是否支持多机多卡分布式训练？

[twwch]

No description provided.

[twwch]

import transformers
import torch
import os
import json
import random
import numpy as np
import argparse
from torch.utils.tensorboard import SummaryWriter
from torch.nn.parallel import DistributedDataParallel as DDP
from datetime import datetime
from tqdm import tqdm
from torch.nn import DataParallel
from tokenizations.bpe_tokenizer import get_encoder

from torch import distributed as dist


def build_files(data_path, tokenized_data_path, num_pieces, full_tokenizer, min_length):
    with open(data_path, 'r', encoding='utf8') as f:
        print('reading lines')
        lines = json.load(f)
        lines = [line.replace('\n', ' [SEP] ') for line in lines]  # 用[SEP]表示换行, 段落之间使用SEP表示段落结束
    all_len = len(lines)
    if not os.path.exists(tokenized_data_path):
        os.mkdir(tokenized_data_path)
    for i in tqdm(range(num_pieces)):
        sublines = lines[all_len // num_pieces * i: all_len // num_pieces * (i + 1)]
        if i == num_pieces - 1:
            sublines.extend(lines[all_len // num_pieces * (i + 1):])  # 把尾部例子添加到最后一个piece
        sublines = [full_tokenizer.tokenize(line) for line in sublines if
                    len(line) > min_length]  # 只考虑长度超过min_length的句子
        sublines = [full_tokenizer.convert_tokens_to_ids(line) for line in sublines]
        full_line = []
        for subline in sublines:
            full_line.append(full_tokenizer.convert_tokens_to_ids('[MASK]'))  # 文章开头添加MASK表示文章开始
            full_line.extend(subline)
            full_line.append(full_tokenizer.convert_tokens_to_ids('[CLS]'))  # 文章之间添加CLS表示文章结束
        with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'w') as f:
            for id in full_line:
                f.write(str(id) + ' ')
    print('finish')


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', default='0,1,2,3', type=str, required=False, help='设置使用哪些显卡')
    parser.add_argument('--model_config', default='config/model_config_small.json', type=str, required=False,
                        help='选择模型参数')
    parser.add_argument('--tokenizer_path', default='cache/vocab_small.txt', type=str, required=False, help='选择词库')
    parser.add_argument('--raw_data_path', default='data/train.json', type=str, required=False, help='原始训练语料')
    parser.add_argument('--tokenized_data_path', default='data/tokenized/', type=str, required=False,
                        help='tokenized语料存放位置')
    parser.add_argument('--raw', action='store_true', help='是否先做tokenize')
    parser.add_argument('--epochs', default=5, type=int, required=False, help='训练循环')
    parser.add_argument('--batch_size', default=6, type=int, required=False, help='训练batch size')
    parser.add_argument('--lr', default=1.5e-4, type=float, required=False, help='学习率')
    parser.add_argument('--warmup_steps', default=2000, type=int, required=False, help='warm up步数')
    parser.add_argument('--log_step', default=1, type=int, required=False,
                        help='多少步汇报一次loss，设置为gradient accumulation的整数倍')
    parser.add_argument('--stride', default=768, type=int, required=False, help='训练时取训练数据的窗口步长')
    parser.add_argument('--gradient_accumulation', default=1, type=int, required=False, help='梯度积累')
    parser.add_argument('--fp16', action='store_true', help='混合精度')
    parser.add_argument('--fp16_opt_level', default='O1', type=str, required=False)
    parser.add_argument('--max_grad_norm', default=1.0, type=float, required=False)
    parser.add_argument('--num_pieces', default=100, type=int, required=False, help='将训练语料分成多少份')
    parser.add_argument('--min_length', default=128, type=int, required=False, help='最短收录文章长度')
    parser.add_argument('--output_dir', default='model/', type=str, required=False, help='模型输出路径')
    parser.add_argument('--pretrained_model', default='', type=str, required=False, help='模型训练起点路径')
    parser.add_argument('--writer_dir', default='tensorboard_summary/', type=str, required=False, help='Tensorboard路径')
    parser.add_argument('--segment', action='store_true', help='中文以词为单位')
    parser.add_argument('--bpe_token', action='store_true', help='subword')
    parser.add_argument('--encoder_json', default="tokenizations/encoder.json", type=str, help="encoder.json")
    parser.add_argument('--vocab_bpe', default="tokenizations/vocab.bpe", type=str, help="vocab.bpe")
    parser.add_argument('--nnodes', default=2, type=int, help="nnodes")
    parser.add_argument('--local_rank', type=int, default=-1, help="local gpu id")

    args = parser.parse_args()
    print('args:\n' + args.__repr__())

    if args.segment:
        from tokenizations import tokenization_bert_word_level as tokenization_bert
    else:
        from tokenizations import tokenization_bert

