Fix quantization dtypes after ORT PR #18043#881
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guotuofeng merged 3 commits intomicrosoft:mainfrom Jan 17, 2024
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@sheng-xiao, would you please help take a look? |
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## Describe your changes With PR #881, the CI pipeline for ORT 1.15 is broken. This PR is to fix the issue. ## Checklist before requesting a review - [ ] Add unit tests for this change. - [ ] Make sure all tests can pass. - [ ] Update documents if necessary. - [ ] Lint and apply fixes to your code by running `lintrunner -a` - [ ] Is this a user-facing change? If yes, give a description of this change to be included in the release notes. ## (Optional) Issue link
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Describe your changes
PR microsoft/onnxruntime#18043 (onnxruntime) extends onnxruntime quantization tools to support float16 weights. To do so, it enforces scale and zerop_point to be strongly typed (as
numpy.array(single_value, dtype=dtype)). scale type should always be the weight type, and zero_point type the quantized weight type. That convention is checked all along the quantization tools to make sure there is loss of information. This change was made to avoid adding new arguments in many functions to carry this information.Checklist before requesting a review
lintrunner -a(Optional) Issue link