[A ERROR in Docker] RuntimeError: CUDA error: no kernel image is available for execution on the device
Labels: needs reproduction, module: cuda, triaged, module: docker

### 🐛 Describe the bug

Traceback (most recent call last):
  File "main.py", line 146, in <module>
    trainer.train(train_loader, valid_loader, max_epoch)
  File "/app/stable-dreamfusion/nerf/utils.py", line 456, in train
    self.train_one_epoch(train_loader)
  File "/app/stable-dreamfusion/nerf/utils.py", line 668, in train_one_epoch
    self.model.update_extra_state()
  File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/app/stable-dreamfusion/nerf/renderer.py", line 586, in update_extra_state
    sigmas = self.density(cas_xyzs)['sigma'].reshape(-1).detach()
  File "/app/stable-dreamfusion/nerf/network_grid.py", line 150, in density
    sigma, albedo = self.common_forward(x)
  File "/app/stable-dreamfusion/nerf/network_grid.py", line 80, in common_forward
    h = self.encoder(x, bound=self.bound)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/app/stable-dreamfusion/gridencoder/grid.py", line 149, in forward
    outputs = grid_encode(inputs, self.embeddings, self.offsets, self.per_level_scale, self.base_resolution, inputs.requires_grad, self.gridtype_id, self.align_corners)
  File "/usr/local/lib/python3.8/dist-packages/torch/cuda/amp/autocast_mode.py", line 110, in decorate_fwd
    return fwd(*args, **kwargs)
  File "/app/stable-dreamfusion/gridencoder/grid.py", line 52, in forward
    outputs = outputs.permute(1, 0, 2).reshape(B, L * C)
RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

### Versions

Collecting environment information...
PyTorch version: 1.12.1+cu116
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.4 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.8.10 (default, Jun 22 2022, 20:18:18)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-3.10.0-957.el7.x86_64-x86_64-with-glibc2.29
Is CUDA available: True
CUDA runtime version: 11.6.124
CUDA_MODULE_LOADING set to: 
GPU models and configuration: 
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB
GPU 4: NVIDIA A100-SXM4-80GB
GPU 5: NVIDIA A100-SXM4-80GB
GPU 6: NVIDIA A100-SXM4-80GB
GPU 7: NVIDIA A100-SXM4-80GB

Nvidia driver version: 470.129.06
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.23.3
[pip3] torch==1.12.1+cu116
[pip3] torch-ema==0.3
[pip3] torchaudio==0.12.1+cu116
[pip3] torchvision==0.13.1+cu116
[conda] Could not collect

cc @ngimel