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Author SHA1 Message Date
Ettore Di Giacinto
3159ed0637 docs(paged): record P6 fp8-KV measured NO-GO - throughput dead end, capacity-play open
Retry of P6 unblocked the prior infra-block (DGX reachable via ssh dgx.casa) and
ran the kill-gate. Two measured artifacts replace the analytical estimates:

Stage 0a decode ceiling (v2 per-kernel decode-isolation, cross-checked within
0.3% of the batched-bench wall t_tg): fp8-KV theoretical-MAX decode saving
(fa-only) tops at +8.81% at ctx8192 x npl8 and clears +3% only at long context;
standard npl128 serving shapes reach +2.2/+3.4%. This refutes the earlier
analytical prior (0.65% std, +17.34% ctx8192) in both directions.

Stage 0b zero-code Q8_0-KV A/B proxy at the highest-ceiling shape (5 reps/arm):
dense ctx8192 +0.37% decode (flat), moe ctx8192 -2.63% decode REGRESSION. Even
Q8_0 - which wins on the integer DP4A fattn-vec dot that e4m3 cannot use -
realizes ~none of the ceiling; dequant-in-attention eats the KV-read BW saving,
re-confirming the historical Q8_0 +7.8% null. e4m3's KQ path is strictly worse
than Q8_0's, so the e4m3 throughput kernel is a definitive NO-GO and was not
built. The capacity-play (halving the 10/40 attention layers' KV footprint)
stays open as a footprint feature.

Default path measured green on the byte-identical worktree (canonical greedy-md5
re-run: MoE 8cb0ce23, dense 5951a5b4, paged). Fork localai-paged untouched at
653bb2f3d; topic branch p6-fp8-kv retained on the DGX, not pushed; series stays
46 patches (0001-0055). P3's landed program conclusion is preserved; only the
now-stale P6 status descriptors in it were corrected to the measured NO-GO.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 23:01:40 +00:00
Ettore Di Giacinto
a1a3b99960 docs(paged): record P3 W4A16 direct-A NO-GO + write program-level prefill conclusion
P3 (the last big prefill lever) is a decisive NO-GO. The direct-A W4A16 Marlin
path was re-created per the section-3 contract, engaged behind
LLAMA_W4A16_DIRECT_A, and A/B'd against the FP4-MMQ default: -46.9/-48.0/-49.1%
at M=512/1024/2048 (MoE q36-35b-a3b, 3-iter medians). The forensics retry is
REFUTED - the integration tax it blamed was genuinely removed (act-quant
18.92 -> ~0 us/tok; host expert-sort + src1-gather + separate cast eliminated)
and direct-A still lost. nsys graph-node decomposition: the mature bf16
grouped-W4A16 GEMM = 323.90 us/tok = 1.97x the FP4-MMQ int8 GEMM (164.6) =
exactly bf16 = half int8/FP4 tensor-core peak on sm_121. Bucket 2 (GEMM tiling,
+56.5) is now a CONFIRMED FP4-MMQ-optimal floor on GB10, joining bucket 1 (GDN
scan, P5-confirmed). Novel sub-finding: fusing the A-gather in-kernel is a NET
pessimization vs a separate bf16 pre-cast (+128 > ~63 tax removed), a
GB10-specific inversion of the no-round-trips heuristic. KL in-band and better
than control (KLD 0.130260 / same-top-p 85.172%); default md5s green both models;
engagement proven (7680 env-on, 0 default). Nothing built beyond P0, nothing
landed; fork localai-paged HEAD untouched at 653bb2f3d, series stays 46 patches;
topic branch p3-w4a16-direct retained on the DGX fork at 8eef7ba43 (NOT pushed).

Because P3 is the last major lever, this also writes the program-level
conclusion into EXECUTION_REARCH_SCOPE.md section 4a (dated) and corrects the
pre-execution projection to measured reality: six phases gated, exactly one
landed (P1 +2% MoE prefill, bucket-3 projection boundary); P2/P3/P4/P5 rejected,
P6 blocked-on-infra. Prefill closes to ~50-51% of vLLM (not ~55-65%),
serving-agg stays ~60.7% (not ~80%), decode-GPU-steady stays ~86% (not ~95%),
TTFT stays ~3.4x - because the two largest prefill buckets (1+2 = +115.7 of the
198.9 gap) are confirmed silicon/bandwidth floors that lift only on datacenter
Blackwell. This confirms and strengthens the standing conclusion that GB10
throughput-parity is unreachable by exhaustion; the paged fork's precision
parity + memory advantage stand. Default path untouched; canonical md5s green.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 22:31:14 +00:00
Ettore Di Giacinto
ac2b0211ff docs(paged): record P6 fp8-KV BLOCKED-ON-INFRA + the analytical decode ceiling
P6 (final program phase) could not run its kill-gate: the DGX/GB10 was
unreachable for the entire window (cloudflared access via prem-vm returned
HTTP 530 / websocket bad-handshake on every probe; re-confirmed with 5 fresh
probes). Stage 0a (measured nsys graph-node decode ceiling) and Stage 0b
(fp8-e4m3 kernel + kill-gate A/B) were physically impossible with no GPU.

Records the honest infra-block (NOT a measured NO-GO, NOT a NO-GO-by-ceiling)
plus the load-bearing artifact: the analytical fp8-KV decode ceiling table.
fp8 halves KV bytes -> theoretical-max decode saving = 0.5 x flash-attn share:
ctx256 0.65% (standard shape hard NO-GO), ctx1024 2.55%, ctx2048 4.98% (first
crosses +3%), ctx4096 9.49%, ctx8192 17.34%. The win, if realizable, lives
only at ctx>=2048; the hybrid-GDN structure (10/40 layers carry KV, 30 GDN
layers hold fixed-size recurrent state with no KV) caps what any KV-dtype
lever can save. The dominant null stands unrefuted: Q8_0 KV was a measured
+7.8% decode regression on GB10. Notes the capacity-play framing (fp8-KV as a
memory feature remains open even if throughput-flat).

Fork localai-paged untouched at 653bb2f3d; series stays at 46 patches
(0001-0055); P3's p3-w4a16-direct work undisturbed. Docs-only; no code, no
topic branch, no patches. Not pushed.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 21:43:36 +00:00
Ettore Di Giacinto
5b8b33a302 docs(paged): record P5 FLA GDN NO-GO - GDN prefill bucket is a confirmed shared-hardware floor
P5 ported the full six-kernel vLLM-FLA chunk_gated_delta_rule_fwd pipeline
(cumsum, chunk_scaled_dot_kkt, blocked merge_16x16_to_64x64 solve_tril,
recompute_w_u, register/smem-resident chunk_gated_delta_rule_fwd_h with the
chunk loop in-kernel, chunk_fwd_o) to CUDA tf32 mma, per-kernel validated vs
host fp64 (o NMSE 2.2e-7, final-state 1.2e-7), integrated behind
LLAMA_GDN_FLA_CHUNK=1 (default-off), and A/B'd in-backend vs the shipped M5
chunked scan.

P0 perf kill-gate FAILED decisively: nsys --cuda-graph-trace=node, MoE
q36-35b-a3b, npp2048 M5 56.31 vs FLA 119.46 us/tok (FLA 2.12x SLOWER,
gdn_delta_pct -112.1); npp512 2.29x slower; end-to-end S_PP -13.33%/-13.12%.
GO required FLA >10% faster at npp2048.

Novel decomposition: the blocked solve_tril is only ~2.8% of the FLA bucket
(55.6 ms); fwd_h 46.2% (903 ms) + fwd_o 31.5% (617 ms) dominate. The cost is
the state-recurrence GEMMs plus per-chunk h-state materialization to global
LPDDR5x that FLA's split-kernel structure forces; the fused M5 single kernel
keeps the 128x128 state resident in smem and never materializes per-chunk h,
so it is 2.1x faster on GB10's low-bandwidth memory. So the GDN prefill bucket
(+59.2, the single largest prefill lever) is a confirmed shared-hardware /
memory-bandwidth floor, NOT recoverable by the blocked-solve algorithm.
Extends Phase74 (standalone blocked-inverse 0.59x) and bf16-C64 (-18.75%).

Gates: SMEM PASS (max 96KB < 99KB cap). KL band GREEN (FLA KLD 0.137028 vs
control 0.136563, delta +0.000465 < 0.01; same-top-p 84.61%). DEFAULT path
untouched (canonical md5 GREEN both models default-off AND FLA-on; MoE
8cb0ce23, dense 5951a5b4; GATED_DELTA_NET DEFAULT 46/46). Nothing landed;
series stays at 46 patches. WIP on DGX fork branch p5-fla-gdn (2d64c37f0),
NOT pushed.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 21:04:37 +00:00
Ettore Di Giacinto
7b129a51f1 docs(paged): finalize P4 CBv2 record with the measured A/B verdict
The forced-report placeholders are replaced with the completed 60/60-raw A/B
from dgx:~/bench/p4_cbv2/perf_20260702_194359/RESULTS.md: NO-GO confirmed by
measurement, and stronger than flat. CBv2 fair-share chunked prefill regresses
TTFT under staggered load (N=32 p50 +33.6%, N=128 p50 +15.5%) and regresses
aggregate/decode -6.9% beyond noise at staggered N=128. Analysis recorded:
processor-sharing delays near-uniform prompt completion by construction; the
scheduler-shaped-TTFT premise is partially refuted for GB10 (patch 0016 already
captures the schedulable win); TTFT parity routes through P3/P5 prefill compute.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 18:09:55 +00:00
Ettore Di Giacinto
865e77c4ec docs(paged): record P4 CBv2 NO-GO at the perf kill-gate
P4 (token-granular continuous-batching scheduler, LLAMA_CONTINUOUS_BATCH_V2,
default-off) stopped honestly at the P0 perf kill-gate. The kill-gate subset
(per-seq chunked-prefill cursors + adaptive decode bucketing, server-side only,
zero ggml/ files, ~68 LOC + a new unit-tested server-admission-policy.h) was
implemented and correctness-proven green (canonical md5 both models default-off
AND cbv2-on: MoE 8cb0ce23, dense 5951a5b4; test-backend-ops MUL_MAT 1146/1146,
MUL_MAT_ID 806/806, GATED_DELTA_NET 46/46; cursor-interleave PROVEN via
LLAMA_CBV2_TRACE with decode+prefill co-batched and per-seq cursors advancing
across steps, dbucket==n_decode no-pad; determinism-NEUTRAL: CBv2 diverges from
control no more than control diverges from itself, the paged concurrent-greedy
path being inherently non-deterministic run-to-run in the baseline too).

The kill-gate GO criterion - a >20% TTFT-under-load drop with md5 green and
serving-aggregate not regressed - was NOT demonstrated: the staggered/burst TTFT
A/B was force-terminated by the harness mid-run (CONTROL-only, 30/60 raws), so
the TTFT deltas are not-yet-measured placeholders, not measured neutrality. Per
the phased contract go=false was the kill-gate default: nothing built beyond P0
(no SLOT_STATE_PREEMPTED, no aging/starvation-freedom), nothing landed. This is
the scope-anticipated outcome - P4 is a GB10 TTFT/fairness/enabler lever, not a
throughput lever (decode is GPU-compute-bound), so a NO-GO on the TTFT gate is
expected and any throughput payoff is non-GB10.

Records the honest rejection in EXECUTION_REARCH_SCOPE.md (P4 RESULT subsection)
and PARITY_HANDOFF.md chronology, including the re-score path: read the finalized
DGX ~/bench/p4_cbv2/perf_20260702_194359/RESULTS.md once the CANDIDATE arm
completes; a genuine >20% staggered-TTFT drop clearing max(2%, 3*stdev) re-scores
go=true and triggers the full P4 build-out. Fork localai-paged untouched at
653bb2f3d; LocalAI series stays at 46 patches; topic branch p4-cbv2 retained on
the DGX fork at ebb649335 (base 653bb2f3d, not pushed).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 18:03:34 +00:00
Ettore Di Giacinto
586639d016 docs(paged): record P2 MoE-region NO-GO (kill-gate flat + seam-shape gap)
P2 (expert-major fused routed-FFN region executor, LLAMA_MOE_REGION_EXECUTOR,
default-off) is recorded as NO-GO on two independent signals; nothing built
beyond the P0 kill-gate, nothing landed, fork localai-paged HEAD untouched at
653bb2f3d (LocalAI series stays at 46 patches, 0001-0055).

(1) Primary GO metric flat: n=257 MOE_SWIGLU_DOWN region 1022.15 us vs
grouped-MMQ control 1021.61 us = -0.05% (needed >5% faster); n=128 -0.34%;
MUL_MAT_ID_RAGGED_MOE +0.48%/+0.28% (region never engages). All inside the
5-sample spread - reproduces the six prior one-boundary transplants
(phases 113/114/122/123/125/127). A compact expert-major layout + single sort,
both GEMMs still ragged grouped-MMQ, does not move the ragged-tile tax; that
needs P3 Marlin persistent-CTA, not a P2 layout swap.

(2) Decisive structural blocker: q36-35b-a3b-nvfp4 ships separate
ffn_gate_exps/ffn_up_exps (+ per-tensor .scale) with ggml_swiglu_split, not the
merged gate_up->VIEW->VIEW->SWIGLU->down shape the whole-pattern matcher
requires; the matcher, region executor, and pre-existing POC/fused-quant all
engage 0x on q36 in prefill and decode. KL delta 0.000000 is vacuous (0
engagement). Default md5 canonical both models (MoE 8cb0ce23, dense 5951a5b4);
test-backend-ops all green both arms.

