A first sample version of FloatQuant
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…on can be found in the `Examples`. `±inf` are clipped to `±max_val`. `±NaN` are mapped to `±NaN`. The zero is always representable. I tested with subnormals (to be intended as subnormals for the output representation) and the quantizer represented the subnormals with no loss (I didn't extensively tested this part though). I tested the function against Brevitas `FloatQuant` implementation: they do not always match. For example I think `0.3125` should be representable (`x == xq`) by a float quantizer with 4bits for mantissa, 4bits for the exponent, 0 bias and 1bit for the sign. Brevitas `FloatQuant` implementation quantize it to `0.25`. Not sure what I should consider correct for this case.
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Brevitas developer here, thanks for this concrete example - I will look into it ASAP! |
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Hi @nickfraser |
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Yes, please do - if you can provide minimal examples as well, this will make it much easier for us as well 🙏. Note, If you have proposed solutions, feel free to make PRs as well (pointing to |
Co-authored-by: Nicolo Ghielmetti <[email protected]>
… provided. Some other tests have been added
nghielme
commented
Dec 12, 2024
| exponent_bias=None, | ||
| max_val=None, | ||
| rounding_mode="ROUND", | ||
| lt_subnorm_to_zero=False, |
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@maltanar , this name is terrible! Please, help me in finding a better one 🤦♂️
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Please, consider that the function should be tested more extensively.
Sample
FloatQuantfunction implemented. A sample use of the function can be found in theExamples.±infare clipped to±max_val.±NaNare mapped toNaN. The zero is always representable. I tested with subnormals (to be intended as subnormals for the output representation) and the quantizer represented the subnormals with no loss (I didn't extensively tested this part though). I tested the function against BrevitasFloatQuantimplementation: they do not always match. For example I think0.3125should be representable (x == xq) by a float quantizer with 4bits for mantissa, 4bits for the exponent, 0 bias and 1bit for the sign. BrevitasFloatQuantimplementation quantize it to0.25. Not sure what I should consider correct for this case.