_num = len(worker_endpoints)
                current_endpoint = worker_endpoints[trainer_id]
            else:
                port = os.getenv("PADDLE_PORT")
                worker_ips = os.getenv("PADDLE_TRAINERS")
                worker_endpoints = []
                for ip in worker_ips.split(","):
                    worker_endpoints.append(':'.join([ip, port]))
                worker_endpoints_env = ','.join(worker_endpoints)
                trainers_num = len(worker_endpoints)
                current_endpoint = os.getenv("POD_IP") + ":" + port
            if trainer_id == 0:
                logger.info("train_id == 0, sleep 60s")
                time.sleep(60)

            logger.info("trainers_num:{}".format(trainers_num))
            logger.info("worker_endpoints:{}".format(worker_endpoints))
            logger.info("current_endpoint:{}".format(current_endpoint))
            config = fluid.DistributeTranspilerConfig()
            config.mode = "nccl2"
            t = fluid.DistributeTranspiler(config=config)
            t.transpile(
                trainer_id,
                trainers=worker_endpoints_env,
                current_endpoint=current_endpoint,
                program=train_prog,
                startup_program=train_startup_prog)
            train_progs = train_prog, train_startup_prog, train_model
            train_loop(args, logger, vocab, train_progs, infer_progs, optimizer,
                       trainers_num, trainer_id, worker_endpoints)
        else:
            port = os.getenv("PADDLE_PORT", "6174")
            pserver_ips = os.getenv("PADDLE_PSERVERS")
            eplist = []
            for ip in pserver_ips.split(","):
                eplist.append(':'.join([ip, port]))
            pserver_endpoints = ",".join(eplist)
            trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "0"))
            current_endpoint = os.getenv("POD_IP") + ":" + port
            trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))

            logger.info("pserver_endpoints:{}".format(pserver_endpoints))
            logger.info("current_endpoint:{}".format(current_endpoint))
            logger.info("trainer_id:{}".format(trainer_id))
            logger.info("pserver_ips:{}".format(pserver_ips))
            logger.info("port:{}".format(port))

            t = fluid.DistributeTranspiler()
            t.transpile(
                trainer_id,
                pservers=pserver_endpoints,
                trainers=trainers,
                program=train_prog,
                startup_program=startup_prog)

            if training_role == "PSERVER":
                logger.info("distributed: pserver started")
                current_endpoint = os.getenv("POD_IP") + ":" + os.getenv(
                    "PADDLE_PORT")
                if not current_endpoint:
                    logger.critical("need env SERVER_ENDPOINT")
                    exit(1)
                pserver_prog = t.get_pserver_program(current_endpoint)
                pserver_startup = t.get_startup_program(current_endpoint,
                                                        pserver_prog)

                exe.run(pserver_startup)
                exe.run(pserver_prog)
            elif training_role == "TRAINER":
                logger.info("distributed: trainer started")
                trainer_prog = t.get_trainer_program()
                train_loop(args, logger, vocab, train_progs, infer_progs,
                           optimizer)
            else:
                logger.critical(
                    "environment var TRAINER_ROLE should be TRAINER os PSERVER")
                exit(1)


def train_loop(args,
               logger,
               vocab,
               train_progs,
               infer_progs,
               optimizer,
               nccl2_num_trainers=1,
               nccl2_trainer_id=0,
               worker_endpoints=None):
    train_prog, train_startup_prog, train_model = train_progs
    infer_prog, infer_startup_prog, infer_model = infer_progs

    # prepare device
    place = core.CUDAPlace(0) if args.use_gpu else core.CPUPlace()
    exe = Executor(place)
    if not args.use_gpu:
        place = fluid.CPUPlace()
        import multiprocessing
        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
    else:
        place = fluid.CUDAPlace(0)
        dev_count = fluid.core.get_cuda_device_count()

    if args.load_dir:
        logger.info('load pretrained checkpoints from {}'.format(args.load_dir))
        fluid.io.load_persistables(exe, args.load_dir, main_program=train_prog)
    elif args.load_pretraining_params:
        logger.info('load pretrained params from {}'.format(
            args.load_pretraining_params))
        exe.run(train_startup_prog)
        init_pretraining_params(
            exe, args.load_pretraining_params, main_program=train_prog)
    else:
        exe.run(train_startup_prog)

    # prepare data
    feed_list = [
        train_prog.global_block().var(var_name)
        for var_name in train_model.feed_order
    ]
    feeder = fluid.DataFeeder(feed_list, place)

    logger.info('Training the model...')
    exe_strategy = fluid.parallel_executor.ExecutionStrategy()
    parallel_executor = fluid.ParallelExecutor(
        loss_name=train_model.loss.name,
        main_program=train_prog,
        use_cuda=bool(args.use_gpu),
        exec_strategy=exe_strategy,
        num_trainers=nccl2_num_trainers,
        trainer_id=nccl2_trainer_id)

    logger.info("begin to load data")
    train_data = data.BidirectionalLMDataset(
        args.train_path,
        vocab,
        test=(not args.shuffle),
        shuffle_on_load=args.shuffle)
    logger.info("finished load vocab")

