Marks given tensors as modified in an in-place operation.
This should be called at most once, only from inside the
forward()method, 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.
>>> 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