In torch, printing a tensor will print the whole tensor, even if that one is huge. This is sometimes annoying, especially when using a notebook, as it crashes it. Numpy has a very nice way to display big tensors by truncating them. Would it be possible so have something similar for pytorch?
Yeah, we haven't implemented it yet, but that's definitely on our roadmap
Implemented in .
I should say that I have some artefacts that appear with while printing a tensor. Here is an example output: gives Curiously, if I simply do It works fine and print nicely, with the truncation for large tensors. I'm using Python 2.7.6 (probably the one that came with my system and not from anaconda). Maybe it's just a bad setup in my machine?
hmmm, it works fine on python 2.7.12. wonder what changed between .6 and .12
ok i got a repro. I opened my ipython and did this:
my example was flawed, sorry. Using works for me, but when only entering it prints the s, as in your example.
Is it possible to tell the truncation point?
yes, use the function


Right now it makes everything .
To be clear, for a tensor, the problem is only when all of N, H, and W are 1. (So BatchNorm2d on batch size of 1 is OK as long as you don't have a 1x1 image). What's the desired behavior? The only reasonable behavior I can think of is: Raise an exception when the dimensions of which you are normalizing are one or Output zero (+ optional affine transform) I'm not sure the outputing zero is a good idea. I can't think of a case where that's what you want.


Hi, I have installed two versions of CUDA. But I want to use one of it, can I specify it during setup process?
Try running
I have tried it, () still returns False. Thx for your reply!
Do you have the cuda driver installed?
Of course I do. It raise assertion error which shows "The NVIDIA driver on your system is too old(found version 8000)".
that's the answer to your problem. upgrade your driver.
Thx!


I think I encountered a memory leak situation potentially due to . I create a to reproduce my observations. Basically, for three different (meaningless) models, once the function is used, the memory usage will keep increasing. To run the code, where the flag controls whether is used. Please run it long enough. I'm on Ubuntu 14.04 with python3.6. I tried two versions of pytorch, 0.1.122 and 0.1.12+. Side Note: Even without using , the memory usauge can also increase for a while, but it will finally stablize at a level. What's more, when (i.e. ) is used, it can take very long before the memory usage stablizes. I'm not sure whether this is a problem or not.
I think I'm sure there is a memory leak due to , and I found a weird way to do a temporary fix under pytorch version "0.1.122". The simplest example is as follows: Basically, if we replace with , and pass in an unpacked list of numbers, (i.e., instead of ), the memory leak is gone. Using will also have the leak issue. Unfortunately, I don't know why this is working. Finally, since there are so many in pytorch code, to do a quick hacky fix, one can change the function in the file as follows:
After some more research, I think we have a much bigger problem here. My current conjecture is that: whenever we use ( object) as an argument to a function, there will be a memory leak. An example is the function in , where of the code is which uses . Again, a minimal example for reproduction is as follows: As in the case, once I change the code to , memory leak is gone. So, in short, the problem is on the usage of .
The same issue. +1
Thanks for tracking this down and for the repro script!
Thanks for the quick fix! BTW, will the fix be included in the release version (0.1.12_?)?
it'll be released in
I see. Thanks again.


Torchvision is considered a basic requirement of the tutorials. Perhaps it makes sense to include it in the docker build.
It's easy to install it by command ;)
It's easy to install torch itself using a similar command? :). I was thinking a docker image should have torchvision included.
Runtime docker file already has torchvision.


Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting I can work on this if this can be to pytorch. Please let me know. Thanks!
This should be fairly straightforward to add in user code using . Eventually it could be to core, but its utility should probably be validated in external repos first.


