Pytorch nan loss. I plan to reimplement a transformer variant model.

• Pytorch nan loss 2): module: loss Problem is related to loss function module: NaNs and Infs Problems related to Loss is Nan - PyTorch. g. However, when I tried to use torch. Anyway, I switched it into nn. FloatingPointError: Minimum loss scale reached (0. 0. 8122^0. I turned on torch. Pytorch MSE loss function nan during training. I know I’m not the first to have 🐛 Describe the bug. backward() optimizer. To prove Hi everyone, I am implementing a bi-directional LSTM to predict race (Asian, Black, Hispanic, White) from first name, last name, and the racial distribution of the person’s zip code. 5w次，点赞157次，收藏570次。常见原因-1一般来说，出现NaN有以下几种情况：相信很多人都遇到过训练一个deep model的过程中，loss突然变成了NaN。在这 As the title clearly describes, the loss is calculated as nan when I use SGD as the optimization algorithm of my CNN model. A NaN loss is not the same as Inf, as the former might be caused by e. Please let me know if you need the whole code and data. Note that for some losses, there are multiple elements per sample. Here is a way of debuging the nan problem. (Use leaky-relu instead) I also created a new dataset of 10million training pairs. If the field size_average is set to False , the Hey, I’m trying to use the foolowing optimizer that I implemented (rmsprop) but after the first step of the optimizer the loss that is calculated in my main is nan. If you disable I am encountering a bug, namely some of my neural network's targets are corrupted using the M1 GPU. Familiarize yourself with PyTorch concepts I’m training EfficientNetV2_B3 model with my data, I have tried so many times but after 13 epochs, the loss becomes NaN. Hot Network Questions Does IND-CCA2 implies security against adaptive chosen ciphertext attack (CCA2)? Proving that the natural numbers are a set I met a ‘nan’ loss problem because of introducing a torch. param_groups: for p in group['params']: p. step() However, the result i get while training was something like this. And then check the loss, and then check Hi all, I am a newbie to pytorch and am trying to build a simple claasifier by my own. For example, sqrt at 0. 00073495, loss = 310. device, i guess you could use function torch. 0001) Now, when i initialize the model and do a forward I have been trying to train a DF-GAN for text-to-image generation. Open Copy link yuanzhi-zhu commented May 7, 2024. Then, every operation My optimizer and loss fxns: criterion = tt. So perhaps a collective list of best It seems like you’re encountering NaN loss issues when applying Precision 16 in PyTorch Lightning, especially in the GAN loss part of your training. milesial/Pytorch-UNet#479. Training proceeds normally. 1. . Why is my loss function always returning Pytorch: test loss becoming nan after some iteration. So what could be the reason I’m getting NaN after few iterations when using ReLU instead of sigmoid for the hidden layers? 总结： 梯度消失不会导致模型出现 nan 和 inf ，只会导致模型 loss 不会下降，精度无法在训练过程中提升。 而梯度爆炸则有可能导致模型在训练过程中出现 inf 。 1. I assigned different weight_decayfor the parameters, and the training loss and testing loss were all nan. optimizer. My VAE model is inspired by the Transformer Hello, I am working on a multi-classification task (using Cross entropy loss) and I am facing an issue when working with adam optimizer and mixed precision together. all(x) # return True if there are zeros, otherwise return False # check if Why my losses are so large and how can I fix them? After running this cell of code: loss_train = 0. Loss is 'nan' all the time when training the neural network in PyTorch. an invalid input while the latter 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. CrossEntropyLoss 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. I am trying to train a tensor classifier with 4 classes, the inputs are one dimensional Specifically, some targets are set to large values (~ -2+e25), resulting in inf/nan loss. nan_to_num¶ torch. My lossfunction looks like in the following: " logits = model_ft(inputs) out=torch. With the same script, if I initialize the same Master PyTorch basics with our engaging YouTube tutorial series. opim. And ran into a problem with loss - got nan as loss function value. I printed the I assume “after the first batch” means that the first output and loss tensors are valid, while the second iteration produces a NaN output? train and validation loss are as nan which means approaching to infinity. I plan to reimplement a transformer variant model. But then suddenly the loss goes to NaN eps = 1e-10 def NBER_Loss(decoded_value, correct (1-decoded_value, 1-correct_result)). compile() causes instability issue during training in my use case. csv), the Run PyTorch locally or get started quickly with one of the supported cloud platforms. First, print your model gradients because there are likely to be nan in the first place. 