Cannot run pipeline with GPU but works with CPU

### Checklist

- [X] I have searched for [similar issues](https://github.com/isl-org/Open3D-ML/issues).
- [X] I have tested with the [latest development wheel](http://www.open3d.org/docs/latest/getting_started.html#development-version-pip).
- [X] I have checked the [release documentation](http://www.open3d.org/docs/release/) and the [latest documentation](http://www.open3d.org/docs/latest/) (for `master` branch).

### Describe the issue

I've been testing Open3D ML pretrained models before I set up a configuration for a custom data set.

I am trying to do this by running the predefined scripts.

The CPU works but GPU is giving me an error:
_RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`_

### Steps to reproduce the bug

```python
$ python scripts/run_pipeline.py torch -c ml3d/configs/randlanet_semantickitti.yml --dataset.dataset_path /home/alex/Desktop/Datasets/SemanticKITTI --pipeline SemanticSegmentation --dataset.use_cache True --split test

Using external Open3D-ML in /home/alex/Desktop/NIST_T3/PROJECT/Open3D-ML
regular arguments
batch_size: null
cfg_dataset: null
cfg_file: ml3d/configs/randlanet_semantickitti.yml
cfg_model: null
cfg_pipeline: null
ckpt_path: null
dataset: null
dataset_path: null
device: gpu
framework: torch
main_log_dir: null
max_epochs: null
mode: null
model: null
pipeline: SemanticSegmentation
seed: 0
split: test

extra arguments
dataset.dataset_path: /home/alex/Desktop/Datasets/SemanticKITTI
dataset.use_cache: 'True'
pipeline.num_workers: '0'

INFO - 2022-04-26 18:17:20,184 - semantic_segmentation - DEVICE : cuda
INFO - 2022-04-26 18:17:20,184 - semantic_segmentation - Logging in file : ./logs/RandLANet_SemanticKITTI_torch/log_test_2022-04-26_18:17:20.txt
INFO - 2022-04-26 18:17:20,222 - semantickitti - Found 20351 pointclouds for test
INFO - 2022-04-26 18:18:40,823 - semantic_segmentation - Initializing from scratch.
INFO - 2022-04-26 18:18:40,825 - semantic_segmentation - Started testing
```

### Error message

Traceback (most recent call last):
File "/home/alex/Desktop/NIST_T3/PROJECT/Open3D-ML/scripts/run_pipeline.py", line 163, in
main()
File "/home/alex/Desktop/NIST_T3/PROJECT/Open3D-ML/scripts/run_pipeline.py", line 151, in main
pipeline.run_test()
File "/home/alex/Desktop/NIST_T3/PROJECT/Open3D-ML/ml3d/torch/pipelines/semantic_segmentation.py", line 233, in run_test
results = model(inputs['data'])
File "/home/alex/anaconda3/envs/o3Dml9/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/alex/Desktop/NIST_T3/PROJECT/Open3D-ML/ml3d/torch/models/randlanet.py", line 266, in forward
feat = self.fc0(feat).transpose(-2, -1).unsqueeze(
File "/home/alex/anaconda3/envs/o3Dml9/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/alex/anaconda3/envs/o3Dml9/lib/python3.9/site-packages/torch/nn/modules/linear.py", line 94, in forward
return F.linear(input, self.weight, self.bias)
File "/home/alex/anaconda3/envs/o3Dml9/lib/python3.9/site-packages/torch/nn/functional.py", line 1753, in linear
return torch._C._nn.linear(input, weight, bias)
RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`

### Expected behavior

When --device is set to CPU everything seems to work, however when set to gpu or cuda I get the error.

### Open3D, Python and System information

```markdown
- Operating system: Ubuntu 20.04
- Python version: 3.9
- Open3D version: (output from python: `print(open3d.__version__)`)
- System type: x84
- Is this remote workstation?: yes or no
- How did you install Open3D?: pip
- Compiler version (if built from source): gcc 7.5
```

### Additional information

I've tried with CUDA 11.6 and 10.1 and got the same error
This error pops up with different datasets as well (SemanticKITTI and Stanford3D)

Any tips or ideas would be great, thank you!