WebJun 27, 2024 · The last post showed how PyTorch constructs the graph to calculate the outputs’ derivatives w.r.t. the inputs when executing the forward pass. Now we will see … WebMar 10, 2024 · Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward. It could only …

torch.autograd.backward — PyTorch 2.0 documentation

WebNov 2, 2024 · 🐛 Bug DDP doesn't work with retain_graph = True when trying to run backwards twice through the same model. To Reproduce To replicate, change only def … Webtensor.backward(gradient, retain_graph) pytoch构建的计算图是动态图，为了节约内存，所以每次一轮迭代完之后计算图就被在内存释放。 如果使用多次 backward 就会报错。 可以通过设置标识 retain_graph=True 来保存计算图，使其不被释放。 import torch x = torch.randn(4, 4, requires_grad=True) y = 3 * x + 2 y = torch.sum(y) … snowgaiter

Backward() to compute partial derivatives without retain_graph

WebDec 12, 2024 · Backward error with retain_graph=True. mpry December 12, 2024, 1:10am #1. for j in range (n_rnn_batches): print x.size () h_t = Variable (torch.zeros (x.size (0), 20)) c_t … WebApr 11, 2024 · Saved intermediate values of the graph are freed when you call .backward () or autograd.grad (). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward. WebApr 7, 2024 · 出于性能原因，我们只能在给定的图形上使用一次 backward 进行梯度计算。 如果我们需要对同一个图多次调用 backward ，我们需要给 backward 的调用传递 retain_graph=True 。 默认情况下，所有 requires_grad=True 的张量都跟踪它们的计算历史并支持梯度计算。 然而，某些情况下，我们不需要这样做，例如，当我们已经训练完模型 … snowgate