Repository: pytorch
Issue: [torch.jit] Crash would be raised when using TorchScript modules
Labels: oncall: jit

### 🐛 Describe the bug

Segmentation fault would be triggered when using TorchScript modules, particularly in the context of saving, loading, and dealing with interface changes and named tuple definitions. The code is as follows:

```python
import io
from typing import NamedTuple
import torch
from torch import Tensor
from torch.testing._internal.jit_utils import clear_class_registry


def script_module_to_buffer(script_module):
    module_buffer = io.BytesIO(script_module._save_to_buffer_for_lite_interpreter(_use_flatbuffer=True))
    module_buffer.seek(0)
    return module_buffer


class MyCoolNamedTuple(NamedTuple):
    a: int

@torch.jit.interface
class MyInterface:

    def bar(self, x: Tensor) -> Tensor:
        pass

@torch.jit.script
class ImplementInterface:

    def __init__(self) -> None:
        pass

    def bar(self, x):
        return x


class Foo(torch.nn.Module):
    interface: MyInterface

    def __init__(self) -> None:
        super().__init__()
        self.foo = torch.nn.Linear(2, 2)
        self.bar = torch.nn.Linear(2, 2)
        self.interface = ImplementInterface()

    def forward(self, x):
        x = self.foo(x)
        x = self.bar(x)
        x = self.interface.bar(x)
        return [x, MyCoolNamedTuple(a=5)]

first_script_module = torch.jit.script(Foo())
first_saved_module = script_module_to_buffer(first_script_module)
clear_class_registry()


@torch.jit.interface
class MyInterface:
    def not_bar(self, x: Tensor) -> Tensor:
        pass

@torch.jit.script
class ImplementInterface:

    def __init__(self) -> None:
        pass

    def not_bar(self, x):
        return x


class MyCoolNamedTuple(NamedTuple):
    a: str

class Foo(torch.nn.Module):
    interface: MyInterface

    def __init__(self) -> None:
        super().__init__()
        self.foo = torch.nn.Linear(2, 2)
        self.interface = ImplementInterface()

    def forward(self, x):
        return x

second_script_module = torch.jit.script(Foo())
second_saved_module = script_module_to_buffer(second_script_module)
clear_class_registry()

class ContainsBoth(torch.nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.add_module('second', torch.jit.load(second_saved_module))
        self.add_module('first', torch.jit.load(first_saved_module))

    def forward(self, x):
        return x

sm = torch.jit.script(ContainsBoth())
contains_both = script_module_to_buffer(sm)
sm = torch.jit.load(contains_both)
```

Error messages:
```shell
Fail to import hypothesis in common_utils, tests are not derandomized
[W831 15:30:16.414731168 ir_emitter.cpp:4523] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[Tensor, __torch__.MyCoolNamedTuple]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
  File "/data/test.py", line 49
        x = self.bar(x)
        x = self.interface.bar(x)
        return [x, MyCoolNamedTuple(a=5)]
               ~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
 (function emitListLiteral)
Segmentation fault (core dumped)

```
The error is reproducible with the nightly-build version `2.5.0.dev20240815+cpu` . 





### Versions

Collecting environment information...
PyTorch version: 2.5.0.dev20240815+cpu
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.3 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31
Is CUDA available: False
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090

Nvidia driver version: 525.116.03
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 57 bits virtual
CPU(s):                          64
On-line CPU(s) list:             0-63
Thread(s) per core:              2
Core(s) per socket:              16
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           106
Model name:                      Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz
Stepping:                        6
Frequency boost:                 enabled
CPU MHz:                         900.000
CPU max MHz:                     3500.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5800.00
Virtualization:                  VT-x
L1d cache:                       1.5 MiB
L1i cache:                       1 MiB
L2 cache:                        40 MiB
L3 cache:                        48 MiB
NUMA node0 CPU(s):               0-15,32-47
NUMA node1 CPU(s):               16-31,48-63
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          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 IBRS, IBPB conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
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 pni pclmulqdq dtes64 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 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi 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 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.5.0.dev20240815+cpu
[pip3] torchaudio==2.4.0.dev20240815+cpu
[pip3] torchvision==0.20.0.dev20240815+cpu
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] torch                     2.5.0.dev20240815+cpu          pypi_0    pypi
[conda] torchaudio                2.4.0.dev20240815+cpu          pypi_0    pypi
[conda] torchvision               0.20.0.dev20240815+cpu          pypi_0    pypi


cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel