torch.autograd.Function.forward¶
- static Function.forward(ctx, *args, **kwargs)¶
This function is to be overridden by all subclasses. There are two ways to define forward:
Usage 1 (Combined forward and ctx):
@staticmethod def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any: pass
It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
See Combined or separate forward() and setup_context() for more details
Usage 2 (Separate forward and ctx):
@staticmethod def forward(*args: Any, **kwargs: Any) -> Any: pass @staticmethod def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None: pass
The forward no longer accepts a ctx argument.
Instead, you must also override the
torch.autograd.Function.setup_context()
staticmethod to handle setting up thectx
object.output
is the output of the forward,inputs
are a Tuple of inputs to the forward.See Extending torch.autograd for more details
The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with
ctx.save_for_backward()
if they are intended to be used inbackward
(equivalently,vjp
) orctx.save_for_forward()
if they are intended to be used for injvp
.- Return type