Repository: pytorch
Issue: JIT tracing a quantized model with hooks is broken
Labels: oncall: jit,oncall: quantization,low priority,triaged

### 🐛 Describe the bug

JIT tracing a quantized model that has forward_pre_hooks throws the following error: 


`RuntimeError: Couldn't find method: 'forward' on class: '__torch__.torch.ao.nn.intrinsic.quantized.modules.conv_relu.___torch_mangle_260.ConvReLU2d (of Python compilation unit at: 0x5a4cdb98abe0)'
`

**Float32, fused, and prepared models with hooks all seem to work just fine.**

Code to replicate (adapted from the Torch's own quantization example) - tested on Torch 2.4.0: 

```python
import torch

# define a floating point model where some layers could be statically quantized
class M(torch.nn.Module):
    def __init__(self):
        super().__init__()
        # QuantStub converts tensors from floating point to quantized
        self.quant = torch.ao.quantization.QuantStub()
        self.conv = torch.nn.Conv2d(1, 1, 1)
        self.relu = torch.nn.ReLU()
        # DeQuantStub converts tensors from quantized to floating point
        self.dequant = torch.ao.quantization.DeQuantStub()
        self.handles = []

    def forward(self, x):
        # manually specify where tensors will be converted from floating
        # point to quantized in the quantized model
        x = self.quant(x)
        x = self.conv(x)
        x = self.relu(x)
        # manually specify where tensors will be converted from quantized
        # to floating point in the quantized model
        x = self.dequant(x)
        return x

# create a model instance
model_fp32 = M()
input_fp32 = torch.randn(4, 1, 4, 4)

# model must be set to eval mode for static quantization logic to work
model_fp32.eval()
def test_hook(module, input):
    return input

model_fp32.conv.register_forward_pre_hook(test_hook)
trace_output = torch.jit.trace(model_fp32, input_fp32)
print("Model Fp32 with hooks - Traced Successfully")
model_fp32.qconfig = torch.ao.quantization.get_default_qconfig('x86')


model_fp32_fused = torch.ao.quantization.fuse_modules(model_fp32, [['conv', 'relu']])
model_fp32.conv.register_forward_pre_hook(test_hook)
trace_output = torch.jit.trace(model_fp32_fused, input_fp32)
print("Fused model with hooks - Traced Successfully")

# Prepare the model for static quantization. This inserts observers in
# the model that will observe activation tensors during calibration.
model_fp32_prepared = torch.ao.quantization.prepare(model_fp32_fused)
model_fp32_prepared(input_fp32)
model_fp32_prepared.conv.register_forward_pre_hook(test_hook)
traced_output = torch.jit.trace(model_fp32_prepared, input_fp32)
print("Prepared model with hooks - Traced Successfully")

print("Int8 Model with hooks: UNSUCCESSFUL")
model_int8 = torch.quantization.convert(model_fp32_prepared)
torch.jit.trace(model_int8, input_fp32)
    
print("Without hooks: works")
model_int8.conv._forward_pre_hooks.clear()
# model_int8.conv._forward_hooks.clear()
# model_int8.conv._forward_pre_hooks_with_kwargs.clear()
torch.jit.trace(model_int8, input_fp32)
```

Outputs: 

```
Model Fp32 with hooks - Traced Successfully
Fused model with hooks - Traced Successfully
Prepared model with hooks - Traced Successfully
Int8 Model with hooks: UNSUCCESSFUL
```
And the above error.

### Versions

Collecting environment information...
PyTorch version: 2.4.0+cu121
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: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-44-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:                      39 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             20
On-line CPU(s) list:                0-19
Vendor ID:                          GenuineIntel
Model name:                         13th Gen Intel(R) Core(TM) i7-13700H
CPU family:                         6
Model:                              186
Thread(s) per core:                 2
Core(s) per socket:                 14
Socket(s):                          1
Stepping:                           2
CPU max MHz:                        5000.0000
CPU min MHz:                        400.0000
BogoMIPS:                           5836.80
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 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities
Virtualisation:                     VT-x
L1d cache:                          544 KiB (14 instances)
L1i cache:                          704 KiB (14 instances)
L2 cache:                           11.5 MiB (8 instances)
L3 cache:                           24 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-19
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] triton==3.0.0
[conda] Could not collect

cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @Xia-Weiwen @leslie-fang-intel @msaroufim