    # get train epoch size
    log_interval = args.log_interval
    total_time = 0.0
    batch_size = args.batch_size
    hidden_size = args.hidden_size
    custom_samples_array = np.zeros(
        (batch_size, args.num_steps, args.n_negative_samples_batch + 1),
        dtype='int64')
    custom_probabilities_array = np.zeros(
        (batch_size, args.num_steps, args.n_negative_samples_batch + 1),
        dtype='float32')
    for i in range(batch_size):
        for j in range(0, args.num_steps):
            for k in range(0, args.n_negative_samples_batch + 1):
                custom_samples_array[i][j][k] = k
                custom_probabilities_array[i][j][k] = 1.0

    start_time = time.time()
    train_data_iter = lambda: train_data.iter_batches(batch_size * dev_count, args.num_steps)
    train_reader = read_multiple(train_data_iter, batch_size, dev_count)
    total_num = 0
    n_batch_loss = 0.0
    n_batch_cnt = 0
    last_hidden_values = np.zeros(
        (dev_count, args.num_layers * 2 * batch_size * args.embed_size),
        dtype='float32')
    last_cell_values = np.zeros(
        (dev_count, args.num_layers * 2 * batch_size * hidden_size),
        dtype='float32')
    n_tokens_per_batch = args.batch_size * args.num_steps
    n_batches_per_epoch = int(args.all_train_tokens / n_tokens_per_batch)
    n_batches_total = args.max_epoch * n_batches_per_epoch
    begin_time = time.time()
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    ce_info = []
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    for batch_id, batch_list in enumerate(train_reader(), 1):
        if batch_id > n_batches_total:
            break
        feed_data = batch_reader(batch_list, args)
        feed = list(feeder.feed_parallel(feed_data, dev_count))
        for i in range(dev_count):
            init_hidden_tensor = fluid.core.LoDTensor()
            if args.use_gpu:
                placex = fluid.CUDAPlace(i)
            else:
                placex = fluid.CPUPlace()
            init_hidden_tensor.set(last_hidden_values[i], placex)
            init_cell_tensor = fluid.core.LoDTensor()
            init_cell_tensor.set(last_cell_values[i], placex)

            feed[i]['init_hiddens'] = init_hidden_tensor
            feed[i]['init_cells'] = init_cell_tensor

        fetch_outs = parallel_executor.run(feed=feed,
                                           fetch_list=[
                                               train_model.loss.name,
                                               train_model.last_hidden.name,
                                               train_model.last_cell.name
                                           ],
                                           return_numpy=False)
        cost_train = np.array(fetch_outs[0]).mean()
        last_hidden_values = np.array(fetch_outs[1])
        last_hidden_values = last_hidden_values.reshape(
            (dev_count, args.num_layers * 2 * batch_size * args.embed_size))
        last_cell_values = np.array(fetch_outs[2])
        last_cell_values = last_cell_values.reshape(
            (dev_count, args.num_layers * 2 * batch_size * args.hidden_size))

        total_num += args.batch_size * dev_count
        n_batch_loss += np.array(fetch_outs[0]).sum()
        n_batch_cnt += len(np.array(fetch_outs[0]))

        if batch_id > 0 and batch_id % log_interval == 0:
            smoothed_ppl = np.exp(n_batch_loss / n_batch_cnt)
            ppl = np.exp(
                np.array(fetch_outs[0]).sum() / len(np.array(fetch_outs[0])))
            used_time = time.time() - begin_time
            speed = log_interval / used_time
            logger.info(
                "[train] step:{}, loss:{:.3f}, ppl:{:.3f}, smoothed_ppl:{:.3f}, speed:{:.3f}".
                format(batch_id, n_batch_loss / n_batch_cnt, ppl, smoothed_ppl,
                       speed))
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            ce_info.append([n_batch_loss / n_batch_cnt, used_time])
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            n_batch_loss = 0.0
            n_batch_cnt = 0
            begin_time = time.time()
        if batch_id > 0 and batch_id % args.dev_interval == 0:
            valid_ppl = eval(vocab, infer_progs, dev_count, logger, args)
            logger.info("valid ppl {}".format(valid_ppl))
        if batch_id > 0 and batch_id % args.save_interval == 0:
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            epoch_id = int(batch_id / n_batches_per_epoch)
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            model_path = os.path.join(args.para_save_dir,
                                      str(batch_id + epoch_id))
            if not os.path.isdir(model_path):
                os.makedirs(model_path)
            fluid.io.save_persistables(
                executor=exe, dirname=model_path, main_program=train_prog)

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    if args.enable_ce:
        card_num = get_cards()
        ce_loss = 0
        ce_time = 0
        try:
            ce_loss = ce_info[-2][0]
            ce_time = ce_info[-2][1]
        except:
            print("ce info error")
        print("kpis\ttrain_duration_card%s\t%s" %
            (card_num, ce_time))
        print("kpis\ttrain_loss_card%s\t%f" %
            (card_num, ce_loss))


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    end_time = time.time()
    total_time += end_time - start_time
    epoch_id = int(batch_id / n_batches_per_epoch)
    model_path = os.path.join(args.para_save_dir, str(epoch_id))
    if not os.path.isdir(model_path):
        os.makedirs(model_path)
    fluid.io.save_persistables(
        executor=exe, dirname=model_path, main_program=train_prog)
    valid_ppl = eval(vocab, infer_progs, dev_count, logger, args)
    logger.info("valid ppl {}".format(valid_ppl))
    test_ppl = eval(vocab, infer_progs, dev_count, logger, args)


if __name__ == '__main__':
    train()