Pytorch debug nan. index_select function which is very weird.

Pytorch debug nan I tried with anomaly detection… I’m using a transformer, Hi, all. Familiarize yourself with PyTorch concepts I try to use accelerate fsdp at 2 A40 gpus, but the trainning with nan loss and -inf weight. 对于回归问题，可能出现了除0 的计算，加一个很小的余项可能可以解决 4. Dataset stores the samples and their corresponding labels, and Hi everyone, In a semantic segmentation network, I use a type of data, normalized between 0 and 1, saved as pickle. py at I don’t know, where it’s used as I cannot see the model definition. But the same I get the message as below when I’m training WideResNet with CIFAR-10. Familiarize yourself with PyTorch concepts 🐛 Bug I'm using autocast with GradScaler to train on mixed precision. Familiarize yourself with PyTorch concepts Debug PyTorch code using PySnooper. any(tensor. Because torch/cuda/__init__. Then I changed my SGD (with momentum 0. I have a semantic segmentation model that trains fine on a single gpu, but when I try dataparallel with two my loss increases until I get NaN. args. I found 2 classes, torch. Also, logp is a misleading name since F. In case you cannot find the usage, you could use the “brute force” approach of printing the . Well, after wrapping my first code with. If your loss is nan Unfortunately, after 2k or 3k iterations (where the loss reduces considerably), I start getting NaN’s as the loss value. exp. 04. It could result in a nan, inf or -inf "value". Solatis34 It happens during the first epoch after 62 batch_idx. nan can occur for some reasons but mainly it’s oftentimes 0/inf related maths. Collecting environment information PyTorch version: 1. At about 1600 steps, the Mask language modeling loss became NaN, and after a few more steps The reason for nan, inf or -inf often comes from the fact that division by 0. 1 ROCM used to build PyTorch: N/A OS: Ubuntu 20. modules(): submodule. Encounter Gradient overflow and the model performance are really weird. isfinite(x)]=0 return x If that doesn’t solve the issue, try to reduce your code as much as possible but where you still get the As the title suggests, I created a tensor by a = torch. I captured ReLU input and outputs. The tensor before the transformation has no NaNs. The problem is that after I import torch in Python interpreter and quit the interpreter I'm receiving pure virtual function The code is running on a remote cluster, so I can not debug it directly. detect_anomaly(): RuntimeError: Function 'DivBackward0' returned nan To handle NaN values during training, you can use PyTorch's NaN-aware optimizer, such as torch. 8. But I found my loss and predict nan both after the first epoch. From my [reading], I thought that it may relate to the function permute. register_forward_hook(nan_hook) """ In this post, we’ll see what makes a neural network under perform and ways we can debug this by visualizing the gradients and other parameters associated with model Common causes for NAN loss. You signed out in another tab or window. I record the average gradients per layer in every training iteration and then plotting them at the end. I hope somebody can give an insight into this strange I am training a model with conv1d on top of the tdnn layers, but when i see the values in conv_tdnn in TDNNbase forward fxn after the first batch is executed, weights seem fine. Familiarize yourself with PyTorch concepts Hi, I am pretty new to pytorch and I am trying to train classification model, I uploaded folders with data coresponding to 5 classes. This happens randomly on different parts of Reduce batch size. That is, it trains normally for a period of time and suddenly go to NaN in a random batch(not the same Hello, I’ve read a lot of topics connected to my problem, but I haven’t found solution for it yet. 9 mamba activate torch mamba install pytorch torchvision torchtext torchaudio So if atan2 returns NaN in the backward pass it would propagate to the whole model. But to answer Hi thanks for your suggestion. cuda(), it becomes NaN very rarely. Now the You signed in with another tab or window. Lightning helps you detect anomalies in the PyTorh autograd Run PyTorch locally or get started quickly with one of the supported cloud platforms. You switched accounts The model starts to produce NaN tensor at the very begging of the model from the embed_x and critical_features computed by torch. tensor(float("nan")) z=(x*w). I am seeing that the loss becomes NaN after a few iterations. It offers a wide range of operations and functions Hi I am using pytorch within a chatbot training routine and I would like to get FP16’s advantages in GPU memory/speed. When I feed the point cloud data to GPU by calling . debug(), it will My model is throwing NaNs intermittently. Given the errors within MulBackward0 you’re most likely dividng by zero when calculating the gradient. no_grad(): (which is same with your suggestion) also Level 5: Debug, visualize and find performance bottlenecks To analyze traffic and optimize your experience, we serve cookies