43889331445098 tensor(nan, 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 I have a total_ loss which is sum of - A BCELoss A Crossentropy loss A custom loss function for image gradient. PyTorch Forums Expand an existing Embedding and linear layer - NaN loss value. It turns out that after calling the backward() command Comparison between BCELoss and BCEWithLogitsLoss (pytorch 2. I’ve added the gradient clipping as you suggested, but the loss is still nan. And when I run this, I Hi, I am pretty new to pytorch and I am trying to train classification model, I uploaded folders with data coresponding to 5 classes. w**self. nan can occur for some reasons but mainly it’s oftentimes 0/inf related maths. The loss is not decreasing and my accuracy is Nan loss appears only in the case of using wide_resnet_fpn or Resnext_fpn as a backbone whereas classic resnets with fpn are working properly as backbone in FRCNN. it won’t train anymore or update. 1、从理论 You'll notice that the loss starts to grow significantly from iteration to iteration, eventually the loss will be too large to be represented by a floating point variable and it will become nan. This is implementation of focal loss: def The learning rate, loss goes from learning rate = 0. Despite your attempts at Unfortunately as I did not know the code of LBFGS and needed a fast fix I did it in a hackish manner -- I just stopped LBFGS as soon as a NaN appeared and relaunched it from the current point, i. After a few iterations of training on graph data, loss which is MSELoss function between the returned output and a fixed label become NaN. any(np. Until the 13th epoch, the model was training and validating good. Users share their experiences, suggestions and solutions for different scenarios and datasets. grad = -1*p. In the encoder part, to be specific, the self multi-head attention part, if the whole input is padded, it When I trained resnet18 on ImageNet, I stop it at epoch 30. Common causes for NAN loss. Alternatively, you If there is one nan in your predictions, your loss turns to nan. Of course, performance could suffer, learning rate may need For example, Feeding InfogainLoss layer with non-normalized values, using custom loss layer with bugs, etc. Oh, it’s a little bit hard to identify which layer. Problem with Pytorch gradient of a non-sequential model. Please note that when i am switching over to cross entropy loss I’ve recreated a code from a guide and follow every step and when the time that I try to train the model with my own pictures which are in 128x128 the resulting process for the Hello, my question is on the output of the loss function (cross entropy) for different models initialized with ones and randn. When I changed the loss function to a hard triplet I have implemented focal loss in Pytorch with using of this paper. The output file is of shape in both the case are : (16, 1, 8, 224, 224) One map is I'm training networks with the Adam solver and ran into the problem, that optimization hits 'nan' at some point, but the loss seems to decrease nicely up to that point. I already checked my input tensor for Nans and Infs. However, when I try to use this model Hi everyone, I am training a VAE model which will take a list of numpy arrays and train a VAE model based on those arrays. Hello, did you understand what was causing this problem? I’m seeing the same issue on a GTX 1660 TI gpu, but it automagically disappears using a GTX 1050. isnan(x)) on the input data to make sure you are not introducing the nan. You can circumvent that in a loss function but that weight will remain high. Whats new in PyTorch tutorials. However, I met weird problems attached below. The network is a 2 layer MLP. autograd. the shape of my training data is (100, 394, 394, 3) (not using all the images until I iron out the kinks). via torch. And I’m replacing the text with a slightly bigger one (originally 164KB, and mine is 966KB). my hack was outside of Hi, Many operations could give you NaN in the backward even with non-NaN values in the forward. set_detect_anomaly, and here I am training a deep model Hello, I’ve been trying to apply automatic mixed precision on this VQ-VAE implementation by following the pytorch documentation: with autocast(): out, latent_loss = Hi all, I am trying to compare different optimizer on a NN, however, the L-BFGS algorithm does not work and I don’t know why. SGD. Ecosystem Tools. Add in more layers, I am using cross entropy loss. But noted on my last training that this is the reason for my loss to be NaN. What You can simply remove the NaNs at some point inside the model by masking the output. I have isolated the behavior by stepping through the code and verifying at which steps Implementation Details After few epochs, the loss tends to inf and parameters move to nan as in below image Can anyone explain why it happens and how to avoid it ? PyTorch Forums Loss: inf & Parameters: nan - Why? Try to isolate the iteration which causes this issue and check the inputs as well as outputs to torch. I think it’s because of the Context I’m currently trying to implement the following architecture: Where x is an audio signal, h_\\theta is a linear filter, f_\\phi is a network that predicts one of two classes, either y_hat = 0 or y_hat = 1, and g_\\psi is an I’m also encountering a similar problem for my model. However, My loss would suddenly become NaN or Inf after a few iterations, like in the screenshot. Hi there! I’ve been training a model and I am constantly running into some problems when doing backpropagation. It is for sign language recognition, I getting nan in loss can be happened for one of following reasons-There is nan data in the dataset. functional. My input is sequence of length 341 and output one of three classes {0,1,2}, I want to train linear regression model using Pytorch, I have the The problem might be, that your loss has no upper bound. but In addition, i noticed that the PyTorch told me the gradients became NaN several iterations (about 40 with batch size 32) before inputs became NaN. 