Automatic Mixed Precision package - torch.amp¶
torch.amp
provides convenience methods for mixed precision,
where some operations use the torch.float32
(float
) datatype and other operations
use lower precision floating point datatype (lower_precision_fp
): torch.float16
(half
) or torch.bfloat16
. Some ops, like linear layers and convolutions,
are much faster in lower_precision_fp
. Other ops, like reductions, often require the dynamic
range of float32
. Mixed precision tries to match each op to its appropriate datatype.
Ordinarily, “automatic mixed precision training” with datatype of torch.float16
uses torch.autocast
and
torch.cuda.amp.GradScaler
together, as shown in the CUDA Automatic Mixed Precision examples
and CUDA Automatic Mixed Precision recipe.
However, torch.autocast
and torch.cuda.amp.GradScaler
are modular, and may be used separately if desired.
As shown in the CPU example section of torch.autocast
, “automatic mixed precision training/inference” on CPU with
datatype of torch.bfloat16
only uses torch.autocast
.
For CUDA and CPU, APIs are also provided separately:
torch.autocast("cuda", args...)
is equivalent totorch.cuda.amp.autocast(args...)
.torch.autocast("cpu", args...)
is equivalent totorch.cpu.amp.autocast(args...)
. For CPU, only lower precision floating point datatype oftorch.bfloat16
is supported for now.
torch.autocast
and torch.cpu.amp.autocast
are new in version 1.10.
Autocasting¶
- class torch.autocast(device_type, dtype=None, enabled=True, cache_enabled=None)[source]¶
Instances of
autocast
serve as context managers or decorators that allow regions of your script to run in mixed precision.In these regions, ops run in an op-specific dtype chosen by autocast to improve performance while maintaining accuracy. See the Autocast Op Reference for details.
When entering an autocast-enabled region, Tensors may be any type. You should not call
half()
orbfloat16()
on your model(s) or inputs when using autocasting.autocast
should wrap only the forward pass(es) of your network, including the loss computation(s). Backward passes under autocast are not recommended. Backward ops run in the same type that autocast used for corresponding forward ops.Example for CUDA Devices:
# Creates model and optimizer in default precision model = Net().cuda() optimizer = optim.SGD(model.parameters(), ...) for input, target in data: optimizer.zero_grad() # Enables autocasting for the forward pass (model + loss) with torch.autocast(device_type="cuda"): output = model(input) loss = loss_fn(output, target) # Exits the context manager before backward() loss.backward() optimizer.step()
See the CUDA Automatic Mixed Precision examples for usage (along with gradient scaling) in more complex scenarios (e.g., gradient penalty, multiple models/losses, custom autograd functions).
autocast
can also be used as a decorator, e.g., on theforward
method of your model:class AutocastModel(nn.Module): ... @torch.autocast(device_type="cuda") def forward(self, input): ...
Floating-point Tensors produced in an autocast-enabled region may be
float16
. After returning to an autocast-disabled region, using them with floating-point Tensors of different dtypes may cause type mismatch errors. If so, cast the Tensor(s) produced in the autocast region back tofloat32
(or other dtype if desired). If a Tensor from the autocast region is alreadyfloat32
, the cast is a no-op, and incurs no additional overhead. CUDA Example:# Creates some tensors in default dtype (here assumed to be float32) a_float32 = torch.rand((8, 8), device="cuda") b_float32 = torch.rand((8, 8), device="cuda") c_float32 = torch.rand((8, 8), device="cuda") d_float32 = torch.rand((8, 8), device="cuda") with torch.autocast(device_type="cuda"): # torch.mm is on autocast's list of ops that should run in float16. # Inputs are float32, but the op runs in float16 and produces float16 output. # No manual casts are required. e_float16 = torch.mm(a_float32, b_float32) # Also handles mixed input types f_float16 = torch.mm(d_float32, e_float16) # After exiting autocast, calls f_float16.float() to use with d_float32 g_float32 = torch.mm(d_float32, f_float16.float())
CPU Training Example:
# Creates model and optimizer in default precision model = Net() optimizer = optim.SGD(model.parameters(), ...) for epoch in epochs: for input, target in data: optimizer.zero_grad() # Runs the forward pass with autocasting. with torch.autocast(device_type="cpu", dtype=torch.bfloat16): output = model(input) loss = loss_fn(output, target) loss.backward() optimizer.step()
CPU Inference Example:
# Creates model in default precision model = Net().eval() with torch.autocast(device_type="cpu", dtype=torch.bfloat16): for input in data: # Runs the forward pass with autocasting. output = model(input)
CPU Inference Example with Jit Trace:
class TestModel(nn.Module): def __init__(self, input_size, num_classes): super().__init__() self.fc1 = nn.Linear(input_size, num_classes) def forward(self, x): return self.fc1(x) input_size = 2 num_classes = 2 model = TestModel(input_size, num_classes).eval() # For now, we suggest to disable the Jit Autocast Pass, # As the issue: https://github.com/pytorch/pytorch/issues/75956 torch._C._jit_set_autocast_mode(False) with torch.cpu.amp.autocast(cache_enabled=False): model = torch.jit.trace(model, torch.randn(1, input_size)) model = torch.jit.freeze(model) # Models Run for _ in range(3): model(torch.randn(1, input_size))
Type mismatch errors in an autocast-enabled region are a bug; if this is what you observe, please file an issue.