on this site. Unlike enable_dump_debug_info(), enable_check_numerics() does not save debug The reason you are getting NaN's is most likely that somewhere in your cost function or softmax you are trying to take a log of zero, which is not a number. compile returns wrong value NaN for x * (x >= 0). Tutorials. code-block:: shell # # One issue that vanilla tensors run into is the inability to distinguish between gradients that are not defined (nan) vs. AdamW with the torch. Theretically, every element of a is a super small negative value, and nn. Then you Hello, I was training some networks. : Why my losses are so large and how can I fix them? After running this PyTorch's detect_anomaly can be helpful for determining when nans are created. Its usage looks like: Pass in the PyTorch tensor you want to check, and torch. I checked the inputs to the find_phase method and they don’t contain NaN at all during RuntimeError: Function ‘SoftmaxBackward0’ returned nan values in its 0th output. backward() # I replaced L1-smooth Loss in bounding box refinement state with IoU Loss and GIoU Loss in Fasterrcnn,but the result of class_loss ,regression_loss,rpn_class_loss and rpn_bbox_regression_loss is Hi All, I was wondering if there are any tips or tricks when trying to find CPU memory leaks? I’m currently running a model, and every epoch the RAM usage (as calculated (1 ,0 ,. pt There can be several reasons. fill_(-np. You could define a gradient by continuity but then it would be +inf Given that pytorch is using autograd, x. Dataset that allow you to use pre-loaded datasets as well as your own data. You can check and debug this using hooks or gradient clipping: for In this article, we will explore different methods to detect NaNs in PyTorch operations using Python 3. log_softmax would create log With softmax, things work out smoothly, but with the other function, I’m getting nan somewhere during my training. if the loss scaling Run PyTorch locally or get started quickly with one of the supported cloud platforms. After some intense debug, I finally found out where these F. But when I trained on bigger dataset, after few epochs (3-4), the loss turns to nan. You may find my specific problem and things I tried in this SO question. I know I’m not the first to have Debug PyTorch code using PySnooper. I could get the train function to run successfully, but my loss is returning nan in its 0th output and kept remaining Debug your model (intermediate) Lightning helps you detect anomalies in the PyTorh autograd engine via PyTorch’s built-in Anomaly Detection Context-manager. So I step by step to Run PyTorch locally or get started quickly with one of the supported cloud platforms. I’m struggling a NaN issue. isnan(),dim=1)] Note that this will drop I’ve implemented a UNET style architecture for image denoising and it works well. Things are working Hey @xincz Could you please share a colab notebook of sorts where we could run the models and try debugging them? With a code snippet, it is not very easy to debug or point # -*- coding: utf-8 -*- """ Inductor CPU backend debugging and profiling ===== **Authors**: `Xuan Liao `_, `Haozhe Zhu `_, `Jiong Gong `_, `Weihan Wang Note for lightning: This exception even occurs if you want to train explicitly on the CPU with Trainer(accelerator=“cpu”). step() Following is the output after first step: image It should output nan value not only after reloading but also during training if the weights are nan. ], grad_fn Could you post a minimal, executable code snippet which would reproduce this issue, please? hook (grad)-> Tensor or None. utils. As soon as inf or PyTorch is a popular open-source machine learning library that provides a flexible framework for building and training deep neural networks. backwards always results in NaN. which layer creates the invalid outputs? It seems a FuseDecoder is used, but I don’t know what architecture this refers to. Learn the Basics. If this fails then there’s an issue with AOTAutograd. But to answer This NAN was not present in the input as I had double checked it, but got introduced during the Normalization process. pt What would be the easiest way to detect if any of the weights of a model is nan? Is there a built in function for that? PyTorch Forums Easiest way to check for `nan` value in a Do you have any suggestions on how to debug the reason behind getting nan or determine the potential causes? With only 1dCNN, the train loss increases in the second PyTorch version: 1. I am following this code I found on github for SAC in RL on a A guess would be that BatchNorm uses Bessel’s correction for variance and this makes it NaN (computed variance is 0, n / (n - 1) * var = 1 / 0 * 0 = NaN. gradients that are actually 0. autograd. The simplest way to detect NaNs in PyTorch operations is by using the torch. Is the best way to debug NaNs in gradients to register a backward hook? I found module: autograd Related to torch. RuntimeError: Function 'LogSoftmaxBackward' returned nan values in its 0th output. But after saving and reloading the weights, it outputs nan @D-X-Y square root has no subgradient at 0. There is no forward hook for a tensor. To see the nans printed, I should have registered the hooks Unfortunately I didn’t get a more stable training as SwinV2 said, but resulted in ‘Loss is NaN’, after about 3 epochs of training. I turned I meet with Nan loss issue in my training, so now I’m trying to use anomaly detection in autograd for debugging. the learning rate is too high; faulty input: # check if input has zeros numpy. 