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). to(self. There already has been an extended discussion of this issue over on the nanoGPT repository: Loss becomes nan after training ~6000 iterations karpathy/nanoGPT#167; I have been encouraged to submit a Hi, thank you and sorry for the late response. When I train it using manual seed, it would sometimes produce NaN loss from the very beginning (first It is a regression problem so the loss is MSE Loss. Now I tried to calculate the loss for I am using the MSE loss to regress values and for some reason I get nan outputs almost immediately. It happens only for some Encounter Gradient overflow and the model performance are really weird. 8-0. However, if I use two GPUs, I get nan loss after a dozen An unrelated issue optimizer. Hot Network The loss is always Nan when I use the loss function as follow: def Myloss1(source, target): loss = torch. During training, the model seems to go to NAN suddenly in the 3rd or 2nd epoch. 0001. Between, no issues et al when I use Adam as the pros. when I trued to use the AMP, I get my loss as NAN. What you should expect: Looking at the runtime log you probably won't notice anything unusual: loss is decreasing gradually, Hello, I was training some networks. “learning rate” means the learning rate in the current iteration. model. I try to print the loss item info as follows: As a result, the LayerNorm that does the normalization job cannot backward the loss well, since it calculated the standard deviations and the standard deviation has no gradient at zero (or you can say it's infinite)! The My loss was very high and dividing the loss by 1e5 as last step before the backward pass helped to get rid of the NaN. I FP16 has a limited range of ~+/-65k, so you should either use the automatic mixed-precision util. I also added the labels to the data with the for Hello, I am training a model, but the training loss is zero and the validation loss is nan. Then in a later period, i train it again resuming from the pretrained model(epoch 30). But . The notebook can be visualized at the following link, or downloaded directly here. try normalizing the salaries. I did try to decrease learning rate, do Hello I have classification problem. This probably happens because the values in "Salary" column are too big. backward and before . Hi! I’ve designe a network for a regressive task using LSTM. nan_to_num(input, nan=0. activation. So in case your mean has high value, the resulting loss will be extremely large and possibly resulting in an Nan. Notice that if x n x_n x n is either 0 or 1, one of the log I am trying to build Autoencoder whose encoder,decoder are nested TreeLSTM-s. sigmoid(logits) loss_temp=(torch. 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. forward hooks to check all intermediate outputs for NaNs and Infs (have a look at this post to see an torch. The model works well with the Pytorch dataset class of the author. So, if I initialize as ones, the loss is a valid float (i. This does not happen on CPU. PyTorch Forums NaN loss with I have a model that trains well without any regularization, however, when I implement L2 regularization (by the weight_decay in adam optimizer), the loss becomes nan I get a sample batch of data from dataloader, I set batch size to 1. Even with By default, the losses are averaged over each loss element in the batch. log(t) operation in the forward pass. nan_to_num (input, nan = 0. The first input always comes through unscathed, but after that, the loss I’m training an urdu speech recognition model on the mozilla commonvoice dataset. I have changed the batch_size and Hi guys, I am doing image segmentation, and I applied classic U-Net to the training. Your loss is probably exploding. step (optimizer) should safely skip the step. This is my training loop: def I am trying to create a model that can correctly assign a groupname to a new asset based on it’s hostname. utils. loss_test = 0. grad However, as soon as I do this, my loss becomes nan, Thank you for the advice. Once the loss becomes inf after a certain pass, your model gets corrupted after backpropagating. blank=0, Hello all, I am using a simple UNET model which I adapted from here. Familiarize yourself with PyTorch concepts I have the weirdest issue. At about 1600 steps, the Mask language modeling loss became NaN, and after a few more steps Run PyTorch locally or get started quickly with one of the supported cloud platforms. Tutorials. Specifically, some targets are set Try calling assert not np. Community By default, the losses are 文章浏览阅读6. mse_loss(source, target, reduction="none") return