Repository: pytorch
Issue: Error compiling the torch.library.custom_op with input mutations with set_
Labels: triaged,module: custom-operators,oncall: pt2,module: pt2-dispatcher

### 🐛 Describe the bug

When we try to wrap a simple set_ into custom_op, we found it cannot be compiled. 

The following simple case failed with error:

```
import torch
from torch import nn

@torch.library.custom_op("mylib::set_data", mutates_args=["param"])
def set_data(param: torch.Tensor, new_data: torch.Tensor) -> None:
    param.set_(new_data) # or param.data = new_data.data


@torch.library.register_fake("mylib::set_data")
def set_data_fake(param: torch.Tensor, new_data: torch.Tensor) -> None:
    param.set_(new_data)
    return None


class SomeModule(nn.Module):
    def __init__(self):
        super().__init__()
        self.param = nn.Parameter(torch.empty(0))

    def forward(self, x):
        x = x * 3
        y = self.param
        set_data(y, x)
        return y / 3


module = SomeModule()  
module = torch.compile(module)

x = torch.randn(3, 3)
with torch.no_grad():
  y = module(x)

print(y)
```
Execute the above program shows exception:

```
File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 1182, in wrapped
    out = f(*tensors)
  File "<string>", line 1, in <lambda>
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 693, in inner_fn
    outs = fn(*args)
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 612, in _functionalized_f_helper
    inpt_old.copy_(inpt_new)
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 1230, in __torch_function__
    return func(*args, **kwargs)
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/_subclasses/functional_tensor.py", line 535, in __torch_dispatch__
    outs_unwrapped = func._op_dk(
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/_meta_registrations.py", line 397, in meta_copy_
    aten.expand_copy.default(intermediate, self.size())
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/_ops.py", line 716, in __call__
    return self._op(*args, **kwargs)
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/_refs/__init__.py", line 2214, in _fn
    result = fn(*args, out=out, **kwargs)
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/_prims_common/wrappers.py", line 273, in _fn
    result = fn(*args, **kwargs)
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/_ops.py", line 1116, in __call__
    return self._op(*args, **(kwargs or {}))
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/_refs/__init__.py", line 2970, in expand
    torch._check(
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/__init__.py", line 1565, in _check
    _check_with(RuntimeError, cond, message)
  File "/home/haifchen/working/envs/env_test_nightly/lib/python3.10/site-packages/torch/__init__.py", line 1547, in _check_with
    raise error_type(message_evaluated)
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
RuntimeError: expand: the requested shape has too few dimensions!

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information


You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True
```

While if I changes "self.param = nn.Parameter(torch.empty(0))" to "self.param = nn.Parameter(torch.empty(3, 3))", it works (but this is not what we need.

Some other variations show different problems. The following example shows the wrong result:

```
import torch
from torch import nn

@torch.library.custom_op("mylib::set_data", mutates_args=["param"])
def set_data(param: torch.Tensor, new_data: torch.Tensor) -> None:
    param.set_(new_data)


@torch.library.register_fake("mylib::set_data")
def set_data_fake(param: torch.Tensor, new_data: torch.Tensor) -> None:
    param.set_(new_data)
    return None


class SomeModule(nn.Module):
    def __init__(self):
        super().__init__()
        self.param = torch.empty(0)

    def forward(self, x):
        x = x * 3
        # using tenstor and reassign makes it pass the compile but shows the wrong result.
        self.param = torch.empty(0)
        y = self.param
        set_data(y, x)
        return y / 3


module = SomeModule()  
module = torch.compile(module)

x = torch.randn(3, 3)
with torch.no_grad():
  y = module(x)

print(y)

```

Execute the above program shows the wrong results as following:
`tensor([])`

The only version that works is as following (set_ to a local torch.empty(0) tensor.)

```
import torch
from torch import nn

@torch.library.custom_op("mylib::set_data", mutates_args=["param"])
def set_data(param: torch.Tensor, new_data: torch.Tensor) -> None:
    param.set_(new_data)


@torch.library.register_fake("mylib::set_data")
def set_data_fake(param: torch.Tensor, new_data: torch.Tensor) -> None:
    param.set_(new_data)
    return None


class SomeModule(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        x = x * 3
        y = torch.empty(0)
        set_data(y, x)
        return y / 3


module = SomeModule()  
module = torch.compile(module)

x = torch.randn(3, 3)
with torch.no_grad():
  y = module(x)

print(y)
```


### 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