LocalAI/backend/cpp/llama-cpp-localai-paged/patches/paged

LocalAI paged-attention llama.cpp patch series

This directory holds the vendored patch series that turns stock llama.cpp into LocalAI's paged-attention variant (llama-cpp-localai-paged). The patches are applied on top of a pinned upstream llama.cpp at build time; nothing here is a fork - it is a source-only *.patch stack plus this single canonical doc.

One-file rule: this README is the canonical reference for the patch series. The only other docs kept in this directory are operational and linked below:

• PIN_SYNC_c299a92c.md - the current pin-sync record (referenced by the canary workflow + scripts).
• PAGED_BITEXACT_NOTE.md - the per-path bit-exactness gate (the canonical paged-MoE md5 reference).
• LOCALAI_LLAMACPP_BACKEND_PLAN.md - the design-of-record for shipping this as its own backend + the NVFP4 gallery items.

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1. What it is

llama-cpp-localai-paged is the LocalAI paged-attention llama.cpp backend: a vendored patch series over upstream llama.cpp that adds

• a paged KV cache (vLLM-style block manager: on-demand fixed-size blocks, free pool, ref-counted blocks) with a block-table flash-attention read so the attention kernels index physical cells instead of a contiguous buffer;
• cross-request prefix sharing - concurrent requests that share a long prefix physically reuse one committed copy of the prefix blocks and prefill only their divergent suffix;
• a decode-first prefill scheduler - a dynamic per-step prefill-token budget decoupled from n_batch, so a long prefill never freezes co-batched decode;
• GB10 / Blackwell NVFP4 decode optimizations for the Qwen3.6 hybrid gated-DeltaNet (SSM) models, where the recurrent-state plumbing - not the FP4 GEMM - dominates the decode step.

It is pinned to llama.cpp c299a92c ("binaries : Improve rpc-server and export-graph-ops names", #25045) and advanced only by a manual, bit-exact-gated pin-sync process, decoupled from the nightly auto-bumper (see section 7).

The build gate is LLAMA_PAGED (default on in this tree); the paged engine is enabled per-model at runtime via the gallery options: knobs (paged_kv:true, max_batch_tokens:, kv_unified:false, ...). Against unpatched llama.cpp the runtime hooks are inert, so a single grpc-server.cpp is shared between the clean and the paged build.

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2. Architecture

The decode step on these models breaks into three cost centers; the patch series attacks each one.

Paged KV manager + block-table flash-attn. A host-side PagedKVManager (FreeBlockQueue / BlockPool / chained-hash content cache) hands out fixed-size KV blocks on demand and reclaims them per-sequence (ref-counted, with copy-on-write for shared prefixes). The attention path reads through a block table - an I32 [n_view, n_stream] position-ordered physical-cell index passed as src[5] of ggml_flash_attn_ext - so the CUDA fattn vec/tile kernels and the CPU reference map logical KV index j to physical cell block_table[seq*ne11+j] and read K/V in place. Token-position ordering keeps the flash-attn online-softmax reduction order identical to stock. A null block table is the stock contiguous read, byte-identical.

The gated-DeltaNet (GDN / SSM) decode path. The Qwen3.6 hybrid models are 48 gated-DeltaNet (linear-attention / SSM) layers + 16 full-attention layers. On GB10 the recurrent-state plumbing, not the weight GEMM, is the dominant decode cost. The series fuses that plumbing to mirror vLLM's fused_recurrent_gated_delta_rule: the recurrent state is read from and written to its cache slot in place (no copy-back, no get_rows materialization), the conv state is updated in place, the output projection is reshaped to route to the tensor-core MMQ GEMM, and the recurrence kernel is occupancy-retuned - all bit-exact (md5-gateable) against the f32 baseline.

NVFP4 native FP4-MMA on Blackwell. The NVFP4 dense/expert weight GEMM uses Blackwell's native FP4-MMA. The series removes a redundant activation-requantize in the MoE broadcast projections (bit-exact byte copy of identical blocks) and keeps CUDA graphs on for the grouped-MMQ MoE decode step. These are the only NVFP4-specific optimizations; on non-Blackwell hardware the FP4 path falls back to dequant.