autocast(enabled=False)
subregions can be nested in autocast-enabled regions. Locally disabling autocast can be useful, for example, if you want to force a subregion to run in a particulardtype
. Disabling autocast gives you explicit control over the execution type. In the subregion, inputs from the surrounding region should be cast todtype
before use:# Creates some tensors in default dtype (here assumed to be float32) a_float32 = torch.rand((8, 8), device="cuda") b_float32 = torch.rand((8, 8), device="cuda") c_float32 = torch.rand((8, 8), device="cuda") d_float32 = torch.rand((8, 8), device="cuda") with torch.autocast(device_type="cuda"): e_float16 = torch.mm(a_float32, b_float32) with torch.autocast(device_type="cuda", enabled=False): # Calls e_float16.float() to ensure float32 execution # (necessary because e_float16 was created in an autocasted region) f_float32 = torch.mm(c_float32, e_float16.float()) # No manual casts are required when re-entering the autocast-enabled region. # torch.mm again runs in float16 and produces float16 output, regardless of input types. g_float16 = torch.mm(d_float32, f_float32)
The autocast state is thread-local. If you want it enabled in a new thread, the context manager or decorator must be invoked in that thread. This affects
torch.nn.DataParallel
andtorch.nn.parallel.DistributedDataParallel
when used with more than one GPU per process (see Working with Multiple GPUs).- Parameters
device_type (str, required) – Device type to use. Possible values are: ‘cuda’, ‘cpu’, ‘xpu’ and ‘hpu’. The type is the same as the type attribute of a
torch.device
. Thus, you may obtain the device type of a tensor using Tensor.device.type.enabled (bool, optional) – Whether autocasting should be enabled in the region. Default:
True
dtype (torch_dtype, optional) – Whether to use torch.float16 or torch.bfloat16.
cache_enabled (bool, optional) – Whether the weight cache inside autocast should be enabled. Default:
True
- class torch.cuda.amp.autocast(enabled=True, dtype=torch.float16, cache_enabled=True)[source]¶
See
torch.autocast
.torch.cuda.amp.autocast(args...)
is equivalent totorch.autocast("cuda", args...)
- torch.cuda.amp.custom_fwd(fwd=None, *, cast_inputs=None)[source]¶
Create a helper decorator for
forward
methods of custom autograd functions.Autograd functions are subclasses of
torch.autograd.Function
. See the example page for more detail.- Parameters
cast_inputs (
torch.dtype
or None, optional, default=None) – If notNone
, whenforward
runs in an autocast-enabled region, casts incoming floating-point CUDA Tensors to the target dtype (non-floating-point Tensors are not affected), then executesforward
with autocast disabled. IfNone
,forward
’s internal ops execute with the current autocast state.
Note
If the decorated
forward
is called outside an autocast-enabled region,custom_fwd
is a no-op andcast_inputs
has no effect.
- torch.cuda.amp.custom_bwd(bwd)[source]¶
Create a helper decorator for backward methods of custom autograd functions.
Autograd functions are subclasses of
torch.autograd.Function
. Ensures thatbackward
executes with the same autocast state asforward
. See the example page for more detail.
- class torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16, cache_enabled=True)[source]¶
See
torch.autocast
.torch.cpu.amp.autocast(args...)
is equivalent totorch.autocast("cpu", args...)
