# Automatic Mixed Precision examples¶

Warning

torch.cuda.amp.GradScaler is not a complete implementation of automatic mixed precision. GradScaler is only useful if you manually run regions of your model in float16. If you aren’t sure how to choose op precision manually, the master branch and nightly pip/conda builds include a context manager that chooses op precision automatically wherever it’s enabled. See the master documentation for details.

Gradient scaling helps prevent gradient underflow when training with mixed precision, as explained here.

Instances of torch.cuda.amp.GradScaler help perform the steps of gradient scaling conveniently, as shown in the following code snippets.

### Typical Use¶

# Creates a GradScaler once at the beginning of training.

for epoch in epochs:
for input, target in data:
output = model(input)
loss = loss_fn(output, target)

# Scales the loss, and calls backward() on the scaled loss to create scaled gradients.
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 the parameters’ .grad attributes between backward() and scaler.step(optimizer), you should unscale them first. For example, gradient clipping manipulates a set of gradients such that their global norm (see torch.nn.utils.clip_grad_norm_()) or maximum magnitude (see torch.nn.utils.clip_grad_value_()) 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 (say optimizer2), you may call scaler.unscale_(optimizer2) separately to unscale those parameters’ gradients as well.

Calling scaler.unscale_(optimizer) before clipping enables you to clip unscaled gradients as usual:

scaler = GradScaler()

for epoch in epochs:
for input, target in data:
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:

# 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 (internally) calling optimizer.step().

Warning

unscale_() should only be called once per optimizer per step() call, and only after all gradients for that optimizer’s assigned parameters have been accumulated. Calling unscale_() twice for a given optimizer between each step() triggers a RuntimeError.

For some operations, you may need to work with scaled gradients in a setting where scaler.unscale_ is unsuitable.

A gradient penalty implementation typically creates gradients out-of-place 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:

for epoch in epochs:
for input, target in data:
output = model(input)
loss = loss_fn(output, target)

# Computes the penalty term and adds it to the loss

loss.backward()
optimizer.step()


To implement a gradient penalty with gradient scaling, the loss passed to torch.autograd.grad() should be scaled. The resulting out-of-place gradients will therefore be scaled, and should be unscaled before being combined to create the penalty value.

Here’s how that looks for the same L2 penalty:

scaler = GradScaler()

for epoch in epochs:
for input, target in data:
output = model(input)
loss = loss_fn(output, target)

# Scales the loss for the out-of-place backward pass, resulting in scaled grad_params

# by any optimizer, so ordinary division is used instead of scaler.unscale_:
inv_scale = 1./scaler.get_scale()

# Computes the penalty term and adds it to the loss

# Applies scaling to the backward call as usual.  Accumulates leaf gradients that are correctly scaled.
scaler.scale(loss).backward()

# step() and update() proceed as usual.
scaler.step(optimizer)
scaler.update()


### Working with Multiple Losses and Optimizers¶

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.

However, 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:
output0 = model0(input)
output1 = model1(input)
loss0 = loss_fn(2 * output0 + 3 * output1, target)
loss1 = loss_fn(3 * output0 - 5 * output1, target)

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 independently checks its gradients for infs/NaNs, and therefore 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 file an issue.