Pytorch output nan. I have couple of question: (1) nan value in its 1th .

Pytorch output nan hojaeklee opened this issue Oct 2, 2019 · 4 comments Labels. I changed loss function to BCE version and Gaussian loss version, but VAE’s Encoder output NaN in training phase. I added. ones(1,1,3,3)) # Make a 'nan' tensor model = nn. 1 Is debug build: No CUDA used to build PyTorch: 10. isnan(target)]=0 loss=torch. 8. angle(complx_spect PyTorch Forums Using a Custom Loss Function produces NAN. If I run my code without Anomaly Detection, I get NaN’s in my data. models import resnet18 net = resnet18(pretrained=True). The problem only appears on GPU and not on CPU. array([ [1, 0], [0, 1], [1, 1]])) b = th. I have a pytorch dataloader and well-trained model. nn. set_detect_anomaly(True), Pytorch returns this error. Can anyone explain this issue? for some operation z on the output y = g(x), the chain rule gives you dz/dy * dy/dx = dz/dy. If you get NaN values this is probably caused at an earlier stage in your network, using a debugger in an IDE might help in that case. output_tensor = squared_norm * input_tensor / ((1. pc_data = h5py. How to take the features of net1 as the input of net2. After sifting through possible issues, I came across that my activations started off as well distributed normalized numbers and eventually an upsampling followed by a 2D If you look at example 1 below, it is a 4x4 matmul z = torch. one solution i found on searching is to use normalized softmaxhowever I can not find any pytorch imlpementaion for this. After I do the backward, on the second epoch nan. marwanj (Marwan) June 6, 2022, 11:30pm 1. Information I have: Fp16 training (autocast, scale(). I use LSTM layer returns nan when fed by its own output in PyTorch. 0. Eecrease the learning rate to e. class Encoder(nn. backward and before . log_prob(x) 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 was doing this cast manually because I though it was the right way. Python version: 3. I have narrowed it I assume “after the first batch” means that the first output and loss tensors are valid, while the second iteration produces a NaN output? If that’s the case, could you check all gradients in the model using: Hi all. nn. softmax(unnorm,dim=2) out = torch. 015876239976466765 0. numpy versions resolved the issue. But I found my loss and predict nan both after the first epoch. This confuses me because both the square and its derivative should not give nans at any point. I do not know which division causes the problem since DivBackward0 does not seem to be a unique name. Really appreciate all your help @ptrblck! I am using a GCP vm, their deep learning image. I run the wide-resnet, while input sub. I am following this code I found on github PyTorch version: 1. RuntimeError Traceback (most recent call last PyTorch Forums Network forward output is Nan, without backward. (zero-mean, and variance value is between 0. 2 Python For some reason the loss is exploding and ultimately returns inf or nan. which is traced to the ‘. You signed out in another tab or window. 1k; Star 85. I. Ask Question Asked 6 years, 6 months ago. 0 20160609 CMake version: version 3. Then, the decoder takes this feature representation I am working with VAE and I don’t know why but during the training process, I am getting the output of VAE as well as that of the encoder as nan. Hi! I face the following So i just average the loss by myself, but after some iterations weights and outputs become nans. isinf()), also the min and max values seem to be reasonable (between -15 to +15) So, I am wondering what can be causing this? ‘WeightNormInterfaceBackward0’ returned nan values in its 0th output. mean and div. no The result is that suddenly the model returns nans even though all weights in the model appear reasonable. Code used for debugging is below. I have to mention that I’m experimenting with a really small model (5 hidden unit), but I’m wondering if there is a way to have a more stable solution (adding an epsilon 1^-6 do not solve PyTorch Forums Network forward output is Nan, without backward. I’m new to Pytorch. PyTorch Conv2D returns non-zero output for an input tensor of zeros? 0. Hi, I am trying extract some features from time-series data of window size 50. I found the attention output is NaN when the sentence is all PAD. set_detect_anomaly(True) and it gives me. The other parameters are exactly the same. which as I mentioned in my first post isn’t very helpful in this case since the NaNs are already present in the input to the When using detect_anomoly, I’m getting an nan in the backward pass of a squaring function. I let you know about the points that I have been able to confirm. All I did is change the input shape, denoted by When I use the suggested method from the discussion, my Nan issue is gone. TransformerEncoder. where(_x <= 0. The issue does not happen every time, but at a very high frequency, e. vision. at the very first step of backward instead of waiting for several epochs to see NaN loss. Nemfor (Nemfor) May 12, 2022, 10:26pm 1. 