multiple AMD GPUs
Labels: needs reproduction, module: rocm, triaged

### 🐛 Describe the bug

When I run multiple GPU's using ROCm, the second GPU does not work.

I use the docker image rocm/pytorch:latest.

I have two GPUs installed:
> rocm-smi

```
========================= ROCm System Management Interface =========================
=================================== Concise Info ===================================
GPU  Temp (DieEdge)  AvgPwr  SCLK  MCLK   Fan  Perf  PwrCap  VRAM%  GPU%  
0    31.0c           5.0W    0Mhz  96Mhz  0%   auto  289.0W    0%   0%    
1    43.0c           5.0W    0Mhz  96Mhz  0%   auto  289.0W    0%   0%    
====================================================================================
=============================== End of ROCm SMI Log ================================
```

they are both the same type of GPU:
$ lspci|grep VGA
03:00.0 VGA compatible controller: Advanced Micro Devices, Inc. [AMD/ATI] Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] (rev c0)
07:00.0 VGA compatible controller: Advanced Micro Devices, Inc. [AMD/ATI] Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] (rev c0)

within the docker image, i run python:
>>> import torch
>>> print(torch.tensor([1.0, 2.0, 3.0], device="cuda:0"))
tensor([1., 2., 3.], device='cuda:0')

and the first device executes simple functions with no problem.

but the second device gives this error:

>>> print(torch.tensor([1.0, 2.0, 3.0], device="cuda:1"))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_tensor.py", line 427, in __repr__
    return torch._tensor_str._str(self, tensor_contents=tensor_contents)
  File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_tensor_str.py", line 636, in _str
    return _str_intern(self, tensor_contents=tensor_contents)
  File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_tensor_str.py", line 567, in _str_intern
    tensor_str = _tensor_str(self, indent)
  File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_tensor_str.py", line 327, in _tensor_str
    formatter = _Formatter(get_summarized_data(self) if summarize else self)
  File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_tensor_str.py", line 115, in __init__
    nonzero_finite_vals = torch.masked_select(
RuntimeError: HIP error: the operation cannot be performed in the present state
HIP kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing HIP_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_HIP_DSA` to enable device-side assertions.


and after that, the GPU% of that GPU shoots up to 99% and stays there:
```
========================= ROCm System Management Interface =========================
=================================== Concise Info ===================================
GPU  Temp (DieEdge)  AvgPwr  SCLK     MCLK   Fan  Perf  PwrCap  VRAM%  GPU%  
0    33.0c           5.0W    0Mhz     96Mhz  0%   auto  289.0W    2%   0%    
1    54.0c           60.0W   2575Mhz  96Mhz  0%   auto  289.0W    1%   99%   
====================================================================================
=============================== End of ROCm SMI Log ================================
```


### Versions

PyTorch version: 2.0.0a0+git70f6d0c
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 5.6.31061-8c743ae5d

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: 16.0.0 (https://github.com/RadeonOpenCompute/llvm-project roc-5.6.0 23243 be997b2f3651a41597d7a41441fff8ade4ac59ac)
CMake version: version 3.26.4
Libc version: glibc-2.31

Python version: 3.8.16 (default, Jun 12 2023, 18:09:05)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: AMD Radeon RX 6900 XT
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: 5.6.31061
MIOpen runtime version: 2.20.0
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
Address sizes:                      39 bits physical, 48 bits virtual
CPU(s):                             16
On-line CPU(s) list:                0-15
Thread(s) per core:                 2
Core(s) per socket:                 8
Socket(s):                          1
NUMA node(s):                       1
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              158
Model name:                         Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz
Stepping:                           13
CPU MHz:                            799.992
CPU max MHz:                        5000.0000
CPU min MHz:                        800.0000
BogoMIPS:                           6199.99
L1d cache:                          256 KiB
L1i cache:                          256 KiB
L2 cache:                           2 MiB
L3 cache:                           16 MiB
NUMA node0 CPU(s):                  0-15
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX unsupported
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Mitigation; Microcode
Vulnerability Tsx async abort:      Mitigation; TSX disabled
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] mypy==0.960
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.18.5
[pip3] torch==2.0.0a0+git70f6d0c
[pip3] torchvision==0.15.0a0+c206a47
[conda] No relevant packages


cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang