Repository: pytorch
Issue: torchrun discarding --rdzv-endpoint when it should not
Labels: triaged,module: elastic

### 🐛 Describe the bug

Hi, I have been going over the torch distributed implementation and documentations to further my understanding and think I might have found an issue.
When we use torchrun in any of the following ways (see documentation's _Usage_ section: https://pytorch.org/docs/stable/elastic/run.html):
```bash
torchrun
    --standalone
    --nnodes=1
    --nproc-per-node=$NUM_TRAINERS
    YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
``` 
or
```bash
torchrun
    --rdzv-backend=c10d
    --rdzv-endpoint=localhost:0
    --nnodes=1
    --nproc-per-node=$NUM_TRAINERS
    YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
```
we are using --rdzv-backend=c10d which is different than "static" !

---
To see why --standalone does use c10d see the following screenshot from https://github.com/pytorch/pytorch/blob/main/torch/distributed/run.py : 
![image](https://github.com/pytorch/pytorch/assets/54705314/f3960d16-5127-427b-accf-617be022d56e)

----

But in  https://github.com/pytorch/pytorch/blob/main/torch/distributed/launcher/api.py#L99, we have:
![image](https://github.com/pytorch/pytorch/assets/54705314/dac6350c-bebb-4c30-a1bb-9c5350285017)

 as I have mentioned before, almost always we pass --rdzv-backend=c10d which makes code run the following if statement in the above image and return None for master_addr and master_port values:
    ```python
    if rdzv_parameters.backend != "static":
            return (None, None)
    ```
    This discards our --rdzv-endpoint values which I believe is the  wrong thing to do ? I assume it should run as:
    ```python
    master_addr, master_port = parse_rendezvous_endpoint(endpoint, default_port=-1)
    ```
and use our provided --rdzv-endpoint.


Am I missing something or is there really an issue here ? This issue is related to my questions 3-5 on https://discuss.pytorch.org/t/torchrun-rdzv-and-master-options-related-questions/191679.
Thank you very much for your help. 

### Versions

Collecting environment information...
PyTorch version: 2.0.1
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 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: 3.8.16 (default, Mar  2 2023, 03:21:46)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.2.0-36-generic-x86_64-with-glibc2.17
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
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:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             16
On-line CPU(s) list:                0-15
Vendor ID:                          AuthenticAMD
Model name:                         AMD Ryzen 7 5800H with Radeon Graphics
CPU family:                         25
Model:                              80
Thread(s) per core:                 2
Core(s) per socket:                 8
Socket(s):                          1
Stepping:                           0
Frequency boost:                    enabled
CPU max MHz:                        4462.5000
CPU min MHz:                        1200.0000
BogoMIPS:                           6387.67
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 rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 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 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms 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 rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualization:                     AMD-V
L1d cache:                          256 KiB (8 instances)
L1i cache:                          256 KiB (8 instances)
L2 cache:                           4 MiB (8 instances)
L3 cache:                           16 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-15
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 Retbleed:             Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET, no microcode
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; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] ament-flake8==0.12.7
[pip3] numpy==1.24.2
[pip3] torch==2.0.1
[pip3] torch-tb-profiler==0.4.3
[pip3] torchvision==0.15.2
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py38h7f8727e_0  
[conda] numpy                     1.24.2                   pypi_0    pypi
[conda] pytorch                   2.0.1           cpu_py38hab5cca8_0  
[conda] pytorch-mutex             1.0                         cpu    pytorch
[conda] torch                     1.13.1                   pypi_0    pypi
[conda] torch-tb-profiler         0.4.3                    pypi_0    pypi
[conda] torchvision               0.15.2                 py38_cpu    pytorch


cc @dzhulgakov