1363317370414734 market_feature_extractor. set_detect_anomaly(True) Pytorch return this error: Function 'PowBackward0' returned nan values in its 0th output. I have noticed that although the input to the model never includes NaN values or values very large in magnitude, the output of the PyTorch Forums After torch::load model and predict, then got NaN. Code below to 🐛 Bug My model returns nan values while evaluating. ones(m1,m2,m3),torch. I can provide the input and weights if needed. I have confirmed on documents that manual backward is essential when using multi-optimizers, and the code runs without issues with precision 32. any()) conv = self. Todo so a build a neural network based on the tutorial here. the returned output is nan. So I tried debugging and found something strange. e. However, if you are finding that the training is consistently producing NaN Besides I am unable to get why convolution output is nan for valid inputs. step() will be skipped and the scaling value will be reduced. 2 ROCM used to build PyTorch: N/A how to deal with this problem. For the step when everything breaks down and becomes NaN, none of the values seem weird, there is no nan (torch. solve output NaN for singular linear algebra Issues related to specialized linear algebra operations in Hi. atan2 then it solves the problem. . Here is the code in forward function print("input",torch. checker. Angle(). However, the loss becomes nan after several iterations. zeros((3, 4)). Try lower learning rate (10^-4 to 10^-6) though, the result does not change from NaN. The input has no nans and infs as I verify with the following: Could you check the stats of the input tensor as well as the parameters of the linear layer, which is causing this issue? E. Angle() returns Nan for some cases and why the suggested solution solves the Nan issue. step, scaler. Is there the train model store the output in pytorch? Same activation at different layers. Hot Network Questions Do Hi everyone, I am trying to use Transformer Encoder Layer with src_key_padding_mask to be the encoder in the multi-turned dialogue generation task, but i get NaN. log(-B*torch. 13042187690734863 Min: -0. Because all tokens in the sentence is converted to -inf, the Softmax returns NaN as results. OS: Ubuntu 16. Can someone please help to let me know what am I missing here. 008856, 9. granth_jain (granth jain) November 15, 2020, 4:28pm 1. Looking at the runtime log you probably won't notice anything unusual: loss is decreasing gradually, and all of a sudden a nan appears. The first 2 layers before the transformer encoder layer are a nn. import torch import torch. import torch import h5py from torch. It seems to pytorch / pytorch Public. eval(), then the model generates NaN output. If the loss is exploding and thus the gradients are large in their magnitude, the parameter updates might yield to overflows. I’m training on 2xL4 with pytorch==2. log from getting nan. I am working on an encoder-decoder architecture to perform regression for a family of sinusoidal functions. Apart from that, it doesn’t differ too much. RuntimeError: Function 'LogSoftmaxBackward' returned nan values in its 0th output. And this is only happening with GPU. digiperson September 2, 2020, Function ‘MseLossBackward0’ returned nan values in its 0th output. nn as nn from torch. The ONNX model is parsed into a TensorRT model, serialized, loaded, and a context created and executed all successfully with no errors logged. Hallo. Since I have a pretty special setup that takes extremely long to reproduce, I’ll just try to explain the problem as clearly as possible. On the other hand, with y = f(x), the Thank you for reply. 8668133321973349 But when I train with FP16 training, LSTM output shows nan value. However, if I use two GPUs, I get nan loss after a dozen epochs. I found some weird situation on ignite’s evaluator. 5. My model is throwing NaNs intermittently. Viewed 2k times On training, the LSTM layer returns nan for its hidden state after one iteration. I added So if i do net_1(torch. If I run a specific code the model goes crazy and returns some part of the output NaN. This problem has been explained by KFrank, but due to my fault that I ask this question in the other topic, so I create a new one. During training (mostly after the first backpropagation) the outputs become nan. bias and half of the weights are becoming NaNs by the second iteration, all of the weights are NaNs by the third Even though most loss functions seem to have this problem some like torch. 下記のLinkに飛び,ページの下の方にある「QUICK START LOCALLY」で自身の環境のものを選択し,現れたコマンドをcmd等で入力する(コ RuntimeError: Function 'BroadcastBackward' returned nan values in its 0th output. Only intermediate result become nan, input normalization is implemented but problem still exist. In this setting we have a batch size of Hello everyone I’m testing how suitable the models made available by torchvision are at, among other things, analyzing both images and audio (In regards to the audio, I first extract MFCC features from the audio clip, and turn said MFCC features into an image, as I saw some people doing it, and saying that apparently it’s somewhat common practice). CPU works as expected. When the input tensor is “nan”, I expected the output to be “nan” as well. could you explain the reason for Hi, I’ve got a network containing: Input → LayerNorm → LSTM → Relu → LayerNorm → Linear → output With gradient clipping set to a value around 1. encoder, self. Here’s my code. The loss function is the mse of the reconstructed Z and the groundtruth, which leads to a RuntimeError: function ‘LinalgSvdBackward0’ returned nan values in its 0th output. I am using cnn in my code. You can also check whether your data itself has bad inputs that are causing the loss to go NaN Your learning rate is too high for the calculated loss, which also sums the sample losses. slavavs (slavavs) February 9, 2020, 1:53pm 1. During training (mostly after the first As a PyTorch user, have you ever seen nan show up in your model‘s outputs and wondered: What does this mean? Where did these nan values come from? How can I detect 🐛 Describe the bug When I use the mps it turns into nan values for just a simple encoder similar to the tutorial on PyTorch. If you are using anomaly detection from the beginning, you might need to disable it. Hello, The models provided in the Torchvision library of PyTorch give NaN output when performing inference with CUDA on the Jetson Nano (Jetpack 4. 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. eye(y. std . Thanks! Versions. What could be the possible reasons? class MelanomaDataset(Dataset): def __init__(self, dataframe, I have a training set with 43 variables and 7471 observations. Open ChocoL0rd opened this issue Feb 7, 2024 · 5 comments Open My problem is that my loss after around 20 iterations prints NaN or (in the rare case) stays constant. Number of training examples: 12907 Number of validation examples: 5 Number of testing examples: 25 Unique tokens in source (en) vocabulary: 2804 Unique tokens in target (hi) vocabulary: 3501 The model has 214,411 trainable parameters I have noticed that there are NaNs in the gradients of my model. If so, than note that invalid gradients are expected when amp is used with float16 and the GradScaler will skip the parameter updates in this iteration before decreasing the scaling factor. Conv2d(in_channels=1, According to softmax function, you need to iterate all elements in the array and compute the exponential for each individual element then divide it by the sum of the exponential of the all elements:. g. My code have to take X numbers (floats) from a list and give me back the X+1 number (float) but all what i become back is: for Output-tensor tensor([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], device='cuda:0', grad_fn=<ThAddBackward>) and for loss: tensor(nan, device='cuda:0', I could have simply assumed that the weight of the pretrained D was the problem, but these nan values don’t always appear and if I’m lucky, the training ends without nan occurring until the 100,000 iterations I set. acos(1+torch. PyTorch version: 1. However, Try to isolate the iteration which causes this issue and check the inputs as well as outputs to torch. mixed-precision training by default. dev20210126+cpu Is debug build: False CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A. 8 out of 10 times, and the occurring time differ from each other (which makes it harder to debug). eval(). Using wrong loss. See the example below, I have changed like this and it works angle = torch. + squared_norm) * torch. The only difference is that I have added a couple of Residual Blocks in the beginning. To summarize, I kept going up the chain and saw that the first layer of my nn. I wonder why Torch. data import DataLoader, Dataset class PCDataset(Dataset): def __init__(self): self. LSTM layer returns nan when fed by its own output in PyTorch. Notifications You must be signed in to change notification settings; Fork 23. Generally when there are NaNs or Inf values in a given training step, it is not possible to “recover” from the training step; a common practice is to simply reject or skip the weight update of that step to avoid propagating the issue to the model weights (so nan_to_num wouldn’t really help). I tried using gradient clipping, but it didn’ work. 0+cu117, FSDP, torchrun with NCCL PyTorch Forums NaN gradient for torch. 7 Is CUDA ‘WeightNormInterfaceBackward0’ returned nan values in its 0th output. Below is a simple example. set_detect_anomaly(True) Pytorch return this error: Function 'PowBackward0' returned nan values in When I debug my code, it says avg_cost becomes nan just after batch_idx is 62. Solved by passing both input and output though a softmax layer, then through BCE loss. pytorch model returns NANs after first round. Particularly, this NaN phenomena only occurs when I initialize hidden and cell state of LSTM w/ Normal distribution. 0. 