Automatic Mixed Precision examples¶
torch.cuda.amp.autocast enable autocasting for chosen regions.
Autocasting automatically chooses the precision for GPU operations to improve performance
while maintaining accuracy.
torch.cuda.amp.GradScaler help perform the steps of
gradient scaling conveniently. Gradient scaling improves convergence for networks with
gradients by minimizing gradient underflow, as explained here.
(Samples here are illustrative. See the Automatic Mixed Precision recipe for a runnable walkthrough.)
# 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 dtype autocast chose 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()
All gradients produced by
scaler.scale(loss).backward() are scaled. If you wish to modify or inspect
.grad attributes between
scaler.step(optimizer), you should
unscale them first. For example, gradient clipping manipulates a set of gradients such that their global norm
torch.nn.utils.clip_grad_norm_()) or maximum magnitude (see
is some user-imposed threshold. If you attempted to clip without unscaling, the gradients’ norm/maximum
magnitude would also be scaled, so your requested threshold (which was meant to be the threshold for unscaled
gradients) would be invalid.
scaler.unscale_(optimizer) unscales gradients held by
optimizer’s assigned parameters.
If your model or models contain other parameters that were assigned to another optimizer
optimizer2), you may call
scaler.unscale_(optimizer2) separately to unscale those
parameters’ gradients as well.
scaler.unscale_(optimizer) before clipping enables you to clip unscaled gradients as usual:
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()
scaler records that
scaler.unscale_(optimizer) was already called for this optimizer
this iteration, so
scaler.step(optimizer) knows not to redundantly unscale gradients before
Gradient accumulation adds gradients over an effective batch of size
batch_per_iter * iters_to_accumulate
* num_procs if distributed). The scale should be calibrated for the effective batch, which means inf/NaN checking,
step skipping if inf/NaN grads are found, and scale updates should occur at effective-batch granularity.
Also, grads should remain scaled, and the scale factor should remain constant, while grads for a given effective
batch are accumulated. If grads are unscaled (or the scale factor changes) before accumulation is complete,
the next backward pass will add scaled grads to unscaled grads (or grads scaled by a different factor)
after which it’s impossible to recover the accumulated unscaled grads
step must apply.
Therefore, if you want to
unscale_ grads (e.g., to allow clipping unscaled grads),
unscale_ just before
step, after all (scaled) grads for the upcoming
step have been accumulated. Also, only call
update at the end of iterations
where you called
step for a full effective batch:
scaler = GradScaler() for epoch in epochs: for i, (input, target) in enumerate(data): with autocast(): output = model(input) loss = loss_fn(output, target) loss = loss / iters_to_accumulate # Accumulates scaled gradients. scaler.scale(loss).backward() if (i + 1) % iters_to_accumulate == 0: # may unscale_ here if desired (e.g., to allow clipping unscaled gradients) scaler.step(optimizer) scaler.update() optimizer.zero_grad()
A gradient penalty implementation commonly creates gradients using
torch.autograd.grad(), combines them to create the penalty value,
and adds the penalty value to the loss.
Here’s an ordinary example of an L2 penalty without gradient scaling or autocasting:
for epoch in epochs: for input, target in data: optimizer.zero_grad() output = model(input) loss = loss_fn(output, target) # Creates gradients grad_params = torch.autograd.grad(outputs=loss, inputs=model.parameters(), create_graph=True) # Computes the penalty term and adds it to the loss grad_norm = 0 for grad in grad_params: grad_norm += grad.pow(2).sum() grad_norm = grad_norm.sqrt() loss = loss + grad_norm loss.backward() # clip gradients here, if desired optimizer.step()
To implement a gradient penalty with gradient scaling, the
torch.autograd.grad() should be scaled. The resulting gradients
will therefore be scaled, and should be unscaled before being combined to create the
Also, the penalty term computation is part of the forward pass, and therefore should be
Here’s how that looks for the same L2 penalty:
scaler = GradScaler() for epoch in epochs: for input, target in data: optimizer.zero_grad() with autocast(): output = model(input) loss = loss_fn(output, target) # Scales the loss for autograd.grad's backward pass, producing scaled_grad_params scaled_grad_params = torch.autograd.grad(outputs=scaler.scale(loss), inputs=model.parameters(), create_graph=True) # Creates unscaled grad_params before computing the penalty. scaled_grad_params are # not owned by any optimizer, so ordinary division is used instead of scaler.unscale_: inv_scale = 1./scaler.get_scale() grad_params = [p * inv_scale for p in scaled_grad_params] # Computes the penalty term and adds it to the loss with autocast(): grad_norm = 0 for grad in grad_params: grad_norm += grad.pow(2).sum() grad_norm = grad_norm.sqrt() loss = loss + grad_norm # Applies scaling to the backward call as usual. # Accumulates leaf gradients that are correctly scaled. scaler.scale(loss).backward() # may unscale_ here if desired (e.g., to allow clipping unscaled gradients) # step() and update() proceed as usual. scaler.step(optimizer) scaler.update()
If your network has multiple losses, you must call
scaler.scale on each of them individually.
