torch.autograd.function.FunctionCtx.mark_dirty¶
- FunctionCtx.mark_dirty(*args)[源代码]¶
将给定的张量标记为在就地操作中修改。
这应该最多调用一次,在
setup_context()
或forward()
方法中,所有参数都应该是输入。在调用
forward()
时,在就地修改的每个张量都应传递给此函数,以确保检查的正确性。该函数是在修改之前还是之后调用并不重要。- 示例:
>>> 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