xformers-0.0.32.post2 ROCm 6.4 with PyTorch 2.8.0 Aborted (core dumped) In WSL2

longer_pathname_so_that_rpms_can_support_packaging_the_debug_info_for_all_os_profiles/src/clr/hipamd/src/hip_global.cpp:158 : 1096688401 us:  Module not initialized
Aborted (core dumped)

location=$(pip show torch | grep Location | awk -F ": " '{print $2}')
cd ${location}/torch/lib/
rm libhsa-runtime64.so*

python3 -c $'import torch\n\
print(f"PyTorch版本: {torch.__version__}")\n\
print(f"ROCm版本: {torch.version.hip}")\n\
print(f"GPU可用: {torch.cuda.is_available()}")\n\
if torch.cuda.is_available():\n\
    print(f"设备名称: {torch.cuda.get_device_name(0)}")\n\
    print(f"显存: {torch.cuda.get_device_properties(0).total_memory/1024**3:.2f} GB")'

PyTorch版本: 2.8.0+rocm6.4
ROCm版本: 6.4.43482-0f2d60242
GPU可用: True
/home/ai/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:816: UserWarning: Can't initialize amdsmi - Error code: 34
  warnings.warn(f"Can't initialize amdsmi - Error code: {e.err_code}")
设备名称: AMD Radeon RX 7900 XTX
显存: 23.98 GB

got prompt
model weight dtype torch.float16, manual cast: None
model_type V_PREDICTION
Using xformers attention in VAE
Using xformers attention in VAE
VAE load device: cuda:0, offload device: cpu, dtype: torch.float32
Requested to load SDXLClipModel
loaded completely 9.5367431640625e+25 1560.802734375 True
CLIP/text encoder model load device: cuda:0, offload device: cpu, current: cuda:0, dtype: torch.float16
Requested to load SDXL
loaded completely 19545.85126953125 4897.0483474731445 True
  0%|                                                                                                                                                                                     | 0/28 [00:00<?, ?it/s]
:0:/longer_pathname_so_that_rpms_can_support_packaging_the_debug_info_for_all_os_profiles/src/clr/hipamd/src/hip_global.cpp:158 : 1096688401 us:  Module not initialized
Aborted (core dumped)

# For security purposes, please check the contents of collect_env.py before running it.
python -m torch.utils.collect_env

PyTorch version: 2.8.0+rocm6.4
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 6.4.43482-0f2d60242

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, May 27 2025, 17:12:29) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: AMD Radeon RX 7900 XTX (gfx1100)
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: 6.4.43482
MIOpen runtime version: 3.4.0
Is XNNPACK available: True

CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Address sizes:                        48 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               24
On-line CPU(s) list:                  0-23
Vendor ID:                            AuthenticAMD
Model name:                           AMD Ryzen 9 7900X 12-Core Processor
CPU family:                           25
Model:                                97
Thread(s) per core:                   2
Core(s) per socket:                   12
Socket(s):                            1
Stepping:                             2
BogoMIPS:                             9399.70
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr arat avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm
Hypervisor vendor:                    Microsoft
Virtualization type:                  full
L1d cache:                            384 KiB (12 instances)
L1i cache:                            384 KiB (12 instances)
L2 cache:                             12 MiB (12 instances)
L3 cache:                             32 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-23
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 Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass:      Vulnerable
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] onnx==1.18.0
[pip3] onnxruntime==1.15.1
[pip3] onnxruntime_extensions==0.14.0
[pip3] onnxruntime-rocm==1.21.0
[pip3] onnxruntime-training==1.21.0+rocm641.83
[pip3] open_clip_torch==2.32.0
[pip3] optree==0.16.0
[pip3] pytorch-lightning==2.5.2
[pip3] pytorch-triton-rocm==3.4.0
[pip3] torch==2.8.0+rocm6.4
[pip3] torchmetrics==1.7.4
[pip3] torchsde==0.2.6
[pip3] torchvision==0.23.0+rocm6.4
[pip3] triton==3.4.0
[conda] Could not collect