WebMay 31, 2024 · The error message effectively said there were no input arguments to the backward method, which means, both ctx and grad_output are None. This then means ‘ctx.save_for_backward (mu, signa, x)’ method did nothing during forward call. Maybe change mu, sigma and x to torch tensors or Variable could solve your problem. 1 Like WebDec 9, 2024 · The graph correctly shows how out is computed from vertices (which seems to equal input in your code). Variable grad_x is correctly shown as disconnected because it isn't used to compute out.In other words, out isn't a function of grad_x.That grad_x is disconnected doesn't mean the gradient doesn't flow nor your custom backward …
Customizing torch.autograd.Function - PyTorch Forums
Webclass LinearFunction (Function): @staticmethod def forward (ctx, input, weight, bias=None): ctx.save_for_backward (input, weight, bias) output = input.mm (weight.t ()) if bias is not None: output += bias.unsqueeze (0).expand_as (output) return output @staticmethod def backward (ctx, grad_output): input, weight, bias = ctx.saved_variables … WebSep 19, 2024 · @albanD why do we need to use save_for_backwards for input tensors only ? I just tried to pass one input tensor from forward() to backward() using ctx.tensor = inputTensor in forward() and inputTensor = ctx.tensor in backward() and it seemed to work.. I appreciate your answer since I’m currently trying to really understand when to … scott erikson high point nc
How to customize the double backward? - PyTorch Forums
WebMay 23, 2024 · class MyConv (Function): @staticmethod def forward (ctx, x, w): ctx.save_for_backward (x, w) return F.conv2d (x, w) @staticmethod def backward (ctx, grad_output): x, w = ctx.saved_variables x_grad = w_grad = None if ctx.needs_input_grad [0]: x_grad = torch.nn.grad.conv2d_input (x.shape, w, grad_output) if … WebJan 18, 2024 · 18 人 赞同了该回答. `saved_ for_ backward`是会保留此input的全部信息 (一个完整的外挂Autograd Function的Variable), 并提供避免in-place操作导致的input … Websave_for_backward() must be used to save any tensors to be used in the backward pass. Non-tensors should be stored directly on ctx. If tensors that are neither input nor output … prepared meal delivery fitness