Repository: pytorch
Issue: [c10d] Workaround the ncclCommAbort() hang issues 
Labels: oncall: distributed,triaged,module: c10d

### 🐛 Describe the bug

In many prod cases, We have found that the processGroups were hung during destruction of ProcessGroupNCCL. The root cause is that the NCCL API ncclCommAbort was called and it was hung due to its 'collective' nature implementation. Note we found that the issue exists only in NCCL version 2.17.1. So we propose the following changes.
1. For the NCCL side, ncclCommAbort need to be made as a 'local' call and should not hang waiting for other ranks. This should be tracked by a separate thread/issue/PR in NCCL.
2. In the meantime, in the pytorch c10d, we propose to implement the following workaround while ncclCommAbort is still a 'collective call': a) explicitly call shutdown (and hence ncclCommAbort) in destroy_process_group and encourage users of c10d to call destroy_process_group explicitly when PGs are no longer used. b) in the destructor of ProcessGroup, avoid calling abort/ncclCommAbort, but checking if the users have explicitly called destroy_process_group, if not, PG will log a warning. Before this propoal, ncclCommAbort are called in a random order depends on when Python garbage collector would destroy the PG. These changes would make sure we make those calls in a deterministic way


### Versions

Collecting environment information...
PyTorch version: 2.3.0a0+git8837df1
Is debug build: False
CUDA used to build PyTorch: 12.0
ROCM used to build PyTorch: N/A

OS: CentOS Stream 9 (x86_64)
GCC version: (GCC) 11.4.1 20230605 (Red Hat 11.4.1-2)
Clang version: 17.0.1 (CentOS 17.0.1-2.el9)
CMake version: version 3.26.4
Libc version: glibc-2.34

Python version: 3.12.0 | packaged by Anaconda, Inc. | (main, Oct  2 2023, 17:29:18) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.12.0-0_fbk16_zion_7661_geb00762ce6d2-x86_64-with-glibc2.34
Is CUDA available: True
CUDA runtime version: 12.0.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA PG509-210
GPU 1: NVIDIA PG509-210
GPU 2: NVIDIA PG509-210
GPU 3: NVIDIA PG509-210
GPU 4: NVIDIA PG509-210
GPU 5: NVIDIA PG509-210
GPU 6: NVIDIA PG509-210
GPU 7: NVIDIA PG509-210

Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib64/libcudnn.so.8.9.4
/usr/lib64/libcudnn_adv_infer.so.8.9.4
/usr/lib64/libcudnn_adv_train.so.8.9.4
/usr/lib64/libcudnn_cnn_infer.so.8.9.4
/usr/lib64/libcudnn_cnn_train.so.8.9.4
/usr/lib64/libcudnn_ops_infer.so.8.9.4
/usr/lib64/libcudnn_ops_train.so.8.9.4
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn.so.8.9.2
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.9.2
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.9.2
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.9.2
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.9.2
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.9.2
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.9.2
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):                          192
On-line CPU(s) list:             0-191
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8339HC CPU @ 1.80GHz
CPU family:                      6
Model:                           85
Thread(s) per core:              2
Core(s) per socket:              24
Socket(s):                       4
Stepping:                        11
Frequency boost:                 enabled
CPU max MHz:                     1801.0000
CPU min MHz:                     800.0000
BogoMIPS:                        3600.00
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 smx 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 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid 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 avx512_bf16 dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       3 MiB (96 instances)
L1i cache:                       3 MiB (96 instances)
L2 cache:                        96 MiB (96 instances)
L3 cache:                        132 MiB (4 instances)
NUMA node(s):                    4
NUMA node0 CPU(s):               0-23,96-119
NUMA node1 CPU(s):               24-47,120-143
NUMA node2 CPU(s):               48-71,144-167
NUMA node3 CPU(s):               72-95,168-191
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          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; Enhanced IBRS, IBPB conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] flake8==6.1.0
[pip3] flake8-bugbear==23.3.23
[pip3] flake8-comprehensions==3.12.0
[pip3] flake8-executable==2.1.3
[pip3] flake8-logging-format==0.9.0
[pip3] flake8-pyi==23.3.1
[pip3] flake8-simplify==0.19.3
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.2
[pip3] optree==0.10.0
[pip3] pytorch-sphinx-theme==0.0.24
[pip3] pytorch-triton==2.2.0+e28a256d71
[pip3] torch==2.3.0a0+git5894af8
[conda] magma-cuda110             2.5.2                         1    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344
[conda] mkl-include               2023.1.0         h06a4308_46344
[conda] numpy                     1.26.2                   pypi_0    pypi
[conda] optree                    0.10.0                   pypi_0    pypi
[conda] pytorch-sphinx-theme      0.0.24                    dev_0    <develop>
[conda] pytorch-triton            2.2.0+e28a256d71          pypi_0    pypi
[conda] torch                     2.3.0a0+gitf54a968          pypi_0    pypi
[conda] torchfix                  0.2.0                    pypi_0    pypi

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