The prefill/decode scheduler. update_slots() already emits one unified mixed prefill+decode batch per step. The scheduler patches change only the count of prefill tokens admitted per step: decode tokens are claimed first (decode-first), then a dynamic budget max(n_ubatch, T - D) (where D is the live decode load and T is LLAMA_MAX_BATCH_TOKENS) admits prefill, auto- shrinking as decode load rises. Pure scheduler policy, byte-identical when off, orthogonal to the paged allocator.

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3. Patch series (0001-0030)

28 patches (0005 and 0027 are intentionally unused). "Bit-exact" = greedy md5 / test-backend-ops byte-identical to the relevant baseline; the gate methodology is in section 5.

Paged-KV core (0001-0012)

#	What it does	Bit-exact
0001	Vendor the host-side paged KV block manager (FreeBlockQueue, BlockPool, PagedKVManager, chained-hash prefix cache). Pure C++17, nothing uses it yet.	n/a (no behavior)
0002	Place each sequence at permuted, non-contiguous block positions in find_slot (proves attention is invariant to physical KV placement).	yes (token-identical)
0003	Gather K/V/mask down to each stream's non-empty cells before build_attn_mha, position-sorted so the FA reduction order matches stock.	yes
0004	Drive paged placement through the vendored manager: blocks popped on demand, returned on seq end. Core kv-cache struct untouched.	yes (stock path byte-identical)
0006	Host-side cross-request prefix caching: hash prefix blocks, reuse matching physical blocks (ref-count++), COW-privatise before a divergent write.	yes (default off)
0007	Wire the prefix cache into the engine so a new sequence physically shares cached prefix blocks and skips recomputing the shared prefix.	yes (verified byte-identical)
0008	Wire cross-request prefix share into the llama-server continuous-batch loop so concurrent shared-prefix requests prefill only the suffix (36x fewer prefill tokens at K=32).	within CUDA batch-shape non-determinism band
0009	Replace the per-step gather with an in-kernel paged read (block table as src[5]); the K/V get_rows is gone. Decode step at batch32 691->696ms (was 1279ms gathered).	yes on CPU/batch1; GPU batch>1 within vec-vs-mma band
0010	Graft the block-table read into the tile kernel; add a dispatch guard so a present block table routes ONLY to vec/tile (never the mma/wmma kernels that ignore it).	yes (CPU byte-identical; vec route)
0011	Route the GQA-grouped F16 decode to the tile kernel (native head-group reuse) by default; vec for everything else. Paged decode to within 1.8% of stock.	vs stock-mma: different-kernel rounding; bit-exact vs vec
0012	Defensive GGML_ASSERT(n_view % 64 == 0) so a future pad/tile change can't silently reintroduce a past-end KV leak on the tile route.	yes (additive assert)

Decode-first scheduler (0013, 0016)

#	What it does	Bit-exact
0013	LLAMA_PREFILL_BUDGET: a static per-step prefill-token budget decoupled from n_batch (vLLM --max-num-batched-tokens analogue). Flattens the decode ITL spike a long prefill inflicts (8.5x smaller worst freeze).	yes (off/short = byte-identical; == -b chunking)
0016	Supersede 0013 with a dynamic decode-first budget: max(n_ubatch, T-D), auto-shrinking as decode load D rises. Policy-only inside update_slots(), zero libllama changes.	yes (default-off byte-identical)

(0014/0015 are the MoE token-tile levers: 0014 adds LLAMA_MOE_MMQ_X (opt-in high-batch decode micro-opt, +4.8% on Qwen3-Coder-30B), 0015 makes it a default-on, density-aware auto-select that is prefill-safe by construction. Both bit-exact. 0017 is the dense FP4-GEMM occupancy-tune track: bit-exact gate green, but every cheap occupancy lever regressed on GB10, so nothing is enabled - it ships as the parity gate + default-off instrumentation only.)

SSM (gated-DeltaNet) decode levers (0018-0022, 0028)

These are the dominant decode levers on the Qwen3.6 hybrid models. All bit-exact.

