[Bug] Segmentation fault when using TVM together with `transformers` (flan-t5-base + bfloat16 + `use_cache=True`)

### Describe the bug

When I import TVM, create a target, and then load a Hugging Face `transformers` model (`google/flan-t5-base` with `torch_dtype=torch.bfloat16` and `use_cache=True`) and call `generate()`, the Python process crashes with a segmentation fault.

The crash happens before any TVM compilation or runtime calls on the model — simply creating a TVM target and then using `AutoModelForSeq2SeqLM.generate()` is enough to trigger a segfault. The stack trace shows the failure occurring during `dlopen` and initialization of LLVM’s COFF option table (`llvm::opt::OptTable::buildPrefixChars()` / `COFFDirectiveParser.cpp`).

This looks like a dynamic linking / LLVM initialization interaction between TVM and other LLVM-using components loaded by `transformers` / PyTorch.

---

### Minimal reproducible example

```python
#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
Minimal repro: TVM + transformers (flan-t5-base) cause a segfault.

Steps:
1) Import TVM and create a target/device.
2) Import AutoModelForSeq2SeqLM("google/flan-t5-base", bfloat16, use_cache=True).
3) Run one generate() on random input_ids.
"""

import torch
from torch import nn
import tvm

def main():
# 1) Load TVM and create a target (triggers LLVM / TVM runtime loading)
if torch.cuda.is_available():
target = tvm.target.Target("cuda")
device = "cuda"
else:
target = tvm.target.Target("llvm")
device = "cpu"
print("TVM target:", target)

# 2) Now import transformers and load flan-t5-base
from transformers import AutoModelForSeq2SeqLM

class MyModel(nn.Module):
def __init__(self):
super().__init__()
self.model = AutoModelForSeq2SeqLM.from_pretrained(
"google/flan-t5-base",
torch_dtype=torch.bfloat16,
use_cache=True,
)

def forward(self, input_ids, attention_mask=None, **gen_kwargs):
return self.model.generate(
input_ids,
attention_mask=attention_mask,
**gen_kwargs,
)

model = MyModel().to(device)
model.eval()

# 3) Single generate() call on random input
input_ids = torch.randint(0, 10000, (1, 512), dtype=torch.long, device=device)
attention_mask = torch.ones_like(input_ids)

with torch.no_grad():
out = model(
input_ids,
attention_mask=attention_mask,
max_new_tokens=8,
)

print("generate() finished, output shape:", out.shape)

if __name__ == "__main__":
main()
```

Run:

```bash
python minimal_tvm_transformers_segfault.py
```

---

### Actual behavior

On my machine, the script prints the TVM target and then immediately crashes with a segmentation fault. The beginning of the output looks like this:

```text
TVM target: cuda -keys=cuda,gpu -arch=sm_86 -max_num_threads=1024 -thread_warp_size=32
!!!!!!! Segfault encountered !!!!!!!
File "./signal/../sysdeps/unix/sysv/linux/x86_64/libc_sigaction.c", line 0, in 0x00007e827de4251f
File "", line 0, in llvm::opt::OptTable::buildPrefixChars()
File "", line 0, in COFFOptTable::COFFOptTable()
File "", line 0, in _GLOBAL__sub_I_COFFDirectiveParser.cpp
File "./elf/dl-init.c", line 70, in call_init
File "./elf/dl-init.c", line 33, in call_init
File "./elf/dl-init.c", line 117, in _dl_init
File "./elf/dl-error-skeleton.c", line 182, in __GI__dl_catch_exception
File "./elf/dl-open.c", line 808, in dl_open_worker
...
Segmentation fault (core dumped)
```

The full stack trace is quite long, but it mainly consists of `dlopen` / `dl-init` frames and LLVM initialization calls such as `llvm::opt::OptTable::buildPrefixChars()` and `COFFDirectiveParser.cpp` global constructors.

---

### Expected behavior

I expect the script to run without a segmentation fault, print the TVM target, run one `generate()` call on `flan-t5-base`, and print the generated output tensor shape.

TVM is not actually compiling or running this model in the repro — only importing TVM and creating a target is required — so ideally it should coexist safely with `transformers` / PyTorch / their dependencies.

---

### Environment

* OS: Linux x86_64 (glibc-based, from backtrace paths such as `./elf/dl-open.c`)
* Python: `3.10.16 | packaged by conda-forge | (main, Apr 8 2025, 20:53:32) [GCC 13.3.0]`
* NumPy: `2.2.6`
* PyTorch: `2.9.0+cu128`
* TVM:

* Version: `0.22.0`
* LLVM version (reported by `tvm.support.libinfo()`): `17.0.6`
* GIT_COMMIT_HASH: `9dbf3f22ff6f44962472f9af310fda368ca85ef2`
* GPU / CUDA:

* TVM target: `cuda -keys=cuda,gpu -arch=sm_86 -max_num_threads=1024 -thread_warp_size=32`
* CUDA toolkit likely 12.8 (from PyTorch build tag `+cu128`)

```python
import tvm, torch, transformers
from tvm import support

print("TVM version:", getattr(tvm, "__version__", "unknown"))
print("TVM LLVM version:", support.libinfo().get("LLVM_VERSION", "unknown"))
print("PyTorch:", torch.__version__)
print("transformers:", transformers.__version__)
```

### Triage

Please refer to the list of label tags [here](https://github.com/apache/tvm/wiki/Issue-Triage-Labels) to find the relevant tags and add them below in a bullet format (example below).

* needs-triage
* bug

Führe zunächst minimal_tvm_transformers_segfault.py aus und vergleiche den Crash-Stack mit den gemeldeten dlopen- und LLVM-Initialisierungs-Frames. Konzentriere dich auf das Zusammenspiel zwischen tvm.target.Target("cuda" oder "llvm"), transformers und der Ladereihenfolge von PyTorch. Als erledigt gilt die Reproduktion, wenn generate() ohne Segmentation Fault abgeschlossen wird und die Form des Ausgabetensors ausgegeben wird.

Vom Indexierungsmodell aus dem Issue-Text verfasst.