Repository: pytorch
Issue: Dynamo capture of tensor.data assignment doesn't identical to eager call of tensor.data assignment
Labels: triaged,oncall: pt2,module: aotdispatch,module: pt2-dispatcher

### 🐛 Describe the bug

DeepSpeed uses a lot "param.data =" statement to updating the param data by gathering the param from other ranks.
While we found param.data= assignment under torch.compile mode behaviors differently with the original eager call of param.data assignment behavior, which causes some strange problems for backward.

Torch dynamo captures param.data attribute assignment into set_ op and we found what set_ op performs on param tensor is not equivalent to the original param.data assignment on eager.
The obvious one is the version counter of the tensor. There may also be other side effects from set_ op while the original param.data assignment doesn't have.

The following test example failed with torch.compile but success with eager mode (This examples shows part of the problem it causes):

```
import torch
import torch.nn as nn

@torch.no_grad()
def do_gather(param):
  # Simulating the deepspeed gather
  r1 = torch.ones(param.shape, dtype=param.dtype, device=param.device)
  r1 = r1.mul(1.0)
  param.data = r1.data

class SomeModule(nn.Module):
    def __init__(self):
        super().__init__()
        self.param = nn.Parameter(torch.ones(5, 5))

    def forward(self, x):
        y = self.param ** 2

        print("HAIFENG version counter before:", self.param._version)
        do_gather(self.param)
        print("HAIFENG version counter after:", self.param._version)
       
        return y


model = SomeModule()
# comment out for eager mode
model = torch.compile(model)

input_tensor = torch.ones(5,5)
output = model(input_tensor)
output.sum().backward()
```

### Versions

Collecting environment information...
PyTorch version: 2.6.0.dev20240914+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04 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 3.22.1
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-5.15.0-122-generic-x86_64-with-glibc2.35
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:                        43 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               12
On-line CPU(s) list:                  0-11
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Gold 6230 CPU @ 2.10GHz
CPU family:                           6
Model:                                85
Thread(s) per core:                   1
Core(s) per socket:                   6
Socket(s):                            2
Stepping:                             0
BogoMIPS:                             4190.15
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 arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xsaves arat pku ospke md_clear flush_l1d arch_capabilities
Virtualization:                       VT-x
Hypervisor vendor:                    VMware
Virtualization type:                  full
L1d cache:                            384 KiB (12 instances)
L1i cache:                            384 KiB (12 instances)
L2 cache:                             12 MiB (12 instances)
L3 cache:                             55 MiB (2 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-11
Vulnerability Gather data sampling:   Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:          KVM: Mitigation: VMX disabled
Vulnerability L1tf:                   Mitigation; PTE Inversion; VMX flush not necessary, SMT disabled
Vulnerability Mds:                    Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown:               Mitigation; PTI
Vulnerability Mmio stale data:        Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Mitigation; IBRS
Vulnerability Spec rstack overflow:   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; IBRS; IBPB conditional; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI SW loop, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==2.1.1
[pip3] torch==2.6.0.dev20240914+cpu
[conda] Could not collect


cc @ezyang @chauhang @penguinwu @zou3519 @bdhirsh @yf225 @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @amjames