onnx model convert trt.int8 failure：fallback fp32
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Description
Description
When I use TensorRT for int8 quantization, I always encounter the accuracy fallback to fp32. The trt.BuilderFlag.OBEY_PRECISION_CONSTRAINTS parameter does not solve the issue. What should I do?"
Environment
TensorRT Version:8.6.16
NVIDIA GPU: A100
CUDA Version:11.4
Operating System:
Python Version (if applicable):3.7
PyTorch Version (if applicable):1.12.1
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
No source file, test, or entry point is named. Start by reproducing the ONNX-to-TensorRT INT8 conversion with TensorRT 8.6.16, CUDA 11.4, an A100, Python 3.7, and PyTorch 1.12.1, then inspect why precision falls back to FP32 despite OBEY_PRECISION_CONSTRAINTS. Done means the fallback cause and an actionable fix or limitation are documented.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, python, pytorch
- Domain
- ai, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100