Softmax gradient nan In the problem I’m trying to solve, it is possible to have 0 probabilities. normalize(out, dim=-1)) is somehow playing up with the gradients. dc_next = d_next # Softmax loss gradient dy = prob. softmax (x, axis = None) [source] # Compute the softmax function. One example of a function that must be stabilized to avoid underflow and overflow is the softmax function. I'm encountering NaNs in subtraction inside _softmax_deprecated: unnormalized = jnp. ones([4], requires_grad=False, dtype=torch. Below, by way of example, we show several different issues where torch. Distinguishing between 0 and NaN gradient¶ One issue that torch. sqrt method would create an Inf gradient for a zero input and a NaN output and gradient for a negative input, so you could add an eps value there as well or make sure the input is a positive number: x = torch. Please note that this library is currently classified as a prototype – that is, this library is at an early stage for feedback and testing, and we encourage users to submit any issues they may encounter, feature requests, etc. Simply exchanging the nn. Instead of selecting one maximum value, it breaks the whole (1) with maximal element getting the largest portion of the distribution, but other smaller elements getting some of it as well. EarlyStopping(monitor Nov 29, 2016 · In this blog post, you will learn how to implement gradient descent on a linear classifier with a Softmax cross-entropy loss function. # array([0. 0 min Jan 27, 2020 · 5-11. Aug 30, 2021 · The first place to look was the values themselves, which you already did. Mar 6, 2020 · 🐛 Bug To Reproduce Steps to reproduce the behavior: simply invoke the gumbel_softmax function many times in fp16 mode. MultiheadAttention layer where the forward Mar 4, 2021 · In forward propagation function my softmax function is returning nan values, I tried solving it bu subtracting maximum value as below. copy Jan 24, 2024 · The gradient will still contain NaN value because the output of logsoftmax contains infinities where mask is False. The softmax function transforms each element of a collection by computing the exponential of each element divided by the sum of the exponentials of all the elements. scaler_unscale_grads() only check the scaled gradient is NaN or not, but in the above case, the problem lies in the unscaled gradient! Jul 23, 2020 · Training: def train_model(model, epochs, batch_size, X_train, Y_train, X_validation, Y_validation): earlystop_callback = tf. ], requires_grad=True) y = torch. com Radu Soricut Google Inc. sqrt(x) y. Is there a better way to implement this, or is there an existing SoftMax implementation in PyTorch that can Feb 16, 2021 · We recommend to use the native mixed-precision training utility via torch. function. 自作softmax関数を使った例. exp, keeping everything else like it was, causes not only the gradients to contain NaN but also the intermediate variable s. exp(a - np. Tensor runs into is the inability to distinguish between gradients that are undefined (NaN) vs. Dec 16, 2022 · As for why we use stop_gradient here, we can show analytically that the max(x) term cancels-out in the gradient computation, and so we know a priori that its gradient cannot affect the gradient of the overall function. stop_gradient(x_max)) I'll attach the code for your reference if needed: You didn't include enough code to reproduce your issue (next time, try to add a minimal reproducible example to allow others to answer your question without guesswork). 公式に書かれている利用方法は下．でもモデルが数値的に不安定な計算（Softmax, division by epsilon）を含んでるといつかnanが出る． ちなみにTransformer, AttentionはSoftmaxを含んでいるので不安定． Oct 9, 2019 · This problem is called exploding gradients, resulting in an unstable network that at best cannot learn from the training data and at worst results in NaN weight values that can no longer be updated. We can conclude that the model might be well defined. def softmax(a): B = np. Relu) the softmax function can saturate. I’ve almost done, but I’ve a problem with the last layer of the model, the F. it is unfortunate because having a 0/1 mask vector can be handy. Negative log-likelihood cost function invlolves natural log computation which does not expect zero. detect_anomaly (), and then the first prompt that nan is in softmax. Even if the value appears reasonable, if the gradient is very steep, backprop will eventually explode the gradient and value. , i. exp. I was initially very confused why my model suddenly diverges when none of the losses "exploded Mar 21, 2019 · Gradient blow up; Your input contains nan (or unexpected values) Loss function not implemented properly; Numerical instability in the Deep learning framework; You can check whether it always becomes nan when fed with a particular input or is it completely random. I have no idea why this is. First, since the NAN loss didn't appear at the very beginning. 