If a norm is zero, its gradient returns nan: Obviously just happening because the gradient divides by the norm, but the (sub)gradient here should probably be zero, or at least not nan, since that will propagate to make all updates nan. Probably low priority, as it's not going to be an issue in 99% of cases, but we're doing a few things with (exact) line searches where this caused a nan to appear, breaking everything downstream.
I'm encountering exactly the same issue! Spent hours on debugging, just to find PyTorch has a bug on such basic thing.
+1 just found this bug too
+1 for this bug. Temporarily changing my code to something like the following for the sake of debugging. x = Variable((1), requires_grad=True) y = x + 1e-16 y.norm().backward() print x.grad
The thing is that in the 2 norm, there is a square root, which has a gradient of at 0. The gradient gives you because you then multiply 0 and an infinity during the backward pass.
For a scalar, norm 2 is basically abs. But x.abs().backward() gives you 0 gradient. In this sense, it's not coherent.
I found this error, too
Alban fixed this behavior in
Hi, the norm function can give use the 0 gradients now. However, the following code still has the nan gradient problem
Ho The square root has no gradient at 0. This is expected behavior.
Hi, but the sub-gradient of the square root should be zero? Also, y = ( x * x ) should equal to x.norm(), why they have different gradient ( 0 and nan )?
I think was right. The left-side derivative of sqrt(x) at x=0 is undefined, so it doesn't even have a subgradient at x=0.
square root has no subgradient at 0. You could define a gradient by continuity but then it would be ... Given that pytorch is using autograd, and (equivalent to your ) are completely different: The first one is a single function that is convex and defined on R, it has a subgradient of 0 at 0. The second one is composed of two function, the first function is the square function which is differentiable and outputs values in . The second function is the square root that is not convex and even though it is defined on , it is only differentiable on and it's gradient in 0 in undefined. Given that, even though and will return the same value, their gradients may differ at points where it is not differentiable, this is because automatic differentiation looks at each step of the computation one by one and even though in some cases a subgradient exist (because we look at multiple operations as a single function), it is not always the case and the gradient remains undefined.
I think math is math. Any root's gradient at zero is either inf or undefined. This issue shall be handled by the users' themselves by adding a small value(as did), but an error(warning) message may be helpful since it is pretty hard to debug. Say: Infinite/Undefined gradient is detected at XfunctionX at line Y. Exit.
Agree, norm is not differentiable at 0 , the bandaid that Alban put there in is wrong (even in the limit sense the gradient at 0 should be 1 not 0), but it should not have been there at all. Norm is norm, if someone want to add epsilons to their norms (like batchrnorm, e.g.) they are welcome to do so in the user code. What would numpy do?
I've also run into a number of problems related to the change introduced in . Is there a reason the subgradient is set to 0, rather than the 1? (The limit as norm-0?) As a minimal example: Produces
Well any value between [-1, 1] is a valid subgradient for the 2-norm. More genereally, any vector in the 1 ball for the dual norm is a valid subgradient. This means that 0 is always going to be a subgradient, while 1 will not be for all p. Anyway, the theory says that any of them could be taken and subgradient descent will work. I'm sure that depending on the application, one will be better than the other. For example, the relu function will also give a 0 subgradient at 0, you could have given 1. The main point here was to remove nans that make your network give nan for everything which is not convenient.


Intermittent test failures occurring on AdaptiveMaxPool3d. and I have seen this in the past where Max pooling has exactly the same values in the same window, then it's differently resolved on CPU/GPU. For example [0 2.5 2.5 3] can give a max index of or on CUDA depending on the runtime. So generating input values that are within epsilon of each other is important (especially at half precision). fix that one.
Will do. Thanks for identifying the issue.


When using classic SGD optimizer with momentum with sparse embeddings the memory keeps garbage collecting / allocating leading to slow down and out of memory error eventually. ! ! The issue dissapears when momentum is not used ! or when embeddings are not sparse ! I'm using the last pytorch version on conda:
I tried out your script with momentum 0.1 on master, it takes roughly 10800mb gpu memory max. This is caused by using sparse buffer. I'm sending out a PR for this.


Looking at the values returned from tn.multinomial(1) and counting them, it seems like it returns the values of the multinomial distribution taken from t, i.e. that it has ignored the signs of the values. However, it seem like always returns a tensor filled with zeros.
looking into this


Hi There, I'm trying to install Pytorch as a module on my university's computing cluster. The node uses CentOS (6.4) x8664. I install running these commands: module load gcclib/5.2.0 cmake/3.8.2 module load gcc/5.2.0 module load anaconda3/4.0.0 export CMAKEPREFIXPATH="(which conda))/../" export NOCUDA=1 git clone --recursive cd pytorch/ python install --prefix=${HOME} Things look fine as it installs but then I run into the following errors, ending in the process terminating with error relating to GCC? I've tried with version GCC 6.2.0 and the same result occurs. Not sure what to even try to fix this! Thanks for any help you can provide!
Same here, with gcc 5.4.0
I don't think building from source works very well with gcc 5.4. Could you install gcc 4.9 and try compiling?
I get this error instead when using gcc 4.9.0, doesn't make it very far at all.
Hi I think this is broken after / due to
I have the same problem ( etc.) with gcc 4.8.5 on Linux with the current head ( ) What works for me is to add at the beginning of the following four files: (see also -- this can be turned into a pull request very easily) This fix is the same as but for different files.
This worked for me Thank you.
fixed in latest master, thanks to


