Repository: pytorch
Issue: [vulkan] Vulkan backend fails creating tensor on aarch64 (Raspberry Pi 5)
Labels: triaged,module: vulkan

### 🐛 Describe the bug


I build torch from source with vulkan backend enabled but when I tried to run some minimal examples with vulkan backed I got same errors as:  https://github.com/pytorch/pytorch/issues/72775. 


Setup:
```
import torch

cpu_device = torch.device('cpu')
vulkan_device = torch.device('vulkan:0' if torch.is_vulkan_available() else 'cpu')
print(torch.__version__)
print(torch.is_vulkan_available())
print(vulkan_device)
```

Output:
```
2.4.0a0+git0465a90
True
vulkan:0
```

Test:

`test_tensor_vulkan = torch.ones(1, dtype=torch.float32, device=vulkan_device)`

Output:

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NotImplementedError: Could not run 'aten::fill_.Scalar' with arguments from the 'Vulkan' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::fill_.Scalar' is only available for these backends: [CPU, Meta, QuantizedCPU, SparseCsrCPU, NestedTensorCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].


```

### Versions


```
python collect_env.py
Collecting environment information...
PyTorch version: 2.4.0a0+git0465a90
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 23.10 (aarch64)
GCC version: (Ubuntu 13.2.0-4ubuntu3) 13.2.0
Clang version: Could not collect
CMake version: version 3.27.4
Libc version: glibc-2.38

Python version: 3.11.6 (main, Oct  8 2023, 05:06:43) [GCC 13.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-1013-raspi-aarch64-with-glibc2.38
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       aarch64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
CPU(s):                             4
On-line CPU(s) list:                0-3
Vendor ID:                          ARM
Model name:                         Cortex-A76
Model:                              1
Thread(s) per core:                 1
Core(s) per cluster:                4
Socket(s):                          -
Cluster(s):                         1
Stepping:                           r4p1
CPU(s) scaling MHz:                 100%
CPU max MHz:                        2400.0000
CPU min MHz:                        1000.0000
BogoMIPS:                           108.00
Flags:                              fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm lrcpc dcpop asimddp
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; CSV2, BHB
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] optree==0.11.0
[pip3] torch==2.4.0a0+git0465a90
[conda] Could not collect
```