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 Before applying exponential max 0. unspecified/invalid, it is forced to rely on NaN or 0 (depending on the use Sep 27, 2017 · Policy-gradient approaches to reinforcement learning have two common and undesirable overhead procedures, namely warm-start training and sample variance reduction. There are few a instances like “Attention”. g. This would cause exp to return NaN. That is, if x is a one-dimensional numpy array: Mar 26, 2022 · Further arithmetic operations might change these infinite values into NaN. The statistical assumption that the input is normal distributed supports the gradient stability of Softmax. As I understood, the softmax function should convert any numbers to the range of [0,1]. This was producing inf values and eventually everything becoming nan was the problem in my code. One way to assure it is exploding gradients, is if loss is unstable and not improving, or if loss shows NaN value during training. keras. the entries are something like 1e-10 or lower. As you can see, this does not affect the result of softmax. Values beyond the predefined threshold are scaled down to be within the predefined range. Sep 21, 2018 · Hi, I observed that using multiplication to create softmax masking does not work because 0 times minus infinity results in nan. Hopefully, you got a good idea of softmax’s gradient and its implementation. Does anyone know 1. The only case that this can happen is if the input itself had a NaN or an Inf For example: ```Python a = torch. special. From this stackexchange answer, softmax gradient is calculated as: Python implementation for above is: Softmax Policy Gradient Nan Ding Google Inc. Our method combines the advantages of policy-gradient methods with the efficiency and simplicity I think the problem for me is the softmax: # suppose I have a layer x with shape [-1, -1, 16] # Normal x = tf. However, when used in attention mechanisms such as transformers, since the correlation scores between embeddings are often not normally distributed, the gradient vanishing MaskedTensor . Aug 16, 2021 · Softmax is widely used in neural networks for multiclass classification, gate structure and attention mechanisms. Jul 19, 2017 · I'm trying to modify the CIFAR-10 tutorial in TensorFlow models repository to feed custom data and customize training parameters and layers, I have 3 convolution layers and 2 fully connected layer. ]) Apr 2, 2020 · I use torch. Safe Softmax . To Reproduce Steps to reproduce the behavior: Backwards pass through nn. It has only positive terms, so we needn't worry about loss of significance, and the denominator is at least as large as the numerator, so the result is guaranteed to fall between 0 and 1. MultiheadAttention causes gradients to become NaN under some use cases. log_softmax) (see DRBM paper, p(y|x), at page 2). softmax(x) # NaN loss on v100 GPU, normal on CPU x = tf. Modified Softmax Function. Apr 5, 2017 · I need to compute softmax for a two dimensional matrix w, batch * seq_length. 🐛 Bug Using key_padding_mask and attn_mask with nn. Policy-gradient approaches to reinforcement learning have two common and undesirable overhead procedures, namely warm-start training and sample variance reduction. callbacks. For example, it is a common approach in gradient clipping to set a threshold or range for computed gradients during training. , LogSoftMax instead of SoftMax. Try normalizing your data, or inspect your normalization process for any bad values introduced. com Abstract Policy-gradient approaches to reinforcement learning have two common and un-desirable overhead procedures, namely warm-start training and sample variance reduction. autograd. float16)` Where ```Python a = torch. This is the expected output, because softmax(x) = 0, so logsoftmax(x) = -inf, and gradients of infinity are NaN because no other value makes sense. May 1, 2019 · As you can see the softmax gradient producers an nxn matrix for input size of n. Mar 5, 2021 · Hi there, I’m trying to implement a NN for the complete MNIST set as suggested at the end for chapter 4. float16, device='cuda:0'). I debug into the gumbel_softmax f Dec 18, 2023 · I'm encountering NaNs in subtraction inside _softmax_deprecated: unnormalized = jnp. Applying this on your softmax: B = np. . They are all similar, just the syntax that differs. 