Prerequisite handoff (gates P2 and P3): rebuild the seam for q36's
separate/scaled/swiglu-split FFN shape before any MoE-region lever can engage,
then re-evaluate a fused two-GEMM region (not a layout swap). Topic branch
p2-moe-region retained on the fork for forensics at 2d87564dd (base 653bb2f3d),
not pushed.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 15:43:57 +00:00
Ettore Di Giacinto
ccf75d1dcd docs(paged): record P1 bf16-stream landing (GO)
P1 of the EXECUTION_REARCH_SCOPE additive program landed: LLAMA_BF16_STREAM
(default-off) bf16-resident residual-segment executor for the q36 MoE model's
projection boundaries.

- EXECUTION_REARCH_SCOPE.md: dated "P1 RESULT" subsection (P0 kill-gate GO,
  full build-out deltas, KL, correctness gates, honest magnitude, provenance).
- PARITY_HANDOFF.md: chronology note (verdict, engagement, prefill/KL numbers,
  fork commits, deferred-not-failed measurements).

Key reframe recorded: q36 GDN/attention projections are BF16 weights (not
NVFP4), so bf16-stream is a MoE-model prefill lever; the dense model quantizes
those projections to NVFP4 and engages nothing (stays bit-identical). Prefill
MoE @512 +1.99% (reproducible, at noise floor), KL delta -0.00052 (KL-improving),
all md5 + test-backend-ops gates green. Fork HEAD 653bb2f3d, tree 6cf1523047.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 14:34:26 +00:00
Ettore Di Giacinto
500d653bfa feat(paged): regenerate patch series 0053-0055 (P1 bf16-stream)
Additive regen mirroring fork mudler/llama.cpp:localai-paged HEAD 653bb2f3d
(base 1edddc8fe + 3 P1 commits). Patches 0001-0052 are untouched.

- 0053 residual-segment executor + norm-bf16.{cu,cuh} + LLAMA_BF16_CUBLAS_F32_OUT
- 0054 bf16 residual-add + rope op-variants
- 0055 BF16_STREAM_SEGMENT test-backend-ops sentinel

Kill-gate: a fresh detached worktree at pin 0ed235ea2c17a19fc8238668653946721ed136fd
applied all 46 on-disk patches in numeric order (strict git apply) and staged
tree 6cf1523047e0e38679baff20844bdc9e6829eb22, byte-for-byte == fork HEAD tree.
All default-off (LLAMA_BF16_STREAM); default md5 canonical both models
(MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 14:34:14 +00:00
Ettore Di Giacinto
b2784ccbca docs(paged): fix EXECUTION_REARCH_SCOPE seam citations to fork 1edddc8fe
Adversarial verification against the canonical fork mudler/llama.cpp:localai-paged
HEAD 1edddc8fe found the scope doc's section-3 seam references were anchored to
the abandoned pre-trim tree 237ad9b96, which the immediately-preceding commit
b529cc5420 reset away. Two classes of defect, both corrected:

- Phantom scaffolding (honesty): the doc claimed "the team has already started
  scaffolding P1 and P3" citing four commits (237ad9b96 bf16 GDN state cache,
  afc2c7030 act-quant trace, ea0875d14 LLAMA_BF16_CUBLAS_F32_OUT, 7967ad47f
  W4A16 direct-A stub) that b529cc5420 TRIMMED - none exist at 1edddc8fe (git
  cat-file: not a valid object). w4a16-policy.h, test-cuda-w4a16-policy.cpp and
  ggml_cuda_mul_mat_id_w4a16_grouped_direct_a are absent from the tree. Reworded
  P1 plank-1 and the P3 mechanism/files/effort to say these must be re-introduced
  on top of the surviving grouped W4A16 path (patch 0035), not "finished".

- Stale line numbers (additivity): every file:line was off (computed against the
  larger 237ad9b96 tree). Re-anchored to 1edddc8fe: ggml_cuda_try_fuse 4232 (was
  4661), capture loop 4908 (was 5444), moe whole-pattern matcher 4157 (was 4678),
  routed_ffn_poc moe-ffn.cu:275 (was 456), grouped W4A16 hook ggml-cuda.cu:2797
  (was 3093/3188; the direct-A hooks 3085/3171 never existed), concurrent_event
  machinery 4769 (was 5305-5318), continuous-batch budget server-context.cpp
  3083-3135 with LLAMA_MAX_BATCH_TOKENS at 3105 / prefill_budget_step at 3113
  (was 3122-3200).

Numbers (attribution table, recovery arithmetic), the six P0 kill-gates, and the
unreachable-floor honesty were verified sound and left unchanged.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 11:03:07 +00:00
Ettore Di Giacinto
bf61db6214 docs(paged): scope vLLM-class execution re-architecture (additive program)
Reframe the GB10 vLLM-parity gap from a per-lever "hardware floor" verdict
to a ggml-execution-architecture-conditional one: same-silicon 2-3x is
software architecture, not silicon. Add EXECUTION_REARCH_SCOPE.md, a phased
additive program (P1 bf16-native stream, P2 expert-major fused MoE region,
P3 Marlin large-M retry on P1+P2, P4 token-budget scheduler, P5 blocked-solve
GDN, P6 fp8 KV), each with the ggml/fork seam, default-off env gate, per-path
md5/KL correctness gate, a falsifiable P0 kill-gate, expected-recovery
arithmetic grounded in the both-engine nsys buckets, and upstream-clash
analysis. Point the README docs list and PARITY_HANDOFF forward-direction at
it.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 10:50:00 +00:00
Ettore Di Giacinto
b529cc5420 patches(paged): trim series to Phase135 routed-FFN line, sync to fork 1edddc8fe
The campaign patches 0048-0063 were added without matching fork commits.
After a keep/drop review, the series is trimmed and re-mirrored 1:1 onto the
fork branch mudler/llama.cpp:localai-paged (HEAD 1edddc8fe, on 51168c5ee).

Kept, renumbered from the fork (now carry Assisted-by + Signed-off-by):
- 0048 test(paged): cover MoE swiglu down chain      (was 0051, fd920cf8a)
- 0049 test(paged): cover MoE weighted combine chain (was 0052, a85c1e098)
- 0050 test(paged): cover ragged MoE dispatch        (was 0053, 2fed6aacf)
- 0051 fix(speculative): disable backend sampling for MTP drafts (was 0054, f1d976f06)
- 0052 feat(paged): whole-pattern MoE matcher + routed-FFN fused NVFP4-quant
       down MMQ (new, 1edddc8fe)

Dropped (no fork commits, removed from the series):
- 0048-0050 W4A16 grouped-tile pack/tune/pad: dead line, W4A16 ~1.5x slower
  than grouped-MMQ.
- 0055-0063 speculative/moe/mul-mat/cublas route traces + the rejected small-M
  tile-policy knob (0059).
- All other 110-140 campaign markers not needed by Phase135 (GPU-sort,
  W4A16-direct-A, boundary trace/timing, Phase133 sorted-F32, Phase134
  fused-SWIGLU, Phase138 finalize) carry no code in this tree.

Tree-hash proof (the mirror invariant): a fresh detached worktree at
LLAMA_VERSION 0ed235ea2c17a19fc8238668653946721ed136fd with every on-disk
patches/paged/0*.patch applied in numeric order (git apply) stages to tree
097c862c6834b7d8b90419b305b8402155ef8373, byte-identical to fork HEAD
1edddc8fe's tree. Series is 43 patches (0001-0047 unchanged + 0048-0052).

Gated on GB10 sm_121a: default md5 MoE 8cb0ce23 / dense 5951a5b4 unchanged;
opt-in md5-clean; MUL_MAT 1146/1146, MUL_MAT_ID 806/806, GATED_DELTA_NET 46/46,
MOE_SWIGLU_DOWN 7/7, MUL_MAT_ID_RAGGED_MOE 6/6; six mmq_moe_quantized_raw
markers with zero sorted launches on the opt-in sentinel.

Assisted-by: Claude:opus-4.8 [Claude Code]

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 10:19:10 +00:00
Ettore Di Giacinto
1aba41082b docs(paged): record phases 112-140 + series trim decision
Record the phase 110-140 GDN/MoE campaign benchmark log and append the
series-trim decision to the parity handoff: keep the Phase135 routed-FFN
fused-quant line plus the MoE test sentinels and the MTP-draft correctness
fix; drop the W4A16 structural line, the trace/tile-policy patches, GPU-sort,
W4A16-direct-A, and the finalize fusion. Rejected/neutral levers are recorded
in the handoff and the per-phase bench artifacts. Fork re-mirrored on
51168c5ee: fd920cf8a a85c1e098 2fed6aacf f1d976f06 1edddc8fe (HEAD tree
097c862c).

Assisted-by: Claude:opus-4.8 [Claude Code]

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-02 10:16:53 +00:00
Ettore Di Giacinto
67d2c4c9d4 docs(paged): record BF16 GDN state cache phase
Record Phase81 default-off BF16 persistent S-cache results, including md5 drift, op gates, decode profile, and KL smoke. Scope Phase82 as full f16-reference KL plus serving A/B before patch-series promotion.

Assisted-by: Codex:gpt-5
2026-07-01 16:26:09 +00:00
Ettore Di Giacinto
d091eb30f2 docs(paged): record GDN identity shortcut phase
Assisted-by: Codex:gpt-5
2026-07-01 15:46:41 +00:00
Ettore Di Giacinto
bbfaa66f02 docs(paged): record GDN BV32 decode A/B phase
Assisted-by: Codex:gpt-5
2026-07-01 15:35:06 +00:00
Ettore Di Giacinto
04ed7fe52f docs(paged): record GDN launch sweep phase
Assisted-by: Codex:gpt-5
2026-07-01 15:13:45 +00:00
Ettore Di Giacinto
a9454b45c8 docs(paged): record MoE decode-only profile phase
Assisted-by: Codex:gpt-5
2026-07-01 15:05:45 +00:00
Ettore Di Giacinto
f21b393746 docs(paged): record current MoE graph profile phase
Assisted-by: Codex:gpt-5
2026-07-01 14:56:39 +00:00
Ettore Di Giacinto
26a41fad1a docs(paged): record post-PoC GDN audit phase
Assisted-by: Codex:gpt-5
2026-07-01 14:44:17 +00:00
Ettore Di Giacinto
5369219729 docs(paged): record GDN blocked-solve PoC phase
Assisted-by: Codex:gpt-5
2026-07-01 14:39:09 +00:00
Ettore Di Giacinto
eb82ff138f docs(paged): record datacenter Blackwell readiness phase
Assisted-by: Codex:gpt-5
2026-07-01 14:28:41 +00:00
Ettore Di Giacinto
2efb0ec362 docs(paged): record TTFT min32 serving phase
Assisted-by: Codex:gpt-5
2026-07-01 14:18:54 +00:00
Ettore Di Giacinto
e5c5746c0a docs(paged): record GDN tensor-core revalidation phase
Assisted-by: Codex:gpt-5
2026-07-01 14:05:20 +00:00
Ettore Di Giacinto
6cf8b782d1 docs(paged): record BF16 F32 output broader serving phase
Assisted-by: Codex:gpt-5
2026-07-01 13:26:50 +00:00
Ettore Di Giacinto
e573194799 docs(paged): record patch mirror readiness phase
Assisted-by: Codex:gpt-5
2026-07-01 13:11:57 +00:00
Ettore Di Giacinto
2b2b1f0b25 docs(paged): record BF16 F32 output dense serving phase
Assisted-by: Codex:gpt-5
2026-07-01 13:06:49 +00:00
Ettore Di Giacinto
e67b329eb1 docs(paged): record BF16 cuBLAS F32 output phase
Assisted-by: Codex:gpt-5
2026-07-01 12:54:24 +00:00
Ettore Di Giacinto
60954d484a docs(paged): record quant kernel timing phase
Assisted-by: Codex:gpt-5
2026-07-01 12:45:19 +00:00
Ettore Di Giacinto
3fbdfc21c9 docs(paged): record quant trace phase
Assisted-by: Codex:gpt-5
2026-07-01 12:42:13 +00:00
Ettore Di Giacinto
55df9100dc docs(paged): record layout trace phase
Assisted-by: Codex:gpt-5
2026-07-01 12:32:05 +00:00
Ettore Di Giacinto
2e19e5c90f docs(paged): record prefill bucket attribution phase
Assisted-by: Codex:gpt-5
2026-07-01 12:20:42 +00:00
Ettore Di Giacinto
6a2618b6dc docs(paged): record MTP verify-cost rejection
Assisted-by: Codex:gpt-5
2026-07-01 11:51:29 +00:00
Ettore Di Giacinto
f7d76389b0 docs(paged): record W4A16 direct activation rejection
Assisted-by: Codex:gpt-5
2026-07-01 11:28:11 +00:00
Ettore Di Giacinto
4645935fa5 docs(paged): mark W4A16 direct routing stub done
Assisted-by: Codex:gpt-5
2026-07-01 11:14:55 +00:00
Ettore Di Giacinto
b425d8ce03 docs(paged): mark W4A16 direct policy tests done
Assisted-by: Codex:gpt-5
2026-07-01 11:06:10 +00:00
Ettore Di Giacinto
ef578866c8 docs(paged): scope W4A16 direct activation experiment
Assisted-by: Codex:gpt-5
2026-07-01 10:59:56 +00:00
Ettore Di Giacinto
fc5d5e4ff3 docs(paged): profile current W4A16 prefill
Assisted-by: Codex:gpt-5
2026-07-01 10:56:48 +00:00
Ettore Di Giacinto
ef7dbfa5f7 docs(paged): compare MoE min32 against vLLM
Assisted-by: Codex:gpt-5
2026-07-01 10:46:32 +00:00
Ettore Di Giacinto
c41d1a5b4f docs(paged): record waiting-threshold TTFT defer
Record Phase58 prompt-backlog threshold A/B, DGX gates, MoE and dense serving results, and the repeat-before-default decision.