Hi, Looking at the formulas of LSTM in , in the third equation (gt), the suffix of the second W should be changed from W{hc} to W{hg}. The correct formula: gt=tanh(W{ig}xt+b{ig}+W{hg}h{(t−1)}+b_{hg}) Cheers, Navid


The three-clause BSD license in file LICENSE says in clause 1 "Redistributions of source code must retain the above copyright notice..." However, there is no longer a copyright notice in the file; it appears to have been removed in commit (see ).
cc


PyTorch version: 0.4.0 Is debug build: No CUDA used to build PyTorch: 9.0.176 OS: Ubuntu 16.04.3 LTS GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.5) 5.4.0 CMake version: version 3.9.4 Python version: 3.6 Is CUDA available: Yes CUDA runtime version: 9.0.176 GPU models and configuration: GPU 0: TITAN X (Pascal) GPU 1: TITAN X (Pascal) Nvidia driver version: 384.98 cuDNN version: Probably one of the following: /usr/lib/x8664-linux- /usr/lib/x8664-linux- /usr/lib/x8664-linux-gnu/libcudnnstaticv7.a /usr/local/lib/python2.7/dist- /usr/local/lib/python3.5/dist- Versions of relevant libraries: [pip3] numpy (1.14.1) [pip3] numpydoc (0.6.0) [pip3] torch (0.4.0) [pip3] torchvision (0.2.0) [conda] cuda90 1.0 h6433d270 pytorch [conda] magma-cuda90 2.3.0 1 soumith [conda] pytorch 0.4.0 py36cuda9.0.176cudnn7.1.21 [cuda90] pytorch [conda] torchvision 0.2.1 py361 pytorch
() returns 7102
Possibly related:
I'm not sure why, but I can't reproduce this on master. I can reproduce on 0.4 though.
Never mind, this error happens when CUDNN is updated regardless of pytorch version.


Einsum currently modifies variables in-place (without an explicit indication that it does so), which prevents pytorch from automatically backpropagating. Results in the following runtime error: Demonstration and discussion can also be found . PyTorch or Caffe2: PyTorch How you installed PyTorch (conda, pip, source): pip OS: PyTorch version: 0.4.0 Python version: 3.6.1
Oops. Thank you for reporting and the minimal example. I'll see to get that fixed.
Saw the same issue over here. Thank you !
Is cloning the tensor before passing to einsum a valid workaround?
Yes, but a somewhat expensive one. If you have the ability to recompile, you could also apply the PR fixing this. Somehow it seems to be stuck in the review queue because it hit an unrelated CI problem...
Okay so how to use it then?
Just get a recent enough master and it will work.