自作softmax関数を定義の通りに実装すると予期しない値を得る可能性がある. sum(np. softmax(-1)) ``` Will return `tensor([0. Let Oct 3, 2021 · Note that softmax calculates an exponential (e xi /Σe xj). Venice, CA 90291 dingnan@google. Default parameters are used (tau=1, hard=False). Feb 26, 2019 · In case of applying softmax on a large numbers, you can try using max normalization: B=np. Usual practice is to reduce the learning rate in step manner after every few The softmax exp(x)/sum(exp(x)) is actually numerically well-behaved. To find the cause of the NaN value, you can add print statements to check for NaN values at various points in the function. normal_(), dim=-1) g. cuda. mean Strictly speaking, gradients are only defined for scalar functions (such as loss functions in ML); for vector functions like softmax it's imprecise to talk about a "gradient"; the Jacobian is the fully general derivate of a vector function, but in most places I'll just be saying "derivative". exp(x - max(x)) C = np. , 0. Softmax is usually used along with cross_entropy_loss, but not always. Mar 14, 2022 · All of the gradients without exception becomes Nan in only 1 step and I don't understand how it is possible since I'm clipping it. PyTorch Issue 10729 - torch. Oct 10, 2021 · softmax is a mathematical function which takes a vector of K real numbers as input and converts it into a probability distribution (generalized form of logistic function, refer figure 1) of K May 22, 2021 · The torch. amp, which will use flaot32 for the softmax operation as given here. grad) > tensor([inf]) Apr 8, 2024 · Gradient clipping is a technique to limit the value of the gradients within a predefined range. 🐛 Bug To Reproduce Steps to reproduce the behavior: g = F. gradients that are actually 0. Dec 18, 2023 · To use the newer algorithm, you can set the jax_softmax_custom_jvp=True configuration. It seems that the gradient explosion only existed in tiny models. using a softmax instead of sigmoid for multiple class classification). The mathematical form of Softmax is: Thank you for your reply. Solution. stop_gradient(x_max)) I'll attach the code for your reference if needed: What was the first non-nan tensor (z_t) for which applying softmax results in nan? – jodag. softmax method. Hopefully, you got a good idea of softmax and its implementation. 001 will generate -6. Our method combines the advantages of policy if NaNs appear suddenly: saturating units yielding non-differentiable gradient; NaN computation due to log(0) NaN due to floating point issues (to high weights) or activations on the output; 0/0, inf/inf, inf*weight solutions: reduce learning rate ; Change the Weight initialization; Use L2 norm; Safe softmax (small value add to log(x I think the NaN problem that you mention in a comment is due to your Softmax function. I have written the following code, however, it runs into all nan after a couple of iterations. I have doubts about checking the nan values. Jul 16, 2015 · When using unbounded activation functions (e. Jun 2, 2017 · As the name suggests, softmax function is a "soft" version of max function. Aug 12, 2016 · I am doing a LSTM implementation with numpy and I've run into nan values after a few training iterations. This can lead to nan gradients when paired with categorical crossentropy cost. Sometimes the output tensor from softmax contains NaN (not a number), while debugging I’ve seen that the input tensor for the softmax contains very large values, and the exponential inside As the results, the optimizer update the NaN unscaled gradient to the network and finally cause the loss become NaN in the next iteration. finfo(torch. Softmax Policy Gradient Nan Ding Google Inc. Tensor falls short and MaskedTensor can resolve and/or work around the NaN gradient problem. i only just realized that this is problematic because the gradient of sqrt(x) is 1/(2*sqrt(x)), so if x is close to 0, the gradient will be NaN. ここでsoftmax関数は以下の式で定義される. Occainonly, it may encounter an nan problem. sum(B) return B/C after trying this, my softmax returns all zeros. The nan arises in the process of calculating the gradient. If the softmax function is replaced with a numerically stable version Oct 11, 2017 · I also tried the logSoftmax+crossEntropy which is much more stable than all the combinations above, but, still leads to gradients = nan, at the very end. May 3, 2020 · You can find a CUDA implementation here, which then calls softmax_warp_forward. , 1. softmax function for a combination which uses tf. When I do backward() softmax# scipy. float16). exp(x)) return B/C. Sequences have different length, and they are denoted by a mask matrix mask_d, also of size batch * seq_length. gumbel_softmax(torch. Is there any suggestion for dealing with this issue? Apr 7, 2023 · In this case, the issue is likely caused by a NaN value being generated somewhere in your get_actor_loss() function. softmax(x, axis=1) NAN normally caused by numerical overflow, means either you have 0 gradience or zero divisions, try use batch normalization on all layers that you need to Apr 4, 2022 · It seems the softmax function returned is NaN, it is