sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: pytorch
Issue: `torch.svd_lowrank` Trigger RuntimeError under UndefinedBehaviorSanitizer
Labels: module: sparse,triaged,module: linear algebra,actionable,module: sanitizers

### 🐛 Describe the bug

The implementation of `torch.svd_lowrank` contains undefined behavior caught by UB sanitizer. Without sanitizers, the test terminates normally.
Test:
```python
import torch

def test():
    arg_1 = torch.rand([5, 5], dtype=torch.float64).to_sparse()
    res = torch.svd_lowrank(arg_1,False,)

test()
```
Error log:
```
pytorch/aten/src/ATen/native/sparse/SparseTensorMath.cpp:1270:19: runtime error: applying non-zero offset 8 to null pointer
```

### Versions

```
PyTorch version: 1.14.0a0+git6e5f736
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 11.1.0-6
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.12 (main, Apr  5 2022, 06:56:58)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.65.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[pip3] torch==1.14.0a0+git6e5f736
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7f8727e_0  
[conda] mkl_fft                   1.3.1            py39hd3c417c_0  
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.21.5           py39he7a7128_1  
[conda] numpy-base                1.21.5           py39hf524024_1  
[conda] numpydoc                  1.2                pyhd3eb1b0_0  
[conda] torch                     1.14.0a0+git6e5f736          pypi_0    pypi
```

cc @ezyang @gchanan @zou3519 @nikitaved @pearu @cpuhrsch @amjames @bhosmer @jianyuh @mruberry @walterddr @IvanYashchuk @xwang233 @Lezcano
Repository: pytorch
Issue: `torch.vander` Trigger RuntimeError with UndefinedBehaviorSanitizer
Labels: triaged,module: linear algebra,module: sanitizers,module: edge cases

### 🐛 Describe the bug

The implementation of `torch.vander` contains undefined behavior caught by UB sanitizer. Without sanitizers, the test terminates normally.
Test:
```python
import torch

def test():
    arg_1 = torch.randint(-1,512,[4], dtype=torch.int64).clone()
    res = torch.vander(arg_1,12,)

test()
```
Error log:
```
pytorch/aten/src/ATen/native/cpu/ReduceOpsKernel.cpp:100:3: runtime error: signed integer overflow: 7179408129339016064 * 494 cannot be represented in type 'long'
```

### Versions

```
PyTorch version: 1.14.0a0+git6e5f736
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 11.1.0-6
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.12 (main, Apr  5 2022, 06:56:58)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.65.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[pip3] torch==1.14.0a0+git6e5f736
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7f8727e_0  
[conda] mkl_fft                   1.3.1            py39hd3c417c_0  
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.21.5           py39he7a7128_1  
[conda] numpy-base                1.21.5           py39hf524024_1  
[conda] numpydoc                  1.2                pyhd3eb1b0_0  
[conda] torch                     1.14.0a0+git6e5f736          pypi_0    pypi
```

cc @jianyuh @nikitaved @pearu @mruberry @walterddr @IvanYashchuk @xwang233 @Lezcano
Repository: pytorch
Issue: `torch.topk` Trigger RuntimError under UndefinedBehaviorSanitizer
Labels: triaged,module: sanitizers

### 🐛 Describe the bug

The implementation of `torch.topk` contains undefined behavior caught by UB sanitizer. Without sanitizers, the test terminates normally.
Test:
```python
import torch

def test():
    arg_1 = torch.rand([3, 5], dtype=torch.float64).clone()
    res = torch.topk(arg_1,0,0,)

test()
```
Error log:
```
pytorch/aten/src/ATen/native/TopKImpl.h:30:45: runtime error: applying non-zero offset 8 to null pointer
```

