python - 累积梯度

Question

我想在向后传递之前累积梯度。所以想知道正确的做法是什么。根据 this article
它的:

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()

而我预计它是:

model.zero_grad()                                   # Reset gradients tensors
loss = 0
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss += loss_function(predictions, labels)       # Compute loss function                              
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        loss = loss / accumulation_steps            # Normalize our loss (if averaged)
        loss.backward()                             # Backward pass
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()     
        loss = 0  

我累积损失，然后除以累积步骤以求平均。

第二个问题，如果我是对的，考虑到我只在每个累积步骤中进行反向传递，您是否希望我的方法更快？

Answer 1

所以根据答案here ，第一种方法是内存高效的。两种方法所需的工作量或多或少相同。

第二种方法不断累积图形，因此需要 accumulation_steps多倍的内存。第一种方法直接计算梯度(并简单地添加梯度)，因此需要较少的内存。