Assisted-by: Codex:gpt-5
2026-07-01 10:31:09 +00:00
Ettore Di Giacinto
9be291e6b0 docs(paged): reject capped TTFT defer sweep
Record Phase57 capped TTFT prefill-first sweep, DGX gates, and the decision to keep the cap as an A/B knob rather than a parity path.

Assisted-by: Codex:gpt-5
2026-07-01 10:18:41 +00:00
Ettore Di Giacinto
902bcc7717 docs(paged): validate TTFT prefill-first A/B
Record Phase56 MoE and lower-concurrency validation for the TTFT prefill-first policy, including DGX gates and the opt-in-only decision.

Assisted-by: Codex:gpt-5
2026-07-01 10:05:23 +00:00
Ettore Di Giacinto
999cf09532 docs(paged): record TTFT prefill-first A/B
Record Phase55 default-off scheduler A/B, DGX md5/op gates, dense serving results, and the pending fork push/mirror status.

Assisted-by: Codex:gpt-5
2026-07-01 09:57:55 +00:00
Ettore Di Giacinto
3dbf34e739 docs(paged): record admission histogram trace
Record Phase54 trace-only histogram work, DGX md5/op gates, dense serving histogram evidence, and the next scheduler decision.

Assisted-by: Codex:gpt-5
2026-07-01 09:40:50 +00:00
Ettore Di Giacinto
347a5c05bd docs(paged): reject admission budget sweep
Assisted-by: Codex:gpt-5
2026-07-01 09:27:20 +00:00
Ettore Di Giacinto
2aa76702df docs(paged): record dense admission trace
Assisted-by: Codex:gpt-5
2026-07-01 09:18:43 +00:00
Ettore Di Giacinto
b5f65152e2 docs(paged): record serving admission trace
Assisted-by: Codex:gpt-5
2026-07-01 09:08:42 +00:00
Ettore Di Giacinto
c299dcd231 docs(paged): record dense true decode profile
Assisted-by: Codex:gpt-5
2026-07-01 08:55:23 +00:00
Ettore Di Giacinto
cd59e5d61f fix(paged): scrub harness vars for vllm serve
Assisted-by: Codex:gpt-5
2026-07-01 08:23:05 +00:00
Ettore Di Giacinto
96825a224e docs(paged): record dense serving snapshot
Assisted-by: Codex:gpt-5
2026-07-01 08:20:26 +00:00
Ettore Di Giacinto
440129c98e fix(paged): harden serving snapshot readiness
Assisted-by: Codex:gpt-5
2026-07-01 08:07:48 +00:00
Ettore Di Giacinto
e69ee0e867 feat(paged): parameterize served model name
Assisted-by: Codex:gpt-5
2026-07-01 07:50:19 +00:00
Ettore Di Giacinto
2a0fc0f4b9 docs(paged): record inference gate guard
Assisted-by: Codex:gpt-5
2026-07-01 07:45:52 +00:00
Ettore Di Giacinto
ae8284f5fb feat(paged): parameterize vllm serving snapshot
Assisted-by: Codex:gpt-5
2026-07-01 07:41:55 +00:00
Ettore Di Giacinto
ecaf406c0b docs(paged): reject persistent gate fusion shortcut
Assisted-by: Codex:gpt-5
2026-07-01 07:34:27 +00:00
Ettore Di Giacinto
b9eff5bca3 docs(paged): reconcile next parity target
Assisted-by: Codex:gpt-5
2026-07-01 07:31:26 +00:00
Ettore Di Giacinto
aa848d5afb docs(paged): record low-concurrency serving check
Assisted-by: Codex:gpt-5
2026-07-01 07:24:28 +00:00
Ettore Di Giacinto
d44e164c96 docs(paged): record max-concurrency parity check
Assisted-by: Codex:gpt-5
2026-07-01 07:13:48 +00:00
Ettore Di Giacinto
52c11b1ce5 docs(paged): reject graph-time gate fusion shortcut
Assisted-by: Codex:gpt-5
2026-07-01 06:56:01 +00:00
Ettore Di Giacinto
5354adcffb docs(paged): scope gate projection policy
Assisted-by: Codex:gpt-5
2026-07-01 06:50:19 +00:00
Ettore Di Giacinto
9f75da01f9 feat(paged): add cublas tensor-name trace patch
Add patch 0063 extending LLAMA_CUBLAS_ROUTE_TRACE with src0/src1/dst tensor names.

Record Phase 37 gates and the conclusion that SGEMM traces to MoE gate tensors.

Assisted-by: Codex:gpt-5
2026-07-01 06:41:00 +00:00
Ettore Di Giacinto
fbdc200886 feat(paged): add cublas route trace patch
Add patch 0062 with default-off LLAMA_CUBLAS_ROUTE_TRACE instrumentation for generic cuBLAS MUL_MAT subroutes.

Record Phase 36 DGX gates, serving trace results, and the next projection follow-up scope.

Assisted-by: Codex:gpt-5
2026-07-01 06:24:46 +00:00
Ettore Di Giacinto
49cce0b5a2 feat(paged): add mul mat route trace patch
Add LocalAI patch 0061 from the llama.cpp fork and record Phase 35 gates, serving route counts, and the updated patch mirror invariant.

Assisted-by: Codex:gpt-5
2026-07-01 05:52:09 +00:00
Ettore Di Giacinto
ba1979a689 feat(paged): add moe mmid route trace patch
Add LocalAI patch 0060 from the llama.cpp fork and record Phase 34 gates, serving route counts, and the updated patch mirror invariant.

Assisted-by: Codex:gpt-5
2026-07-01 05:37:53 +00:00
Ettore Di Giacinto
7665422bfa feat(paged): add moe small-m mmq tile policy gate
Assisted-by: Codex:gpt-5
2026-07-01 05:20:18 +00:00
Ettore Di Giacinto
70a4c31f36 feat(paged): add moe small-m mmq candidate trace
Assisted-by: Codex:gpt-5
2026-07-01 05:08:31 +00:00
Ettore Di Giacinto
e189e5a4ca feat(paged): add moe mmq launch trace patch
Assisted-by: Codex:gpt-5
2026-07-01 04:54:33 +00:00
Ettore Di Giacinto
b28b448c68 docs(paged): record mmq shape serving profile
Assisted-by: Codex:gpt-5
2026-07-01 04:36:04 +00:00
Ettore Di Giacinto
2148fa466b feat(paged): add moe mmq shape trace patch
Assisted-by: Codex:gpt-5
2026-07-01 04:32:12 +00:00
Ettore Di Giacinto
3b9ec3e1f1 docs(paged): record mmq occupancy rejection
Assisted-by: Codex:gpt-5
2026-07-01 04:18:12 +00:00
Ettore Di Giacinto
3c2cb9f4ab docs(paged): record graph-node serving profile
Record the Phase 27 current-stack llama.cpp n128 serving profile captured with CUDA graph node tracing and gated before and after the run.

Assisted-by: Codex:gpt-5
2026-07-01 04:00:14 +00:00
Ettore Di Giacinto
ace1ffab28 docs(paged): record audited current snapshot
Record the Phase 26 current-stack paged-vs-vLLM serving snapshot with hardware classification and compact pre/post inference gates.

Assisted-by: Codex:gpt-5
2026-07-01 03:48:27 +00:00
Ettore Di Giacinto
a0194125f5 chore(paged): summarize snapshot inference gates
Emit a compact gate_summary.tsv from current serving snapshots so each artifact records the pre/post MoE md5, dense md5, and backend op checks. Add a summary-only mode for auditing existing artifacts and document the Phase 25 backfill on the Phase 20 snapshot.

Assisted-by: Codex:gpt-5
2026-07-01 03:35:54 +00:00
Ettore Di Giacinto
7108b68a70 chore(paged): record snapshot hardware class
Add a hardware report to the current serving snapshot harness so every paged-vs-vLLM artifact records the GPU identity and conservative hardware class before any server starts. Document the Phase 24 dry run and the GB10 classification for future parity comparisons.

Assisted-by: Codex:gpt-5
2026-07-01 03:31:11 +00:00
Ettore Di Giacinto
7aa15ce539 docs(paged): refresh parity handoff coordinates
Update the paged parity handoff to the current fork head, patch count, mirror invariant, current serving harness, and LocalAI AI-attribution policy after Phases 20-22.

Assisted-by: Codex:gpt-5
2026-07-01 03:25:14 +00:00
Ettore Di Giacinto
6c165747a9 docs(paged): verify patch-series mirror invariant
Record the Phase 22 strict git-apply mirror check proving the LocalAI paged patch series reconstructs the canonical llama.cpp fork tree after patch 0055.

Assisted-by: Codex:gpt-5
2026-07-01 03:22:43 +00:00
Ettore Di Giacinto
ff3f0620de chore(paged): add current serving snapshot harness
Add a reusable current-stack paged-vs-vLLM serving snapshot harness that targets the clean DGX mirror, enforces idle/lock preflight, runs pre/post inference gates, and records ratio summaries.

Assisted-by: Codex:gpt-5
2026-07-01 03:19:36 +00:00
Ettore Di Giacinto
c99678da42 docs(paged): refresh current serving snapshot
Record the Phase 20 same-session MoE paged-vs-vLLM serving snapshot on the current clean DGX mirror, including pre/post inference gates and the resulting GB10 parity decision.

Assisted-by: Codex:gpt-5
2026-07-01 03:15:30 +00:00
Ettore Di Giacinto
310eb3c866 docs(paged): reject MTP draft-shape scheduler
Record the Phase 19 trace-only serving entropy run and close the group/defer-by-draft follow-up based on measured shape distribution, throughput regression, and green inference gates.

Assisted-by: Codex:gpt-5
2026-07-01 03:03:49 +00:00
Ettore Di Giacinto
cced07c7fe docs(paged): add MTP shape trace patch
Add the next incremental llama.cpp patch for default-off speculative batch-shape tracing and record the Phase 18 red/green and inference-gate results.

Assisted-by: Codex:gpt-5
2026-07-01 02:54:29 +00:00
Ettore Di Giacinto
6e35476340 docs(paged): scope MTP graph-shape follow-up
Record Phase 17 source inspection: MTP verification rows change hard graph dimensions, padding is not a safe shortcut, and any future work should start with shape instrumentation before an opt-in scheduler experiment.

Assisted-by: Codex:gpt-5
2026-07-01 02:37:21 +00:00
Ettore Di Giacinto
ae76d42a96 docs(paged): profile MTP graph reuse loss
Record Phase 16 nsys evidence that current MTP serving loses paged decode graph reuse and increases GPU work, explaining the Phase 15 serving regression.

Assisted-by: Codex:gpt-5
2026-07-01 02:32:49 +00:00
Ettore Di Giacinto
4d171e62bb docs(paged): reject MTP serving lever
Add the repeatable MTP serving A/B runner and record Phase 15 results showing current llama-server MTP regresses GB10 serving throughput despite passing inference gates.

Assisted-by: Codex:gpt-5
2026-07-01 02:29:28 +00:00
Ettore Di Giacinto
70394364a3 docs(paged): gate MTP rollback safety
Record Phase 14 MTP rollback evidence, normalized greedy-prefix checks, and canonical inference gates.

Assisted-by: Codex:gpt-5
2026-07-01 02:15:11 +00:00
Ettore Di Giacinto
e169058e73 chore(paged): add DGX inference gate runner
Add a reusable paged llama.cpp gate script for DGX work. It checks docker/local-ai-worker/GPU lock state, runs the canonical MoE and dense transcript md5 gates, and runs selected test-backend-ops filters.

Verified on dgx.casa: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT_ID 806/806. Artifact: /home/mudler/bench/paged_inference_gates/20260701_040048.

Assisted-by: Codex:gpt-5
2026-07-01 02:01:55 +00:00
Ettore Di Giacinto
ede23df333 docs(paged): close W4A16 pad checklist
Mark the rejected-branch disposition as not taken because Phase 4 was kept as patch 0050 with recorded md5, op, perf, and mirror gates.

Assisted-by: Codex:gpt-5
2026-07-01 01:58:22 +00:00
Ettore Di Giacinto
abc70c209e docs(paged): close ragged MoE dispatch shortcut
Record the Phase 8 safety rerun, canonical transcript md5 gates, full and ragged MUL_MAT_ID op gates, and the no-production-patch decision for metadata-only fused dispatch work.

Assisted-by: Codex:gpt-5
2026-07-01 01:57:45 +00:00
Ettore Di Giacinto
2074b4fb5b docs(paged): reject GDN global Ai32 prototype
Record the default-off Global-Ai32 implementation, exact md5 gates, GB10 A/B regression, rejected diff artifact, and the resulting stop decision for GDN kernel work on GB10.

Assisted-by: Codex:gpt-5
2026-07-01 01:51:53 +00:00
Ettore Di Giacinto
adabd11919 docs(paged): scope GDN global Ai32 prototype
Record the shared-A/Ai GB10 cost model, the GO decision for one default-off f32 Ai prototype, and the Phase 13 implementation plan.

Assisted-by: Codex:gpt-5
2026-07-01 01:38:51 +00:00
Ettore Di Giacinto
1b5ae227eb docs(paged): reject GDN M5 QS-early phase
Record the Phase 11 default-off QS-early GDN experiment, its canonical md5 gates, the same-session GB10 A/B regression, and the rejected diff artifact.

Assisted-by: Codex:gpt-5
2026-07-01 01:29:44 +00:00
Ettore Di Giacinto
24e778de47 docs(paged): scope GDN M5 state-boundary phase
Add the Phase 11 design and implementation plan for a default-off C16 M5 QS-early GDN experiment after rejecting C32 slabs.

Assisted-by: Codex:gpt-5
2026-07-01 01:21:05 +00:00
Ettore Di Giacinto
3da3b169fb docs(paged): reject GDN C32 slab phase
Record the default-off C32 slab experiment, its md5 gates, the dense tail-row fix, and the performance regression that rejects the source patch.

Assisted-by: Codex:gpt-5
2026-07-01 01:15:00 +00:00
Ettore Di Giacinto
ff3ad84191 docs(paged): record GDN C32 slab baseline
Record the Phase 10 current-M5 prefill baseline and the source inspection finding that C32 M5 needs a real U-staging implementation rather than a launcher-only shortcut.

Assisted-by: Codex:gpt-5
2026-07-01 00:58:54 +00:00
Ettore Di Giacinto
9bbe02c161 fix(paged): gate MTP backend sampling
Record the Phase 9 MTP smoke gate, mirror the fork patch that disables backend sampling for MTP drafts, and scope the follow-up C32 slab GDN prefill phase.

Assisted-by: Codex:gpt-5
2026-07-01 00:54:25 +00:00
Ettore Di Giacinto
b862e2c568 docs(paged): stop ragged dispatch source shortcut
Assisted-by: Codex:gpt-5
2026-07-01 00:42:36 +00:00
Ettore Di Giacinto
b009de0ee0 test(paged): mirror ragged MoE dispatch gate
Assisted-by: Codex:gpt-5
2026-07-01 00:41:21 +00:00
Ettore Di Giacinto
89ef3a4020 docs(paged): record ragged MoE profile gate
Assisted-by: Codex:gpt-5
2026-07-01 00:35:21 +00:00
Ettore Di Giacinto
ef14748f06 docs(paged): scope ragged MoE dispatch phase
Assisted-by: Codex:gpt-5
2026-07-01 00:26:01 +00:00
Ettore Di Giacinto
b6885aa446 docs(paged): reject weighted combine fusion candidate
Assisted-by: Codex:gpt-5
2026-07-01 00:20:53 +00:00
Ettore Di Giacinto
4b6fc0fa1c test(paged): mirror MoE weighted combine gate
Assisted-by: Codex:gpt-5
2026-06-30 23:51:52 +00:00
Ettore Di Giacinto
22a93ce1a3 docs(paged): select weighted combine candidate
Assisted-by: Codex:gpt-5
2026-06-30 23:47:34 +00:00
Ettore Di Giacinto
3cf7fa1715 docs(paged): reject swiglu down fusion candidate
Assisted-by: Codex:gpt-5
2026-06-30 23:41:38 +00:00
Ettore Di Giacinto
d0fa463eac test(paged): mirror MoE swiglu down gate
Mirror the llama.cpp Phase 7 test gate for the merged MoE gate_up/SWIGLU/down chain and record the DGX md5/op gate evidence.

Assisted-by: Codex:gpt-5
2026-06-30 23:20:52 +00:00
Ettore Di Giacinto
34c4b5ce8d docs(paged): scope phase7 serving candidates
Mark the Phase 6 serving classifier complete, preserve the old parity final as historical, and scope Phase 7 source candidates with explicit md5 and op gates.

Assisted-by: Codex:gpt-5
2026-06-30 23:12:09 +00:00
Ettore Di Giacinto
b647460dee docs(paged): record phase6 serving classifier
Record both-engine serving nsys buckets, rejected sampler short-circuit, and rejected GDN/MMQ env grids for the GB10 parity work.

Assisted-by: Codex:gpt-5
2026-06-30 23:04:15 +00:00
Ettore Di Giacinto
f9e015d8e2 docs(paged): record W4A16 Wq padding rejection
Record the Phase 5 Wq shared-memory padding experiment, its gates, sub-threshold benchmark gain, and the decision to ship no 0051 patch.

Assisted-by: Codex:gpt-5
2026-06-30 22:23:14 +00:00
Ettore Di Giacinto
85c88320ef patches(paged): pad W4A16 A shared tile stride
Mirror fork commit d9b9be0be as patch 0050 and record the Phase 4 W4A16 shared-memory padding gates, benchmarks, and mirror verification.

Assisted-by: Codex:gpt-5
2026-06-30 22:15:21 +00:00
Ettore Di Giacinto
8b413d1cbd docs(paged): record W4A16 scale broadcast rejection
Record the Phase 3 scale-broadcast experiment, its md5 and MUL_MAT_ID gates, the prefill regression, and the decision to ship no 0050 patch.

Assisted-by: Codex:gpt-5
2026-06-30 22:06:17 +00:00
Ettore Di Giacinto
c5f2545cdd patches(paged): tune W4A16 grouped tile shape
Mirror fork commit 7dfa0e175 as patch 0049 and record the Phase 2 GB10 W4A16 shape sweep, md5 gates, MUL_MAT_ID checks, and mirror verification.

Assisted-by: Codex:gpt-5
2026-06-30 21:57:42 +00:00
Ettore Di Giacinto
d8edc615e7 patches(paged): mirror W4A16 packed metadata
Mirror the fork-first W4A16 packed tile metadata commit into the LocalAI paged patch series, record the Phase 1 benchmark result, and keep the implementation plan checked off.

Assisted-by: Codex:gpt-5
2026-06-30 21:21:53 +00:00
Ettore Di Giacinto
1c0709b700 docs(paged): record W4A16 phase1 kill gate
Record the clean forced W4A16 baseline, default comparison, selected metadata target, and completed plan checkpoint for the GB10 parity reopen.

Assisted-by: Codex:gpt-5
2026-06-30 20:40:40 +00:00
Ettore Di Giacinto
337ebb8a37 docs(paged): record phase0 decode repro
Record comparable graph-node-traced paged and vLLM decode difference-method artifacts for the GB10 parity reopen.

Assisted-by: Codex:gpt-5
2026-06-30 20:35:43 +00:00
Ettore Di Giacinto
ef5d4af203 docs(paged): record phase0 prefill baseline
Record clean-source MoE and dense prefill baselines for the GB10 parity reopen and mark the plan checkpoint complete.

Assisted-by: Codex:gpt-5
2026-06-30 20:22:18 +00:00
Ettore Di Giacinto
a9a2efb296 docs(paged): record phase0 clean build gates
Record the clean DGX build retry, binary provenance, canonical greedy md5 gates, and completed plan steps for the GB10 parity reopen.

Assisted-by: Codex:gpt-5
2026-06-30 20:19:14 +00:00
Ettore Di Giacinto
b1a1b721bd docs(paged): record GB10 parity artifact gaps
Add the read-only DGX artifact review for the Phase 0 parity reopen, including supported paged measurements and missing vLLM difference-method evidence.

Assisted-by: Codex:gpt-5
2026-06-30 15:55:16 +00:00
Ettore Di Giacinto
b3cfdfac4a docs(paged): record GB10 parity source provenance
Add the clean llama.cpp fork state, base merge point, patch count, and tree-match result for the Phase 0 parity reopen workflow.

Assisted-by: Codex:gpt-5
2026-06-30 15:54:23 +00:00
Ettore Di Giacinto
6ac06734e9 docs(paged): start GB10 parity phase0 record
Create the Phase 0 results record for the parity reopen workflow, including preflight, provenance, and baseline sections.

Assisted-by: Codex:gpt-5
2026-06-30 15:51:57 +00:00
Ettore Di Giacinto
d288a0300f docs(paged): add GB10 parity implementation plan
Add the Superpowers implementation plan for the GB10 parity reopen, including Phase 0 provenance, decode repro, W4A16 kill gates, and later kernel workstream entry criteria.

Assisted-by: Codex:gpt-5
2026-06-30 15:50:01 +00:00
Ettore Di Giacinto
f8d7b026cf docs(paged): scope GB10 parity reopen plan
Add a phased follow-up spec for challenging the GB10 vLLM-parity closure, including provenance gates, W4A16/GDN/MoE workstreams, and subagent ownership boundaries.

Assisted-by: Codex:gpt-5
2026-06-30 15:44:11 +00:00
Ettore Di Giacinto
de34cd5954 docs(paged): refresh parity handoff state
Reconcile the paged backend pin prose with the current Makefile pin, mark the 0044 patch tracking note as resolved, and add DGX Docker worker idleness to the benchmark preflight.

Assisted-by: Codex:gpt-5
2026-06-30 15:27:44 +00:00
Ettore Di Giacinto
1b9176c2c8 docs(paged): codify fork-first patch workflow as mandatory policy
The fork mudler/llama.cpp branch localai-paged is the canonical source of
truth for all paged-backend kernel/patch work. Always update it FIRST: commit
the change on the fork branch and push it, then regenerate the LocalAI patch
series (backend/cpp/llama-cpp-localai-paged/patches/paged/) from the fork via
git format-patch so the series is a 1:1 drift-free mirror of the branch. Never
edit the LocalAI patch files directly, and never add a patch with no
corresponding fork-branch commit. The series is a derivative; the fork is the
source. The fork branch is also where the build and the per-path bit-exact md5
gate actually run, so it is the only place a change is truly validated.

Codified in two places:
- .agents/llama-cpp-localai-paged-backend.md: new "Fork-first workflow
  (MANDATORY)" section at the top of the patch/pin-sync material, plus the
  "Encapsulating your work" bullet now points at it.
- backend/cpp/llama-cpp-localai-paged/docs/PARITY_HANDOFF.md: strengthened the
  hard-gate (section 2.5) into "Fork-first is MANDATORY", and corrected a stale
  numbering example (fork 51168c5ee "patch 0044" maps to worktree 0044, not the
  f32-only M5 which is worktree 0047).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-30 15:12:36 +00:00
Ettore Di Giacinto
2033086f60 patches(paged): track 0044 GatedRMSNorm patch, sync LocalAI series to fork 51168c5
The fork mudler/llama.cpp branch localai-paged is the canonical source of
truth for the paged-backend patch series. This file is the git format-patch
of fork commit 51168c5ee ("feat(paged): fused gated RMSNorm + SiLU gate-mul
CUDA op (patch 0044)"), verified byte-identical to that commit's format-patch
output. The full on-disk series applies clean in numeric order on the pinned
base and the resulting tree is byte-identical to the fork commit tree (tree
hash a73d759350277532a14e853e1fe78f08bbb74ce8), so the LocalAI series is a
drift-free 1:1 mirror of the fork branch.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-30 15:10:13 +00:00
Ettore Di Giacinto
8bb47e5a8a docs(paged): correct PARITY_HANDOFF ahead/behind + note dense CDEF gate md5
Ground-check follow-up to 2431090ff. Two factual corrections:

- Section 7 worktree line had the ahead/behind counts swapped ("25 ahead,
  197 behind"); the branch is actually ~199 ahead / 25 behind origin/master.
- Discrepancy item 5 flagged only the MoE CDEF PAGED_GATE_MD5 (0921716...);
  the dense run is symmetric (COMBINED_DEFINITIVE.txt records ecfe924d... for
  dense, which likewise differs from the canonical dense gate 5951a5b4). Both
  CDEF values come from combined_definitive.sh's own gate command, not the
  canonical bit-exact gate in section 3.3, so neither is sanctioned and both
  must be KL-validated.

Everything else in the handoff verified accurate: fork branch localai-paged
HEAD 51168c5ee (patch 0044) on dgx:~/llama-paged-fork, dev-tree HEAD a7d439e,
all md5/KL numbers, the 86%/1078/924 decode record, bench env, and all
referenced file/artifact paths.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-30 14:49:06 +00:00
Ettore Di Giacinto
2431090ff3 docs(paged): future-agent vLLM-parity HANDOFF guide (GB10, how-to companion to FINAL)
Adds docs/PARITY_HANDOFF.md: the operational how-to for an agent with zero
context picking up the GB10 vLLM-parity work. Complements VLLM_PARITY_FINAL.md
(the why/record) with TL;DR state, the hard gates (per-path bit-exact md5,
KL-gate, no LLAMA_MAX_BATCH_TOKENS, fork-is-canonical), a copy-pasteable
operational quickstart (ssh/lock/build/bench + the --cuda-graph-trace=node
decode-profiling rule that caused 4 wrong analyses), the complete tested-and-
rejected lever map, methodology lessons, the three forward directions, and a
key file/artifact index with the open discrepancies to reconcile.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-30 14:42:44 +00:00
Ettore Di Giacinto
baf1025245 docs(paged): correct decode-serving record to ~86% GPU-steady parity (graph-node-traced)
The decode-serving section characterized the high-N gap as "BW-floored, vLLM
pays equally / 56-68%". A clean uncontended graph-node-traced profile
(dgx ~/highN_prof2 + ~/highN_vllm, 2026-06-30) shows that was a profiling
artifact: decode runs as a replayed CUDA graph, and nsys without
--cuda-graph-trace=node collapses each replay into one opaque launch, so every
prior decode decomposition (159 us/tok, "host-bound", "5.4x more efficient")
was wrong. Corrected via --cuda-graph-trace=node + the ntg=64-minus-ntg=16
difference method.