#	What it does	Effect (dense q36-27b / MoE q36-35b-a3b @npl128)
0018	In-place SSM state write-back - the recurrence writes its final state directly into the cache slot, removing the ~225MB/copy D2D memcpy (18.9% of decode time).	dense +23.5% / MoE +18.9%
0019	Fused recurrent-state gather - the op reads each sequence's prior state directly from cache[ids[seq]] (no get_rows materialization); race-free in-place + ids read.	dense +37.8% / MoE +35.3%
0020	o_proj MMVQ->MMQ reshape - collapse the GDN output to 2D so the output projection routes to the M=128 tensor-core MMQ GEMM (was a batch<=8 MMVQ GEMV). The single biggest decode-parity lever.	dense +31.7% (->85.9% of vLLM) / MoE +23.3%
0021	Conv-state in-place fusion - one ggml_ssm_conv_update_inplace op replaces the 4-op conv chain (transpose+concat+conv+silu+ring-cpy), writing the shifted ring state in place.	dense +3.2% / MoE +3.5%
0022	GDN recurrence occupancy/coalescing retune - column-folding (NUM_WARPS/COLS_PER_WARP) raises memory-level parallelism on the bandwidth-bound B=128 recurrence kernel; per-column f32 FMA order unchanged. 73.4%->84.6% of GB10 peak BW.	dense +11.1% / MoE +8.3%
0028	Recurrent conv-tap gather fusion - the last k_get_rows in the GDN decode path (the conv-state tap gather) becomes an indexed in-kernel read.	dense ~377 t/s / MoE ~784 t/s

MoE NVFP4 quant (0023, 0025)

#	What it does	Bit-exact
0023	NVFP4 activation-quantize de-dup - the broadcast up/gate projections re-quantize the same token activation once per expert; quantize the unique token activations once and byte-copy them into the expert-gathered layout. The only NVFP4-specific patch.	yes (byte-identical)
0025	MoE decode re-graph - keep CUDA graphs on for the grouped-MMQ MoE decode step (the upstream guard disables graphs conservatively; the grouped path has no host sync). Env-gated LLAMA_MOE_FORCE_GRAPHS.	yes (graph replay re-issues identical kernels)

Pool reclaim, block-table cache, backend gate, opt-in bf16-SSM

#	What it does	Bit-exact
0024	Paged-pool burst-reclaim - truncate trailing blocks on partial-tail seq_rm, defrag the free queue when idle, release blocks on slot completion. Fixes the long-server burst-degradation bug (post-burst prefill collapse 488->44 t/s, restored to 532). Host-side accounting only.	yes
0029	Block-table within-step host cache - the block table is fixed for the whole step; cache it on first build and memcpy it for the other full-attention layers (get_block_table -87%/-91%).	yes, per path (paged-MoE ref 8cb0ce23)
0030	Fused-op backend gate - the fused GDN / discriminated SSM_CONV ops are CUDA-family + CPU only; force them off on any non-CUDA compute backend so a Vulkan/SYCL/Metal build can't silently run the wrong plain-conv kernel.	yes on CUDA (byte-identical pre-0030); safety gate elsewhere
0026	Hybrid per-head bf16 SSM state (opt-in) - --ssm-bf16-tau / option ssm_bf16_tau: fast-decaying GDN heads (memory length below the tau threshold) persist state as bf16, halving that head's decode byte stream (~+12% decode).	default tau=0 = f32 = bit-exact; the bf16 mode is NOT bit-exact (~91% same-top-p)

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4. Benchmarks

Hardware: GB10 / DGX Spark (CUDA 13, sm_121). Models: dense Qwen3.6-27B-NVFP4 and MoE Qwen3.6-35B-A3B-NVFP4. Metric: decode_agg S_TG (t/s) from llama-batched-bench, -fa on, npp 128 / ntg 128, swept over serving width npl. Plots: qwen36_dense_decode_vs_npl.png, qwen36_moe_decode_vs_npl.png; raw data final_benchmark.csv.

