Pytorch nan gradients. I use VGG 16 from torchvision.
Pytorch nan gradients The forward of the net compute the log-conditional probabilities. 1 documentation), it says that the behavior of torch. The various cases follow import torch torch. Could anyone help me understand when torch. Previously the function would return zero for all real For e. amp. r. Sep 16, 2024 · I have a pytorch tensor with NaN inside, when I calculate the loss function using a simple MSE Loss the gradient becomes NaN even if I mask out the NaN values. autograd. sometimes we simply want to automatically skip these samples as if they never existed (perhaps with a warning), and continue training. Aug 5, 2020 · Use torch. float16). To handle skew in the classes, I’m using the Dice loss. So they have a tendancy to propagate. this code successfully identifies nan/inf gradients, and skips parameter update by zeroing gradients for the specific batch; support multi-gpu (at least ddp which I tested). , if the computed function is sqrt(x^2), then the ideal representation of the gradient would be x, but weirdly enough, the gradient computation seemed to calculate 2*x / (2*sqrt(x^2)) and the output value was NaN as autograd apparently (though I couldn't confirm this with further debugging) due to that calculation working out to be 0/0 May 9, 2021 · How can I compute this function in a way that handles gradients correctly? def f(x): return torch. Dec 2, 2020 · pros. t. But the model’s parameters won’t update anymore. named_parameters(): if torch. 125. Jun 13, 2022 · To check if any of the gradients is nan, you can use. 1 → Everything works Sep 25, 2022 · Hi, thank you and sorry for the late response. w**self. for name, param in net. , requires_grad=True) y = f(x) print(y) y. For simplicity consider the following example: def f1(x): return 0/x def f2(x): return x def g(x): r1 = f1(x) r2 = f2 Jan 15, 2024 · Hi all! I am currently training different diffusion models by using the [Imagen-pytorch] repository from Phil Wang, which works super fine when trained on a Nvidia A6000 GPU of a colleague. ====== Note ======= Starting in PyTorch 1. Anyway, I switched it into nn. I guess this is a float16 related bug. But in a second network, the outputs for each pixel are parameters of a Beta distribution, and samples are taken from it. In the problem I’m trying to solve, it is possible to have 0 probabilities. In other words, I think that if I were able to substitute NaN gradients for any other value (e. 0 torch: 1. but. isnan(param. Tensor falls short and MaskedTensor can resolve and/or work around the NaN gradient problem. tensor(1. Jul 16, 2018 · If my pytorch version is 0. After utilizing torch. autocast some of the gradients are immediatly either infinite or NAN. (The grad here is manually saved and printed) There loss looks good during the triaining, no nan or inf in the loss. Oct 2, 2020 · Hello everyone! I am trying to train a RBM in a discriminative way. Nov 28, 2017 · I am aware that in pytorch 0. detect_anomaly to check which layer is creating the invalid gradients, then check its operations and inputs. Then I create a dummy input and target and use MSE loss. The transformer gradients are fine. Oct 17, 2019 · Unfortunately, any nan will create nan for any number it touches. CrossEntropyLoss but the loss is NaN again. Aug 18, 2020 · The result of a. where, however it results in unexpected gradients. I have tried to set. angle — PyTorch 1. backward() on the objective function shown above, while the gradients of the \phi and \psi of f and g respectively do not contain NaN’s. I couldn't produce the behavior when using float32. Below, by way of example, we show several different issues where torch. Probably low priority, as it's not going to be an issue in 99% of cases, but we're doing a few things with (exact) line searches where this caused a nan to Apr 15, 2024 · I’m using MAE to pretrain a ViT model on my custom dataset with 4 A800 GPU. Then, every operation involving Nan result in Nan. 13. But when I call y. In your second example, the gradient at point 1. Dec 2, 2020 · one approach is to automatically stop training (use terminate_on_nan) and then somehow isolate all these samples and remove them from the data permanently. 8, angle returns pi for negative real numbers, zero for non-negative real numbers, and propagates NaNs. 8. 9. torch. set_detect_anomaly(True) and it points to that Function 'DivBackward0' returned nan values in its 1th output on this line. I found that all gradients are nan after epoch 486. PyTorch Issue 10729 - torch Jul 30, 2023 · Despite this, I still get NaN gradients for the final result (final_out), even though the values which result in NaN gradients are not used in calculating final_out, since torch. Jul 28, 2017 · After some intense debug, I finally found out where these NaN’s initially appear: they appear due to a 0/0 in the computation of the gradient of the loss w. I think the gradient should be 0. Following is the note from the link. It works well with a baseline network that just predicts the probability of the pixel being 1. Setups I experimented with: GPU: A6000 Nvidia Driver Version: 525. . Dec 2, 2020 · The problem is that at the point where the final result is -inf, the gradient is infinite. div = x / scale So I try to print the nan gradient by doing Aug 15, 2017 · Obviously just happening because the gradient divides by the norm, but the (sub)gradient here should probably be zero, or at least not nan, since that will propagate to make all updates nan. the means of the gaussian. 0. I am using negative log-likelihood as the loss function, L=-sum(log(p_i)). Disabling cuda. When trained on my Quadro RTX 8000 I do get nan Losses caused by nan gradients. 