Real picture (paged npl=256): 99% GPU-busy (idle 1.4%), NOT host-bound. GDN
recurrent scan 553 us/tok (51%, linear in batch, dominant), NVFP4 expert GEMM
254 (23%), bf16 proj 73 (7%), elementwise 57, SSM conv 31. Gap reconciled:
vLLM-server 1177 -> vLLM true GPU-steady 1078 (chunked-prefill overlap inflates
its window ~8pt) -> llama GPU-steady 924 (= 86% of 1078) -> llama-server 718
(61%, the ~17pt S3-recoverable serving graph-reuse overhead). So vs vLLM's true
GPU-steady decode we are ~86%, not 56%. GDN is a shared BW floor where paged
leads (83% vs 79% of 273 GB/s peak; both 1.17-1.18x for 2x batch).

The residual ~14pt is vLLM's mature fused kernels (Marlin MoE +11ms, Triton
elementwise +10ms); both ggml fusions rejected: act-quant-into-MMQ -79.4%
(ggml MMQ re-quantizes y per row-tile x stream-k split, no single-pass tiling),
norm+quant+silu infeasible via ggml_cuda_can_fuse. Added rejected levers:
Q8_0/FP8 projection (regime error, closes <=6%; vLLM FP8-proj confirmed from
hf_quant_config.json MIXED_PRECISION), the two decode fusions; refined BV-block
GDN occupancy to -1.04% (wave-hidden).

Revised verdict: PREFILL genuinely capped (36-43%, not graph-replayed so real);
DECODE-SERVING near-parity ~86% of vLLM true GPU-steady (headline 56% was a
measurement/operating-point artifact). GB10-vs-datacenter framing kept.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-30 14:16:06 +00:00
Ettore Di Giacinto
6edbb56b06 docs(paged): definitive vLLM-parity final-state record (GB10, CLOSED)
Add docs/VLLM_PARITY_FINAL.md: the standing, never-re-litigate record of the
exhaustive GB10 (sm_121) vLLM-parity investigation for the Qwen3.6 NVFP4 hybrid
models. Captures the definitive same-session both-engine benchmark (prefill
S_PP, decode/serving per-seq + aggregate, TTFT, PEAK_GB, paged-as-%-of-vLLM for
both the MoE 35B-A3B and dense 27B models), the complete lever map (every
prefill-GEMM, prefill-GDN, decode and serving/engine attempt with its verdict
and key number), the structural floors (LPDDR5x bandwidth, FP4-MMQ optimality,
GDN O(C^2) intra-chunk + serial recurrence, vLLM's HBM-tuned FLA/Marlin), the
shipped bit-exact wins, and the parity verdict: parity is a hardware ceiling on
GB10, not missing optimizations; the path to parity is datacenter Blackwell.

Every number cites its artifact (dgx:~/bench/COMBINED_DEFINITIVE.txt, the
marlin_gate / gdn_p1_ab A/Bs, PREFILL_GEMM_RESULTS, VLLM_PARITY_LEVER_MAP,
DECODE_SERVING_SCOPE, the patch headers); figures not pinned to an artifact are
marked estimated. Add a section-9 summary + link in the backend README.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-30 11:57:36 +00:00
Ettore Di Giacinto
bd100dd20a fix(paged): repair the patch series, sync to the fork branch (drop dev-tree 0044/0045, add f32-only M5 as 0047)
The 0044/0045 patches were exported from the old bf16/hybrid dev tree and no
longer apply on the f32-only series (0026 ssm_bf16_tau is dropped), so the
build broke at `git apply`. Re-sync the vendored series to the now
feature-complete fork branch mudler/llama.cpp:localai-paged, which is the
canonical source (pin 0ed235ea + the paged patch commits in order).

- git rm the dev-tree-based 0044 (GDN M5, bf16-machinery base) and 0045
  (Marlin W4A16 offline-repack, not part of the fork branch).
- Add the fork branch's newest commit (2c32ab8b7, "GDN M5 tensor-core
  chunked-scan prefill, f32-only re-port") as 0047, generated with a single
  git format-patch off that branch. It sequences after 0046 (its parent on
  the branch) and recovers the prefill win 0044 encoded (+3.5% S_PP @npp512,
  +17.7% @npp2048), bit-exact per-path (test-backend-ops GATED_DELTA_NET
  46/46 default and force-M5; greedy md5 default-on == M5-forced == canonical).
- Track patch 0046 (dense-prefill geometry gate), which was on disk but never
  committed, so the series is complete in git.
- README: patch-table header 0001-0046 -> 0001-0047, replace the 0044 row with
  the f32-only 0047 row, fix the dangling 0044 prose references, note the
  bf16 M6/M7/M8 variants are not part of this f32-only series, and add a
  maintenance bullet that the series is now generated from the fork branch so
  there is no more patch-export drift.

Verified: on a pristine llama.cpp at pin 0ed235ea the full series 0001-0043,
0046, 0047 applies clean in sorted order with the Makefile's exact
`git apply --verbose` method (37/37 OK), and the resulting tree is
byte-identical to the fork branch tip 2c32ab8b7.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-30 07:54:46 +00:00
Ettore Di Giacinto
be65438eac docs(paged): record MoE-prefill engine-gap decomposition + GEMM-port negatives (default-off)
nsys cross-engine decomposition: the MoE prefill 64% gap vs vLLM is engine plumbing, not the kernel (GPU 97% busy, 443 vs 197 us/tok). Three buckets: per-expert W4A4 M-fragmentation (58%), GDN scan (24%), f32<->bf16 casts (15%). Offline-repack (0045) and verbatim vLLM-marlin port both trail FP4-MMQ via wrapper overhead, kept default-off as recorded negatives.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-29 17:20:07 +00:00
Ettore Di Giacinto
7b38c6b2a3 feat(paged): GDN M5 tensor-core chunked-scan prefill, default-on under paged KV (patch 0044)
Land the tensor-core forms of the chunked gated-DeltaNet prefill scan (0031)
as a single GDN_TC-selected build and ship the M5 variant (full TC form-T
solve + state-update mma) default-ON when LLAMA_KV_PAGED is set.

The dispatch defaults GDN_TC=5 and GDN_CHUNK_MIN=64 under paged KV (both
env-overridable; OFF/INT_MAX when not paged, so stock/non-paged stays
regression-free). GDN_CHUNK_MIN is the per-call engage threshold and stays > 1
so decode (1 tok/call) keeps the sequential recurrence; 64 was tuned from a
{1,32,64,128,256} sweep (32/64/128 all win on prefill, 256 barely fires because
the MoE-prefill per-call count is < 256, 1 collapses decode S_TG ~25%).

Measured GB10, q36-35b-a3b-nvfp4, LLAMA_KV_PAGED=1 LLAMA_MOE_FORCE_GRAPHS=1,
llama-batched-bench -ngl 99 -fa on -ntg 4 -npl 32:
  -npp 512  S_PP 2208.96 -> 2286.5 t/s  (+3.5%, mean of 3 interleaved A/B)
  -npp 2048 S_PP 2021.5  -> 2379.8 t/s  (+17.7%)
Decode S_TG unchanged (~399 vs ~397 t/s, within noise).

Bit-exactness (per-path greedy md5, n=48 --temp 0 --seed 1, paged): default-on
== M5-forced == canonical on the gate prompt - MoE 8cb0ce23, dense 5951a5b4.
test-backend-ops GATED_DELTA_NET 94/94 vs CPU with M5 forced (incl. multi-chunk
up to n_tokens=256). On a long MoE prompt the default (M5 fires at >=64 tokens)
and the sequential path agree word-for-word until one benign greedy token-flip;
dense is byte-identical. The chunked scan is a NEW per-path result (different FP
reduction order), NMSE-validated benign.

CUDA-only, gencode arch=compute_121a,code=sm_121a (GB10 / sm_121a). README
sections 3 (0044 row, 0031 superseded note) and 5 (dev-notes verdict) updated.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-29 06:42:11 +00:00
Ettore Di Giacinto
042deab40e docs(paged): vLLM-parity lever map + tensor-core GDN build plan (both-engine profile-validated)
Lever map records the full prefill/decode gap decomposition vs vLLM, the ranked levers, and the rejected dead ends. GDN build plan is the per-product mma mapping + A-inverse + occupancy design.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-29 06:15:10 +00:00
Ettore Di Giacinto
c4058eb4da feat(paged): tail-fusion (0042) + full-step decode CUDA graph default-on (0043); FP4-MMA W4A4 (0034) + Marlin W4A16 (0035) MoE-GEMM scaffolds default-off
0042 fuses the pre-norm residual add into RMSNorm (+0.5% prefill, bit-exact). 0043 makes the full-step MoE decode CUDA graph default-on (+2-4% decode, bit-exact; removes ~18x per-step host kernel re-issue, A/B-confirmed). 0034 (native FP4-MMA W4A4) and 0035 (Marlin-style W4A16 grouped MoE GEMM) are correct + bit-exact but regress vs the int8 FP4-MMQ in-backend on GB10 (bf16 MMA is ~half the int8 rate); shipped default-off as validated mechanisms and recorded negatives per the parity methodology.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-29 06:15:10 +00:00
Ettore Di Giacinto
f1c98ff0b9 fix(paged): revert S3 decode-stable scheduler to default-OFF (A/B regression)
Patch 0041 (LLAMA_PAGED_DECODE_STABLE) was made default-on-when-paged, but a
measured end-to-end A/B proved that is a serving mistake. S3 defers prefill
admission on the period-8 cadence, which delays prompt admission: 2.5x worse
TTFT (60s vs 24s at N=256) and 20-29% lower end-to-end throughput, with no
end-to-end win at any concurrency. Its apparent decode_agg gain was a metric
artifact (faster per-step decode bought by starving prefill).

Flip the s3_enabled default so an unset LLAMA_PAGED_DECODE_STABLE means OFF; the
mechanism stays available as an explicit opt-in (LLAMA_PAGED_DECODE_STABLE=1) for
decode-dominated, low-arrival traffic where TTFT is not a concern. The default now
prefers prompt prefill admission for good TTFT. S1 (patch 0040) keeps shipping
default-on; only S3's default changes.

Re-exports patch 0041 (change folded into its source commit) and updates the
README 0041 row plus the decode-serving narrative to record the A/B finding.

Greedy md5 gate unchanged (single-sequence llama-completion path, not
update_slots): paged MoE 8cb0ce23, dense 5951a5b4.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-29 05:00:11 +00:00
Ettore Di Giacinto
b028c81eda docs(paged): record padded/fixed-slot decode shape as tested-and-rejected
The S1 section-(a) padded/fixed-slot decode shape (the scoped follow-up to push
serving graph reuse from ~72% toward ~100%) was implemented in an isolated
worktree off the committed S1/S3/tail base, built CUDA-only, and benched on GB10.
Verdict: REJECTED. It is bit-exact and provably inert, but it regresses serving
throughput at every concurrency and does not close the vLLM gap.

Implementation (default-off, LLAMA_PAGED_PAD_DECODE): on a pure-decode step
(n_prompt_budgeted == 0) emit a masked-inert dummy decode for every idle slot so
n_tokens / n_seqs / n_seqs_unq / n_outputs and the seq-id set stay constant; a
release()-side guard keeps a finished slot warm under padding. Each dummy is its
own sequence (private recurrent state, per-stream paged attention, logits
discarded), so it cannot perturb a real stream.

Gates: single-seq greedy md5 bit-exact (dense 5951a5b4, paged-MoE 8cb0ce23). The
literal per-stream ON-vs-OFF identity gate is unachievable - concurrent cuBLAS/FA
decode is not bit-reproducible run-to-run even with padding off (OFF-vs-OFF
diverging streams: dense 3/16, MoE 8/16). The achievable inertness gate passed:
ON-vs-OFF per-stream prefix-agreement equals the OFF-vs-OFF noise floor exactly
(MoE 0.940/0.940, dense 0.812/0.812), so the dummy slots leak nothing.

Bench (MoE Qwen3.6-35B-A3B-NVFP4, GB10), burst decode tok/s/seq: n=8 S1+S3 28.16
/ PAD 6.05 / vLLM 44.8; n=128 S1+S3 4.53 / PAD 4.32 / vLLM 6.87. Staggered
aggregate tok/s: baseline (reuse 0%) 757.6, S1+S3 (reuse 72%) 763.3, PAD
(reuse 38%) 558.0.

Why it fails: (1) serving decode here is GPU-compute-bound, not host-rebuild-bound
- baseline reuse 0% ~= S1+S3 reuse 72% on aggregate tok/s, so closing reuse buys
~nothing (the earlier 542->762 host-bound delta did not reproduce); (2) padding
adds dummy-row compute proportional to pad_width - real_load, catastrophic at low
load; (3) in continuous serving padding cannot hold a constant width (perpetual
prefill churn) so reuse drops 72% -> 38%; (4) the completion-driven batch shrink
padding prevents is itself a throughput win in a compute-bound regime. The
residual burst gap is GPU-compute, which a host-side reuse lever cannot close.

Patch series unchanged: this rejected lever is NOT added to patches/paged/.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 20:47:43 +00:00
Ettore Di Giacinto
2fa8ef8fc5 fix(paged): make patch 0031 apply on the 0001-0030 base; default S3 on under paged KV
FIX A (patch 0031 compose break): the chunked GDN prefill patch carried
'#include <cuda_bf16.h>' and '#include <type_traits>' as CONTEXT lines, but
those were introduced by the dropped bf16-tau patch 0026, so on the
bf16-tau-free 0001-0030 base only '#include <cstdlib>' is present and 'git
apply' failed. The same 0026 drop also shifted 0031's later hunks off their
context (the ', hyb' kernel-launch arg, the 'STATE_BF16, HYBRID' template
params, and the GDN_LAUNCH_ARGS list). Regenerated 0031 against a fresh
pin(0ed235ea) + 0001-0030 tree: the chunked kernel now SELF-PROVIDES the
cuda_bf16.h / type_traits includes (adds them, plus the climits it needs for
INT_MAX) and the dispatch guard is the 2-param 'if constexpr (!KDA &&
!keep_rs_t)' form. Behaviour is unchanged: 0031 stays opt-in, default OFF
(GDN_CHUNK_MIN), a recorded negative. The full 0001-0042 series now applies
clean on 0ed235ea ('git apply --check' green for every patch).

FIX B (patch 0041 S3 default): the decode-shape-stable scheduler defaulted OFF.
Make it default ON whenever paged KV is active (LLAMA_KV_PAGED set), still
overridable to off via LLAMA_PAGED_DECODE_STABLE=0. Minimal host-side change in
update_slots(); re-exported from the dev tree, README 0041 row updated to match.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 19:37:05 +00:00
Ettore Di Giacinto
d706980c2b feat(paged): close the continuous-serving decode gap (S1+S3, patches 0040/0041)
Add the two decode-serving graph-reuse levers (validated on GB10) that close the
host-bound serving gap (paged dropped to ~3.7 vs vLLM ~5.9 tok/s/seq in real
continuous serving while tying it in static batched-bench).

- 0040 S1 paged decode-graph reuse: the paged decode inputs never overrode
  llm_graph_input_i::can_reuse (defaults false), so the host rebuilt the ggml
  graph on EVERY decode step (layer-A reuse 0%). Add a 256-bucketed-shape
  can_reuse + a live-mctx refresh from the owning attn input. Bit-exact (md5
  byte-identical reuse on/off). Static batched-bench: paged reuse 0% -> 95.5%.
- 0041 S3 decode-shape-stable scheduling: keep co-batched prefill out of decode
  steps so the scheduler emits the reuse-stable pure-decode shape S1 can reuse.
  Default-off policy on top of 0016; bit-exact (per-stream independent).

S1+S3 together (128-client staggered serving, MoE Qwen3.6-35B-A3B-NVFP4): graph
reuse 0% -> 72.2%, hostproc 15.98 -> 6.31 ms/step, decode 4.05 -> 5.52 tok/s/seq
median (4.24 -> 5.96 mean, at vLLM's ~5.9). S1 alone is insufficient (13.8%);
S3 is the multiplier. S2 (double-buffer set_inputs) dropped: Phase-0 put
set_inputs at ~0.05 ms/step, so it has nothing to recover. README patch table +
DECODE_SERVING_SCOPE.md updated with results and the padded/fixed-slot follow-up.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 18:04:28 +00:00
Ettore Di Giacinto
000705321f feat(paged): FP4 prefill large-M dequant->bf16 cuBLAS scaffold (patch 0033, default-off)
Option (a) of PREFILL_GEMM_SCOPE.md: route large-M (prefill) NVFP4 dense weight
GEMMs off the decode-tuned FP4-MMQ kernel onto the dequant->bf16 cuBLAS (nvjet)
tensor-core path, wired via an M-threshold in ggml_cuda_should_use_mmq. Lands the
validated, bit-exact-gated mechanism and records the honest GB10 result: it is a
regression, so it ships default-off (== stock), mirroring the patch-0017
default-off discipline.

Three-edit scaffold (no new kernel): should_use_mmq routes NVFP4+Blackwell+dense
M>LLAMA_FP4_PREFILL_M to cuBLAS; op_mul_mat_cublas gains an NVFP4 branch that
dequants the FP4 weights to a transient bf16 pool buffer (not cached - stays
FP4-resident) and runs cublasGemmEx CUDA_R_16BF/COMPUTE_32F; ggml_get_to_bf16_cuda
gains the NVFP4 case.

Bit-exact gate PASS (benign): test-backend-ops MUL_MAT 1146/1146 + MUL_MAT_ID
806/806; the forced path (LLAMA_FP4_PREFILL_M=64) is green CUDA-vs-CPU at NVFP4
large-M shapes; greedy md5 on q36-27b is byte-identical to FP4-MMQ both for
short prefill (5951a5b4, decode untouched) and for a >threshold prefill that
exercises the bf16 path (5f3967df - no greedy argmax flips).

Performance REGRESSES on GB10 (S_PP, q36-27b dense, A/B via env): M=512 958.99
-> 486.65 (-49%), M=1024 1013.65 -> 587.27 (-42%), M=2048 918.46 -> 649.42
(-29%). The scope premise (FP4-MMQ ~3% of FP4 peak at large M) is false here:
FP4-MMQ beats bf16-cuBLAS because bf16 peak is ~half FP4 peak and the per-step
weight dequant + 4x bf16 weight traffic (~8x total vs the FP4 read) dominate,
only partially amortizing as M grows. Default-off keeps stock S_PP (966.98).

Phase 2 (MoE grouped large-M) not implemented: it inherits the same
bf16-peak<FP4-peak ceiling plus a per-expert dequant, so grouped bf16-cuBLAS
would regress for the same reason; a real prefill GEMM win needs option (b), a
native FP4-MMA large-M kernel. Full A/B in docs/PREFILL_GEMM_RESULTS.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 17:42:15 +00:00
Ettore Di Giacinto
4bdd26a7f0 docs(paged): scope tensor-core (mma) chunked GDN prefill kernel
Scopes the follow-up recorded by patch 0031 + README section 5: replace the
serial per-thread reductions of the chunked gated-DeltaNet prefill scan with
mma.sync tensor-core matmuls and lift the 1-block/SM occupancy ceiling, the
path that would beat the tuned sequential scan and close the GDN prefill
bucket toward vLLM's ~2.5x-cheaper chunked scan.

Confirmed (not assumed) the GB10/sm_121a tensor-core reality: consumer
Blackwell (SM12x) has NO wgmma (Hopper-only) and NO tcgen05/TMEM (sm_100a
data-center only); the usable path is the extended mma.sync family. So the
kernel is a warp-synchronous mma.sync + cp.async design (reusing ggml's
mma.cuh tiles), not a wgmma/TMA/tcgen05 design - patch 0031's 'mma/wgmma'
shorthand reads as mma only on this part.

Design: register-resident state frees the 64KB that forced C=16, admitting
C=64 under the 99KB shared opt-in; tf32 inputs / f32 accumulate with a 3xtf32
precision ladder; decays/gamma/beta stay f32 outside the mma to preserve the
bounded de-gating; A-inverse via blocked forward substitution (FLA UT
transform) with mma off-diagonal coupling. Mechanism: chunking cuts state-BW
~Cx, mma absorbs the O(C^2) intra-chunk flops the serial 0031 could not.
Honest: multi-week, high risk, no vendor kernel to route to on sm_121; gains
beat the sequential scan and close most of the bucket but not full sm_100-class
parity. KL-gate binding (NMSE likely fails at reduced precision). Phased:
re-profile -> two-product PoC -> full intra-chunk + C=64 + reg-state ->
occupancy/cp.async; opt-in default-OFF until A/B-proven.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 17:23:51 +00:00
Ettore Di Giacinto
9a28f23134 docs(paged): scope the continuous-serving decode gap (host-bound, design-only)
Add DECODE_SERVING_SCOPE.md: the decode KERNEL is at parity in static
batched-bench (~6.1 tok/s/seq ~ vLLM ~5.9 at npl128) but continuous serving
through llama-server update_slots() drops to ~3.7 (-39%) while vLLM sustains
~5.9. Scope shows the gap is the scheduler/host loop, not the kernel.

Root-cause hypothesis from source: continuous batching's batch-shape + seq-set
churn breaks BOTH graph-reuse layers every step - llama-context can_reuse/
allow_reuse (n_tokens + seq-set must match) and the CUDA ggml_cuda_graph
update_required memcmp (ne/nb/data ptrs) - so the GPU idles while the host
rebuilds + re-captures the graph and runs un-graphed set_inputs. vLLM avoids
this with padded/bucketed decode shapes + piecewise CUDA graphs. Documents that
the shipped scheduler patches (0008/0013/0016/0024/0025/0029) target prefill
freezing + burst collapse, NOT decode-step graph reuse, which is why the serving
gap survives them; notes the README s.5 'lever 2 graph coverage FLAT' verdict was
static-regime and is reopened here for serving only.

Ranks host-side, bit-exact-safe levers: S1 bucketed/padded decode-step shape for
graph reuse, S2 double-buffer/overlap per-step host work, S3 graph-shape-stable
scheduling (extend 0016). Specifies a Phase-0 profile to confirm host-bound
before any build, reusing the in-tree [L5INSTR] hostproc/set_inputs/
get_block_table timers, the 'graphs reused' perf counter, LLAMA_GRAPH_REUSE_DISABLE
and nsys GPU-busy%, with vLLM ground-truthed at the same concurrency. No kernel
code; no GPU run in this pass.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 17:14:51 +00:00
Ettore Di Giacinto
e610347367 feat(paged): chunked parallel-scan GDN prefill kernel (patch 0031)
Adds patch 0031 to the paged llama.cpp series: an FLA-style chunked
parallel-scan prefill kernel for gated DeltaNet (the upstream
gated_delta_net.cu "Add chunked kernel for even faster pre-fill" TODO).
Scope: non-KDA scalar gate, f32 state, final-state-only, homogeneous.

Bit-exact-benign (NEW per-path): test-backend-ops GATED_DELTA_NET 91/91 within
the 1e-7 NMSE gate vs the CPU reference (patch adds 8 S_v=128 prefill cases:
exact-multiple / tail / multi-seq / GQA / permuted); numpy prototype confirms
f32 chunked-vs-sequential NMSE ~1e-13.

OPT-IN, default OFF: GB10's 99KB dynamic-smem opt-in forces C=16 (the 128x128
f32 state is 64KB of the all-shared layout), pinning the kernel to 1 block/SM
with serial dk-reductions. Measured ~761 t/s chunked vs ~971 t/s sequential
(~22%% slower) on q36-27b-nvfp4 prefill, so it defaults OFF (enable with
GDN_CHUNK_MIN=<n>); the backend default is regression-free. Beating the
84.7%-of-peak sequential scan needs tensor-core matmuls / register-resident
state with larger chunks (recorded in README section 5).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 17:09:38 +00:00
Ettore Di Giacinto
11128cb080 docs(paged): scope the large-M NVFP4 prefill GEMM lever (design only)
Design + plan for the #1 prefill lever: NVFP4 weight GEMM at large M, where
MMQ (decode/M<=128-tuned, 1 CTA/SM, 128-col tile cap) is ~3.4x slower than
vLLM's marlin/cutlass large-M path (~51% of the prefill gap).

Recommends (a) dequant->bf16 cuBLAS routed by an M-threshold (dense first,
MoE grouped-cuBLAS second); rejects (b) a from-scratch Marlin/FP4 kernel as a
multi-week project. Key enabling finding: NVFP4->bf16 dequant kernels already
exist, and NVFP4 is currently force-excluded from the tensor-core cuBLAS path
(falls to f32 Sgemm) - relaxing that one guard is the pivot. Honest: bf16-cuBLAS
banks ~60-75% of the GEMM gap, not full 68us/tok parity (bf16 TC peak ~half FP4).

Design only - no kernel, no GPU run.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
2026-06-28 16:42:23 +00:00
Ettore Di Giacinto
4cd90bfae9 paged: drop bf16-tau (patch 0026), subsumed by decode fusions (tau=100000 flat, zero speed benefit)
The opt-in hybrid per-head bf16 SSM-state lever (ssm_bf16_tau, patch 0026) is
removed from the llama-cpp-localai-paged patch series. Clean re-measurement after
the decode fusions (0028 recurrent-state gather-fusion + 0029 block-table cache)
landed shows it buys nothing: forcing ALL gated-DeltaNet heads to bf16
(tau=100000, the most aggressive setting) gives flat decode throughput, 780.6 vs
780.0 t/s. The mode engages but adds zero speed because it is subsumed by the
fusions. The earlier "+12%" was measured before the fusions completed. bf16-tau
was a precision trade (not bit-exact, ~91% same-top-p) plus extra bug surface and
extra CUDA template-instantiation compile cost with no offsetting benefit.

Dependency check: no later patch (0028/0029/0030) depends on 0026. 0030's only
mention is a description comment; its code keys off fused_gdn_ar/ch/auto_fgdn,
which originate in 0018/0019/0021 (before 0026). The remaining series (0001-0025,
0028-0030) applies clean with git apply --check against the pin
0ed235ea2c17a19fc8238668653946721ed136fd. The Makefile applies the series by glob
(patches/paged/0*.patch); the resulting gap at 0026 is tolerated (0005/0027 are
already absent).

Removed:
- patches/paged/0026-qwen35-hybrid-perhead-ssm-state.patch
- the dead ssm_bf16_tau / ssm_hybrid_tau option handler in the shared
  grpc-server.cpp (it only set LLAMA_SSM_BF16_TAU, now a no-op the library no
  longer reads)
- the patched+bf16-tau benchmark columns and llama-patched-bf16tau rows
  (README + final_benchmark.csv), the ssm_bf16_tau option text in backend
  index.yaml, the gallery NOTE block, and the docs/features/backends.md mention.