    # os.environ["CUDA_VISIBLE_DEVICES"] = args.device  # 此处设置程序使用哪些显卡
    os.environ['MASTER_PORT'] = '29507'

    model_config = transformers.modeling_gpt2.GPT2Config.from_json_file(args.model_config)
    print('config:\n' + model_config.to_json_string())

    n_ctx = model_config.n_ctx
    if args.bpe_token:
        full_tokenizer = get_encoder(args.encoder_json, args.vocab_bpe)
    else:
        full_tokenizer = tokenization_bert.BertTokenizer(vocab_file=args.tokenizer_path)
    full_tokenizer.max_len = 999999
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    print('using device:', device)

    raw_data_path = args.raw_data_path
    tokenized_data_path = args.tokenized_data_path
    raw = args.raw  # 选择是否从零开始构建数据集
    epochs = args.epochs
    batch_size = args.batch_size
    lr = args.lr
    warmup_steps = args.warmup_steps
    log_step = args.log_step
    stride = args.stride
    gradient_accumulation = args.gradient_accumulation
    fp16 = args.fp16  # 不支持半精度的显卡请勿打开
    fp16_opt_level = args.fp16_opt_level
    max_grad_norm = args.max_grad_norm
    num_pieces = args.num_pieces
    min_length = args.min_length
    output_dir = args.output_dir
    tb_writer = SummaryWriter(log_dir=args.writer_dir)
    assert log_step % gradient_accumulation == 0

    if not os.path.exists(output_dir):
        os.mkdir(output_dir)

    if raw:
        print('building files')
        build_files(data_path=raw_data_path, tokenized_data_path=tokenized_data_path, num_pieces=num_pieces,
                    full_tokenizer=full_tokenizer, min_length=min_length)
        print('files built')

    if not args.pretrained_model:
        model = transformers.modeling_gpt2.GPT2LMHeadModel(config=model_config)
    else:
        model = transformers.modeling_gpt2.GPT2LMHeadModel.from_pretrained(args.pretrained_model)
    model.train()
    model.to(device)

    num_parameters = 0
    parameters = model.parameters()
    for parameter in parameters:
        num_parameters += parameter.numel()
    print('number of parameters: {}, rank'.format(num_parameters, args.local_rank))

    multi_gpu = False
    full_len = 0
    print('calculating total steps')
    for i in tqdm(range(num_pieces)):
        with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'r') as f:
            full_len += len([int(item) for item in f.read().strip().split()])
    total_steps = int(full_len / stride * epochs / batch_size / gradient_accumulation)
    # total_steps = 31916
    print('total steps = {}, rank = {}'.format(total_steps, args.local_rank))

    if torch.cuda.device_count() > 1:
        print("Let's use", torch.cuda.device_count(), "GPUs!")
        multi_gpu = True
        if args.nnodes == 1:
            model = DataParallel(model, device_ids=[int(i) for i in args.device.split(',')])
        else:

            dist.init_process_group(backend='nccl', init_method='env://')
            torch.cuda.set_device(args.local_rank)

            model.cuda()
            torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
            model = DDP(model, device_ids=[args.local_rank], output_device=args.local_rank)

    optimizer = transformers.AdamW(model.parameters(), lr=lr, correct_bias=True)
    scheduler = transformers.WarmupLinearSchedule(optimizer, warmup_steps=warmup_steps,
                                                  t_total=total_steps)
    if fp16:
        try:
            from apex import amp
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
        model, optimizer = amp.initialize(model, optimizer, opt_level=fp16_opt_level)

    print('starting training')
    overall_step = 0
    running_loss = 0
    for epoch in range(epochs):
        print('epoch {}'.format(epoch + 1))
        now = datetime.now()
        print('time: {}'.format(now))
        x = np.linspace(0, num_pieces - 1, num_pieces, dtype=np.int32)
        random.shuffle(x)
        piece_num = 0
        for i in x:
            with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'r') as f:
                line = f.read().strip()
            tokens = line.split()
            tokens = [int(token) for token in tokens]
            start_point = 0
            samples = []
            while start_point < len(tokens) - n_ctx:
                samples.append(tokens[start_point: start_point + n_ctx])
                start_point += stride
            if start_point < len(tokens):
                samples.append(tokens[len(tokens) - n_ctx:])
            random.shuffle(samples)
            for step in range(len(samples) // batch_size):  # drop last

                #  prepare data
                batch = samples[step * batch_size: (step + 1) * batch_size]
                batch_inputs = []
                for ids in batch:
                    int_ids = [int(x) for x in ids]
                    batch_inputs.append(int_ids)
                batch_inputs = torch.tensor(batch_inputs).long().to(device)

                #  forward pass
                outputs = model.forward(input_ids=batch_inputs, labels=batch_inputs)
                loss, logits = outputs[:2]

                #  get loss
                if multi_gpu:
                    loss = loss.mean()
                if gradient_accumulation > 1:
                    loss = loss / gradient_accumulation

                #  loss backward
                if fp16:
                    with amp.scale_loss(loss, optimizer) as scaled_loss:
                        scaled_loss.backward()
                        torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), max_grad_norm)
                else:
                    loss.backward()
                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)

                #  optimizer step
                if (overall_step + 1) % gradient_accumulation == 0:
                    running_loss += loss.item()
                    optimizer.step()
                    optimizer.zero_grad()
                    scheduler.step()
                if (overall_step + 1) % log_step == 0:
                    tb_writer.add_scalar('loss', loss.item() * gradient_accumulation, overall_step)
                    print('now time: {}:{}. Step {} of piece {} of epoch {}, loss {}'.format(
                        datetime.now().hour,
                        datetime.now().minute,
                        step + 1,
                        piece_num,
                        epoch + 1,
                        running_loss * gradient_accumulation / (log_step / gradient_accumulation)))
                    running_loss = 0
                overall_step += 1
            piece_num += 1

        print('saving model for epoch {}'.format(epoch + 1))
        if not os.path.exists(output_dir + 'model_epoch{}'.format(epoch + 1)):
            os.mkdir(output_dir + 'model_epoch{}'.format(epoch + 1))
        model_to_save = model.module if hasattr(model, 'module') else model
        model_to_save.save_pretrained(output_dir + 'model_epoch{}'.format(epoch + 1))
        # torch.save(scheduler.state_dict(), output_dir + 'model_epoch{}/scheduler.pt'.format(epoch + 1))
        # torch.save(optimizer.state_dict(), output_dir + 'model_epoch{}/optimizer.pt'.format(epoch + 1))
        print('epoch {} finished'.format(epoch + 1))

        then = datetime.now()
        print('time: {}'.format(then))
        print('time for one epoch: {}'.format(then - now))

    print('training finished')
    if not os.path.exists(output_dir + 'final_model'):
        os.mkdir(output_dir + 'final_model')
    model_to_save = model.module if hasattr(model, 'module') else model
    model_to_save.save_pretrained(output_dir + 'final_model')
    # torch.save(scheduler.state_dict(), output_dir + 'final_model/scheduler.pt')
    # torch.save(optimizer.state_dict(), output_dir + 'final_model/optimizer.pt')


if __name__ == '__main__':
    main()

我将代码修改成这样

run.sh ，执行sh run.sh 0 2

# m每台机器使用显卡数目
nproc_per_node=4
# 主机器ip
MASTER_ADDR=xxx.xxx.xxx.xx
# 主机器端口号，可以随意，只要不冲突
MASTER_PORT=29507
# world_size
WORLD_SIZE=4
# 机器编号，主机器必须为0
node_rank=$1
# 使用的机器数量
nnodes=$2
# 每个进程的线程数目
export OMP_NUM_THREADS=1
# 训练命令
DISTRIBUTED_ARGS="--nproc_per_node $nproc_per_node --node_rank $node_rank --nnodes $nnodes --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
CUDA_VISIBLE_DEVICES=0,1,2,3 python -W ignore -m torch.distributed.launch $DISTRIBUTED_ARGS train_distributed.py

子节点sh run.sh 1 2

出现一下问题：
0卡上起了多个任务

代码报异常

原来的train.py单机多卡（4张卡）可以正常运行

能否提供支持？

[cywjava]

我改了一个，支持多机多卡，分布式训练 ：