### Versions

```
PyTorch version: 1.14.0a0+git6e5f736
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 11.1.0-6
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.12 (main, Apr  5 2022, 06:56:58)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.65.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[pip3] torch==1.14.0a0+git6e5f736
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7f8727e_0  
[conda] mkl_fft                   1.3.1            py39hd3c417c_0  
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.21.5           py39he7a7128_1  
[conda] numpy-base                1.21.5           py39hf524024_1  
[conda] numpydoc                  1.2                pyhd3eb1b0_0  
[conda] torch                     1.14.0a0+git6e5f736          pypi_0    pypi
```
Repository: pytorch
Issue: `torch.linalg.eigvals` Trigger RuntimeError under UndefinedBehaviorSanitizer
Labels: triaged,module: sanitizers

### 🐛 Describe the bug

The implementation of `torch.linalg.eigvals` contains undefined behavior caught by UB sanitizer. Without sanitizers, the test terminates normally.
Test:
```
import torch

def test():
    arg_1 = torch.rand([0, 3, 3], dtype=torch.float64).clone()
    res = torch.linalg.eigvals(arg_1,)

test()
```
Error log:
```
pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:954:18: runtime error: applying non-zero offset 24 to null pointer
```

### Versions

```
PyTorch version: 1.14.0a0+git6e5f736
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 11.1.0-6
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.12 (main, Apr  5 2022, 06:56:58)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.65.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[pip3] torch==1.14.0a0+git6e5f736
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7f8727e_0  
[conda] mkl_fft                   1.3.1            py39hd3c417c_0  
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.21.5           py39he7a7128_1  
[conda] numpy-base                1.21.5           py39hf524024_1  
[conda] numpydoc                  1.2                pyhd3eb1b0_0  
[conda] torch                     1.14.0a0+git6e5f736          pypi_0    pypi
```
Repository: pytorch
Issue: `torch.Tensor.msort` Trigger RuntimeError under UndefinedBehaviorSanitizer
Labels: triaged,module: sanitizers

### 🐛 Describe the bug

The implementation of `torch.Tensor.msort` contains undefined behavior caught by UB sanitizer. Without sanitizers, the test terminates normally.
Test:
```python
import torch

def test():
    arg_1 = torch.rand([0, 16, 1024, 1024], dtype=torch.float16).clone()
    res = torch.Tensor.msort(arg_1,)

test()
```
Error log:
```
pytorch/aten/src/ATen/TensorIteratorInternal.h:34:17: runtime error: applying non-zero offset 1048576 to null pointer
```

### Versions

```
PyTorch version: 1.14.0a0+git6e5f736
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 11.1.0-6
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.12 (main, Apr  5 2022, 06:56:58)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.65.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[pip3] torch==1.14.0a0+git6e5f736
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7f8727e_0  
[conda] mkl_fft                   1.3.1            py39hd3c417c_0  
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.21.5           py39he7a7128_1  
[conda] numpy-base                1.21.5           py39hf524024_1  
[conda] numpydoc                  1.2                pyhd3eb1b0_0  
[conda] torch                     1.14.0a0+git6e5f736          pypi_0    pypi
```
Repository: pytorch
Issue: torch.linalg.matrix_rank memory leak
Labels: needs reproduction,module: memory usage,triaged,module: linear algebra

### 🐛 Describe the bug

Below is a minimal script for reproducing.

```python
    import torch
    import os, psutil
    
    a = torch.randn(100, 100, 512)
    for _ in range(10):
        torch.linalg.matrix_rank(a)
        print(psutil.Process(os.getpid()).memory_info().rss)
```

The above script gives the following output:
```
4214136832
8331022336
10537857024
12173451264
13269585920
14028787712
14656843776
15086813184
15620276224
16046718976
```
I did some test, and it seems that it only happens when the tensor is high dimensional. For example, if the dimension of `a` is (100, 100, 100) then everything is fine.

