[converter] Fix negative axis in quantize/dequantize lowering - #24
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jakesabathia2 merged 3 commits intoJul 8, 2026
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The quantize/dequantize lowering normalized a negative axis as axis + rank - 1, off by one from the eager op, which resolves it as axis + rank. A per-channel axis=-1 was applied one dimension early, so the converted model used the wrong channel; when the channel and a neighbor dim share a size this is silent, with no shape error. Normalize a negative axis as axis + rank, matching the eager op. Add per-channel negative-axis numerical tests for both ops. They use a (2, 4, 4) shape with equal middle and channel dims so a wrong axis surfaces as a numerical mismatch rather than a reshape error.
pkmandke
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Jul 6, 2026
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| model = Model() | ||
| x = torch.randn(2, 4, 4) |
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Maybe we can use a different dim value at [1], so something like (2,3,4) for clarity on which axis is picked via the negative indexing.
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Added (2, 3, 4) as you suggested and kept (2, 4, 4) too. Equal dims are the case where a wrong axis is silent instead of a reshape error, so both stay covered.
pkmandke
reviewed
Jul 6, 2026
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| model = Model() | ||
| x = torch.randint(-128, 127, (2, 4, 4), dtype=torch.int8) |
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Thank you for the fix @eyupcanakman , can you please rebase on the latest main and re-push? |
pkmandke
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Jul 7, 2026
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Lgtm!
@yixingli-apple can you please take a look as well?
yixingli-apple
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Jul 8, 2026
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Testing