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 Hi, I have a network which outputs NaNs after some epochs. In some situations I have encountered nans as probability as well. detect_anomaly yields RuntimeError: Function 'MseLossBackward' returned nan values in its 0th output. I am reading a CSV file with rows as my data. export(). encoder_1 = Hello, I’m training a model to predict landmarks on faces. – jumelet. If the scaler sees these invalid gradients, the optimizer. Does it have weighs that are updated? Yes, convolution layers are trainable and have a weight (filters) and bias parameter. The loss doesn’t contain NaN Hi, I am using the following generator model for a project, which is similar to DCGAN tutorial. I know I’m not the first to have these problems, so here is what I’ve already tried My input doesn’t contain any NaNs, I replaced them with the average of the df column I have tried NL1Loss and MSELoss and both have this Ah, thank you both @Andrei_Cristea and @ptrblck!This was definitely an issue of converting from TensorFlow without fully understanding the differences; TF has a from_logits argument in its BinaryCrossentropy class, while Torch provides two separate classes. 1e-8 and remove the size_average=False argument. Pytorch loss is nan. Adam(model. Model: from Custom losses tend to be way less stable But just check you are not passing negative values to a log, doing anything/0 these kind of things. Actually, it is due to the BatchNorm2d layers, and the way pytorch handles them in train() and eval() mode. Sequential( nn. I’m using Pytorch’s torch C++ frontend on a Nvidia Orin NX (Arm64). 2. 1810 (Core) GCC version: (GCC) 4. Can anyone post a minimum working code with BCE loss ? PyTorch Forums BCE loss returning NaN. However, Hi, I’m doing a small test run of DinoV2 GitHub - facebookresearch/dinov2: PyTorch code and models for the DINOv2 self-supervised learning method. Screen Shot 2022-06-05 at 22 Function 'MulBackward0' returned nan values in its 0th output. When I am running my RL project, it gives me nan (The Error below) after a few iterations while I clipped the gradient of my model using this: Hi everyone, I am getting the error in the title after 10 epochs of training. In some cases, they are not all nan, instead part of the embedding is nan and the remaining is a float. My batches are of size (68, 45, 100) and initialized my hidden states with a uniform dist between [1, 0]. Module): def __init__(self, input_size, I’m also encountering a similar problem for my model. But if i replace in init of RMSLE class to just MSELoss() with reduction as default- so it avarages by itself, I've finally figure out where the problem comes from. exp(a)) 0. It used to work fine with the following loss function: distrib = torch. This is my network (I’m not sure about the number of ne Hello, i am a Newbie in PyTorch and AI and make this for privacy. detect_anomaly(): RuntimeError: Function 'DivBackward0' returned nan values in its 1th output. OS: Ubuntu 18. I’m trying to build my own classifier. 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 nan. atan2 might have occurred as I haven’t used torch. I have couple of question: (1) nan value in its 1th RuntimeError: Function 'LogSoftmaxBackward0' returned nan values in its 0th output. with no trainable parameters). the initial declared net is : NetDSpace ((conv1): Conv2d(1, 6, kernel_size=(3, 3), stride=(1, 1)) It gives me nan out, even if I apply softmax on the labels. step then you delete the gradients needed for the update before applying the update. hoda_fallah (hoda fallah) April 7, 2022, 3:37pm 1. cb_zhang (Cb Zhang) September 29, 2019, 7:58am 1. Based on your code I cannot find anything obviously wrong. 8668133321973349 You signed in with another tab or window. bmm(attn,emb) I tried the below line as alternative, but the values that should be masked Tou would have to specify what kind of model you are using. Nowadays I use PyTorch version >2 and haven't ran into this problem since. I think this is because the model ends up having 0 variances. Softmax function doesn't predict. like the image below. parameters(), lr = learning_rate) nb_epochs = num_epochs train_hist = This happens rarely, but consistently: given the same input and weights, there are NaN values in the output every time. The model passes onnx. unnorm. I carefully checked the parameters of the model and found that some of them were particularly strange, the values of the parameters were particularly I get the message as below when I’m training WideResNet with CIFAR-10. Hello, I am experiencing issues applying Precision 16 in PyTorch Lightning. any tool on every tensor the first nan appears in the tensor resulted by the suqashing operation (output_tensor) and then this will be propagated to the rest of tensors till the loss turn on to be nan (out of PyTorch Forums Problem with nan values of the model Parameters: weight and bias. 4 LTS (x86_64) GCC version: Could not collect Clang version: Could not collect RuntimeError: Function 'ExpBackward' returned nan values in its 0th output. sum(np. However, the output vector is always all “nan”. After a few iterations of training on graph data, loss which is MSELoss function between the returned output and a fixed label become NaN. exp. cos() / torch. sudri (sudri) September 30, 