Repository: pytorch
Issue: Inductor autograd raises an error in the second run may because of fx graph cache
Labels: high priority,triaged,bug,oncall: pt2,module: inductor

### 🐛 Describe the bug

```python
import torch
import os

os.environ["CUDA_LAUNCH_BLOCKING"] = "1"

@torch.compile
def func(x):
    return x * x

x = torch.tensor(0.0, device="cuda", requires_grad=True)

func(x).backward()
print(x.grad)
```
run the code twice will get a triton error in the second run.
```
Traceback (most recent call last):
  File "/root/dev/temp/tst.py", line 14, in <module>
    func(x).backward()
  File "/root/dev/pytorch/torch/_tensor.py", line 648, in backward
    torch.autograd.backward(
  File "/root/dev/pytorch/torch/autograd/__init__.py", line 353, in backward
    _engine_run_backward(
  File "/root/dev/pytorch/torch/autograd/graph.py", line 815, in _engine_run_backward
    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
  File "/root/dev/pytorch/torch/autograd/function.py", line 307, in apply
    return user_fn(self, *args)
  File "/root/dev/pytorch/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 1958, in backward
    return impl_fn()
  File "/root/dev/pytorch/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 1944, in impl_fn
    out = CompiledFunction._backward_impl(ctx, all_args)
  File "/root/dev/pytorch/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 2079, in _backward_impl
    out = call_func_at_runtime_with_args(
  File "/root/dev/pytorch/torch/_functorch/_aot_autograd/utils.py", line 126, in call_func_at_runtime_with_args
    out = normalize_as_list(f(args))
  File "/root/dev/pytorch/torch/_inductor/output_code.py", line 464, in __call__
    return self.current_callable(inputs)
  File "/root/dev/pytorch/torch/_inductor/utils.py", line 2228, in run
    return model(new_inputs)
  File "/tmp/torchinductor_root/ra/crazrzms2jyia4lhreqvggnuhmqpq44ag44s5qjmcvsbwhbd2hdr.py", line 95, in call
    triton_poi_fused_add_mul_0.run(tangents_1, primals_1, buf0, 1, grid=grid(1), stream=stream0)
  File "/root/dev/pytorch/torch/_inductor/runtime/triton_heuristics.py", line 961, in run
    return launcher(
  File "<string>", line 6, in launcher
  File "/usr/local/lib/python3.10/dist-packages/triton/backends/nvidia/driver.py", line 435, in __call__
    self.launch(*args, **kwargs)
RuntimeError: Triton Error [CUDA]: an illegal memory access was encountered
```
set `TORCHINDUCTOR_FX_GRAPH_CACHE=0` can fix it.

### Versions

Collecting environment information...
PyTorch version: 2.7.0a0+git62ce3e6
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: 20.0.0git (https://github.com/llvm/llvm-project.git ece4e1276e2140d84b05b8c430a0e547a1f23210)
CMake version: version 3.31.4
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-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Laptop GPU
Nvidia driver version: 551.61
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.3.0
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:                           12th Gen Intel(R) Core(TM) i7-12700H
CPU family:                           6
Model:                                154
Thread(s) per core:                   2
Core(s) per socket:                   10
Socket(s):                            1
Stepping:                             3
BogoMIPS:                             5376.02
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 tsc_reliable nonstop_tsc cpuid 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 ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves avx_vnni umip waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize flush_l1d arch_capabilities
Virtualization:                       VT-x
Hypervisor vendor:                    Microsoft
Virtualization type:                  full
L1d cache:                            480 KiB (10 instances)
L1i cache:                            320 KiB (10 instances)
L2 cache:                             12.5 MiB (10 instances)
L3 cache:                             24 MiB (1 instance)
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 Reg file data sampling: Vulnerable: No microcode
Vulnerability Retbleed:               Mitigation; Enhanced 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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==2.1.0
[pip3] optree==0.14.0
[pip3] pytorch-triton==3.2.0+git0d4682f0
[pip3] torch==2.7.0a0+git62ce3e6
[pip3] torch-xla==2.5.0+git3d860bf
[conda] Could not collect

cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @aakhundov @ezyang @gchanan @zou3519 @msaroufim @avikchaudhuri @gmagogsfm @zhxchen17 @tugsbayasgalan @angelayi @suo @ydwu4 @atalman @malfet @ptrblck @nWEIdia @xwang233