segmentation fault while using onnxruntime==1.21.0

[vmnit]

onnxruntime getting crashed with segmentation fault when using version 1.21.0. It is not crashing when using 1.20.1 release.

Steps to reproduce:

import onnxruntime as ort
sess_options = ort.SessionOptions()
sess = ort.InferenceSession('hf_Qwen2-7B-Instruct_model.onnx', sess_options)

hf_Qwen2-7B-Instruct_model.onnx.gz

[yuslepukhin]

The model is referring to an external weights file. Would you like to supply it?

[yuslepukhin]

here is the exception message that is issued. I am not seeing a segmentation fault on the main build:

unknown file: error: C++ exception with description "Load model from D :/dev/data/SegmentationFault_gh_24144/hf_Qwen2 - 7B - Instruct_model.onnx failed:Load model D :/dev/data/SegmentationFault_gh_24144/hf_Qwen2 - 7B - Instruct_model.onnx failed" thrown in the test body.

[vmnit]

Hi @yuslepukhin,

Thanks for looking into it.
The data file is huge around 29 GB. Can you please suggest a way to share the data file?

[vmnit]

Hi @yuslepukhin ,

I'm using model from the following location: https://huggingface.co/Qwen/Qwen2-7B-Instruct/tree/main
Can you please try generating onnx_model from there because I'm unable to find a way to upload the big data file?

[yuslepukhin]

Please, share exactly what you did.

Also, please, share any console messages, enable logging and share, specifically what makes you think there is a segmentation fault. Also, please, fill out the template as to the version of your Linux OS etc.

[amd-vivekag]

@yuslepukhin I'm trying to create a script which can reproduce the issue at your end. I'll try to share with you soon.

[vmnit]

Steps to reproduce:

1. Create virtual environment: python -m venv myenv.env
2. Activate it: source myenv.env/bin/activate
3. Pip upgrade: pip install --upgrade pip
4. Install some libraries: pip install onnx optimum[exporters] onnxruntime
5. Set CACHE_DIR: export CACHE_DIR=<SOME_PATH>
6. run script: python test_seg_fault.py

# test_seg_fault.py
import os
from optimum.exporters.onnx import main_export

cache_dir = os.environ["CACHE_DIR"]
os.environ["HF_HOME"] = cache_dir
os.environ["HUGGINGFACE_HUB_CACHE"] = cache_dir

main_export(
        "Qwen/Qwen2-7B-Instruct",
        os.getcwd(),
        task='text-generation',
        cache_dir=cache_dir,
        local_files_only=False,
        monolith=True,
        framework="pt",
        optimize=None,
        )

import onnxruntime as ort
sess_options = ort.SessionOptions()

print("before ort.InferenceSession")
sess = ort.InferenceSession('model.onnx', sess_options)

print(sess)

Please let me know if you need any information from my side in this regard.

Thanks

[yuslepukhin]

I have followed the procedure and got the model. Produced a debug build from the tip of main.
Tried from a C++ test and your python script simply loading the model.
I did not have a repro. At one-point physical memory usage clocked 31Gb and total commit was 44 Gb, so it had its share of page faults, but the process completed normally. The closest release is next month.

[vmnit]

Hi @yuslepukhin,

Were you able to run the complete script without any segmentation fault? If yes, can you please check the onnxruntime version?
I'm able to reproduce with following library versions:

Successfully installed MarkupSafe-3.0.2 certifi-2025.1.31 charset-normalizer-3.4.1 coloredlogs-15.0.1 filelock-3.18.0 flatbuffers-25.2.10 fsspec-2025.3.0 huggingface-hub-0.29.3 humanfriendly-10.0 idna-3.10 jinja2-3.1.6 mpmath-1.3.0 networkx-3.4.2 numpy-2.2.4 nvidia-
cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 n
vidia-cusparselt-cu12-0.6.2 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.4.127 onnx-1.17.0 onnxruntime-1.21.0 optimum-1.24.0 packaging-24.2 pillow-11.1.0 protobuf-6.30.2 pyyaml-6.0.2 regex-2024.11.6 requests-2.32.3 safetensors-0.5.3 sym
py-1.13.1 timm-1.0.15 tokenizers-0.21.1 torch-2.6.0 torchvision-0.21.0 tqdm-4.67.1 transformers-4.48.3 triton-3.2.0 typing-extensions-4.13.0 urllib3-2.3.0

While running inference step, I was getting Seg Fault: sess = ort.InferenceSession('model.onnx', sess_options)
I was not getting print after that. But if you are getting valid sess object then it is working for you, it seems.

[yuslepukhin]

The bug reproduces with 1.21.0, but is not there with the latest code.

D:\dev\data\SegmentationFault_gh_24144$ pip list
Package Version

---

certifi 2025.1.31
charset-normalizer 3.4.1
colorama 0.4.6
coloredlogs 15.0.1
filelock 3.18.0
flatbuffers 25.2.10
fsspec 2025.3.0
huggingface-hub 0.29.3
humanfriendly 10.0
idna 3.10
Jinja2 3.1.6
MarkupSafe 3.0.2
mpmath 1.3.0
networkx 3.4.2
numpy 1.24.3
onnx 1.17.0
onnxruntime 1.22.0
optimum 1.24.0
packaging 24.2
pip 25.0.1
protobuf 6.30.2
pyreadline3 3.5.4
PyYAML 6.0.2
regex 2024.11.6
requests 2.32.3
safetensors 0.5.3
setuptools 65.5.0
sympy 1.13.1
tokenizers 0.21.1
torch 2.6.0
tqdm 4.67.1
transformers 4.50.2
typing_extensions 4.13.0
urllib3 2.3.0

[vmnit]

The bug reproduces with 1.21.0, but is not there with the latest code.

D:\dev\data\SegmentationFault_gh_24144$ pip list Package Version

certifi 2025.1.31 charset-normalizer 3.4.1 colorama 0.4.6 coloredlogs 15.0.1 filelock 3.18.0 flatbuffers 25.2.10 fsspec 2025.3.0 huggingface-hub 0.29.3 humanfriendly 10.0 idna 3.10 Jinja2 3.1.6 MarkupSafe 3.0.2 mpmath 1.3.0 networkx 3.4.2 numpy 1.24.3 onnx 1.17.0 onnxruntime 1.22.0 optimum 1.24.0 packaging 24.2 pip 25.0.1 protobuf 6.30.2 pyreadline3 3.5.4 PyYAML 6.0.2 regex 2024.11.6 requests 2.32.3 safetensors 0.5.3 setuptools 65.5.0 sympy 1.13.1 tokenizers 0.21.1 torch 2.6.0 tqdm 4.67.1 transformers 4.50.2 typing_extensions 4.13.0 urllib3 2.3.0

@yuslepukhin It is great that you are able to reproduce the issue. I think we should add this as a testcase to avoid such a regression in the future. What do you say? Let me know if you want me to add it. If yes, can you please share some documentation or guide me on how to add it and verify the same.