Gradient Scaling¶
If the forward pass for a particular op has float16
inputs, the backward pass for
that op will produce float16
gradients.
Gradient values with small magnitudes may not be representable in float16
.
These values will flush to zero (“underflow”), so the update for the corresponding parameters will be lost.
To prevent underflow, “gradient scaling” multiplies the network’s loss(es) by a scale factor and invokes a backward pass on the scaled loss(es). Gradients flowing backward through the network are then scaled by the same factor. In other words, gradient values have a larger magnitude, so they don’t flush to zero.
Each parameter’s gradient (.grad
attribute) should be unscaled before the optimizer
updates the parameters, so the scale factor does not interfere with the learning rate.
Note
AMP/fp16 may not work for every model! For example, most bf16-pretrained models cannot operate in the fp16 numerical range of max 65504 and will cause gradients to overflow instead of underflow. In this case, the scale factor may decrease under 1 as an attempt to bring gradients to a number representable in the fp16 dynamic range. While one may expect the scale to always be above 1, our GradScaler does NOT make this guarantee to maintain performance. If you encounter NaNs in your loss or gradients when running with AMP/fp16, verify your model is compatible.
- class torch.cuda.amp.GradScaler(init_scale=65536.0, growth_factor=2.0, backoff_factor=0.5, growth_interval=2000, enabled=True)[source]¶
An instance
scaler
ofGradScaler
.Helps perform the steps of gradient scaling conveniently.
scaler.scale(loss)
multiplies a given loss byscaler
’s current scale factor.scaler.step(optimizer)
safely unscales gradients and callsoptimizer.step()
.scaler.update()
updatesscaler
’s scale factor.
Example:
# Creates a GradScaler once at the beginning of training. scaler = GradScaler() for epoch in epochs: for input, target in data: optimizer.zero_grad() output = model(input) loss = loss_fn(output, target) # Scales loss. Calls backward() on scaled loss to create scaled gradients. scaler.scale(loss).backward() # scaler.step() first unscales gradients of the optimizer's params. # If gradients don't contain infs/NaNs, optimizer.step() is then called, # otherwise, optimizer.step() is skipped. scaler.step(optimizer) # Updates the scale for next iteration. scaler.update()
See the Automatic Mixed Precision examples for usage (along with autocasting) in more complex cases like gradient clipping, gradient accumulation, gradient penalty, and multiple losses/optimizers.
scaler
dynamically estimates the scale factor each iteration. To minimize gradient underflow, a large scale factor should be used. However,float16
values can “overflow” (become inf or NaN) if the scale factor is too large. Therefore, the optimal scale factor is the largest factor that can be used without incurring inf or NaN gradient values.scaler
approximates the optimal scale factor over time by checking the gradients for infs and NaNs during everyscaler.step(optimizer)
(or optional separatescaler.unscale_(optimizer)
, seeunscale_()
).If infs/NaNs are found,
scaler.step(optimizer)
skips the underlyingoptimizer.step()
(so the params themselves remain uncorrupted) andupdate()
multiplies the scale bybackoff_factor
.If no infs/NaNs are found,
scaler.step(optimizer)
runs the underlyingoptimizer.step()
as usual. Ifgrowth_interval
unskipped iterations occur consecutively,update()
multiplies the scale bygrowth_factor
.
The scale factor often causes infs/NaNs to appear in gradients for the first few iterations as its value calibrates.
scaler.step
will skip the underlyingoptimizer.step()
for these iterations. After that, step skipping should occur rarely (once every few hundred or thousand iterations).- Parameters
init_scale (float, optional, default=2.**16) – Initial scale factor.
growth_factor (float, optional, default=2.0) – Factor by which the scale is multiplied during
update()
if no inf/NaN gradients occur forgrowth_interval
consecutive iterations.backoff_factor (float, optional, default=0.5) – Factor by which the scale is multiplied during
update()
if inf/NaN gradients occur in an iteration.growth_interval (int, optional, default=2000) – Number of consecutive iterations without inf/NaN gradients that must occur for the scale to be multiplied by
growth_factor
.enabled (bool, optional) – If
False
, disables gradient scaling.step()
simply invokes the underlyingoptimizer.step()
, and other methods become no-ops. Default:True
- get_backoff_factor()[source]¶
Return a Python float containing the scale backoff factor.
