Repository: pytorch
Issue: [DDP + Dynamo] Failed to get compiled graphs
Labels: oncall: distributed,triaged,oncall: pt2,module: dynamo,pt2d-triage-nov2024

### 🐛 Describe the bug

There is a popular method of getting compiled graphs  using a custom backend.

E.g.

```
def my_backend(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]):
    print("graph:")
    gm.graph.print_tabular()
    return gm.forward

model = nn.Linear(10, 10)
model = torch.compile(model, backend=my_backend)
```

However, when I use the DDP module, this backend is not called (without the DDP everything works fine).
Moreover, most of the information when running with the TORCH_COMPILE_DEBUG=1 variable is not displayed when using the DDP.

My code:
```
def my_backend(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]):
    print('graph:')
    gm.graph.print_tabular()
    return gm.forward

def linear(rank, world_size, model):
    dist.init_process_group("gloo", rank=rank, world_size=world_size)
    inputs = torch.randn(20, 100)
    labels = torch.randn(20, 100)

    model = model.to(rank)
    model = DDP(model, device_ids=[rank])
    model = torch.compile(model, backend=my_backend)

    loss_fn = nn.MSELoss()
    optimizer = optim.SGD(model.parameters(), lr=0.001)

    for i in range(2):
        outputs = model(inputs.to(rank))

        labels = labels.to(rank)
        loss_fn(outputs, labels).backward()
        optimizer.step()

def main():
    model = nn.Sequential(nn.Linear(100, 100), nn.Linear(100, 100))
    world_size = 2
    mp.spawn(linear,
        args=(world_size, model),
        nprocs=world_size,
        join=True)

if __name__=="__main__":
    os.environ["MASTER_ADDR"] = "localhost"
    os.environ["MASTER_PORT"] = "29500"
    main()
```

Output:
```
> python3 ddp_compile.py
[rank0]:W1112 19:38:21.277000 139076842178368 torch/_logging/_internal.py:1034] [0/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
[rank1]:W1112 19:38:21.289000 133097161074496 torch/_logging/_internal.py:1034] [0/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignore
```


I guess this is incorrect behavior. 

Anyway, is there any other way to get compiled graphs?

### Error logs

_No response_

### Versions

Collecting environment information...
PyTorch version: 2.4.0
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 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: 3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 15:12:24) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-45-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090

Nvidia driver version: 535.183.01
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:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             32
On-line CPU(s) list:                0-31
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) w5-2465X
CPU family:                         6
Model:                              143
Thread(s) per core:                 2
Core(s) per socket:                 16
Socket(s):                          1
Stepping:                           8
CPU max MHz:                        4700,0000
CPU min MHz:                        800,0000
BogoMIPS:                           6192.00
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 tsc_known_freq 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 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          768 KiB (16 instances)
L1i cache:                          512 KiB (16 instances)
L2 cache:                           32 MiB (16 instances)
L3 cache:                           33,8 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-31
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: Not affected
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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.4.0
[pip3] torchaudio==2.4.0
[pip3] torchvision==0.19.0
[pip3] torchviz==0.0.2
[pip3] triton==3.0.0
[conda] blas                      1.0                         mkl  
[conda] cuda-cudart               12.1.105                      0    nvidia
[conda] cuda-cupti                12.1.105                      0    nvidia
[conda] cuda-libraries            12.1.0                        0    nvidia
[conda] cuda-nvrtc                12.1.105                      0    nvidia
[conda] cuda-nvtx                 12.1.105                      0    nvidia
[conda] cuda-opencl               12.5.39                       0    nvidia
[conda] cuda-runtime              12.1.0                        0    nvidia
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libcublas                 12.1.0.26                     0    nvidia
[conda] libcufft                  11.0.2.4                      0    nvidia
[conda] libcurand                 10.3.6.82                     0    nvidia
[conda] libcusolver               11.4.4.55                     0    nvidia
[conda] libcusparse               12.0.2.55                     0    nvidia
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] libnvjitlink              12.1.105                      0    nvidia
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0           py312h5eee18b_1  
[conda] mkl_fft                   1.3.8           py312h5eee18b_0  
[conda] mkl_random                1.2.4           py312hdb19cb5_0  
[conda] numpy                     1.26.4          py312hc5e2394_0  
[conda] numpy-base                1.26.4          py312h0da6c21_0  
[conda] pytorch                   2.4.0           py3.12_cuda12.1_cudnn9.1.0_0    pytorch
[conda] pytorch-cuda              12.1                 ha16c6d3_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.4.0               py312_cu121    pytorch
[conda] torchtriton               3.0.0                     py312    pytorch
[conda] torchvision               0.19.0              py312_cu121    pytorch
[conda] torchviz                  0.0.2                    pypi_0    pypi

cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @amjames @ezyang