(a) + (b) Patched vs stock vs vLLM

The stock and patched columns are the same binary, env-toggled, on the same harness (llama-batched-bench) - so "x over stock" is an exact apples-to-apples measure of the patch series' contribution. The vLLM column is a different harness (vLLM server + client continuous batching), so the cross-engine "% of vLLM" is indicative, not apples-to-apples.

Dense Qwen3.6-27B-NVFP4 (t/s):

npl	stock	patched	vLLM	patched % of vLLM	patched x over stock
8	65.7	84.0	71.1	118%	1.28x
32	113.7	204.0	207.6	98%	1.79x
64	134.3	294.9	309.7	95%	2.20x
128	143.5	371.2	422.4	88%	2.59x

MoE Qwen3.6-35B-A3B-NVFP4 (t/s):

npl	stock	patched	vLLM	patched % of vLLM	patched x over stock
8	181.4	227.4	315.1	72%	1.25x
32	260.8	455.7	681.9	67%	1.75x
64	306.8	612.3	765.5	80%	2.00x
128	331.3	772.6	1011.7	76%	2.33x

Caveat on the vLLM column. Besides the different harness, the vLLM MoE @npl128 number here (1011.7 at 128/128) runs hotter than the 901 t/s reference config (512/256), so the MoE "% of vLLM" reads 76% here vs ~86% at the groundtruth config. Memory: llama uses 1.5-3x lower memory than vLLM.

Takeaway. The patch series gives up to 2.59x (dense) / 2.33x (MoE) over stock on the same harness. Dense is parity-to-ahead of vLLM; MoE trails - the remaining gap is structural (see section 5).

(c) Apple M4 (16GB) - for curiosity only

No t/s: the 24GB NVFP4 GGUF did not finish downloading and would not fit in 16GB RAM (= SSD paging). Architectural findings:

• Metal supports_op excludes NVFP4 from MUL_MAT / MUL_MAT_ID / GET_ROWS, so the FP4 matmuls fall back to CPU - there is no Apple FP4-MMA.
• GATED_DELTA_NET and SSM_CONV / SSM_SCAN do have Metal kernels.

Verdict: NVFP4 Qwen3.6 needs Blackwell FP4-MMA + >24GB RAM; a 16GB M4 is not a fit. A Metal-native Q4_K Qwen3.6 would be a different artifact.

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5. Dev notes - what we learned

Bit-exact methodology. Every bit-exact patch is gated two ways: (1) a greedy md5 gate - llama-completion -m MODEL -ngl 99 -fa on -p "The capital of France is" -n 48 --temp 0 --seed 1 | md5sum, paged paths prefixed with LLAMA_KV_PAGED=1 (+ LLAMA_MOE_FORCE_GRAPHS=1 for paged MoE), on the default chat-template path; and (2) test-backend-ops (CUDA0 vs CPU oracle) for every touched op (SSM_CONV*, GATED_DELTA_NET, MUL_MAT, MUL_MAT_ID).

The gate is per-path (see PAGED_BITEXACT_NOTE.md). Dense is bit-exact across paged/non-paged (5951a5b4). The paged MoE md5 (8cb0ce23) does not byte-match the non-paged MoE md5 (07db32c2); this is a benign FP-accumulation-order difference of the paged attention reduction, KL-validated against the f16 reference: KLD(paged||f16) 0.13600 <= KLD(nonpaged||f16) 0.13660, PPL within +/-0.29, ~zero probability bias - two equivalent FP-reorderings of the same quantized model, not a regression. Future paged-MoE regressions therefore compare to 8cb0ce23, not 07db32c2.

MoE-parity conclusion (the residual gap is structural). The two heaviest MoE decode kernels - the GDN-SSM recurrence and the NVFP4-expert GEMM - are llama wins after this series (the recurrence runs at 102.6% of vLLM's bandwidth; the GEMM ties vLLM at the LPDDR5x BW floor). The residual gap is bf16-projection bandwidth + the host scheduling loop, both at the LPDDR5x floor - not a kernel llama is losing. The MoE GEMM kernel is not where the gap lives.

