torch.autograd.function.FunctionCtx.mark_dirty — PyTorch 2.7 documentation (original) (raw)

FunctionCtx.mark_dirty(*args)[source][source]¶

Mark given tensors as modified in an in-place operation.

This should be called at most once, in either the setup_context()or forward() methods, and all arguments should be inputs.

Every tensor that’s been modified in-place in a call to forward()should be given to this function, to ensure correctness of our checks. It doesn’t matter whether the function is called before or after modification.

Examples::

class Inplace(Function): @staticmethod def forward(ctx, x): x_npy = x.numpy() # x_npy shares storage with x x_npy += 1 ctx.mark_dirty(x) return x

@staticmethod
@once_differentiable
def backward(ctx, grad_output):
    return grad_output

a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone() b = a * a Inplace.apply(a) # This would lead to wrong gradients! # but the engine would not know unless we mark_dirty b.backward() # RuntimeError: one of the variables needed for gradient # computation has been modified by an inplace operation