Hi, I try to run the following example from here, but run into some issues: ld: warning: ignoring file , file was built for unsupported file format ( 0x7F 0x45 0x4C 0x46 0x02 0x01 0x01 0x03 0x00 0x00 0x00 0x00 0x00 0x00 0x00 0x00 ) which is not the architecture being linked (x8664): ld: warning: ignoring file , file was built for unsupported file format ( 0x7F 0x45 0x4C 0x46 0x02 0x01 0x01 0x03 0x00 0x00 0x00 0x00 0x00 0x00 0x00 0x00 ) which is not the architecture being linked (x8664): ld: warning: ignoring file , file was built for unsupported file format ( 0x7F 0x45 0x4C 0x46 0x02 0x01 0x01 0x03 0x00 0x00 0x00 0x00 0x00 0x00 0x00 0x00 ) which is not the architecture being linked (x86_64): full error log: i installed pytorch: im running pytorch on: osx 10.11.6 i installed libtorch following these steps: here the cmake output:
should i use: wget -c but the problem is that libtorch-macos- does not contain any libraries in ./lib folder. how to create it? instead of: wget -c when i use libtorch-macos-, i get this error:
I've got the same image not found example in this same spot, by following the minimal example of the docs. Running macOS Mojave here.
we need to build libtorch manually because it doesnt come with all libs...
Getting the same issue as with macOS High Sierra 10.13.5. When I look in I see but not as it requires.
cc: looks like a missing copying of in our OSX libtorch builds.
Seeing the same thing.
Fixed this issue by downloading the two missing libraries from and copying them both (, ) to .
Correct me if I am wrong, but that solution is not answering the original issue: I was having the same issue when trying to run the minimal cpp frontend example here: I grabbed libtorch linked on the main pytorch site: You are right, had neither nor in . However, copying those files to that location did not solve the issue for me. I was able to get the minimal example by giving cmake the for Pytorch's torch library (compiled from source): I am going to try compiling the DCGAN example for the cpp frontend in this same way. I assume that this is not going to work, that we will ultimately need to point to for something like data loading, etc. I am on Mac 10.14.3
I was facing almost same problems here and managed to fix it by downloading and from . Just in case anyone needs this, please do NOT download the newest post there! Instead find the file instead of file. ! I didn't find the difference between and at first, but succeeded once downloaded the ones.
Any idea whats going on with these missing MKL libraries for the mac c++ libtorch? Somehow this is still an issue dating back since 2018, and even after pytorch 1.3, I just tried it out again. The workaround done by above works for me, which involves manually copying the dylib files from old (pre v1.0) intel binaries.
I fixed all such issues for v1.3.0 in or so I thought. But I only fixed them for pip and conda packages, not libtorch. I'm looking into it right now and will fix libtorch too.
will kick off new binary builds as the fix went in. will close the issue after the builds are uploaded and live.
this is fixed now with fixed binaries that are re-uploaded.


I'm trying to implement distributed adversarial training in PyTorch. Thus, in my program pipeline I need to forward the output of one DDP model to another one. When I run the code in distributed setting, the following error is throwed: frame : c10::Error::Error(c10::SourceLocation, std::string const&) + 0x45 (0x7fa248f906d5 in /export/home/haoran/anaconda3/envs/torch1.2/lib/python3.6/site-) frame : c10d::Reducer::prepareforbackward(std::vector<torch::autograd::Variable, std::allocator<torch::autograd::Variableconst&) + 0x666 (0x7fa26f9d0896 in /export/home/haoran/anaconda3/envs/torch1.2/lib/python3.6/site-) frame : <unknown function+ 0x6df3b8 (0x7fa26f9c43b8 in /export/home/haoran/anaconda3/envs/torch1.2/lib/python3.6/site-) frame : <unknown function+ 0x1fc7f0 (0x7fa26f4e17f0 in /export/home/haoran/anaconda3/envs/torch1.2/lib/python3.6/site-) frame : main + 0x16d (0x400c1d in /export/home/haoran/anaconda3/envs/torch1.2/bin/python) frame : _libcstartmain + 0xf5 (0x7fa285e06c05 in ) frame : /export/home/haoran/anaconda3/envs/torch1.2/bin/python() [0x4009e9] Running the following code with DistributedDataParallel <!-- If you have a code sample, error messages, stack traces, please provide it here as well --The code could run successfully without throwing error. Please copy and paste the output from our (or fill out the checklist below manually). You can get the script and run it with: PyTorch version: 1.2.0.dev20190701 Is debug build: No CUDA used to build PyTorch: 9.0.176 OS: CentOS Linux 7 (Core) GCC version: (GCC) 5.4.0 CMake version: version 3.6.2 Python version: 3.6 Is CUDA available: Yes CUDA runtime version: 9.0.176 GPU models and configuration: GPU 0: Tesla P40 GPU 1: Tesla P40 GPU 2: Tesla P40 GPU 3: Tesla P40 Nvidia driver version: 390.25 cuDNN version: Could not collect Versions of relevant libraries: [pip] numpy==1.16.4 [pip] torch==1.2.0.dev20190701 [conda] blas 1.0 mkl defaults [conda] mkl 2019.4 243 defaults [conda] mklfft 1.0.12 py36ha843d7b0 defaults [conda] mklrandom 1.0.2 py36hd81dba30 defaults [conda] pytorch-nightly 1.2.0.dev20190701 py3.6cuda9.0.176cudnn7.5.1_0 pytorch <!-- Add any other context about the problem here. --
Please, try one of the suggested options: (1) passing the keyword argument findunusedparameters=True to ; (2) making sure all forward function outputs participate in calculating loss. If you already have done the above two steps, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's forward function. Please include the loss function and the structure of the return value of forward of your module when reporting this issue (e.g. list, dict, iterable). If the issue will still exist, please, reopen this issue.
I double checked your repro to figure out if this is a real issue or not and found that even setting won't help you here. It's a simple example of course, but you're not using the variable in your loss computation. The model's forward function returns it which is an indication for DDP that it will receive a gradient. Then when it doesn't, and you try to run the next iteration, it raises an error, because the previous iteration didn't finish. I recommend you return only the tensor you plan to use in the loss computation.
Is there any way to see which parameters on earth are unused? It is useless to simply give this RuntimeError or set findunusedparameters to True.
I had the same problem with just one GPUI have modified the code according to what you said
I met the same problem and I found this topic. It works for me. (though it's been a long time for you)