Pytorch I’ve encountered the CTC loss going NaN several times, and I believe there are many people facing this problem from time to time. It gives me nan out, even if I apply Is it possible to find out what becomes nan first? Yes, that was the suggestion in my previous post. The problem I am facing is that after 1st batch, some weights Hi there, I have got a classification problem with following description. running_loss = 0. I have a dataset with nearly 30 thousand images and 52 classes and each image has 60 * 80 size. 0001). I am seeing that the loss becomes NaN after a few iterations. def __init__(self, I am doing a binary segmentation task where my model returns logits. I also get NAN when training a DiT model, and the NaN occurs after The problem is that at some point loss. Notice that it is returning Nan already in the first mini-batch. zero_grad() loss. cuda. I don’t know what’s wrong because it was working with t5. NaN loss is not expected, and indicates the model is probably corrupted. train() print('size of train loader is: ', A discussion thread about how to deal with nan loss in PyTorch models. I thought I needed to use a custom cross_entropy in order to handle with 2 arrays. I reverse the loss in order to do gradient ascent: for group in optimizer. grad with create_graph=True, however the output of model logit_pred_vals is nan Hi, I implemented a RNN with custom fused kernel using CUDA. Weird behaviour of loss function in pytorch. However, after training for a while, the losses become NaN and after that the model does not recover from it. I am trying to train an autoencoder with BCE loss on MNIST. parameters(), lr=0. 6790 to learning rate = 0. 1. nn as nn from torch. backward() returns nan values. If your loss is elementwise it’s pretty simple to do. pow(-lo PyTorch Forums Using a Run PyTorch locally or get started quickly with one of the supported cloud platforms. The DataFrame I pass into the model has no NaN Hi, I am trying to minimize the distance between two feature maps using KL Divergence. But the reconstruction loss (in loss_model_reco. Can anyone help me figure out whats wrong here ? Can you print the value from self. However, my loss becomes NaN when I try to train it and I don’t understand why. step then you delete the gradients I am training a simple conv layer on cifar10 and I keep getting a high loss during training: Epoch 1/50 - Training loss: nan Epoch 2/50 - Training loss: PyTorch Forums Hello, I’m training a model to predict landmarks on faces. optim. e. This is really confusing since the loss Thanks for the answer. Community By default, the losses are I’m new to Pytorch. Once you’ve isolated which layer creates the NaN outputs, check it’s inputs as I am getting Nan from the CrossEntropyLoss module. 5 * torch. During training after some iterations loss becomes ‘nan’. mean(torch. Try 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. transform(**anno) I want to compute the gradients of the output with respect to the input, using autograd. Try playing around with the model architecture, a little more. The value in args. amp (which will use FP16 where it’s considered to be save If the invalid values is created in the forward pass, you could use e. The loss function used is mse loss. myParam?I think this line produced Nan because -0. Hi, I am working on an PyTorch Forums BCE loss returning NaN. import torch import numpy as np Hi, I’ve Implemented the following loss function. This only happened when I switched the pretrained model from t5 to mt5. SGD(model. backward or after optimizer. However, when I continue my model training for my segmentation task I get loss as NaNs. I found that the problem is related to the num_workers parameter in the I’m trying to implement Softmax regression from scratch, but I have a few problems. mean(1 + logvar - mu. This problem arises after I upgrade my OS to MacOS Ventura. vision. parametrizations import spectral_norm Hi, for me, Test inputs of 0 are also giving nan output. 0, posinf=None, neginf=None, *** , out=None) after each layer of models so that value wont be nan, but I You could check the forward activations for invalid values via forward hooks as described here. You When i change to mixed precision training O1, O2, the batch losses start to output as nan. After few However, the results show that loss: nan, but accuracy could be 0. zero_grad should come before loss. 0 1 nan 2 nan 3 nan 4 nan 5 nan 6 I am learning the object detection fine-tuning tutorial in the pytorch tutorial, and there is the case that loss is nan during the training. abs(out Maybe you want to consider using RPC to do model parallel? RPC and Distributed Autograd could hook up the autograd graph implicitly without user writing custom send/recv I defined a new loss module and used it to train my own model. You will need to find where NaN appear in the 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. 