2021, 2:23am 1. Collecting environment information PyTorch version: 1. Reduce the learning rate smaller, 1e-10, but the loss still nan I write the break switch when I get nan I don’t know what your training wrapper does and if Lightning is using e. 6. update, zerograd) diverges to Reason: you have an input with nan in it! What you should expect: once the learning process "hits" this faulty input - output becomes nan. I was on pytorch version 1. con3d1 I am using a tansformer model (on the CPU) based on nn. Why is dropout outputing NaNs? Model is being trained in mixed precission. However after some training of a3c, outputs of nn. Conv1D(), nn. Why does my pytorch NN return a tensor of nan? 1. Here is my code def train_model(model, train_df, num_epochs = None, lr = None, verbose = 20, patience = 10): criterion = nn. org. autograd import Variable class RNN(nn. And I have checked the data with numpy. 6k. I’ve checked that the nan arises i After some iterations my grads goes to NaN. matmul(x, y), and it is a situation where nan should be output by accumulating +inf and inf. Below is my And the model params min max value: market_feature_extractor. This is a note from pytorch: """AMP/fp16 may not work for every model! For example, most bf16-pretrained models cannot operate in I have noticed that if I use layer normalization in a small model I can get, sometimes, a nan in the gradient. A similar issue is reported here. Thanks in advance!! Here is part of the code: self. backward, unscale, clip_grad_norm, scaler. However, I have added asserts to all divisions (like assert getting nan in loss can be happened for one of following reasons-There is nan data in the dataset. neither the model output, nor the parameters or the gradients were having invalid values, but the optimizer. C++. manual_seed(seed), numpy seed and random seed, I sometimes get the following error: Function ‘AddmmBackward0’ returned nan values in its 1th output. It looks like this: self. Pytorch CNN not learning. Finally, you would make the problem more sensible for MSE by downscaling the output The models provided in the Torchvision library of PyTorch give NaN output when performing inference with CUDA on the Jetson Nano (Jetpack 4. When I then want to use the VAE model I am working on Melanoma Classification task where I have to classify the patients into two categories on the basis of their skin images. I want to implement a supervised regression model. You switched accounts on another tab or window. The input, denoted by X, has as shape of (7471, 43), and the output, denoted by y , has a shape of (7471, 6). The corresponding embedding is like below. Code below to reproduce: import torch import torchvision from torchvision. My model handle time-series sequence, if there are one vector ‘infected’ Hi, Are you sure x_std is not 0 in your case? Could you print x juste before giving it to the linear layer? I’m trying to implement a variant of capsule network where the matrix multiplication is replaced by element-wise multiplication with a vector. but from second batch, When I checked the Hi! Afer a 3d convolution ,all of the reslut of the output are nan. cuda() input = torch. Module (a Conv2d layer) just outputs nan. What is the Problem in my Building Softmax from Scratch in Pytorch. h5", pyTorchを初めて使用する場合,pythonにはpyTorchがまだインストールされていないためcmdでのインストールをしなければならない. Hi, I’m trying out the code from the awesome practical-python codes. Viewed 2k times 0 . So if, you can afford to use batch size > 1, that would solve the NaN Hi, I am using softmax at the end of my model. MultivariateNormal(y, torch. File("shapenet. PyTorch Softmax Output Doesn't Sum to 1. After training, I called If I have a loss function is the form torch. 0000) On the other hand, zero initialization of LSTM cell and hidden states doesnt show this NaN phenomena. And when I run on GPU:0, it is ok, but run on GPU:1, it is wrong. ## Training data loading 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. Getting nan as loss value. (eg. Modified 6 years, 6 months ago. What makes me confused is that before each mha outputs weights (probabilities), I use assert all When I debug my code, it says avg_cost becomes nan just after batch_idx is 62. I wrote a function called debug to demonstrate this. However, if I set the model to eval mode using . data import Hello, By using torch. size()[0]*sigma) loss = -distrib. QuantScientist (Solomon K ) December 13, 2017, 1:06pm 4. It sometimes fixes itself after nn. step. Training runs just fine on a single GPU. use a debugger, ensure that your loss (forward output) contains non-finite values (perhaps at some epoch > 1), re-run forward() step-by-step to find the problem (you can use conditional breakpoints and “jump to cursor” in If the invalid values is created in the forward pass, you could use e. However after some training softmax is giving negative probability. I’m working with MNIST dataset and I’m normalizing it before training. 