- Return type
- get_scale()[source]¶
Return a Python float containing the current scale, or 1.0 if scaling is disabled.
Warning
get_scale()
incurs a CPU-GPU sync.- Return type
- load_state_dict(state_dict)[source]¶
Load the scaler state.
If this instance is disabled,
load_state_dict()
is a no-op.- Parameters
state_dict (dict) – scaler state. Should be an object returned from a call to
state_dict()
.
- scale(outputs: Tensor) Tensor [source]¶
- scale(outputs: List[Tensor]) List[Tensor]
- scale(outputs: Tuple[Tensor, ...]) Tuple[Tensor, ...]
- scale(outputs: Iterable[Tensor]) Iterable[Tensor]
Multiplies (‘scales’) a tensor or list of tensors by the scale factor.
Returns scaled outputs. If this instance of
GradScaler
is not enabled, outputs are returned unmodified.- Parameters
outputs (Tensor or iterable of Tensors) – Outputs to scale.
- set_backoff_factor(new_factor)[source]¶
Set a new scale backoff factor.
- Parameters
new_scale (float) – Value to use as the new scale backoff factor.
- set_growth_factor(new_factor)[source]¶
Set a new scale growth factor.
- Parameters
new_scale (float) – Value to use as the new scale growth factor.
- set_growth_interval(new_interval)[source]¶
Set a new growth interval.
- Parameters
new_interval (int) – Value to use as the new growth interval.
- state_dict()[source]¶
Return the state of the scaler as a
dict
.It contains five entries:
"scale"
- a Python float containing the current scale"growth_factor"
- a Python float containing the current growth factor"backoff_factor"
- a Python float containing the current backoff factor"growth_interval"
- a Python int containing the current growth interval"_growth_tracker"
- a Python int containing the number of recent consecutive unskipped steps.
If this instance is not enabled, returns an empty dict.
Note
If you wish to checkpoint the scaler’s state after a particular iteration,
state_dict()
should be called afterupdate()
.
- step(optimizer, *args, **kwargs)[source]¶
Invoke
unscale_(optimizer)
followed by parameter update, if gradients are not infs/NaN.step()
carries out the following two operations:Internally invokes
unscale_(optimizer)
(unlessunscale_()
was explicitly called foroptimizer
earlier in the iteration). As part of theunscale_()
, gradients are checked for infs/NaNs.If no inf/NaN gradients are found, invokes
optimizer.step()
using the unscaled gradients. Otherwise,optimizer.step()
is skipped to avoid corrupting the params.
*args
and**kwargs
are forwarded tooptimizer.step()
.Returns the return value of
optimizer.step(*args, **kwargs)
.- Parameters
optimizer (torch.optim.Optimizer) – Optimizer that applies the gradients.
args (Any) – Any arguments.
kwargs (Any) – Any keyword arguments.
- Return type
Warning
Closure use is not currently supported.
- unscale_(optimizer)[source]¶
Divides (“unscales”) the optimizer’s gradient tensors by the scale factor.
unscale_()
is optional, serving cases where you need to modify or inspect gradients between the backward pass(es) andstep()
. Ifunscale_()
is not called explicitly, gradients will be unscaled automatically duringstep()
.Simple example, using
unscale_()
to enable clipping of unscaled gradients:... scaler.scale(loss).backward() scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm) scaler.step(optimizer) scaler.update()
- Parameters
optimizer (torch.optim.Optimizer) – Optimizer that owns the gradients to be unscaled.
Note
unscale_()
does not incur a CPU-GPU sync.Warning
unscale_()
should only be called once per optimizer perstep()
call, and only after all gradients for that optimizer’s assigned parameters have been accumulated. Callingunscale_()
twice for a given optimizer between eachstep()
triggers a RuntimeError.Warning
unscale_()
may unscale sparse gradients out of place, replacing the.grad
attribute.
- update(new_scale=None)[source]¶
Update the scale factor.
If any optimizer steps were skipped the scale is multiplied by
backoff_factor
to reduce it. Ifgrowth_interval
unskipped iterations occurred consecutively, the scale is multiplied bygrowth_factor
to increase it.Passing
new_scale
sets the new scale value manually. (new_scale
is not used directly, it’s used to fill GradScaler’s internal scale tensor. So ifnew_scale
was a tensor, later in-place changes to that tensor will not further affect the scale GradScaler uses internally.)- Parameters
new_scale (float or
torch.cuda.FloatTensor
, optional, default=None) – New scale factor.