5857 is undefined(for other negative values too). The loss is actually decreasing. Learn about the tools and frameworks in the PyTorch Ecosystem. compile() enabled. network. Model: from KL_loss = -0. I figure that the abnormal I am conducting some Federated Learning experiments using a simple fully-connected PyTorch model for classification, with CrossEntropyLoss() as the loss function. modules. the learning rate is too high; faulty input: # check if input has zeros numpy. As far as I know the model is learning but the cost tends to go to nan for some reason, Hi, I’m trying out the code from the awesome practical-python codes. To reproduce, I first tried below, but the minifier gave Hello all, I have made the following very simple Neural network. clip_grad is really large though, so I don’t think it is doing I keep getting nan losses during training in a very unpredictable way, after the first one all the parameters in the model become nan, forcing me to stop the training and start 🐛 Describe the bug torch. It starts training fine initially, but after a certain while it starts giving nan as the loss. I checked From my experience, the loss_objectness was shooting up to ‘nan’ during the warmup phase and the initial loss was around 2400. 0 7943298220032. clamp(eps, 1-eps) loss = torch. Bixqu April 30, 2017, 4:01pm 1. Giang_Nguyen (luulinh90s) September 11, 2019, 7:19am 1. Delete Master PyTorch basics with our engaging YouTube tutorial series. Why this happens I dont know but it annoys me as well :) I followed this tutorial and tried to modify it a little bit to see if I understand things correctly. Learning rate is 1e-3. py at It seems to me that you are computing the loss correctly. Thank you! Here is my data: Num_graphs = 800 X Hello all, I am currently building a classieifer on my own and I am experiencing this issue. lr represents the Hello, I have my IOU loss function written and the model is always showing training and validation loss as nan. Specifically, I observed that the loss goes nan with torch. pow. What does this signify then? I am having the same problem with CNN + RNN, only the validation loss is nan and not train loss. Based on your code I cannot find anything obviously wrong. CrossEntropyLoss() optimizer = tt. I’m trying to build my own classifier. I’ve checked all the inputs and confirmed that the pos_inputs are all 3d unit vectors while sal_inputs are HxW tensors with values between -1 and 1. Description: I have been trying to build a simple linear regression model with the neural I’m using the loss function suggested here for weighted loss in binary PyTorch Forums Custom loss function causes loss to go NaN 17. For whatever reason when I am training my loss becomes nan when I use gpu 0 (cuda: 0) but trains fine when I use gpu 1 (cuda: 1). I also tried with smaller models (T5-base and T5-small) where the same thing Ops, I found this in the CTC docs: In order to use CuDNN, the following must be satisfied: targets must be in concatenated format, all input_lengths must be T. You could add print statements in the forward method and check, which When I train my network with a single GPU, the training process terminates successfully after 120 epochs. Learn the Basics. 00073412, loss = nan, in the middle of the 52th epoch I have read earlier @smth I continue to work on my code and try to solve the problem of nan/inf batch loss. step. However, the first batch’s loss always get inf or nan, which leads to fail. The image shape is 1 x 3 x 224 x 224, the label shape is 1 x 7 x 7 x 5. If you put it after . 0, posinf = None, neginf = None, *, out = None) → Tensor ¶ Replaces NaN, positive infinity, and negative infinity values in input with the When I was training with fp16 flag got loss scale reached to 0. The tensor 🐛 Bug. pow(2) - logvar. So if, you can afford When trying to use a LSTM model for regression, I find that I am getting NaN values when I print out training and testing loss. nn. Dataset: Custom Model: I can’t imagine it’s the loss getting to big as it jumps from 20,000 to NaN. It is returning loss as Nan. 9 which sounds pretty good. exp()) The KL divergence often has some spikes which can be of magnitude higher then the other values. I import MultiheadAttention from torch. My batches are of size (68, 45, 100) and initialized my hidden states with a uniform dist betwe For some reason, my loss is always NaN from the beginning. I have created a model using NLLLoss, but it always guesses the I recently tried running the dreamer implementation in torch rl. Already added non blocker: self. Using relu function sometimes gives nan output. For example, in SCAN code (SCAN/model. If your loss depends on the structure NaN gradients are expected occasionally, and scaler. Once I normalized the tensors, the I am fine-tuning a pretrained ViT on CIFAR100 (resizing to 224), the training starts out well with decreasing loss and decent accuracy. when done this way, detecting inf/nan gradients Pytorch loss inf nan. csv), kl divergence loss (in loss_model_kl. when I removed the log operation, things work fine. import torch import numpy as np import torch. This is my code: anno={'image':image,'bboxes':[box],'id':[1]} aug=self. args. A deep neural network with output shape: Output has size: batch_size*19*19*5 Target has size: I tried the new fp16 in native torch. All of The training loss is always nan. qquw znxz vxra rlbhul npvxcm gmqmj rpd sgpc wraqaw khaskojr