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[ROCm][Bugfix] Keep zero MiniMax MXFP8 activation blocks finite - #58089
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shen-shanshan merged 2 commits intoSep 23, 2026
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Use finite reciprocal scaling to prevent all-zero MXFP8 activation blocks from generating NaNs on CDNA. The existing regression fails without the fix; all 68 AMD MiniMax kernel tests pass with it. Co-authored-by: OpenAI Codex Signed-off-by: Andreas Karatzas <akaratza@amd.com>
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September 22, 2026 17:34
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September 22, 2026 17:34
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✅ Triggered Buildkite CI #90463 for commit |
Fangzhou-Ai
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Sep 22, 2026
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LGTM! |
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| rescale = tl.exp2(127.0 - sb) | ||
| aq = (act * rescale[:, None]).to(aq_ptr.dtype.element_ty) |
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If this doesn't influence the model accuracy, then LGTM.
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mm .. let me then do a quick lm eval
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How does this look?
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Run Source commit Correct Accuracy
base74370a0e30441256/1319 95.2237%
pra10595a429791252/1319 94.9204%
base-repeat74370a0e30441256/1319 95.2237%
pr-repeata10595a429791252/1319 94.9204%
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LGTM.
shen-shanshan
approved these changes
Sep 23, 2026
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Oct 20, 2026
…-project#58089) Signed-off-by: Andreas Karatzas <akaratza@amd.com>
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0/0when the byte-zero E8M0 divisor flushes to zero on CDNA, while preserving the encoded scale bytes.The MI355 parity run exposed NaNs in an all-zero activation block. Merged #53674 corrected the fused kernel’s scale formula but retained division by the decoded scale. The generic quantizer already uses reciprocal multiplication from merged #53110, and this change applies the same arithmetic to the fused path. Fresh searches and the related PR review found no open change covering this remaining fused-kernel defect.
AI assistance: OpenAI Codex assisted with diagnosis, implementation and validation.