Repository: pytorch
Issue: DTensor RNG state for non CUDA backends
Labels: oncall: distributed,triaged

### 🐛 Describe the bug

DTensor random numbers provide an offset based RNG state tracker OffsetBasedRNGTracker for CUDA. However, for CPU, this offset based RNG state tracker is not available, and it produces a warning -

```
$ !cat dtensor_test.py

import torch
import os
import torch
from torch.distributed._tensor import init_device_mesh, Shard, distribute_tensor

mesh = init_device_mesh("cpu", (int(os.environ["WORLD_SIZE"]),))

t = torch.randn(100, 8)
dt = distribute_tensor(t, mesh, [Shard(dim=0)])

dt.uniform_(0, 1)
```
The warning comes as -
```
$ !torchrun --nnodes=1 --nproc-per-node=4 dtensor_test.py
...
warnings.warn(
/usr/local/lib/python3.10/dist-packages/torch/distributed/tensor/_random.py:45: UserWarning: DTensor random operators may not have complete support on cpu device mesh
```
Random ops mesh support for a backend is checked with is_rng_supported_mesh(), which checks the presence of hasattr(device_handle, “set_rng_state”). 

If a backend uses CPU RNG state and has a set_rng_state() implemented, it sets is_rng_supported_mesh() to be True and DTensor random mechanism tries to use OffsetBasedRNGTracker. The seed and offset based APIs assume the RNG state to be a CUDA like offset based. CPU RNG state doesn’t work and fails as shown below -

```
[rank0]:   File ".../.local/lib/python3.10/site-packages/torch/distributed/_tensor/random.py", line 176, in _distribute_region
[rank0]:     old_offset = self.get_offset("parallel-rng")
[rank0]:   File ".../.local/lib/python3.10/site-packages/torch/distributed/_tensor/random.py", line 195, in get_offset
[rank0]:     return int(offset_tensor.item())
[rank0]: RuntimeError: a Tensor with 631 elements cannot be converted to Scalar
```

It would be better if is_rng_supported_mesh() has an explicit support from the backend RNG, maybe something like an hasattr(device_handle, "rng_mesh_supported"). This way, backends that use CPU RNG state and has the set_rng_state() implemented will not try to use the OffsetBasedRNGTracker.

Also, is there a plan to support the DTensor random ops for CPU better? It will not be offset based RNG tracker, but the seed can be advanced correctly for the CPU RNG state after the DTensor random op.


### Versions


7s
!python collect_env.py
Collecting environment information...
PyTorch version: 2.6.0.dev20240923+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
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: 14.0.0-1ubuntu1.1
CMake version: version 3.30.4
Libc version: glibc-2.35

Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.85+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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):                               2
On-line CPU(s) list:                  0,1
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family:                           6
Model:                                79
Thread(s) per core:                   2
Core(s) per socket:                   1
Socket(s):                            1
Stepping:                             0
BogoMIPS:                             4399.99
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            32 KiB (1 instance)
L1i cache:                            32 KiB (1 instance)
L2 cache:                             256 KiB (1 instance)
L3 cache:                             55 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0,1
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Mitigation; PTE Inversion
Vulnerability Mds:                    Vulnerable; SMT Host state unknown
Vulnerability Meltdown:               Vulnerable
Vulnerability Mmio stale data:        Vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Vulnerable
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Vulnerable
Vulnerability Spectre v1:             Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:             Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Vulnerable (Syscall hardening enabled)
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.1.105
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] nvtx==0.2.10
[pip3] optree==0.13.0
[pip3] pynvjitlink-cu12==0.3.0
[pip3] pytorch-triton==3.1.0+5fe38ffd73
[pip3] torch==2.6.0.dev20240923+cu121
[pip3] torchaudio==2.5.0.dev20240923+cu121
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.20.0.dev20240923+cu121
[conda] Could not collect

cc @XilunWu @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o