3, is there any method to check where causes the gradient NAN?Or, could you please give some examples? albanD (Alban D) July 24, 2018, 8:57am Aug 14, 2020 · Hello, full code and link to Google Colab below. models and remove the FC layers and the Average Pooling layer. gradients that are actually 0. This is a niche bug, but it might cause troubles in advanced users who like to use masking to filter out NaN losses. angle() has been changed since 1. I tried to use torch. detect_anomaly it returns LogBackward . At first, I think it was a trivial coding problem and after a week of debugging I can’t really figure out how this occurs. where discards them. grad). grad is tensor([nan], device='cuda:0', dtype=torch. Mar 2, 2024 · I see nan gradients in my model parameters. So during backprop, the gradient becomes nan. masked_scatter(x < 0, x / (1 - x)) Mar 11, 2020 · Can you print the value from self. After the first training epoch, I see that the input’s LayerNorm’s grads are all equal to NaN, but the input in the first pass does not contain NaN or Inf so I have no idea why this is happening or how to prevent it from happening Jul 22, 2019 · 🐛 Bug. I thought I needed to use a custom cross_entropy in order to handle with 2 arrays. myParam?I think this line produced Nan because -0. In most cases only one or two parameters are impacted at a time. set_detect_anomaly(True) One issue that vanilla tensors run into is the inability to differentiate between gradients that are not defined (nan) vs. Sample Output of Gradients during Training with CPU: Oct 1, 2021 · Hi, I’ve got a network containing: Input → LayerNorm → LSTM → Relu → LayerNorm → Linear → output With gradient clipping set to a value around 1. detect_anomaly() to figure out where the issue comes from: /usr Oct 14, 2022 · Encounter Gradient overflow and the model performance are really weird. Feb 12, 2021 · As I am trying to implement this, I keep getting all NaN’s in the gradients of the filter parameters \theta once I call . And this is the expected behavior here. The mean of these samples is Oct 9, 2018 · Hello, I am trying to calculate gradients of a function that uses torch. Simply put, when NaN losses are masked out using masked_fill, performing backward on the sum of the losses should produce valid gradients (assuming that the gradient graph is smooth everywhere except for the masked losses). , 0), the gradient calculations would work perfectly. When passed negative values to grad we obtain -1/|x| as the gradient and whilst formally that may be incorrect its an odd choice as it “feels” correct. Let Oct 27, 2018 · I have a network which I’m trying to train a network for 2-class pixel-wise segmentation. The normalization I need to perform in order to get the probabilities, however, does not involve a softmax (hence, I cannot use F. grad) I tried using masked_scatter but it also doesn’t work: def f(x): return x. backward(), one of the components of gradient is NaN, it traces back to the input during BP and most parameters become NaN. 8122^0. I basically use it to choose between some real case, complex case and limit case where some of the cases will have a Nan gradient for some specific input. 0 there is this problem of the gradient of zero becoming NaN (see issue #2421 or some posts in this forum. set_detect_anomaly(True) x = torch. log_softmax) (see DRBM paper, p(y|x), at page 2). angle() returns Nan as its gradient? Or is my understanding on the documentation is wrong? (Code is tested in pytorch 1. angle() description (torch. At about 1600 steps, the Mask language modeling loss became NaN, and after a few more steps everything crashed down to NaN. I set torch. 0) Dec 8, 2024 · The exploding gradients exclusively occur in the backbone params, or the single Conv1D layer directly after the backbone. Nov 7, 2017 · When you do backprogation with the first, at some point you’ll run into the derivative of acos(x), which is - 1 / sqrt( 1 - x^2 ). py as indicated in commit #2775 (I somehow cannot build everything from source). any(): print("nan gradient found") raise SystemExit One issue that vanilla tensors run into is the inability to distinguish between gradients that are not defined (nan) vs. 5857 is undefined(for other negative values too). 06 Cuda Version: 12. That can be nasty and lead to your NaNs if x is close to 1 or -1 at times. You definitely want to perform the masking before using them in any computations as much as possible. rand(10, 10) y Oct 4, 2021 · If my understanding to the note is correct, the gradient from angle() when its input is real value should be Nan, but it is not. autocast works fine and does not Oct 26, 2021 · To clarify by correct gradient I meant the function 1/x as the gradient of log(x) is only defined in R^+ however its definition extends to the whole real line. when done this way, detecting inf/nan gradients (instead of inf/nan loss), we avoid a potential cases of losing synchronization between different processes, because typically one of the processes would generate an Oct 4, 2021 · Seeing the torch. g. where(x > 0, x, x / (1 - x)) This issue causes an incorrect nan gradient at x == 1: x = torch. When enabling cuda. backward() print(x. is finite and everything works fine. I’m rereading this part and am unsure how to understand it. I want to use a basic VGG 16 as a feature extractor. 2. Weirdly this happens only when the mask is applyied after calculating the loss and only when the loss has a pow operation inside. I have therefore modified reduce. I use VGG 16 from torchvision. As stated previously, training on GPU works without exploding gradients. mnvxm ddevcb nkiqhq mjf hzp swz rpx ypan rfxxov tsbn