not in the range of [0,1]. max print(a. Venice, CA 90291 rsoricut@google. max(a)) C = np. One issue that vanilla tensors run into is the inability to distinguish between gradients that are not defined (nan) vs. Abstract. There are no nan's there, it is a normal number. forward propagation function looks like the following: Apr 23, 2018 · Improve gradient stability of logsumexp, softmax, log_softmax, logsigmoid at -inf (replace nan by zero) #31829 Closed Sign up for free to join this conversation on GitHub . Paper. Jun 1, 2020 · These values don’t seem to be quite large, I am attaching the logs of max/min values of input and output to torch. Anything above e 709 results in a numerical overflow in Python. Mar 31, 2020 · Hi, I am trying to train an existing neural network from a published paper, using custom dataset. Also, as described, I would then check the output, loss, gradients, and parameters. Verify that you are using the right activation function (e. sum(B) return B/C. If my hypothesis is true and more importantly 2. gradients, an. Jul 2, 2019 · 🐛 Bug 'torch. Solutions: I searched the Pytorch forum and Stackoverflow and found out the accurate reason for this NAN instance. I recently had to implement this from scratch, during the CS231 course offered by Stanford on visual recognition. e. This isn’t true. First note that applying softmax() to, say, a one-dimensional tensor returns a Jul 16, 2021 · When inspecting the first gradient descent step, it has nans, but only for the query model and not the doc model. Softmax computes the exponential function, exp(x) which can easily exceed the range of single or double precision floats for moderate values of x. backward() print(x. In this paper, we describe a reinforcement learning method based on a softmax value function that requires neither of these procedures. The second place to look would be the gradients. Because PyTorch does not have a way of marking a value as specified/valid vs. This repository contains an implementation of the reinforcement learning method described in the paper "Cold-Start Reinforcement Learning with Softmax Policy Gradient" by Nan Ding and Radu Soricut from Google Inc. Shouldn't tensorflow transform the nan gradients into a clipped vector ? Here is the input data when the nan gradients appear : Nov 14, 2024 · I find that the gradient of the softmax input data obtained by using the softmax output data to differentiate is always 0. One of the issues that commonly comes up is the necessity for a safe softmax – that is, if there is an entire batch that is “masked out” or consists entirely of padding (which in the softmax case translates to being set to -inf, then this will result in NaNs, which can lead to training divergence. This library is a part of the PyTorch project. I am watching some videos for Stanford CS231: Convolutional Neural Networks for Visual Recognition but do not quite understand how to calculate analytical gradient for softmax loss function using numpy. float16) a[1] = torch. for some models, the diff tensor is very small - almost 0. exp(x - lax. As you can see, there is usually a flag that defines whether or not softmax will be computed using the log. ], dtype=torch. This could exclude the input, but I would nevertheless check if, as your preprocessing might create invalid values. exp(x) C=np. ones([4], requires_grad=False Oct 2, 2020 · Hello everyone! I am trying to train a RBM in a discriminative way. To Reproduce The following code generate random logits on CPU and on GPU and print a message if NaNs a May 15, 2016 · Use RMSProp with heavy regularization to prevent gradient explosion. nn. Commented Jun 8, 2020 at 13:36. where Nan Ding, Radu Soricut. The normalization I need to perform in order to get the probabilities, however, does not involve a softmax (hence, I cannot use F. Mar 9, 2021 · The NAN values disappeared. However, why trainng this I am getting NAN as my predictions even before completeing the first batch of training (batch size = 32). randn(128,128,30152, dtype=torch. For example, if the logits are generated by something like log(x), an x of 0. gumbel_softmax' yields NaNs on CUDA device (but not on CPU). My hypothesis is that normalizing the return values for doc and query models (return F. The thing is that the loss itself counts correctly. So, this is technically not a gradient exploding problem which is why it couldn't be solved with gradient clipping. Motivation . 9. tensor([0. Although softmax function is widely used in deep learning literature, it might result in underflow and overflow. Nov 3, 2016 · I guess it may be "dead relu" problem which leads to a mathematical mistake. The forward of the net compute the log-conditional probabilities. qczwef vvi ftxep ybfohb gjz tsi crlldl dyo oniy msok