# \[resolved\] BatchNorm1d -\> CUDNN\_STATUS\_NOT\_SUPPORTED **URL:** \ **Category:** Uncategorized\ **Created:** [May 16, 2017, 2:29am UTC](https://discuss.pytorch.org/t/resolved-batchnorm1d-cudnn-status-not-supported/3049 "2017-05-16T02:29:51Z")\ **Posts on this page:** 9\ **Page:** 1
**Author:** ![pchalasani](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/pchalasani/32/223_2.png) [@pchalasani](https://discuss.pytorch.org/u/pchalasani)\ **Post date:** [May 16, 2017, 2:29am UTC](https://discuss.pytorch.org/t/resolved-batchnorm1d-cudnn-status-not-supported/3049/1 "2017-05-16T02:29:51Z")
Here’s a minimal example (never mind that it looks strange): ```auto import torch.nn as nn x = Variable( t.rand(10000000,1)).cuda() bn = nn.BatchNorm1d(1) bn.cuda() xbn = bn(x) ``` I get the stack-trace below. I recall from some other thread that **I would need to build PyTorch from branch R4** to get rid of this? Is that still the case? I’m using PyTorch built from master a month ago on the AWS Deep Learning Ubuntu AMI instance, and I get this error on that instance. Thanks ! ```auto --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) in () 6 bn = nn.BatchNorm1d(1) 7 bn.cuda() ----> 8 x1cbn = bn(x) 9 x1cbn.size() /home/ubuntu/src/anaconda2/lib/python2.7/site-packages/torch/nn/modules/module.pyc in __call__ (self, *input, **kwargs) 200 201 def __call__ (self, *input, **kwargs): --> 202 result = self.forward(*input, **kwargs) 203 for hook in self._forward_hooks.values(): 204 hook_result = hook(self, input, result) /home/ubuntu/src/anaconda2/lib/python2.7/site-packages/torch/nn/modules/batchnorm.pyc in forward(self, input) 41 return F.batch_norm( 42 input, self.running_mean, self.running_var, self.weight, self.bias, ---> 43 self.training, self.momentum, self.eps) 44 45 def __repr__ (self): /home/ubuntu/src/anaconda2/lib/python2.7/site-packages/torch/nn/functional.pyc in batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps) 387 training=False, momentum=0.1, eps=1e-5): 388 f = torch._C._functions.BatchNorm(running_mean, running_var, training, momentum, eps) --> 389 return f(input, weight, bias) 390 391 RuntimeError: CUDNN_STATUS_NOT_SUPPORTED. This error may appear if you passed in a non-contiguous input. ``` ---
**Author:** ![pchalasani](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/pchalasani/32/223_2.png) [@pchalasani](https://discuss.pytorch.org/u/pchalasani)\ **Post date:** [May 17, 2017, 1:13am UTC](https://discuss.pytorch.org/t/resolved-batchnorm1d-cudnn-status-not-supported/3049/2 "2017-05-17T01:13:51Z")
This is with Cuda 8.0, cudnn 6, Ubuntu 14.04 (the Amazon Deep Learning AMI), with PyTorch installed from source a couple months ago. And incidentally it works fine if I use a smaller tensor size, like say 100000 instead of 10000000 ---
**Author:** ![albanD](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/alband/32/215_2.png) [@albanD](https://discuss.pytorch.org/u/albanD)\ **Post date:** [May 17, 2017, 8:38am UTC](https://discuss.pytorch.org/t/resolved-batchnorm1d-cudnn-status-not-supported/3049/3 "2017-05-17T08:38:31Z")
Hi, You may want to wait for a response from an nvidia guy, but from what I remember, some very weird input shapes are not supported by cudnn (for various reasons like for example your gpu not having enough memory for the required workspace). Given that in your case feeding a smaller tensor works, it may be the reason. You can try disabling cudnn with `torch.backends.cudnn.enabled=False` and see if it works. ---
**Author:** ![pchalasani](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/pchalasani/32/223_2.png) [@pchalasani](https://discuss.pytorch.org/u/pchalasani)\ **Post date:** [May 17, 2017, 2:09pm UTC](https://discuss.pytorch.org/t/resolved-batchnorm1d-cudnn-status-not-supported/3049/4 "2017-05-17T14:09:25Z")
Thanks @albanD I did this and it did not fix it: ```auto import torch import torch.nn as nn torch.backends.cudnn.enabled=False x = Variable( t.rand(1000000,1).contiguous()).cuda() print torch.backends.cudnn.version() bn = nn.BatchNorm1d(1) bn.cuda() xbn = bn(x) xbn.size() ``` ---
**Author:** ![albanD](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/alband/32/215_2.png) [@albanD](https://discuss.pytorch.org/u/albanD)\ **Post date:** [May 17, 2017, 4:12pm UTC](https://discuss.pytorch.org/t/resolved-batchnorm1d-cudnn-status-not-supported/3049/5 "2017-05-17T16:12:19Z")
Hi, I cannot reproduce your problem. Running the code sample that you gave does not raise any cudnn error anymore for me. ---
**Author:** ![pchalasani](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/pchalasani/32/223_2.png) [@pchalasani](https://discuss.pytorch.org/u/pchalasani)\ **Post date:** [May 17, 2017, 4:13pm UTC](https://discuss.pytorch.org/t/resolved-batchnorm1d-cudnn-status-not-supported/3049/6 "2017-05-17T16:13:30Z")
did you try with a larger size like 10 Million ? ---
**Author:** ![albanD](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/alband/32/215_2.png) [@albanD](https://discuss.pytorch.org/u/albanD)\ **Post date:** [May 17, 2017, 4:16pm UTC](https://discuss.pytorch.org/t/resolved-batchnorm1d-cudnn-status-not-supported/3049/7 "2017-05-17T16:16:41Z")
Yes, this exact code works for me (after freezing my computer for few second due to memory usage) and outputs: 6021 (100000000L, 1L) ```auto import torch from torch.autograd import Variable import torch.nn as nn torch.backends.cudnn.enabled=False x = Variable( torch.rand(100000000,1).contiguous()).cuda() print torch.backends.cudnn.version() bn = nn.BatchNorm1d(1) bn.cuda() xbn = bn(x) print(xbn.size()) ``` ---
**Author:** ![pchalasani](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/pchalasani/32/223_2.png) [@pchalasani](https://discuss.pytorch.org/u/pchalasani)\ **Post date:** [May 17, 2017, 4:19pm UTC](https://discuss.pytorch.org/t/resolved-batchnorm1d-cudnn-status-not-supported/3049/8 "2017-05-17T16:19:01Z")
Ah my version shows 5110, so looks like I’m still on cudnn 5, although I **thought** I installed 6.0 ---
**Author:** ![pchalasani](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/pchalasani/32/223_2.png) [@pchalasani](https://discuss.pytorch.org/u/pchalasani)\ **Post date:** [May 17, 2017, 4:24pm UTC](https://discuss.pytorch.org/t/resolved-batchnorm1d-cudnn-status-not-supported/3049/9 "2017-05-17T16:24:32Z")
Turned out I had forgotten to re-build PyTorch from source after installing the cudnn 6.0 files. Now it works fine.