Issue 1:
🐛 Bug

I'm using rtx 3090 to create a simple float32 tensor in cuda. It's very weird it would take too much time after importing huggingface transformers library. However the same code below doesn't have any issues in 20-series cards.

To Reproduce

The following code would reproduce the behavior:

import torch
import time
import transformers

if __name__ == '__main__':
t = time.time()
x = torch.zeros((50273,), dtype=torch.float32).cuda()
print('took:', time.time() - t)
print('done')

result:

took: 137.26064133644104
done

I think the size doesn't matter, any size could reproduce this result in 3090 cards. I don't have rtx 3080, therefore don't know if the issue would exist in 3080 card.

Expected behavior

Here is the result after commenting the import transformers:

took: 0.8175389766693115
done

Environment

The pytorch is built from the master branch of pytorch github. The transformer library is 3.3.1.

Collecting environment information...
PyTorch version: 1.7.0a0+9d5607f
Is debug build: True
CUDA used to build PyTorch: 11.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.1 LTS (x86_64)
GCC version: (Ubuntu 9.3.0-10ubuntu2) 9.3.0
Clang version: Could not collect
CMake version: version 3.18.2

Python version: 3.8 (64-bit runtime)
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: GeForce RTX 3090
Nvidia driver version: 455.23.05
cuDNN version: Probably one of the following:
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.19.1
[pip3] numpydoc==1.1.0
[pip3] pytorch-pretrained-bert==0.6.2
[pip3] torch==1.7.0a0
[conda] blas 1.0 mkl
[conda] cudatoolkit 10.2.89 hfd86e86_1
[conda] mkl 2020.2 256
[conda] mkl-include 2020.2 256
[conda] mkl-service 2.3.0 py38he904b0f_0
[conda] mkl_fft 1.1.0 py38h23d657b_0
[conda] mkl_random 1.1.1 py38h0573a6f_0
[conda] numpy 1.18.5 pypi_0 pypi
[conda] numpy-base 1.19.1 py38hfa32c7d_0
[conda] numpydoc 1.1.0 py_0
[conda] pytorch-pretrained-bert 0.6.2 pypi_0 pypi
[conda] torch 1.7.0a0 pypi_0 pypi

Additional context

I don't know the issue come from transformer library or pytorch. But since the same code under same setting works in rtx 2080. I guess there is a higher chance the problem come from pytorch itself. Thanks for any help.

cc @ngimel @csarofeen @ptrblck

Issue 2:
Edit: If you see this error, please go to https://pytorch.org/get-started/locally/ and download a wheel built with cuda 11.x

**Hi

I recently purchased RTX 3080 and got this error

GeForce RTX 3080 with CUDA capability sm_86 is not compatible with the current PyTorch installation.

The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_61 sm_70 sm_75 compute_37.

and my cuda version is following:

torch.cuda_version

Out[3]: '10.2'

Should I install the latest cudatoolkit 11.0? but It seems Pytorch only provides cudatoolkit 10.2 like below screenshot.

Is there any solution for this issue?

How do I get sm_86 binary capability?

thanks in advance**

cc @malfet @seemethere @walterddr @ngimel