PyTorch重大更新:将支持自动混合精度训练!
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2020-07-30 00:16
AI编辑:我是小将
混合精度训练(mixed precision training)可以让模型训练在尽量不降低性能的情形下提升训练速度,而且也可以降低显卡使用内存。目前主流的深度学习框架都开始支持混合精度训练。对于PyTorch,混合精度训练还主要是采用NVIDIA开源的apex库。但是,PyTorch将迎来重大更新,那就是提供内部支持的混合精度训练,而且是自动混合精度训练:
torch.cuda.amp.autocast
:自动为GPU op选择精度来提升训练性能而不降低模型准确度。torch.cuda.amp.GradScaler
: 对梯度进行scale来加快模型收敛,因为float16梯度容易出现underflow(梯度过小)
两者结合在一起,可以实现自动混合精度训练:
# Creates model and optimizer in default precision
model = Net().cuda()
optimizer = optim.SGD(model.parameters(), ...)
# Creates a GradScaler once at the beginning of training.
scaler = GradScaler()
for epoch in epochs:
for input, target in data:
optimizer.zero_grad()
# Runs the forward pass with autocasting.
with autocast():
output = model(input)
loss = loss_fn(output, target)
# Scales loss. Calls backward() on scaled loss to create scaled gradients.
# Backward passes under autocast are not recommended.
# Backward ops run in the same precision that autocast used for corresponding forward ops.
scaler.scale(loss).backward()
# scaler.step() first unscales the gradients of the optimizer's assigned params.
# If these gradients do not contain infs or NaNs, optimizer.step() is then called,
# otherwise, optimizer.step() is skipped.
scaler.step(optimizer)
# Updates the scale for next iteration.
scaler.update()
可以看到,为了防止梯度的underflow,首先scaler.scale(loss).backward()会对loss乘以一个scale因子,然后backward时所有梯度都会乘以相同的scale因子,这样保证梯度有较大的magnitude而不会出现为0。我们不希望这个scale因子对学习速率产生影响,那么scaler.step(optimizer)会先unscale要更新的梯度然后再更新,如果梯度出现infs或者NaNs,optimizer将忽略这次迭代训练。
如果你想在梯度更新前对梯度进行clip,也是可以的:
scaler = GradScaler()
for epoch in epochs:
for input, target in data:
optimizer.zero_grad()
with autocast():
output = model(input)
loss = loss_fn(output, target)
scaler.scale(loss).backward()
# Unscales the gradients of optimizer's assigned params in-place
scaler.unscale_(optimizer)
# Since the gradients of optimizer's assigned params are unscaled, clips as usual:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
# optimizer's gradients are already unscaled, so scaler.step does not unscale them,
# although it still skips optimizer.step() if the gradients contain infs or NaNs.
scaler.step(optimizer)
# Updates the scale for next iteration.
scaler.update()
当然,混合精度训练肯定要支持分布式训练,由于autocast
是thread local的,所以要注意以下不同的情形:
如果使用torch.nn.DataParallel
:
此时只有一个进程,而不同GPU上是各自的线程跑forward过程的,所以下面操作时无效的:
model = MyModel()
dp_model = nn.DataParallel(model)
# Sets autocast in the main thread
with autocast():
# dp_model's internal threads won't autocast. The main thread's autocast state has no effect.
output = dp_model(input)
# loss_fn still autocasts, but it's too late...
loss = loss_fn(output)
此时你需要对model的forward方法用autocast
装饰:
MyModel(nn.Module):
...
@autocast()
def forward(self, input):
...
# Alternatively
MyModel(nn.Module):
...
def forward(self, input):
with autocast():
...
model = MyModel()
dp_model = nn.DataParallel(model)
with autocast():
output = dp_model(input)
loss = loss_fn(output)
如果使用torch.nn.parallel.DistributedDataParallel
:
一般情形下是单GPU进程的,此时原来的用来就没有问题,但是如果是多GPU一个进程那么就和上述问题一样,需要用autocast装饰model的forward。
更多内容见: https://pytorch.org/docs/master/notes/amp_examples.html#amp-examples
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