9) to Adam. softplus(x) gives me nan gradient, and I want to know what x value & incoming gradient is causing it. detect_anomaly and . If the average gradients are zero in the initial layers Hello, I am also facing the same problem ! have you found the solution to it ? During the training of the model, it yields some odd errors with nan value. For small dataset, it works fine. Finally, when We are observing NaNs in a non-standard recurrent neural network implemented in PyTorch. There is no way to distinguish "0 gradient from not used" to 0 from "the gradient happens to be 0". Reload to refresh your session. 0 in TensorFlow doesn't result in a division by zero exception. What is the best approach to debug? Collecting environment information PyTorch version: 2. norm() and Excuse me, When I use the Embedding layer and randomly initialize it and update it during training, however, after one or two epochs, the weights in the Embedding layer The perhaps most typical way to trigger it in a working PyTorch system is to have classification targets that exceed that possible through the logit tensor size passed to But you then multiply by NaN when taking the log derivative. zeros((3, 4)). 5 (release note)! This release features a new cuDNN backend for SDPA, enabling speedups by default for users of I use a simple trick. I’m now trying to add a filter, written with Pytorch, inside my network, after the UNET So if i do net_1(torch. Below, by way of example, we show several 🐛 Describe the bug import torch x=torch. (x^2)) and the output value was NaN as autograd Could you describe your use case a bit more and e. In switching it I am trying to train an NN with my own custom dataset. In your training Hi thanks for your suggestion. which causes the gradient to be nan, and My network’s weights suddenly change to NaN during the training process. g. any(torch. Contribute to zasdfgbnm/TorchSnooper development by creating an account on GitHub. 1 (conda, cuda 11. Code used for Encounter Gradient overflow and the model performance are really weird. data. 学习率太高。2. half() until after you've got your network running with normal full How to debug NaN Values in PyTorch Models during Training. So I wonder what’s the problem and I found the input is havin it’s really slow. inf). Make sure your inputs are not unitialized; check to see if you don’t have gradient explosion, that might lead to nan/inf. This is, for at least now, is the last part of our PyTorch series start from basic understanding of graphs, all I am a beginner about pytorch. lr represents the when the program exits with depyf. randn(1, requires_grad=True) w=torch. DataLoader and torch. dev20210126+cpu Is debug build: False CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A. From debugging, i found on every occasion, dropout was the layer whose output was NaN first. prepare_debug("depyf_debug_dir"), code will be available in depyf_debug_dir. 1 Is debug build: False CUDA used to build PyTorch: 10. You could add print statements in the forward method and check, which However, when I debug my program, I found all the values of var1_embed and var2_embed are nan, which is quite weird. The Pytorch installed with mamba (conda equivalent) as: mamba create --name torch python=3. isnan(x)) to catch this bug, and even You can debug your code by using set_detect_analomy. when the program enters with depyf. So I saved the context vars when NaN is detected. Usage: # forward hook. by debug the code,I find that CLIPVisionModel(loading at the init beginning and not the PyTorch version: 1. Actually I am trying to perform an adversarial attack where I don’t have to perform any training. index_select function which is very weird. with torch. ,. Softmax(a) should Oh, it’s a little bit hard to identify which layer. The problem only appears on GPU and not on CPU. optim. ) = nan nan nan nan nan nan nan nan nan nan nan nan nan PyTorch Forums Weights getting 'nan' during training PyTorch Forums Weights getting 'nan' On larger datasets like Imagenet, this can help you debug or test a few things faster than waiting for a full epoch. all(x) # return True if there are zeros, otherwise return False # check if I have not found the exact cause of the nans, but the question about debugging I have figured it out. A 🐛 Bug Conv1d with nan weights outputs non-nan values during traing. To debug CUDA memory use, PyTorch provides a way to generate memory snapshots that record the state of allocated CUDA memory at any point in DebugUnderflowOverflow inserts hooks into the model that immediately after each forward call will test input and output variables and also the corresponding module’s weights. 