If your network has multiple optimizers, you may call
scaler.unscale_ on any of them individually,
and you must call
scaler.step on each of them individually.
scaler.update should only be called once,
after all optimizers used this iteration have been stepped:
scaler = torch.cuda.amp.GradScaler() for epoch in epochs: for input, target in data: optimizer0.zero_grad() optimizer1.zero_grad() with autocast(): output0 = model0(input) output1 = model1(input) loss0 = loss_fn(2 * output0 + 3 * output1, target) loss1 = loss_fn(3 * output0 - 5 * output1, target) # (retain_graph here is unrelated to amp, it's present because in this # example, both backward() calls share some sections of graph.) scaler.scale(loss0).backward(retain_graph=True) scaler.scale(loss1).backward() # You can choose which optimizers receive explicit unscaling, if you # want to inspect or modify the gradients of the params they own. scaler.unscale_(optimizer0) scaler.step(optimizer0) scaler.step(optimizer1) scaler.update()
Each optimizer checks its gradients for infs/NaNs and makes an independent decision whether or not to skip the step. This may result in one optimizer skipping the step while the other one does not. Since step skipping occurs rarely (every several hundred iterations) this should not impede convergence. If you observe poor convergence after adding gradient scaling to a multiple-optimizer model, please report a bug.
torch.nn.DataParallel spawns threads to run the forward pass on each device.
The autocast state is thread local, so the following will not work:
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)
The fix is simple. Enable autocast as part of
MyModel(nn.Module): ... @autocast() def forward(self, input): ... # Alternatively MyModel(nn.Module): ... def forward(self, input): with autocast(): ...
The following now autocasts in
dp_model’s threads (which execute
forward) and the main thread
model = MyModel() dp_model = nn.DataParallel(model) with autocast(): output = dp_model(input) loss = loss_fn(output)
torch.nn.parallel.DistributedDataParallel’s documentation recommends one GPU per process for best
performance. In this case,
DistributedDataParallel does not spawn threads internally,
so usages of
GradScaler are not affected.
torch.nn.parallel.DistributedDataParallel may spawn a side thread to run the forward pass on each
torch.nn.DataParallel. The fix is the same:
apply autocast as part of your model’s
forward method to ensure it’s enabled in side threads.
takes multiple floating-point Tensor inputs,
wraps any autocastable op (see the Autocast Op Reference), or
requires a particular
dtype(for example, if it wraps CUDA extensions that were only compiled for
In all cases, if you’re importing the function and can’t alter its definition, a safe fallback
is to disable autocast and force execution in
float32 ( or
dtype) at any points of use where errors occur:
with autocast(): ... with autocast(enabled=False): output = imported_function(input1.float(), input2.float())
If you’re the function’s author (or can alter its definition) a better solution is to use the
torch.cuda.amp.custom_bwd() decorators as shown in
the relevant case below.
custom_bwd (with no arguments) to
backward respectively. These ensure
forward executes with the current autocast state and
executes with the same autocast state as
forward (which can prevent type mismatch errors):
class MyMM(torch.autograd.Function): @staticmethod @custom_fwd def forward(ctx, a, b): ctx.save_for_backward(a, b) return a.mm(b) @staticmethod @custom_bwd def backward(ctx, grad): a, b = ctx.saved_tensors return grad.mm(b.t()), a.t().mm(grad)
MyMM can be invoked anywhere, without disabling autocast or manually casting inputs:
mymm = MyMM.apply with autocast(): output = mymm(input1, input2)
Consider a custom function that requires
custom_bwd (with no arguments) to
forward runs in an autocast-enabled region, the decorators cast floating-point CUDA Tensor
float32, and locally disable autocast during
class MyFloat32Func(torch.autograd.Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) def forward(ctx, input): ctx.save_for_backward(input) ... return fwd_output @staticmethod @custom_bwd def backward(ctx, grad): ...
MyFloat32Func can be invoked anywhere, without manually disabling autocast or casting inputs:
func = MyFloat32Func.apply with autocast(): # func will run in float32, regardless of the surrounding autocast state output = func(input)