Warning
update()
should only be called at the end of the iteration, afterscaler.step(optimizer)
has been invoked for all optimizers used this iteration.Warning
For performance reasons, we do not check the scale factor value to avoid synchronizations, so the scale factor is not guaranteed to be above 1. If the scale falls below 1 and/or you are seeing NaNs in your gradients or loss, something is likely wrong. For example, bf16-pretrained models are often incompatible with AMP/fp16 due to differing dynamic ranges.
Autocast Op Reference¶
Op Eligibility¶
Ops that run in float64
or non-floating-point dtypes are not eligible, and will
run in these types whether or not autocast is enabled.
Only out-of-place ops and Tensor methods are eligible.
In-place variants and calls that explicitly supply an out=...
Tensor
are allowed in autocast-enabled regions, but won’t go through autocasting.
For example, in an autocast-enabled region a.addmm(b, c)
can autocast,
but a.addmm_(b, c)
and a.addmm(b, c, out=d)
cannot.
For best performance and stability, prefer out-of-place ops in autocast-enabled
regions.
Ops called with an explicit dtype=...
argument are not eligible,
and will produce output that respects the dtype
argument.
CUDA Op-Specific Behavior¶
The following lists describe the behavior of eligible ops in autocast-enabled regions.
These ops always go through autocasting whether they are invoked as part of a torch.nn.Module
,
as a function, or as a torch.Tensor
method. If functions are exposed in multiple namespaces,
they go through autocasting regardless of the namespace.
Ops not listed below do not go through autocasting. They run in the type defined by their inputs. However, autocasting may still change the type in which unlisted ops run if they’re downstream from autocasted ops.
If an op is unlisted, we assume it’s numerically stable in float16
.
If you believe an unlisted op is numerically unstable in float16
,
please file an issue.
CUDA Ops that can autocast to float16
¶
__matmul__
,
addbmm
,
addmm
,
addmv
,
addr
,
baddbmm
,
bmm
,
chain_matmul
,
multi_dot
,
conv1d
,
conv2d
,
conv3d
,
conv_transpose1d
,
conv_transpose2d
,
conv_transpose3d
,
GRUCell
,
linear
,
LSTMCell
,
matmul
,
mm
,
mv
,
prelu
,
RNNCell
CUDA Ops that can autocast to float32
¶
__pow__
,
__rdiv__
,
__rpow__
,
__rtruediv__
,
acos
,
asin
,
binary_cross_entropy_with_logits
,
cosh
,
cosine_embedding_loss
,
cdist
,
cosine_similarity
,
cross_entropy
,
cumprod
,
cumsum
,
dist
,
erfinv
,
exp
,
expm1
,
group_norm
,
hinge_embedding_loss
,
kl_div
,
l1_loss
,
layer_norm
,
log
,
log_softmax
,
log10
,
log1p
,
log2
,
margin_ranking_loss
,
mse_loss
,
multilabel_margin_loss
,
multi_margin_loss
,
nll_loss
,
norm
,
normalize
,
pdist
,
poisson_nll_loss
,
pow
,
prod
,
reciprocal
,
rsqrt
,
sinh
,
smooth_l1_loss
,
soft_margin_loss
,
softmax
,
softmin
,
softplus
,
sum
,
renorm
,
tan
,
triplet_margin_loss
CUDA Ops that promote to the widest input type¶
These ops don’t require a particular dtype for stability, but take multiple inputs
and require that the inputs’ dtypes match. If all of the inputs are
float16
, the op runs in float16
. If any of the inputs is float32
,
autocast casts all inputs to float32
and runs the op in float32
.
addcdiv
,
addcmul
,
atan2
,
bilinear
,
cross
,
dot
,
grid_sample
,
index_put
,
scatter_add
,
tensordot
Some ops not listed here (e.g., binary ops like add
) natively promote
inputs without autocasting’s intervention. If inputs are a mixture of float16
and float32
, these ops run in float32
and produce float32
output,
regardless of whether autocast is enabled.
