NVIDIA / NVIDIA/Model-Optimizer
How to set the right quant cfg ?
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Description
I have an ONNX, I have some doubts:
1, If I don't consider prediction accuracy, what is the fastest inference time and how can I obtain it ?
2, I found that, FP8 quantization is not much faster than INT8 quantization, sometimes even slower.
3, If I choose int8+fp16 as computational precision, use the default int8-max quant config setting is not get good performance, so do I still need to manual adjust the config based on the info of the engine by trex ?
4, Set autotune: bool = True in modelopt.onnx.quantization.quantize() get worse performance than manual setting ?
Thanks !
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Research direction
Start with modelopt.onnx.quantization.quantize() and the int8-max configuration, then review the FP8, INT8, int8+fp16, autotune, and TREX cases described in the issue. Use the ONNX model and engine information referenced by the report to compare inference performance; done means documenting clear guidance for selecting or adjusting the quantization configuration.
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Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100