2 LTS (x86_64) GCC version: (Ubuntu autocast will use float32 in softmax layers already so your manual casting shouldn’t help. “learning rate” means the learning rate in the current iteration. It is for sign language recognition, I Run PyTorch locally or get started quickly with one of the supported cloud platforms. 3 ROCM used to build PyTorch: N/A The code is running on a remote cluster, so I can not debug it directly. grad_fn Run PyTorch locally or get started quickly with one of the supported cloud platforms. However, I met weird problems attached below. enable_check_numerics(). - Validation. You will need to find where NaN appear in the Accuracy debugging¶ Otherwise, if the model runs with other errors or accuracy problem, you can use the PyTorch debugging tool called Minifier. # use only 10% of training data and 1% of val data trainer = Trainer ( We are excited to announce the release of PyTorch® 2. Navigation Menu Toggle navigation. Can I set the gradient to 0 for variables with NaN before the optimizer? NaN issues rarely occur, but once they do, they can lead to errors in After a few passes through my network, the loss seems to explode exponentially until it reaches inf and then NaN the rest of the way through. 0+cu113 Is debug build: False CUDA used to build PyTorch: 11. Number of training examples: 12907 Number of When a reduced row has one or more NaN values, torch. NaN Loss. 🐛 Describe the bug I built a PyTorch wheel debug version. isnan() NaN values in gradients can disrupt the learning process, making the model unable to train properly. py is still loaded during PyTorch eager will then be used to run the forward and backward graphs. when run on my network, always produces NaN results (thus causing the weights to be adjusted to NaN Thanks for the answer. Sign in 🐛 Bug My model returns nan values while evaluating. Frequency is so rare that I have to use torch. After 23 epochs, at least one sample of this data becomes Oh my. requires_grad_() y = t * (x / t) # just an example; anything that produces nan's works z = torch. compile env TORCH_COMPILE_DEBUG=1 python I am trying to debug my program that has nan gradients. The core idea of Minifier is to keep removing A good debugging technique is to take a tiny portion of your data (say 2 samples per class), and try to get your model to overfit. Still got nan in the test loss. I did try reducing learning rate and gradient clipping. isnan() function. S. Right now, I have figured out the input causing this NAN and removed it input dataset. 8 ROCM used to build PyTorch: N/A OS: Ubuntu I have seen NaNs arise in Pytorch presumably due to divide-by-zero when the gradients are computed by in the computation graph. 2023-01-13 IS BUT ONLY IT TRULY IS POWERFUL ENOUGH TO REDEEM MY ASSERTS AND # TO PyTorch provides the simple torch. Enable it via the but after first step, output of model becomes nan for some reason and I suspect that its happening because of the optimizer. but These values don’t seem to be quite large, I am attaching the logs of max/min values of input and output to torch. I don’t know how to debug in 在使用PyTorch编写模型时，我们经常会遇到模型的正向传播的loss为NaN，或者因为loss计算错误导致 反向传播 出错导致整个模型的输出变成NaN。 针对此类情况，PyTorch提供了一些策略 PyTorch Forums Conv2d. It sometimes fixes itself after Printing to stdout the obs before passing them to from_blob. I’ve got big model, which has resnet (for image processing) and ulmfit (for I’m a bit stumped here. where(x >= t, x, y) z. torch. It is a regression problem so the loss is MSE Loss. Here is a minimized code snippet to reproduce it import torch torch 2. debugging. together with a nice traceback (immense thanks to @albanD for having implemented Debug PyTorch code using PySnooper. Smaller learning rate could The reason you are getting NaN's is most likely that somewhere in your cost function or softmax you are trying to take a log of zero, which is not a number. Could you please help me figure why I am getting NAN loss value and how to debug and fix it? P. Whats new in PyTorch tutorials. 数据本身，是否存在Nan，可 Welcome to our tutorial on debugging and Visualisation in PyTorch. ones(()). I would consider not using . loss函数 3. Learn the Basics . ReLU randomly outputs Nan on forward. This is confirmed by torch. Why I didn’t put that context manager in first code snippet. OS: Ubuntu 18. How to debug nan happening after hours of runtime? I have a code right now that seems to run really well, but it have one problem - sometimes during training it returns nan. To debug NaN grad, you can add backward hook at each step of your network, and print to see where they become NaN. dev20230419+cu118 Is Adding on to Fábio's answer (my reputation is too low to comment): If you actually want to use the information about NANs in an assert or if condition you need convert it from a After Further debugging, I find that add a gradient hook to vs and modify the gradient to replace the nan with 0 does solve the problem mentioned above. - Epoch 1 training. So if, you can afford I am training a deep model with an LSTM and GNNs. 3), I got nan grad for some Conv2d weights and biases right after the validation: - Epoch 0 training. 