Repository: pytorch
Issue: All processes running torch.distributed.destroy_process_group() create CUDA context on device 0
Labels: oncall: distributed,triaged,module: c10d

### 🐛 Describe the bug

All distributed local processes create a new CUDA context on local CUDA device 0 when calling `torch.distributed.destroy_process_group()`. Extra context shouldn't be created, because it may lead to out of memory errors on local rank 0 if the workload was almost maxing out GPU memory and there is no space to accommodate extra contexts created by all local ranks.

Repro:
```
import torch
import time

import os


torch.cuda.set_device(int(os.getenv('LOCAL_RANK')))
torch.distributed.init_process_group('nccl')
device = torch.device('cuda')
a = torch.rand(128, device=device)
torch.distributed.all_reduce(a)
torch.distributed.destroy_process_group()

time.sleep(3600)
```

Launch with `python3 -m torch.distributed.run --nproc-per-node 2 destroy_context.py` on a machine with at least 2 GPUs, and watch output of `nvidia-smi`, when `destroy_process_group` is called `nvidia-smi` shows that process that was supposed to run on device 1 is creating CUDA context on device 0:

```
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A   3828821      C   /usr/bin/python3                              234MiB |
|    0   N/A  N/A   3828822      C   /usr/bin/python3                              162MiB |
|    1   N/A  N/A   3828822      C   /usr/bin/python3                              234MiB |
+-----------------------------------------------------------------------------------------+
```

In the case above process `3828822` that called `torch.cuda.set_device(1)` created CUDA context on both devices. If `destroy_process_group()` is commented out, extra context is not present.

### Versions

```
Collecting environment information...
PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-97-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: Quadro RTX 8000
GPU 1: Quadro RTX 8000

Nvidia driver version: 550.54.15
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             20
On-line CPU(s) list:                0-19
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Core(TM) i9-9820X CPU @ 3.30GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 10
Socket(s):                          1
Stepping:                           4
CPU max MHz:                        4200.0000
CPU min MHz:                        1200.0000
BogoMIPS:                           6599.98
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 vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          320 KiB (10 instances)
L1i cache:                          320 KiB (10 instances)
L2 cache:                           10 MiB (10 instances)
L3 cache:                           16.5 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-19
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.3.0
[pip3] torchaudio==2.3.0
[pip3] torchvision==0.18.0
[pip3] triton==2.3.0
[conda] Could not collect
```

cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu @penguinwu @fegin @XilunWu @wanchaol @fduwjj @wz337 @tianyu-l @wconstab @yf225 @chauhang @d4l3k