torch.distributed.all_reduce() has inconsistent behavior
Labels: oncall: distributed, triaged

### 🐛 Describe the bug

This code does not perform an all_reduce() reduction across workers:

```
      torch.distributed.all_reduce( total_loss.cuda(), op=torch.distributed.ReduceOp.AVG )
      total_loss = total_loss_cuda.cpu()
```

while this code performs the operation correctly:  

```
      total_loss_cuda = total_loss.cuda()
      torch.distributed.all_reduce( total_loss_cuda, op=torch.distributed.ReduceOp.AVG )
      total_loss = total_loss_cuda.cpu()

```  

In my opinion, this is a bug, at least a usability one since I don't see a reason why the temporary returned by total_loss.cuda() in the first version shouldn't be usable as an argument to torch.distributed.all_reduce(). Version: PyTorch/1.12.0-CUDA-11.7.


### Versions

collect_pyenv (the code is running on a large supercomputer so collect_pyenv might not be entirely accurate/usefule:

Collecting environment information...
PyTorch version: 1.12.0
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Rocky Linux release 8.7 (Green Obsidian) (x86_64)
GCC version: (GCC) 11.3.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.28

Python version: 3.10.4 (main, Oct  4 2022, 08:48:24) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28
Is CUDA available: False
CUDA runtime version: 11.7.64
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
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:                   43 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          96
On-line CPU(s) list:             0-95
Vendor ID:                       AuthenticAMD
Model name:                      AMD EPYC 7402 24-Core Processor
CPU family:                      23
Model:                           49
Thread(s) per core:              2
Core(s) per socket:              24
Socket(s):                       2
Stepping:                        0
Frequency boost:                 enabled
CPU(s) scaling MHz:              83%
CPU max MHz:                     2800.0000
CPU min MHz:                     1500.0000
BogoMIPS:                        5599.93
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 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 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 avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es
Virtualization:                  AMD-V
L1d cache:                       1.5 MiB (48 instances)
L1i cache:                       1.5 MiB (48 instances)
L2 cache:                        24 MiB (48 instances)
L3 cache:                        256 MiB (16 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-23,48-71
NUMA node1 CPU(s):               24-47,72-95
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:          Mitigation; untrained return thunk; SMT enabled with STIBP protection
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, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.22.3
[pip3] torch==1.12.0
[conda] Could not collect

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