1. Here is an example, a = th. encoder_layer. Closed hojaeklee opened this issue Oct 2, 2019 · 4 comments PyTorch version: 1. nn as nn x = torch. (Use leaky-relu instead) Sometimes zero into square_root from torch gives nan output. In train() mode, pytorch computes the BN trainable parameters Hello! I’ve trained a stand-alone VAE based on the PyTorch example and a few other bits of code found on github - it works well and my output images look quite good. However, the output is Hello, I’m training a model to predict landmarks on faces. Similarly, they swap the order of true and false labels when applying the loss function. conv1d producing nans in a3c. 0001~1. MSELoss(reduction='sum') optimizer = optim. ones([1, 3, 48, 48]). dev20230720+cu121 Is debug build: False CUDA used to build PyTorch: 12. The pipeline is: images are first fed into a DDP model for feature extraction, then those features are Hi, all. inf). LogSoftmax outputs values between -inf and 0. Therefore, it seems the Torch. Both of these do the same thing. I’ve tried to: set a very small learning rate (1e-10) play with the batchsize; monitor the forward pass and look for Inf, NaN and Zeros But the forward pass looks ok. Using relu function sometimes gives nan output. solve output NaN for singular matrix [Feature Request] Make torch. autograd. 4. data = files def __getitem__(self, i): tmp = self. What makes it print NaN? I can’t imagine it’s the loss getting to big as it jumps from 20,000 to NaN. I have a dataset with nearly 30 thousand images and 52 classes and each image has 60 * 80 size. And I’m replacing the text with a slightly bigger one (originally 164KB, and mine is 966KB). backward() " Not all Landmarks are everytime provided, so thats the reason I assign the loss a zero for Hi, I’m trying to understand and solve a problem where my loss goes to nan. 1 ended up fixing the issue but causing slowness in dataloader, so I compiled using source from github (assuming this way I also got all the computation support libraries specific to the Could you check the stats of the input tensor as well as the parameters of the linear layer, which is causing this issue? E. 11) 5. When I was training and validating the model, the output was all normal. From debugging, i found on every occasion, dropout was the layer whose output was NaN first. 12. abs(out-target))**potenz loss_temp[torch. step() caused the parameters to become NaNs? Before I saw the other posts I was trying to reason The loss function is the mse of the reconstructed Z and the groundtruth, which leads to a RuntimeError: function ‘LinalgSvdBackward0’ returned nan values in its 0th output. exp(X)) what should be the best way to tackle the torch. Dataset): def __init__(self, files): self. 1 Is debug build: False CUDA used to build PyTorch: 10. This is confirmed by torch. Also have a look at I am trying to understand how masking works with the scaled_dot_product_attention, I’m using the one implemented in torch. gradients turns to NaN after several iterations. I suspect that with each step your hidden state is getting closer and closer to -inf. toTensor(); starts returning NaN even with small values. I’ve been working on this project with a collaborator lately and we’ve been trying to train a large Unet model (~800k params). Softmax(a) should produce near zero output. exp and torch. Linear projection layer and a fixed positional encoding layer (i. I printed the prediction_train,loss_train,running_loss_train,prediction_test,loss_test,and running_loss_test,they were all nan. But after a simple Conv2d layer the output becomes “-inf”. When i debug the code i find the model parametrs are nan also. if your input contains Infs or very large values in their magnitude, the result might overflow and could be set to NaN in further operations. sqrt(squared_norm)) return output_tensor. I’m trying to Could you try running with Trainer(detect_anomaly=True)?This should give an informative stacktrace of where the NaN might be coming from. backward or after optimizer. optim. Environment. Forward pass is handled correctly (epoch = 0). PyTorch Forums 'CudnnConvolutionBackward' returned nan values in its 0th output. Sigmoid() ) The input vector is valid, doesn’t contain any NaNs. I’m struggling a NaN issue. isnan(input). torch. But matrix output (z matrix) are all composed of +/inf. The loss value is 0. scaled_dot_product_attention So I wanted to test how the masks work, I create three tensors to simulate the queries, keys and values. ReLU randomly outputs Nan on forward. On the other hand, if you think that the backward pass might create invalid gradients, which would then create invalid parameters, you could use nn. mean(loss_temp) loss. forward hooks to check all intermediate outputs for NaNs and Infs (have a look at this post to see an example usage). Why does my convolutional model does not learn? 1. distributions. In train mode, everything works fine and proper results are generated. For single GPU I use a batch size of 2 and for 2 GPUs I use a batch size of 1 for each GPU. I try to use pre-train model to do classification problem. nesrine (NG) March 15, 2018, 10:20am 1. exp’ operation. For example in one of the calculations where output contained a single Nan the input tensor was size [2, 64, 1056, 800] Hi all, I want to know what may be the reasons for getting nan after a convolution, if my inputs are all properly initialized (not for loss but for the input). I tried to create a minimal appli PyTorch Forums Libtorch returns NaN on Arm64. conv1d turns to nans. I captured ReLU input and outputs. Also have a look at 🐛 Describe the bug When I use the mps it turns into nan values for just a simple encoder similar to the tutorial on PyTorch. Any thoughts would be appreciated. 2017, 7:33am 3. When I train my network with a single GPU, the training process terminates successfully after 120 epochs. But I am getting nan as the model output while training. 4. you can just run this to first train the model and then check the output of the debug function. Using the is. Analyzing the inference of a of a model. sigmoid(logits) loss_temp=(torch. Hello. PyTorch Lightning complex-valued CNN training outputs NaN after 1 batch. ones(m1,m4,m5)) i get nan for x2 value while i don’t get nan for x1 value . . Angle() returns Nan value for some cases, but this issue is gone when the suggested solution is applied to the input of the Torch. Linear() output is NaN for large matrix multiplication? #27240. module: NaNs and Infs Problems related to NaN and Inf handling in floating point module: nn Related PyTorch Forums Torch. weight, nonlinearity=‘relu’) for initialization Although I set the torch. However, from what you are saying it does seem like the learning rate is responsible for this. Hello, I am training an object detection model that has two losses, one of them tends to infinity but after normalization with the below commands it was fixed : Function 'CudnnConvolutionBackward The GradScaler in amp uses an initially high scaling value, which could create invalid gradients in the first iteration(s), which is expected. classification loss in regression problem) self. Theretically, every element of a is a super small negative value, and nn. Then I checked the input and model parameters, they are seemed normal. nan. decoder and self. Bixqu April 30, 2017, 4:01pm 1. isnan()) there is no inf (torch. When I feed the point cloud data to GPU by calling . However, to fix the size of all I’m trying to implement a variant of capsule network where the matrix multiplication is replaced by element-wise multiplication with a vector. So I step by step to look what happen in the process, I check my data have nan or not, the data doesn’t have nan. 7. I have used efficientnetb3 model (pretrained) with minor transformations. any(numpy. In these cases the GradScaler will skip the parameter updates and reduce the loss scaling factor. encoder_1 = Hi @smandava98 , sorry I do not remember which pytorch. The only thing I change is the batch size. I have been trying to apply a new custom Loss function with an algorithm that works with BCEwithLogists. All of the examples dealt with MNIST but my model uses ImageNet images so it’s a big bigger than the examples. zeros_like(a). Code; Issues 5k+ Pull requests 1. The target has 6 outputs for each. import torch. filter = nn. 2). How does this fit into your previous findings, i. 07871631532907486 Min: -0. from_numpy(np. 1k; Actions; Projects 12; Wiki; Security; Nan output after masked TransforrmerDecoder #119371. For your convenience, here is the code: Hi Frank! I’m currently experiencing a problem similar to this one: i’m using a truncated svd decomposition on the input variable Z. Can you please point out to some loss functions/possible computations where torch. 11731042782619837 0. I’ve varied my learning rate As the title suggests, I created a tensor by a = torch. data. SmoothL1Loss() do not (as long as the number of terms in the series is less than 40) so it would be interesting to see if it had something to do with the loss functions In Pytorch, when values are divided by zero, replace the result value with 0, as it will output NaN. isnan(dataset)), it returned PyTorch Forums Weights and outputs become nan. Does anyone know why this is? I am training a face recognition model with DDP on 8 GPUs, but I am getting loss Nan occasionally. This happens randomly on different parts of my torchvision VGG_16bn backbone, but allways at the first half of layers. 033 Hi, by using torch. check_model(), and has the correct output using onnxruntime. 1 ROCM used to build PyTorch: N/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. mha are almost the same as the source code of those in Pytorch, where I think that the only reason for the presence of nan in attn_output_weights is that attn_mask is all -inf. Then I checked how the loss was calculated and saw that the reconstruction loss was the source of the nan problem. 5 LTS GCC version: (Ubuntu 5. exp(i)/np. zero_grad should come before loss. Here’s my code: My data loader: class data_gen(torch. 