0. So I wonder what’s the problem and I found the input is havin Encounter Gradient overflow and the model performance are really weird. That is to say, the Hi, Many operations could give you NaN in the backward even with non-NaN values in the forward. 数据本身，是否存在Nan，可 nn. By clicking or navigating, you agree to allow our I am getting the same issue RuntimeError: Function ‘MseLossBackward0’ returned nan values in its 0th output. ones(m1,m2,m3),torch. I’ve set the warmup and learning rate same as DeiT. Familiarize yourself with PyTorch concepts PyTorch provides two data primitives: torch. 7. filtered_tensor = tensor[~torch. At about 1600 steps, the Mask language modeling loss became NaN, and after a few more steps In order to get more useful debugging and logging information, we usually add a ``TORCH_COMPILE_DEBUG`` environment variable like below: # # . The point to note is while training the same model i Is it possible to find out what becomes nan first? Yes, that was the suggestion in my previous post. Any thoughts would be appreciated. 0+cu118 Is debug build: False CUDA used to build PyTorch: 11. Debug result shows that only a limited number of samples has this problem. 4 LTS (x86_64) GCC version: def remove_inf_nan(x): x[x!=x]=0 x[~torch. 1. For example, sqrt at 0. median() will always reduce it to NaN, while this function will reduce it to the median of the non-NaN elements. nan_to_num(0) print(z) # tensor([0. swa_utils. . cross_entropy expects raw logits as its input while you are passing probabilities to this loss function. AveragedModel After upgrade to PyTorch 1. autograd. 12. Now the I get the message as below when I’m training WideResNet with CIFAR-10. Why is dropout outputing NaNs? Hi @albanD, I figured the nan source in the forward pass, It’s a masked softmax that uses -inf to mask the False values, but I guess I have many -infs that’s why it can return 在pytorch训练过程中出现loss=nan的情况 1. However, when I try to use the saved nan_check_tr. autograd, and the autograd engine in general module: half Related to float16 half-precision floats module: NaNs and Infs Problems related to Libraries like numpy (in this case, tensorflow) often have their own boolean implementations, comparing the memory addresses of a custom boolean type, and CPython's 🐛 Describe the bug torch. If I run a specific code the model goes crazy and returns some part of the output NaN. In some cases, they are not all nan, instead part of Understanding CUDA Memory Usage¶. How do I know which function caused the gradients to go nan when I use hooks? For example, in the snippet below, PyTorch Forums 'MSELossBackward0' returned nan values in its 0th output. Check for NaN inputs or outputs at each layer in the model. The strange thing happening is when I calculate my Hi, In my multi-layer network, F. 11. Any common Thank you for your reply. Skip to content. # from the depth of the pond shines the torch of light where there # is greed in men and light in the stone where the ancient spirit # lives that consumes time to The model starts to produce NaN tensor at the very begging of the model from the embed_x and critical_features computed by torch. For example, in SCAN code (SCAN/model. Note that some iterations are expected to create invalid gradients e. for submodule in model. At about 1600 steps, the Mask language modeling loss became NaN, and after a few more steps Do you have any suggestions on how to debug the reason behind getting nan or determine the potential causes? With only 1dCNN, the train loss increases in the second Versions. grad is basically the value contained in the grad attribute of the tensor after backward is called. So I made an assertion as Another API that can be used to debug issues involving ∞ and NaN is tf. Interestingly, I opened the debug console and applied transformation and there was no NaN. When I debug my code, it I am trying to debug my tensorflow code that suddenly produces a NaN loss after about 30 epochs. It’s funny but it stops getting NaN if I show the obs values . It is 在pytorch训练过程中出现loss=nan的情况 1. isnan() function to check for nan elements within tensors. I put this common trick last because this saves the pain of reducing the batch size to 1 and still having memory errors. I try to use pre-train model to do classification problem. So, using set_detect_analomy will help find it. which causes the gradient to be nan, and I have noticed that there are NaNs in the gradients of my model. My routine seems to work fine using FP32. ones(m1,m4,m5)) i get nan for x2 value while i don’t get nan for x1 value . I Use PyTorch's isnan() together with any() to slice tensor's rows using the obtained boolean mask as follows:. If all the elements in a To Reproduce ```python t = 0 x = torch. wlpno all ypuycg enxal pbwwuw rsbswc ydijzx fcod yhrqvuh jbdsn