# Automatic Mixed Precision examples¶

Ordinarily, “automatic mixed precision training” means training with
`torch.cuda.amp.autocast`

and `torch.cuda.amp.GradScaler`

together.

Instances of `torch.cuda.amp.autocast`

enable autocasting for chosen regions.
Autocasting automatically chooses the precision for GPU operations to improve performance
while maintaining accuracy.

Instances of `torch.cuda.amp.GradScaler`

help perform the steps of
gradient scaling conveniently. Gradient scaling improves convergence for networks with `float16`

gradients by minimizing gradient underflow, as explained here.

`torch.cuda.amp.autocast`

and `torch.cuda.amp.GradScaler`

are modular.
In the samples below, each is used as its individual documentation suggests.

(Samples here are illustrative. See the Automatic Mixed Precision recipe for a runnable walkthrough.)

## Typical Mixed Precision Training¶

```
# 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()
```

## Working with Unscaled Gradients¶

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.

### Gradient clipping¶

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:
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
(internally) calling `optimizer.step()`

.

## Working with Scaled Gradients¶

### Gradient accumulation¶

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),
call `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()
```

### Gradient penalty¶

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 `outputs`

Tensor(s)
passed to `torch.autograd.grad()`

should be scaled. The resulting gradients
will therefore be scaled, and should be unscaled before being combined to create the
penalty value.

Also, the penalty term computation is part of the forward pass, and therefore should be
inside an `autocast`

context.

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()
```

## Working with Multiple Models, 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:
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.

## Working with Multiple GPUs¶

The issues described here only affect `autocast`

. `GradScaler`

’s usage is unchanged.

### DataParallel in a single process¶

`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.forward`

:

```
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
(which executes `loss_fn`

):

```
model = MyModel()
dp_model = nn.DataParallel(model)
with autocast():
output = dp_model(input)
loss = loss_fn(output)
```

### DistributedDataParallel, one GPU per process¶

`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 `autocast`

and `GradScaler`

are not affected.

### DistributedDataParallel, multiple GPUs per process¶

Here `torch.nn.parallel.DistributedDataParallel`

may spawn a side thread to run the forward pass on each
device, like `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.

## Autocast and Custom Autograd Functions¶

If your network uses custom autograd functions
(subclasses of `torch.autograd.Function`

), changes are required for
autocast compatibility if any function

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`dtype`

).

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_fwd()`

and `torch.cuda.amp.custom_bwd()`

decorators as shown in
the relevant case below.

### Functions with multiple inputs or autocastable ops¶

Apply `custom_fwd`

and `custom_bwd`

(with no arguments) to `forward`

and
`backward`

respectively. These ensure `forward`

executes with the current autocast state and `backward`

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)
```

Now `MyMM`

can be invoked anywhere, without disabling autocast or manually casting inputs:

```
mymm = MyMM.apply
with autocast():
output = mymm(input1, input2)
```

### Functions that need a particular `dtype`

¶

Consider a custom function that requires `torch.float32`

inputs.
Apply `custom_fwd(cast_inputs=torch.float32)`

to `forward`

and `custom_bwd`

(with no arguments) to `backward`

.
If `forward`

runs in an autocast-enabled region, the decorators cast floating-point CUDA Tensor
inputs to `float32`

, and locally disable autocast during `forward`

and `backward`

:

```
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):
...
```

Now `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)
```