Consider the following: A reasonable reading of this message is that the order of the parameters should be reversed, but this isn't equivalent: The problem is that the second signature has a keyword-only argument but it's printed. It also doesn't include defaults and such, but that's probably secondary. In this case the problem is at least flagged, but it's not clear to me whether there are other deprecated signatures that silently suggest the wrong thing. Please copy and paste the output from our (or fill out the checklist below manually). You can get the script and run it with: Collecting environment information... PyTorch version: 1.5.0a0+ Is debug build: Yes CUDA used to build PyTorch: Could not collect OS: CentOS Linux 7 (Core) GCC version: (GCC) 7.4.0 CMake version: version 3.14.0 Python version: 3.7 Is CUDA available: No CUDA runtime version: 9.2.88 GPU models and configuration: GPU 0: Tesla M40 GPU 1: Tesla M40 Nvidia driver version: 396.69 cuDNN version: /usr/local/cuda-9.2/targets/x8664- Versions of relevant libraries: [pip] numpy==1.17.2 [pip] torch==1.5.0a0+ [conda] blas 1.0 mkl [conda] mkl 2019.4 243 [conda] mkl-include 2019.4 243 [conda] mkl-service 2.3.0 py37he904b0f0 [conda] mklfft 1.0.14 py37ha843d7b0 [conda] mklrandom 1.1.0 py37hd6b4f25_0 [conda] torch 1.5.0a0+ <pip<!-- Add any other context about the problem here. --


See Docker run fails with , which is due to the fact that is specified in docker container: Which results in: CI cc
Check that adding to manywheels image results in a failure:
Tentatively marking hi-pri since nightly testing are failing
This is resolved, here is the successful run:


After merging cuda 12.4 for nightly. I observe following failures: libtorch-cuda124-shared-with-deps-cxx11-abi-build: Build Official Docker Images 124: We need to resolve these before continuing adding CUDA 12.4 CI workflows 0 cc
attempt 3 worked for libtorch build issue:
For Docker image, related discussion:
I will instance a new bug for the docker job failure.


Here, I can't export trivial net with sort() using dynamoexport(). Legacy () works fine, dynamoexport errors out : Unsupported FX nodes: {'callfunction': ['']}. Pytorch nightly 06/03 cc
a missing op, could you take this?
Not sure if related:
Hmmm. Ideally, ONNX should add a sort op. It doesn't have one. I wonder what the legacy exporter converts it to.
should fix this issue.


Previously, worked to install CPU version explicitly. However, does not work. Are the whl files for 2.4 missing? N/A cc
Hi, this link is deprecated since 2.4. Please use the official installation guide instead.
Still it is not available for macosx x86 platform
Hi and Please use official install commands from For linux its: MacOS x86 binaries where deprecated since release 2.3.0.
Closing as resolved. We previously used the command in original post for building dockerfiles for specific versions. I have now replaced it with:
This still installs torch 2.2.2 but not 2.4.0 On Monday, August 19, 2024 at 10:41:25 PM GMT+5:30, Mihir Patel Closing as resolved. We previously used the command in original post for building dockerfiles for specific versions. I have now replaced it with: pip${PYTHONVERSION} install --no-cache-dir --find-links  torch=={CUDAVERSIONTAG} — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID:
This still installs torch 2.2.2 but not 2.4.0
python --version Python 3.12.5 pip3 install torch torchvision torchaudio --index-url Collecting torch Downloading (151.0 MB) 151.0/151.0 MB 11.3 MB/s eta 0:00:00 Successfully installed torch-2.2.2 torchaudio-2.2.2 torchvision-0.17.2 but not 2.4.0