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fix: import dense subprocess KV into hybrid GPU cache groups - #259
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This was referenced Oct 23, 2026
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) PagedKV.write_span splits dense model-layer-order KV (subprocess producers) into the receiver's per-geometry groups and validates all shapes before any copy. From fix/258-dense-hybrid-kv (#259). Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
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Landed on optimus-rm via #283. Closing; the branch is archived locally (experiment records and evidence included). |
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Background
The first live Gemma3-1B RNGD→GPU request crashed: the subprocess producer returned all26 model layers in one KV tensor, while the GPU receiver tried to copy it into its22-layer sliding-attention group. Existing grouped Torch producers did not exercise this contract.
Changes
PagedKV.write_span maps dense model-layer KV into receiver groups, preserves existing single-group/recurrent and grouped inputs, and validates group counts and shapes before copying. The subprocess wire format is unchanged. Added nonzero-offset layer-mapping and malformed-input regressions plus dense Gemma PD outputs compared against independent HF greedy beyond the sliding window.
Verification
Code32ce5d5, base273952a (#250); actual failed runtime3e16c33, BF16 Gemma3-1B, RNGD SDK2026.4, GPU Torch2.13.0+cu130, A10080GB, width8192,4slots/context9216/page64/cacheoff. Original failure retained; no completed hardware performance claim.
Compatibility and limits
Dense spans require uniform head geometry; grouped tensors still support heterogeneous geometry. No default routing, cache policy or transport changes. The recorded hardware matrix now passes. This does not establish arbitrary-prompt quality or all numerical configurations; draft retained for maintainer review. Separate Qwen/KONI/Gemma numerical investigations remain open.
Related Issue
Refs #258, #201. Stacked on #250; maintainer reviews and merges.