# autocast

Source: https://www.tensorplay.cn/docs/generated/tensorplay.autocast.html

# autocast

class tensorplay.autocast(device_type: [str](https://docs.python.org/3/library/stdtypes.html#str), dtype: [Any](https://docs.python.org/3/library/typing.html#typing.Any) | [None](https://docs.python.org/3/library/constants.html#None) = None, enabled: [bool](https://docs.python.org/3/library/functions.html#bool) = True, cache_enabled: [bool](https://docs.python.org/3/library/functions.html#bool) | [None](https://docs.python.org/3/library/constants.html#None) = None)[[source]](../_modules/tensorplay/amp/autocast_mode.html#autocast)

Instances of [autocast](#tensorplay.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.

When entering an autocast-enabled region, Tensors may be any type.
You should not call half() or bfloat16() on your model(s) or inputs when using autocasting.

[autocast](#tensorplay.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 tensorplay.autocast(device_type="cuda"):
        output = model(input)
        loss = loss_fn(output, target)

    # Exits the context manager before backward()
    loss.backward()
    optimizer.step()
```

[autocast](#tensorplay.autocast) can also be used as a decorator, e.g., on the forward method of your model:

```
class AutocastModel(nn.Module):
    ...

    @tensorplay.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 to float32 (or other dtype if desired).

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 particular dtype.

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.

Parameters:

- device_type ([str](https://docs.python.org/3/library/stdtypes.html#str) , required ) – Device type to use. Possible values are: ‘cuda’ and ‘cpu’. The type is the same as the type attribute of a [tensorplay.device](tensorplay.device.html#tensorplay.device). Thus, you may obtain the device type of a tensor using Tensor.device.type .

- enabled ([bool](https://docs.python.org/3/library/functions.html#bool) , optional ) – Whether autocasting should be enabled in the region. Default: True

- dtype ( tensorplay.dtype , optional ) – Data type for ops run in autocast. It uses the default value ( tensorplay.float16 for CUDA and tensorplay.bfloat16 for CPU), given by [get_autocast_dtype()](tensorplay.get_autocast_dtype.html#tensorplay.get_autocast_dtype), if [dtype](tensorplay.dtype.html#tensorplay.dtype) is None . Default: None

- cache_enabled ([bool](https://docs.python.org/3/library/functions.html#bool) , optional ) – Whether the weight cache inside autocast should be enabled. Default: True