TImeout in NCCL doesn't work
Labels: oncall: distributed, triaged

### 🐛 Describe the bug

The document about `init_process_group` https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group

suggest `timeout` has been avaliable in NCCL backend. But I cannot get the following work:

```python
import os
import time
from datetime import timedelta

import torch.distributed as dist
from torch.distributed.elastic.multiprocessing.errors import record

print(os.environ["NCCL_BLOCKING_WAIT"])

local_rank = int(os.environ["RANK"])
local_world_size = int(os.environ["WORLD_SIZE"])
server_store = dist.TCPStore(
    host_name="localhost",
    port=24950,
    world_size=local_world_size,
    is_master=(local_rank == 0),
    # timeout=timedelta(seconds=60),
)

dist.init_process_group(
    backend="nccl",
    world_size=local_world_size,
    rank=local_rank,
    store=server_store,
    timeout=timedelta(seconds=2),
)


@record
def main():
    if dist.get_rank() == 1:
        dist.barrier()
    else:
        time.sleep(1000)


main()

```

and run it by
```
NCCL_BLOCKING_WAIT=1 torchrun --nnode 1 --nproc_per_node 2 test.py
```

But the code still hang on and doesn't raise a timeout error. Is this a bug or did I misunderstand the usage of 'timeout' here?


### Versions

Collecting environment information...
PyTorch version: 2.0.0+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (conda-forge gcc 11.3.0-19) 11.3.0
Clang version: Could not collect
CMake version: version 3.26.3
Libc version: glibc-2.31

Python version: 3.9.16 | packaged by conda-forge | (main, Feb  1 2023, 21:39:03)  [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.4.0-148-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.7.99
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100 80GB PCIe
GPU 1: NVIDIA A100 80GB PCIe

Nvidia driver version: 530.30.02
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.1
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
Byte Order:                      Little Endian
Address sizes:                   43 bits physical, 48 bits virtual
CPU(s):                          256
On-line CPU(s) list:             0-254
Off-line CPU(s) list:            255
Thread(s) per core:              1
Core(s) per socket:              64
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       AuthenticAMD
CPU family:                      25
Model:                           1
Model name:                      AMD EPYC 7763 64-Core Processor
Stepping:                        1
Frequency boost:                 enabled
CPU MHz:                         1497.171
CPU max MHz:                     2450.0000
CPU min MHz:                     1500.0000
BogoMIPS:                        4890.81
Virtualization:                  AMD-V
L1d cache:                       2 MiB
L1i cache:                       2 MiB
L2 cache:                        32 MiB
L3 cache:                        256 MiB
NUMA node0 CPU(s):               0-63,128-191
NUMA node1 CPU(s):               64-127,192-254
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 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; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
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 constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate sme ssbd mba sev ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.3
[pip3] pytorch-lightning==1.6.4
[pip3] torch==2.0.0
[pip3] torchaudio==2.0.2
[pip3] torchmetrics==0.11.4
[pip3] torchvision==0.15.2
[pip3] triton==2.0.0
[conda] blas                      2.116                       mkl    conda-forge
[conda] blas-devel                3.9.0            16_linux64_mkl    conda-forge
[conda] libblas                   3.9.0            16_linux64_mkl    conda-forge
[conda] libcblas                  3.9.0            16_linux64_mkl    conda-forge
[conda] liblapack                 3.9.0            16_linux64_mkl    conda-forge
[conda] liblapacke                3.9.0            16_linux64_mkl    conda-forge
[conda] mkl                       2022.1.0           h84fe81f_915    conda-forge
[conda] mkl-devel                 2022.1.0           ha770c72_916    conda-forge
[conda] mkl-include               2022.1.0           h84fe81f_915    conda-forge
[conda] numpy                     1.24.3           py39h6183b62_0    conda-forge
[conda] pytorch-cuda              11.7                 h778d358_5    pytorch
[conda] pytorch-lightning         1.6.4                    pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torch                     2.0.0                    pypi_0    pypi
[conda] torchaudio                2.0.2                py39_cu117    pytorch
[conda] torchmetrics              0.11.4                   pypi_0    pypi
[conda] torchtriton               2.0.0                      py39    pytorch
[conda] torchvision               0.15.2               py39_cu117    pytorch


cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu