如何使用多 GPU _ BERT答案

【问题标题】：how to use the Multi GPU _ BERT如何使用多 GPU _ BERT
【发布时间】：2020-10-23 14:05:52
【问题描述】：

我使用以下 BERT 代码对多个 GPU 进行了分析。

model = BertForSequenceClassification.from_pretrained(
    "beomi/kcbert-large", 
    num_labels = len(df['label'].unique()), 
    output_attentions = False, 
    output_hidden_states = False,
)

model = torch.nn.DataParallel(model)
model.cuda()

当使用一个 GPU 进行分析时，它的分析没有问题。（没有模型 = torch.nn.DataParallel(model)）

但是之后

model = torch.nn.DataParallel(model)

有错误

import random
import numpy as np

# This training code is based on the `run_glue.py` script here:
# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128

# Set the seed value all over the place to make this reproducible.
seed_val = 42

random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)

training_stats = []
total_t0 = time.time()
for epoch_i in range(0, epochs):
    
    # ========================================
    #               Training
    # ========================================
    
    # Perform one full pass over the training set.

    t0 = time.time()

    total_train_loss = 0
    total_train_accuracy = 0


    for step, batch in enumerate(train_dataloader):

        if step % 40 == 0 and not step == 0:
            elapsed = format_time(time.time() - t0)
            print('  Batch {:>5,}  of  {:>5,}.    Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))


        b_input_ids = batch[0].to(device)
        b_input_mask = batch[1].to(device)
        b_labels = batch[2].to(device)


        model.zero_grad()        


        loss, logits = model(b_input_ids, 
                             token_type_ids=None, 
                             attention_mask=b_input_mask, 
                             labels=b_labels)


        total_train_loss += loss.item()
        
        logits = logits.detach().cpu().numpy()
        label_ids = b_labels.to('cpu').numpy()
        
        total_train_accuracy += flat_accuracy(logits, label_ids)

        loss.backward()

        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

        optimizer.step()
        scheduler.step()
    avg_train_loss = total_train_loss / len(train_dataloader)            
    
    training_time = format_time(time.time() - t0)
    avg_train_accuracy = total_train_accuracy / len(train_dataloader)

我遇到了以下问题： ValueError: 只有一个元素张量可以转换为 Python 标量

-> total_train_loss += loss.item()

我不知道发生了什么错误，

请帮忙。谢谢

【问题讨论】：

标签： python gpu bert-language-model

【解决方案1】：

DataParallel 将返回在每个 gpu 上计算的部分损失，因此您可以这样做

loss.backward(torch.Tensor([1, 1]))

或

loss.sum().backward()

或

loss.mean().backward()

loss.mean()要求所有批次大小相等

它们中的任何一个都将帮助您获得缩放器值。

【讨论】：