### Versions

PyTorch version: 1.13.0a0
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: SUSE Linux Enterprise Server 15 SP3 (x86_64)
GCC version: (SUSE Linux) 7.5.0
Clang version: Could not collect
CMake version: version 3.17.0
Libc version: glibc-2.31

Python version: 3.9.7 (default, Sep 16 2021, 13:09:58)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.3.18-150300.59.87-default-x86_64-with-glibc2.31
Is CUDA available: False
CUDA runtime version: 11.6.124
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy==0.982
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.20.3
[pip3] numpydoc==1.1.0
[pip3] torch==1.13.0a0+gitunknown
[pip3] torchaudio==0.13.0+bc8640b
[pip3] torchtext==0.14.0a0+e2b27f9
[pip3] torchvision==0.14.0a0
[conda] blas                      1.0                         mkl
[conda] mkl                       2021.4.0           h06a4308_640
[conda] mkl-include               2021.4.0           h06a4308_640    <unknown>
[conda] mkl-service               2.4.0            py39h7f8727e_0
[conda] mkl_fft                   1.3.1            py39hd3c417c_0
[conda] mkl_random                1.2.2            py39h51133e4_0
[conda] numpy                     1.20.3           py39hf144106_0
[conda] numpy-base                1.20.3           py39h74d4b33_0
[conda] numpydoc                  1.1.0              pyhd3eb1b0_1


cc @jianyuh @nikitaved @pearu @mruberry @walterddr @IvanYashchuk @xwang233 @Lezcano
Repository: pytorch
Issue: `torch.set_rng_state` Trigger RuntimeError under UndefinedBehaviorSanitizer
Labels: triaged,module: sanitizers

### 🐛 Describe the bug

The implementation of `torch.set_rng_state` contains undefined behavior caught by UB sanitizer. 
Test:
```python
import torch

def test():
    arg_1 = torch.randint(0,64,[5056], dtype=torch.uint8).clone()
    _ = torch.set_rng_state(arg_1,)

test()
```
Error log:
```
pytorch/aten/src/ATen/CPUGeneratorImpl.cpp:167:20: runtime error: load of value 46, which is not a valid value for type 'bool'
```

### Versions

```
PyTorch version: 1.14.0a0+git6e5f736
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 11.1.0-6
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.12 (main, Apr  5 2022, 06:56:58)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.65.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[pip3] torch==1.14.0a0+git6e5f736
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7f8727e_0  
[conda] mkl_fft                   1.3.1            py39hd3c417c_0  
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.21.5           py39he7a7128_1  
[conda] numpy-base                1.21.5           py39hf524024_1  
[conda] numpydoc                  1.2                pyhd3eb1b0_0  
[conda] torch                     1.14.0a0+git6e5f736          pypi_0    pypi
```
Repository: pytorch
Issue: `torch.nn.functional.embedding_bag` Trigger RuntimeError under UndefinedBehaviorSanitizer
Labels: triaged,module: sanitizers

### 🐛 Describe the bug

The implementation of `torch.nn.functional.embedding_bag` contains undefined behavior caught by UB sanitizer. Without sanitizers, the test terminates normally.
Test:
```python
import torch

def test():
    arg_1 = torch.randint(-1024,1024,[6], dtype=torch.int32).clone()
    arg_2 = torch.rand([5, 2], dtype=torch.float32).clone()
    arg_3 = torch.randint(-8192,0,[0], dtype=torch.int64).clone()
    arg_9 = torch.rand([6], dtype=torch.float32).clone()
    _ = torch.nn.functional.embedding_bag(arg_1, arg_2, arg_3, None, 2.0, False, "sum", False, arg_9, False, None)

test()
```
Error log:
```
pytorch/aten/src/ATen/native/EmbeddingBag.cpp:708:11: runtime error: null pointer passed as argument 2, which is declared to never be null
/usr/include/string.h:44:28: note: nonnull attribute specified here
```

### Versions

```
PyTorch version: 1.14.0a0+git6e5f736
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 11.1.0-6
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.12 (main, Apr  5 2022, 06:56:58)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.65.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[pip3] torch==1.14.0a0+git6e5f736
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7f8727e_0  
[conda] mkl_fft                   1.3.1            py39hd3c417c_0  
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.21.5           py39he7a7128_1  
[conda] numpy-base                1.21.5           py39hf524024_1  
[conda] numpydoc                  1.2                pyhd3eb1b0_0  
[conda] torch                     1.14.0a0+git6e5f736          pypi_0    pypi
```