Files
LocalAI/backend/cpp/llama-cpp/patches
Ettore Di Giacinto 37cbc089b0 bench(dense): Qwen3-32B dense parity - dense has the kernel gap too (PP 7.6-32x)
vLLM W4A16 vs llama Q4_K_M dense: prefill 7.6-32x behind (llama plateaus ~765,
vLLM scales to 24.4k); decode ~parity at B=1 (weight-bandwidth-bound), 2.2x at
B=64. Full NVFP4 (W4A4) hangs on this vLLM/GB10 stack - W4A16 used. Decision:
the Lever-3 kernel track must ALSO deliver a non-grouped FP4 dense GEMM, not just
the MoE grouped GEMM (dense GEMM is the simpler first kernel to land).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 03:55:58 +00:00
..

llama.cpp patch series — paged attention (vLLM-parity engine)

A stacking series: each patch is a small, self-contained, independently-buildable step toward an in-model paged-attention engine. They apply in numeric order on top of the pinned LLAMA_VERSION (backend/cpp/llama-cpp/Makefile). The build applies them automatically after checkout (see the llama.cpp: target). Keeping the work as ordered patches — rather than one big diff — is what lets us rebase cleanly across llama.cpp bumps and avoid drift: when a patch stops applying, only that small patch needs fixing, and the failure points at exactly which step the upstream change touched.

Base

  • LLAMA_VERSION pin in ../Makefile. All patches are generated against that exact commit. Bumping the pin = re-run the regen workflow below and fix only the patches that no longer apply.

The series (phases → patches)

# Patch What Verifies
0001 0001-vendor-paged-kv-manager.patch Add src/paged-kv-manager.{h,cpp} (vLLM-parity block manager, CPU foundation) + CMake; no behavior change builds; unit-tested separately under ../paged/
0002 0002-paged-kv-storage.patch Shared block-pool KV tensor + set_rows-by-slot writes, behind LLAMA_KV_PAGED builds; write/gather round-trip
0003 0003-paged-gather-read.patch build_attn_paged gather-read in llama-graph.cpp Gate 0: token-identical greedy gen, single + multi-seq
0004 0004-paged-ondemand-alloc.patch On-demand block allocation via PagedKVManager max concurrent seqs before OOM
0005 0005-paged-continuous-batching.patch Block-granular admit/evict in the server slot path tok/s vs concurrency, mixed-length
0006 0006-paged-prefix-caching.patch Block-hash cross-request prefix dedup TTFT + memory on shared prefixes

Each row is a separate git commit on the dev branch (below), exported 1:1 as a patch. Default off (LLAMA_KV_PAGED) until Gate 0 (0003) is green, so partial series never changes stock behavior.

Regen workflow (the anti-drift recipe)

# 1. check out the exact pin into a dev tree
git -C /tmp clone https://github.com/ggml-org/llama.cpp llama-dev && cd /tmp/llama-dev
git checkout <LLAMA_VERSION from ../Makefile>
git checkout -b paged

# 2. apply the current series (each becomes a commit), or develop the next patch
git am /path/to/backend/cpp/llama-cpp/patches/00*.patch     # or `git apply` + commit per patch

# 3. iterate a phase as ONE commit, then export the whole series 1:1
git format-patch <LLAMA_VERSION>..paged -o /path/to/backend/cpp/llama-cpp/patches/ --zero-commit -N

# 4. on a pin bump: rebase `paged` onto the new pin; only conflicting patches need edits; re-export.

Build integration

../Makefile's llama.cpp: target runs, after git checkout -b build $(LLAMA_VERSION):

for p in $(CURRENT_MAKEFILE_DIR)/patches/0*.patch; do git apply --verbose "$p"; done

All variants (avx/avx2/avx512/cuda/…) copy the patched llama.cpp/ tree, so the series ships everywhere.

Status

  • 0001 vendor manager — DONE. Applies clean to the pin; builds into libllama.
  • 0002 block placement — DONE + VERIFIED. Built llama-simple at the pin; greedy generation is token-identical stock vs LLAMA_KV_PAGED=1 (Qwen3-0.6B), paged branch confirmed firing.
  • 0003 gather-read — NEXT. The intricate build_attn graph surgery; the real engine compute. Multi-session.
  • 00040006 follow.

Honest parity note (important)

This series delivers the paged-attention engine (capacity + scheduling + prefix sharing). It does not by itself reach vLLM throughput parity, because the measured prefill bottleneck is the FP4 MoE GEMM kernel (Lever 3: mul_mat_q<MXFP4> ~22 TFLOP/s, ~27× behind vLLM) — a per-token compute gap that paging does not touch. Paged attention closes the concurrency/memory gap (more sequences, prefix reuse); the prefill/throughput gap additionally needs the tcgen05/CUTLASS grouped-GEMM (deferred, upstream-grade, no shortcut — see ../paged/UPSTREAM_GGML_ISSUE.md and DGX_BLACKWELL_PLAN.md). So full vLLM parity = this series AND the kernel; neither alone suffices.