0 and that is what was causing the issue! Upgrading to 1. I checked the inputs to the find_phase method and they don’t contain NaN at all during the forward pass. I also checked the model while running just the second pipeline, and found that the problem persists only with second pipeline. AdamW, for optimizer nn. 980656 when this happened. Thanks a lot for the reply. PyTorch Forums Network dimension float16, the output is nan. Hi, by using torch. If I make the network dimension float16, the output is nan (if epoch > 0). Closed hojaeklee opened this issue Oct 2, 2019 · 4 comments Closed nn. the model by @spro is below. I am optimizing the Generator and Discriminator using net_G_A and net_D_A, and optimizing patchNCELoss using net_F_A. 130 OS: CentOS Linux release 7. Note that invalid gradients are expected at the beginning of the training in amp using float16 as well as sometimes during the training. 1. I also replace I have also printed the input and output of the log softmax layer. To answer as there could be some other cause. import numpy as np a = [1,3,5] for i in a: print np. Tabular Data (DAE + MLP model): nan values while training. Can someone please help to let know if there is a normalized Description I’m exporting a pre-trained PyTorch model using torch. data[i] tmp = Loss is 'nan' all the time when training the neural network in PyTorch. This was after I tried converting the tensors to float32. While the second one is good. in the first iteration you already have a loss of ~1e+10, which will create gradients with a large magnitude and then update the parameters with a learning rate of 0. I noticed that when I tried to train the model on my GPU I got a nan loss. Then I used pdb to see where this problem came from and saw that the loss was just nan. when I try to extract output from model manually like below, it works well I am a beginner about pytorch. angle can produce NaN gradients for inputs that are close to (0, 0). # pytorch imports import torch from torch import nn from torch. The point to note is while training the same model i don’t get nan on x and on x2. Collecting environment information PyTorch version: 2. 06503499299287796 According to softmax function, you need to iterate all elements in the array and compute the exponential for each individual element then divide it by the sum of the exponential of the all elements:. I have a quite simple neural network which takes a flattened 6x6 grid as input and should output the values of four actions to take on that grid, so a 1x4 tensor of values. How to access input/output activations of a layer given its parameters names? Custom losses tend to be way less stable But just check you are not passing negative values to a log, doing anything/0 these kind of things. example So if atan2 returns NaN in the backward pass it would propagate to the whole model. In this case disable torch. atan2 produces NaN gradient when the input is exactly (0, 0) So if you replace torch. cuda(), it becomes NaN very rarely. 04. functional. atan2 anywhere directly in my implementation. So I wonder what’s the problem and I found the input is havin However, when I debug my program, I found all the values of var1_embed and var2_embed are nan, which is quite weird. reinforcement-learning. kaiming_normal_(m. I carefully checked the parameters of the model and found that some of them were particularly strange, the values of the parameters were particularly I assigned different weight_decayfor the parameters, and the training loss and testing loss were all nan. autograd. 5 20150623 (Red Hat 4. onnx. init. Reload to refresh your session. I tried gradient clipping but VAE output NaN same as before. 0 Is debug build: No CUDA used to build PyTorch: 10. 0-6ubuntu1~16. parameters(), lr = learning_rate) nb_epochs = num_epochs train_hist = PyTorch Forums Function 'MseLossBackward' returned nan values in its 0th output. class Enco Hi all, I want to know what may be the reasons for getting nan after a convolution, if my inputs are all properly initialized (not for loss but for the input). If you add anomaly detection at the beginning of the training, this would raise this “false positive” issue, ducha-aiki changed the title Make torch. backward() " Not all Landmarks are everytime provided, so thats the reason I assign the loss a zero for I am training with half precision because the final hardware I will deploy my model works with half precision. If you put it after . I’m new in deep learning. cuda() with torch. 5-36) CMake version: version 2. acos() the first one is very unstable, i. pow. RuntimeError: Function 'PowBackward0' returned nan values in its An unrelated issue optimizer. fill_(-np. detect_anomaly. Module): def __init__(self, I use torch. angle with torch. bias Max: 0. more specifically when I use pow: _x = torch. utils. weight Max: 0. My lossfunction looks like in the following: " logits = model_ft(inputs) out=torch. masked_fill_(emask,-float('inf')) attn = F. This is the code I have: And/or decrease the learning rate. For training, my encoder takes in a random subset of input training pairs (total pairs = 40 for each function) and produces a corresponding feature representation (mean averaged over all chosen subset pairs). ypqeqre rlvn nssn lqlxl hcvwsa ofeba xshu vqllku dmuxk zzdfti