Rejected / flat levers (recorded so they are not re-tried):

• Lever 2 - graph/stream coverage: FLAT. Bit-exact graph coverage was exhausted by 0025; more graph/stream overlap is a no-op or small regression on this model.
• Lever 3 - act-quant fusion: FLAT. The W4A4 act-quant tax is removable only by W4A16 (a precision change, rejected) or a structural kernel rewrite; no further bit-exact lever clears it. 0023 already banks the de-dup.
• Lever 4 - NVFP4 the bf16 GDN/attn projections: REJECTED (KL-gate fail). Quantizing the projections to NVFP4 costs ~+6% PPL; vLLM deliberately keeps the same bf16 projections. No-ship.
• W4A16-Marlin MoE GEMM: REJECTED. It would be a precision upgrade nobody needs bought with a ~5% slower kernel; both kernels are already at the BW floor. (The "the win was NVFP4-dense-quant, not the Marlin kernel" dense verdict carries over to MoE.)

Opt-in bf16-SSM fast mode (patch 0026, ssm_bf16_tau). The design premise - that bf16 KL error concentrates in long-memory heads and can be removed by keeping them f32 - is empirically refuted: the error scales with the bf16 head count and saturates (~0.06 MeanKLD / ~91% same-top-p) far below any useful byte saving, and the carry is byte-exact (genuine bf16 rounding, not a bug). The byte-saving (and ~+12% decode) is real but cannot meet a strict KL bar, so it ships default-off (f32, bit-exact) and opt-in only. Do not put a hybrid tau in a recommended/gallery config.

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6. Architecture and quant generality

(From the arch-generality and quant-generality audits.)

• 15 of 16 optimizations are quant-AGNOSTIC. Only 0023 (NVFP4 activation-quantize de-dup) is NVFP4-specific. The SSM/paged/MMQ optimizations help any quant of these models (the GDN recurrence, conv, gather and o_proj-MMQ levers operate on the f32 recurrent state and the routing layout, not on the weight dtype).
• Arch-safe to build everywhere. NVFP4 use is Blackwell-gated and falls back to dequant on other hardware; the GB10-tuned occupancy params (0022) are perf-only and env-selectable (GDN_NW / GDN_CPW), so they never change correctness on other GPUs. Patch 0030 makes the fused-op emission CUDA-family + CPU only, so a non-CUDA paged build routes to the safe upstream non-fused path.

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7. Pin + maintenance policy

• Pinned to llama.cpp c299a92c. The pin is advanced only by the manual PIN_SYNC process: rebase the source-only patch series onto the new tip, rebuild on GPU, and pass the bit-exact gate on every path (dense + MoE, paged + non-paged) plus test-backend-ops. The 9d5d882d -> c299a92c jump (23 upstream commits) needed zero patch changes and did not change decode output.
• Decoupled from the nightly auto-bumper. There is deliberately no bump_deps.yaml entry for this backend - a naive LLAMA_VERSION bump could silently shift the tree out from under the patches.
• Weekly canary. .github/workflows/llama-cpp-paged-canary.yml (via .github/scripts/paged-canary-apply.sh) tries the patch series against the latest upstream tip with the build's own strict git apply. Red = upstream drifted past the series -> run a PIN_SYNC (do not bump the pin blindly). The canary references PIN_SYNC_c299a92c.md.

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8. Models

The benchmarked NVFP4 GGUFs are published and wired into the LocalAI gallery:

Gallery entry	Weights (HuggingFace)	Notes
qwen3.6-27b-nvfp4-paged	mudler/Qwen3.6-27B-NVFP4-GGUF	Dense, native Blackwell NVFP4 (FP4-MMA).
qwen3.6-35b-a3b-nvfp4-paged	mudler/Qwen3.6-35B-A3B-NVFP4-GGUF	MoE (256 experts, top-8), file_type MOSTLY_NVFP4.

Both gallery entries set backend: llama-cpp-localai-paged and the paged serving config (paged_kv:true, max_batch_tokens, kv_unified:false, parallel, flash_attention:on, context_size). They intentionally stay bit-exact (no ssm_bf16_tau). The full backend-split + gallery plan is in LOCALAI_LLAMACPP_BACKEND_PLAN.md.