Prefer binary_cross_entropy_with_logits
over binary_cross_entropy
¶
The backward passes of torch.nn.functional.binary_cross_entropy()
(and torch.nn.BCELoss
, which wraps it)
can produce gradients that aren’t representable in float16
. In autocast-enabled regions, the forward input
may be float16
, which means the backward gradient must be representable in float16
(autocasting float16
forward inputs to float32
doesn’t help, because that cast must be reversed in backward).
Therefore, binary_cross_entropy
and BCELoss
raise an error in autocast-enabled regions.
Many models use a sigmoid layer right before the binary cross entropy layer.
In this case, combine the two layers using torch.nn.functional.binary_cross_entropy_with_logits()
or torch.nn.BCEWithLogitsLoss
. binary_cross_entropy_with_logits
and BCEWithLogits
are safe to autocast.
CPU Op-Specific Behavior¶
The following lists describe the behavior of eligible ops in autocast-enabled regions.
These ops always go through autocasting whether they are invoked as part of a torch.nn.Module
,
as a function, or as a torch.Tensor
method. If functions are exposed in multiple namespaces,
they go through autocasting regardless of the namespace.
Ops not listed below do not go through autocasting. They run in the type defined by their inputs. However, autocasting may still change the type in which unlisted ops run if they’re downstream from autocasted ops.
If an op is unlisted, we assume it’s numerically stable in bfloat16
.
If you believe an unlisted op is numerically unstable in bfloat16
,
please file an issue.
CPU Ops that can autocast to bfloat16
¶
conv1d
,
conv2d
,
conv3d
,
bmm
,
mm
,
baddbmm
,
addmm
,
addbmm
,
linear
,
matmul
,
_convolution
CPU Ops that can autocast to float32
¶
conv_transpose1d
,
conv_transpose2d
,
conv_transpose3d
,
avg_pool3d
,
binary_cross_entropy
,
grid_sampler
,
grid_sampler_2d
,
_grid_sampler_2d_cpu_fallback
,
grid_sampler_3d
,
polar
,
prod
,
quantile
,
nanquantile
,
stft
,
cdist
,
trace
,
view_as_complex
,
cholesky
,
cholesky_inverse
,
cholesky_solve
,
inverse
,
lu_solve
,
orgqr
,
inverse
,
ormqr
,
pinverse
,
max_pool3d
,
max_unpool2d
,
max_unpool3d
,
adaptive_avg_pool3d
,
reflection_pad1d
,
reflection_pad2d
,
replication_pad1d
,
replication_pad2d
,
replication_pad3d
,
mse_loss
,
ctc_loss
,
kl_div
,
multilabel_margin_loss
,
fft_fft
,
fft_ifft
,
fft_fft2
,
fft_ifft2
,
fft_fftn
,
fft_ifftn
,
fft_rfft
,
fft_irfft
,
fft_rfft2
,
fft_irfft2
,
fft_rfftn
,
fft_irfftn
,
fft_hfft
,
fft_ihfft
,
linalg_matrix_norm
,
linalg_cond
,
linalg_matrix_rank
,
linalg_solve
,
linalg_cholesky
,
linalg_svdvals
,
linalg_eigvals
,
linalg_eigvalsh
,
linalg_inv
,
linalg_householder_product
,
linalg_tensorinv
,
linalg_tensorsolve
,
fake_quantize_per_tensor_affine
,
eig
,
geqrf
,
lstsq
,
_lu_with_info
,
qr
,
solve
,
svd
,
symeig
,
triangular_solve
,
fractional_max_pool2d
,
fractional_max_pool3d
,
adaptive_max_pool3d
,
multilabel_margin_loss_forward
,
linalg_qr
,
linalg_cholesky_ex
,
linalg_svd
,
linalg_eig
,
linalg_eigh
,
linalg_lstsq
,
linalg_inv_ex
CPU Ops that promote to the widest input type¶
These ops don’t require a particular dtype for stability, but take multiple inputs
and require that the inputs’ dtypes match. If all of the inputs are
bfloat16
, the op runs in bfloat16
. If any of the inputs is float32
,
autocast casts all inputs to float32
and runs the op in float32
.
cat
,
stack
,
index_copy
Some ops not listed here (e.g., binary ops like add
) natively promote
inputs without autocasting’s intervention. If inputs are a mixture of bfloat16
and float32
, these ops run in float32
and produce float32
output,
regardless of whether autocast is enabled.