NXP backend: Unify quantization function inplementations #15044
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Let's wait for a review from one of my colleagues from NXP this time. 😀 In a meantime, I will check the red signals... |
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Those failing tests are unrelated to this PR. Also, I noticed the same error messages in other PRs. |
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| model = model.module() | ||
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| if not quantizer: | ||
| from executorch.backends.nxp.quantizer.neutron_quantizer import NeutronQuantizer |
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Is it necessary to conditionally import? It could be in default param.
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There is a circular import btw utils.py and neutron_quantizer.py. It would be possible but this was the easier (and cleaner) way of dealing with it.
backends/nxp/quantizer/utils.py
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| def post_training_quantize( | ||
| model: [ExportedProgram | fx.GraphModule], | ||
| calibration_inputs: list[tuple[torch.Tensor, ...]], |
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Will call from aot_neutron_compile work? The type hint changed.
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Yeah, you are right. Both my syntax highlighter and linter didn't mark it as an issue. I will change it to Iterable to include both options.
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Summary
Unifies implementation of model quantization between
executorch_pipeline.pyand exampleaot_neutron_compile.pyand introduces documented single function interface.Test plan
Should be covered by already existing unit tests.
cc @robert-kalmar @JakeStevens @digantdesai