core dump in bart trt engine
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Description
Description
when i use trtexec to generate bart trt engine, there is core dump.
Environment
TensorRT Version: 8.2.1
NVIDIA GPU: T4
NVIDIA Driver Version: 470.82.01
CUDA Version: 11.5.50
CUDNN Version:
Operating System: Ubuntu 20.04.3 LTS
Python Version (if applicable): 3.8.10
Tensorflow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if so, version):
onnx 1.7.0
onnx-graphsurgeon 0.3.14
onnxruntime 1.8.0
onnxruntime-gpu 1.8.0
Relevant Files
Steps To Reproduce
https://github.com/huggingface/transformers/tree/v4.15.0/examples/onnx/pytorch/summarization
1.python run_onnx_exporter.py --model_name_or_path facebook/bart-base
2.trtexec --onnx=BART.onnx --workspace=64 --minShapes=input_ids:1x1 --optShapes=input_ids:1x32 --maxShapes=input_ids:1x64 --buildOnly --saveEngine=test.engine
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the crash with run_onnx_exporter.py and the supplied trtexec command using BART.onnx in the listed TensorRT, CUDA, driver, and Ubuntu environment. Capture the core-dump stack and trace it through the relevant TensorRT or trtexec entry point; done means the same command builds test.engine without a core dump.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, python
- Domain
- machine-learning, tooling
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100