The rejected-lever lesson is kept (why it was dropped: subsumed, tau=100000 flat)
in the backend README section 5, the paged-backend agent guide, and the
vLLM-parity methodology, so it is not re-tried.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 16:06:06 +00:00
Ettore Di Giacinto
2c59805267 fix(paged): rpc cmake target renamed rpc-server -> ggml-rpc-server at pin 0ed235ea
llama.cpp renamed the RPC tool target (tools/rpc/CMakeLists.txt: set(TARGET
ggml-rpc-server)) at the 0ed235ea pin. master already updated the stock
llama-cpp Makefile to match (--target ggml-rpc-server, cp bin/ggml-rpc-server);
the paged backend's separate Makefile copy was left stale and its -grpc (RPC)
variant failed with 'No rule to make target rpc-server' (grpc-server itself
built to 100%). Mirror the stock rename in the paged Makefile.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 11:10:07 +00:00
Ettore Di Giacinto
c51ff4cec9 docs(paged): scope porting the portable benefits to Metal/SYCL/Vulkan (+ROCm)
Add ACCELERATOR_PORTING_SCOPE.md, the umbrella scope for taking the paged
backend's accelerator-portable wins off the CUDA family. It builds on (does
not duplicate) UPSTREAM_LAYER2_SCOPE.md, which stays the GDN/SSM-fusion
detail (benefit #1), and adds:

- Benefit #2 (paged KV in-kernel block-table flash-attn read, 0009-0011):
  new per-backend feasibility from source analysis of the Metal/SYCL/Vulkan
  flash-attn kernels. SYCL EASY (near line-for-line CUDA mirror), Metal
  EASY-MEDIUM (decode already routes to the vec kernel), Vulkan MEDIUM (the
  fast coopmat2 NVIDIA decode path cannot do the indexed read; push-constants
  are full). Universal constraint: only the vec/scalar decode kernel admits
  the per-cell indexed read, so route block-table ops onto vec (as CUDA's
  0009-0010 dispatch guard already does) and leave the fast MM/coopmat2 path
  contiguous-only. This is the lever that flips paged KV from
  neutral-to-slightly-negative to non-negative off CUDA.
- Benefit #3 (decode-first scheduler, 0013/0016): confirmed a free portable
  win - host-side update_slots() policy, zero kernel work, runs on any
  accelerator as-is.
- Benefit #4 (NVFP4 FP4-MMA, 0017/0023/0025): out of scope (Blackwell only);
  flags the backend-agnostic analogues of the act-quant dedup and the
  graph-coverage lever without over-claiming a port.
- A ROCm note: ROCm rides the CUDA/HIP path (validate, don't re-port);
  FP4-MMA stays Blackwell-only.

Benefits #1 and #2 share the port shape and rank Metal->SYCL->Vulkan, so they
bundle into one per-backend PR behind a shared ops-first PR. Cross-link added
from UPSTREAM_LAYER2_SCOPE.md. All gates are test-backend-ops on-target (no
Metal/SYCL/Vulkan/ROCm hardware here).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 08:34:32 +00:00
Ettore Di Giacinto
ea72a56e2c Merge origin/master + pin-sync paged backend to 0ed235ea
master auto-bumped the stock llama-cpp pin 9d5d882d -> 0ed235ea and updated the
shared grpc-server.cpp. The paged backend's pin must track the stock pin (the
grpc-server.cpp is shared), so bump its LLAMA_VERSION to match. All 28 paged
patches apply clean on 0ed235ea (verified against a fresh upstream clone). The
bf16-tau state-serialization fix (patch 0026) is included. Bit-exact gate + full
grpc-server build verify on GPU/CI to follow.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 07:56:47 +00:00
Ettore Di Giacinto
1f3e5ba301 fix(paged): serialize both SSM partitions in hybrid bf16-tau state save/restore (patch 0026)
The opt-in ssm_bf16_tau hybrid mode splits a gated-DeltaNet layer's
recurrent SSM state into an f32 partition (s_l) and a bf16 partition
(s_l_bf16). The recurrent state serialization paths (state_write_data /
state_read_data) were never updated for the split: they read/wrote s_l
using the FULL hparams.n_embd_s() (S_v*S_v*H) row width, but a split
layer's s_l only holds S_v*S_v*n_f32, so the access overruns the smaller
tensor (a ggml_backend tensor read out of bounds), and the bf16
fast-head partition was never persisted at all.

This is what broke high-concurrency serving with --ssm-bf16-tau: the
server's context-checkpoint feature serializes per-sequence state via
state_seq_get_data. With a checkpoint enabled, even a single request
triggered the out-of-bounds read; at higher concurrency the cell range
starts at a higher base slot so the overrun reaches further (hard abort
in a debug build, silent state corruption then 1-token-then-EOS on
restore in a release build). The static batched-bench never exercises
save/restore so it did not catch it; the GDN decode kernel and per-head
partition offsets were already correct (decode with checkpoints disabled
is fine at N=8/16/32).

Fix: serialize the f32 partition and, when the layer is split, the bf16
partition right after it, each with its OWN row width (tensor ne[0]).
head_slot is rebuilt deterministically at load (same model + tau), so it
is not serialized. Non-split layers have ne[0] == n_embd_s() and no bf16
partition, so their on-disk format and behavior are byte-identical (the
default f32 path and the bit-exact gate are unaffected).

Verified on GB10/DGX with Qwen3.6-35B-A3B-NVFP4 + --ssm-bf16-tau 64 via a
continuous-batching llama-server: with context checkpoints enabled, N=8,
N=16 and N=32 (slot reuse + restore) all now produce full coherent
128-token output and the server stays up; pre-fix the same config
aborted on the first checkpoint.

Assisted-by: Claude:claude-opus-4-8[1m] [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 07:47:17 +00:00
Ettore Di Giacinto
4da769c1ca paged headers: self-include <cstddef>/<cstdint> for size_t/uintN_t (fix amd64/non-arm64 build; compile-only)
Vendored paged headers used size_t / uintN_t without including <cstddef> /
<cstdint>. The arm64 DGX toolchain provides them transitively so the build
passed there, but amd64/older toolchains do not, failing the CI amd64 build one
header at a time ('size_t' does not name a type -> cascade).

paged-kv-manager.h was already fixed. This adds the missing includes to the
remaining vendored headers at the point each is created/rewritten in the patch
series so every src/paged*.h self-includes both:

  * paged-attn.h     (0003): add <cstddef> (had <cstdint>)
  * paged-alloc.h    (0007): add <cstddef> (had <cstdint>)
  * paged-prefix-api.h (0007): add <cstddef> + <cstdint> (had only llama.h)

The .cpp units include their own paged header, so they inherit the includes
transitively. Whole series still applies clean on the pinned llama.cpp.

Compile-only change: no runtime behavior change, bit-exactness unaffected.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 06:18:56 +00:00
Ettore Di Giacinto
23b11a5239 paged-kv-manager.h: add missing <cstddef> for size_t
Fixes cuda-13 amd64 / non-arm64 build where size_t was used without the
header (arm64 cuda-13 pulled it in transitively; amd64/cuda-12 toolchains
do not). Compile-only change, bit-exactness unaffected.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 04:09:16 +00:00
Ettore Di Giacinto
9bb8994c4e chore(paged): drop CUDA-12 variants of llama-cpp-localai-paged, keep CUDA-13 only
The paged backend targets Blackwell sm_121a, which CUDA 12.0 cannot target
at all, so the CUDA-12 variants were pointless. They were also broken: the
cublas-12 / nvidia-l4t / arm64 build failed to compile paged-kv-manager.cpp
("no declaration matches ...", a ~10-function mismatch the older
cuda-12-base gcc rejects). CUDA-13 compiles it fine (confirmed on GB10).

Removed (config-only, scoped to the paged backend):
- backend-matrix.yml: the two CUDA-12 paged rows
  (-gpu-nvidia-cuda-12-llama-cpp-localai-paged,
   -nvidia-l4t-arm64-llama-cpp-localai-paged)
- backend/index.yaml: CUDA-12 capability keys (nvidia-cuda-12,
  nvidia-l4t-cuda-12, nvidia-l4t) on both meta-backends, repointed
  default/nvidia to the cuda13 amd64 variant, and dropped the orphaned
  cuda12-* / nvidia-l4t-arm64-* variant definitions (latest + -development).

Kept CUDA-13 only: cuda13-llama-cpp-localai-paged (amd64) and
cuda13-nvidia-l4t-arm64-llama-cpp-localai-paged (l4t arm64). Matrix
tag-suffixes <-> index variant URIs form a clean 2:2 bijection.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-28 01:37:54 +00:00
Ettore Di Giacinto
0b84fda496 docs(paged): add the bf16-tau opt-in line to the decode plots
Per request, the plots now show all four series: llama.cpp (standard), vLLM,
LocalAI's llama.cpp patches (bit-exact hero), and LocalAI's patches + bf16-tau
(opt-in ceiling, +3% to +17% over the patches, ahead of vLLM at every dense width
and MoE npl>=32). Subtitle flags bf16-tau as opt-in / not bit-exact.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 22:25:02 +00:00
Ettore Di Giacinto
1431f72b92 docs(paged): regenerate decode plots (3-way) from re-measured data + overview
Rebuild the two committed decode plots from the re-measured CSV and add a combined
overview. Three series per the comparison that matters: llama.cpp (standard) vs
vLLM vs LocalAI's llama.cpp patches; x-over-standard called out at npl128. bf16-tau
stays out of the plot (it remains in the CSV + the README table as the opt-in row).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 22:20:12 +00:00
Ettore Di Giacinto
266fcc79ad docs(agents): fix A/B-bench gotcha - env-toggle != stock for compiled-in wins
The DGX re-run showed toggling LLAMA_KV_PAGED on/off on the patched binary does
NOT reproduce stock: the dominant SSM decode fusions are compiled in, not
runtime-gated, so the toggle measures only the (here ~neutral) paged-KV part.
True stock needs a separately-built unpatched binary at the same pin. Correct the
methodology skill's per-lever discipline + apples-to-apples rule accordingly.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 22:09:05 +00:00
Ettore Di Giacinto
3466094c68 docs(paged): re-measure DGX benchmarks on one harness (stock/patched/bf16-tau)
Re-run the GB10/DGX-Spark llama-batched-bench matrix (dense q36-27b + MoE
q36-35b-a3b, npl 8/32/64/128, -fa on -ngl 99 -npp 128 -ntg 128) so the CSV and
README section 4 carry a single consistent set of llama numbers with all three
configs:

- stock: separately-built unpatched llama.cpp at this backend's exact pin
  9d5d882d (toggling LLAMA_KV_PAGED on the patched binary does NOT reproduce
  stock - the SSM decode fusions are compiled in, not env-gated).
- patched: paged binary, LLAMA_KV_PAGED=1 (+LLAMA_MOE_FORCE_GRAPHS=1 for MoE).
- patched+bf16-tau: patched plus --ssm-bf16-tau 64 (opt-in, NOT bit-exact,
  ~91% same-top-p).

final_benchmark.csv now has stock + patched + bf16-tau + vllm rows for both
models at all four widths (the prior CSV had no stock and no bf16-tau rows).
peak_gb is dropped: the GB10's unified LPDDR5x reports [N/A] to nvidia-smi and
the bench does not print it, so per-run peak could not be captured this session.

Patch series gives up to 2.46x (dense) / 2.26x (MoE) over true-stock; opt-in
bf16-tau adds a further +3% to +17% on top of patched (growing with width).
vLLM column is kept from the prior session (not re-run) and labeled as such.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 22:05:59 +00:00
Ettore Di Giacinto
ed5eb705c7 docs(paged): drop moot PIN_SYNC_c299a92c record, repoint to README sec 7
The paged backend's llama.cpp pin was reverted from c299a92c back to
9d5d882d (== stock), so docs/PIN_SYNC_c299a92c.md (a blow-by-blow of the
reverted sync) is dead weight. The pin-sync PROCESS stays documented in
the three live places: the Makefile comment, README section 7 (Pin +
maintenance policy), and .agents/llama-cpp-localai-paged-backend.md.

Delete the doc and repoint every reference to it (Makefile, README,
.agents, canary script + workflow) at README section 7. No functional
paths change: the canary's patches-dir glob (patches/paged/0*.patch)
is untouched.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 21:34:10 +00:00
Ettore Di Giacinto
53f66a6f03 fix(paged): revert pin to 9d5d882d (== stock); c299a92c broke grpc-server link
The c299a92c bump diverged 23 commits ahead of the stock llama-cpp pin.
grpc-server.cpp is SHARED with the stock backend and tracks the stock pin;
c299a92c's upstream server-API refactor pulled stream_* helpers into the headers
grpc-server.cpp includes, whose definitions the stock-aligned build does not
compile -> every paged variant failed to LINK (undefined reference to
stream_aware_should_stop / stream_pipe_producer::cleanup /
stream_session_attach_pipe). The bump was greedy-md5 bit-exact, but the bit-exact
gate never exercises the full grpc-server build, so it slipped through.

Revert LLAMA_VERSION to 9d5d882d (== stock pin, where the patches are bit-exact
AND grpc-server links - the original DGX-proven baseline). Document the hard
constraint in the Makefile, README, PIN_SYNC record, and the .agents guide: the
paged pin must track the stock pin, and a pin-sync must pass the full CI
grpc-server build, not only the bit-exact gate.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 20:28:28 +00:00
Ettore Di Giacinto
08b754f910 chore(paged): keep patches/ patch-only; README to backend root, docs to docs/
The llama-cpp-localai-paged patches/ dir had accumulated docs, plots, a csv,
dev .cpp harnesses, and a dead FP4-MoE kernel scaffold after an earlier git-mv.
Restore the invariant that patches/ holds only the .patch series.

Moves:
- patches/paged/README.md -> README.md (canonical doc at the backend root)
- patches/paged/{PIN_SYNC_c299a92c,PAGED_BITEXACT_NOTE,LOCALAI_LLAMACPP_BACKEND_PLAN,UPSTREAM_LAYER2_SCOPE}.md,
  final_benchmark.csv, qwen36_*.png, paged-burst-bench.cpp, paged-reclaim-unit.cpp -> docs/
- patches/README.md -> docs/PATCH_MAINTENANCE.md (unique patch-regen recipe not in the canonical README)

Deletes:
- patches/BENCHMARKS.md (superseded by README section 4 + the dev-notes section)
- patches/kernel/ (dead FP4-MoE scaffold, never in the 0001-0030 apply glob, zero refs repo-wide)

Repoint every reference to the moved files: README internal links (docs/ + the
.github links drop from 5x ../ to 3x ../), .agents/llama-cpp-localai-paged-backend.md,
.github/scripts/paged-canary-apply.sh, .github/workflows/llama-cpp-paged-canary.yml,
the wrapper Makefile, backend/cpp/llama-cpp/grpc-server.cpp, backend/index.yaml,
docs/content/features/backends.md, gallery/index.yaml.

The build apply glob PAGED_PATCHES_DIR/0*.patch (PAGED_PATCHES_DIR := .../patches/paged)
is unchanged and still resolves to the 28 patches.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 13:20:05 +00:00
Ettore Di Giacinto
db14006fcd docs(agents): add paged-backend maintenance + vLLM-parity methodology skills
Two .agents guides (indexed in AGENTS.md):
- llama-cpp-localai-paged-backend.md: what the CUDA-only paged backend is, the
  patchset scope, the bit-exact gate, the manual pin-sync + weekly canary, the
  CUDA-only / stock-stays-pure invariants, and the Metal/SYCL/Vulkan follow-up scope.
- vllm-parity-methodology.md: the decode-parity playbook (bit-exact gating,
  profile-don't-assume, both-engine ground-truth, per-lever A/B, recording rejected
  levers, multi-agent GPU orchestration).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 12:58:01 +00:00
Ettore Di Giacinto
a4e730979d feat(paged): restrict llama-cpp-localai-paged to CUDA-only build targets
The paged backend previously built for cublas/cuda, cpu, vulkan, sycl,
hipblas and darwin/metal. On non-CUDA the patchset's wins are inert: the
GDN fusions are gated off (patch 0030) and NVFP4 falls back to dequant,
so the backend is neutral-to-negative there (README section 4c). The
darwin grpc-server link also fails on undefined upstream server symbols,
turning CI red. Both broken and pointless off-CUDA, so ship CUDA-only.

- backend-matrix.yml: drop the hipblas, sycl f32/f16, cpu amd64/arm64,
  vulkan amd64/arm64 and metal-darwin rows for this backend; keep the
  four cublas rows (cuda-12, cuda-13, nvidia-l4t cuda-12 and cuda-13).
- index.yaml: meta-backend (and -development) capabilities are now
  CUDA-only with default pointing at cuda12 (mirrors faster-qwen3-tts);
  removed the orphaned cpu/rocm/sycl/vulkan/metal variant entries.
- Removed the now-unused darwin build script and its Makefile target /
  .NOTPARALLEL entry / backend_build_darwin.yml step.
- Documented the CUDA-only build coverage in the patch README and plan.

Non-CUDA users should use the stock llama-cpp backend.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 12:29:15 +00:00
Ettore Di Giacinto
9115c2c52c docs(paged): correct Vulkan/SYCL note (GDN op IS upstream) + CUDA-only rationale
The gated-DeltaNet + SSM_CONV ops have upstream Metal/Vulkan/SYCL kernels, so the
Qwen3.6 hybrids run there (non-fused) - the earlier 'no Vulkan kernel' note was
wrong. The patchset's fusions are gated off off-CUDA, so the backend ships
CUDA-only; non-CUDA users use stock llama-cpp.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 12:18:11 +00:00
Ettore Di Giacinto
984c8fcbea docs(paged): Layer-2 upstream scope for native fused-GDN kernels (Metal/Vulkan/SYCL)
Source-only analysis of what it would take to give the gated-DeltaNet decode
fusions (0018 in-place state write-back, 0019 fused recurrent-state gather,
0021 ssm_conv_update_inplace, 0028 conv-tap gather fusion) native kernels on
the non-CUDA compute backends, so the patch-series decode win extends past
CUDA-family hardware.

Key findings:
- The base GGML_OP_GATED_DELTA_NET and GGML_OP_SSM_CONV kernels ALREADY exist
  upstream on Metal, Vulkan AND SYCL (the README's no-Vulkan-kernel line is
  stale). The Qwen3.6 hybrids run on all three today via the non-fused path;
  Layer-2 is the decode SPEEDUP, not enabling the model to run.
- Per backend the new work is only the FUSION plumbing: redirect the GDN state
  write (in-place), add the ids read, write one new conv-update kernel + its
  ids variant, two tiny gather kernels, plus supports_op + op-handler + (Vulkan)
  pipeline/push-constant/descriptor wiring. Builders, CPU refs, model graph and
  test-backend-ops cases are shared and already done.
- Bit-exactness is feasible per backend by construction (the fusions redirect
  addresses, not the f32 reduction order); test-backend-ops (backendX-vs-CPU)
  is the gate.
- The 0030 name allow-list should become capability-driven (make supports_op
  authoritative for the discriminated src slots).
- Ranked: ops-first PR, then Metal (highest value/effort, fixed simdgroup =
  simplest bit-exactness), then SYCL (near-verbatim CUDA mirror, cheapest to
  author), then Vulkan (widest hardware reach but the shader-gen + variant
  matrix + subgroup variance make it the capstone).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 12:11:24 +00:00
Ettore Di Giacinto
4a9a1dd247 docs(paged): Mac stock-vs-patched bench + Vulkan note + cross-backend learnings
Section 4(c): real Apple M4/Metal numbers (Qwen3-8B Q4_K_M, stock vs patched) -
patchset is neutral-to-slightly-negative on Metal (the in-kernel block-table read
is CUDA-only; NVFP4/GDN-fusions inert), so prefer stock llama-cpp on Apple Silicon.
Vulkan: same picture, worse (no upstream GDN op). Section 6: cross-backend learnings
+ upstream candidates (the GDN decode-plumbing fusions are the portable, bit-exact,
CPU-mirrored win worth upstreaming).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 11:05:37 +00:00
Ettore Di Giacinto
78fac9a28f refactor(paged): stock llama-cpp is patch-free; paged backend owns its patch series
Move ALL paged-attention content out of the stock backend/cpp/llama-cpp
backend and into backend/cpp/llama-cpp-localai-paged, so the stock backend is
pure upstream llama.cpp and the paged backend owns and applies its own vendored
patch series.

- Delete the dead early-exploration scaffold backend/cpp/llama-cpp/paged/
  (kernel/w4a16 Marlin scaffold, standalone paged_kv_manager, bench/loadgen,
  its own 0001-0002 patches, dense-era design docs, tests). Zero references
  repo-wide.
- Move backend/cpp/llama-cpp/patches/ (the 28-patch paged series + paged/README
  + 3 operational docs, plus the kernel/ scaffold patch and the top-level paged
  README/BENCHMARKS) to backend/cpp/llama-cpp-localai-paged/patches/. The stock
  backend keeps no patches/ dir; it had no non-paged base patches.
- Purify the stock backend: remove the LLAMA_PAGED make variable, the
  patches/paged apply loop, and the LLAMA_PAGED passthrough to prepare.sh;
  remove the paged-series handling from prepare.sh. The stock llama.cpp target
  now only clones the pin and applies its own (currently empty) base patches/
  series. The runtime paged option hooks in the shared grpc-server.cpp are
  untouched (inert without the patches).
- The paged backend's Makefile now applies its OWN patches/paged/0*.patch onto
  each freshly cloned tree via strict git apply (apply-paged-patches), after the
  copied stock infra clones the pin and applies base patches.
- Repoint every reference to the old patches/paged path: the upstream canary
  workflow + apply script, bump_deps.yaml, gallery/index.yaml, the docs,
  backend/index.yaml, backend-matrix.yml, the top-level Makefile comments, and
  the moved PIN_SYNC / README docs. Drop the now-removed LLAMA_PAGED=on
  build-toggle from comments.

Verified: the full 28-patch series applies strict-clean (git apply, exit 0) to
a clean ggml-org/llama.cpp checkout at the pinned c299a92c, and the repointed
canary apply script resolves and applies the series end to end.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 11:01:22 +00:00
Ettore Di Giacinto
fb2dc33d52 docs(paged): consolidate the dev-trail docs into one canonical README
The paged-attention patch directory had accumulated ~55 scattered dev docs
(results, progress, scope, lever, and gap-analysis notes). Consolidate the
durable content of all of them into one canonical
backend/cpp/llama-cpp/patches/paged/README.md covering: what the patchset is,
the architecture (paged KV + block-table flash-attn, the gated-DeltaNet SSM
decode path, NVFP4 FP4-MMA, the decode-first scheduler), the full 0001-0030
patch series table with bit-exact status, the GB10 benchmarks
(patched-vs-stock-vs-vLLM + the Apple M4 architectural note), the dev notes
(bit-exact methodology, the per-path gate, the MoE-parity conclusion, the
rejected/flat levers, the opt-in bf16-SSM mode), arch+quant generality, the
pin + canary maintenance policy, and the published NVFP4 gallery models.

Delete the consolidated-away dev trail. Keep the three operational docs the
README links to: PIN_SYNC_c299a92c.md (canary reference), PAGED_BITEXACT_NOTE.md
(per-path gate reference) and LOCALAI_LLAMACPP_BACKEND_PLAN.md (the
ship-as-own-backend design-of-record), plus the benchmark plots + csv. The
.patch files and the unit/bench .cpp are untouched.

Repoint every external reference to a deleted doc at the new README:
grpc-server.cpp, docs/content/features/backends.md, gallery/index.yaml, the
canary apply script (PIN_BUMP_APPLY_CHECK.md -> README), and the base
patches/README.md (ADDITIVE_DESIGN.md -> README). The canary's PIN_SYNC
reference still resolves; its inert SSM_DECODE_FIX_RESULTS.md glob (a
patch-internal path matcher, not a repo-doc link) is left intact.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 09:25:47 +00:00
Ettore Di Giacinto
a5a5b2ad80 feat(paged): bump llama.cpp pin 9d5d882d -> c299a92c (bit-exact verified)
Advance the paged-attention backend's owned llama.cpp pin by 23 upstream
commits. The shipped source-only patch series (0001-0030, 28 patches) applies
strict-clean (git apply, exit 0) on a fresh c299a92c checkout with no re-export
needed, and the bit-exact gate is GREEN on every path on GB10 (CUDA sm_121):

- md5 greedy decode (-ngl 99 -fa on -n 48 --temp 0 --seed 1): dense
  non-paged/paged 5951a5b4, MoE non-paged 07db32c2, MoE paged 8cb0ce23; all
  match the established baselines.
- test-backend-ops CUDA0: SSM_CONV 45/45, SSM_CONV_UPDATE 16/16,
  SSM_CONV_UPDATE_IDS 16/16, GATED_DELTA_NET 84/84, MUL_MAT 1146/1146,
  MUL_MAT_ID 806/806; all OK.

The 23-commit upstream jump did not change our decode output. The .patch files
are kept byte-identical (they already apply strict-clean at the new pin); only
the pin, the PIN_SYNC evidence doc, and the canary/gallery doc references change.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 08:57:33 +00:00
Ettore Di Giacinto
7e1832b868 fix(paged): strip stray dev-doc hunks so patch series applies on a clean checkout
The shipped from-patches build applies the paged series with strict `git apply`
(backend/cpp/llama-cpp/Makefile `llama.cpp` target:
`git apply --verbose "$p" || { ...; exit 1; }`), which is atomic: a hunk against
a file missing from the tree rejects the whole patch and fails the build. Four
patches carried hunks against dev-only docs that live in the DGX dev tree but are
absent from a clean ggml-org/llama.cpp checkout, so the build only succeeded on
the DGX and FAILED on CI / any clean checkout:

  0019 -> SSM_DECODE_FIX_RESULTS.md   (modify hunk = the root reject)
  0020 -> LEVER1_OPROJ_MMQ_RESULTS.md (create)
  0021 -> CONV_STATE_FUSION_RESULTS.md (create)
  0028 -> LEVER1_GATHER_PROGRESS.md, LEVER1_GATHER_RESULTS.md (create)

0019's reject cascaded to 0021/0022/0026/0028 (which build on 0019's code). Strip
each `diff --git a/<devdoc>` section plus its diffstat line, `create mode`
trailer, and correct the summary count. Every llama.cpp SOURCE hunk is left
byte-identical (verified by sha256 of each patch's source-diff tail).

Verified on a fresh clone of ggml-org/llama.cpp at the pin 9d5d882d: BEFORE,
strict `git apply` failed at 0019 (cascade 0019/0021/0022/0026/0028); AFTER, the
full series 0001-0030 applies with exit 0 (sentinel created, zero stray docs).
The tolerant `patch -p1` fallback in prepare.sh also applies with zero rejects.

PIN_SYNC_9d5d882d.md documents the durable fix: re-exports/pin-syncs must keep
patches source-only (export with a source pathspec / `:!*.md`, gate with a strict
`git apply` on a clean checkout). The upcoming c299a92c pin-bump re-export must
produce source-only patches too.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 08:39:27 +00:00
Ettore Di Giacinto
2bee7a5ab1 ci(paged): add early-warning canary for vendored llama.cpp paged patches
The paged backend (backend/cpp/llama-cpp-localai-paged) pins its own verified
llama.cpp tip and is excluded from the nightly auto-bumper so a naive bump can
never silently break the shipped build. That exclusion also removed the early
warning of upstream drift. This restores the signal without touching the pin.

Add .github/workflows/llama-cpp-paged-canary.yml (weekly + workflow_dispatch):

- apply-check job (ubuntu-latest, toolchain-free): resolve the latest
  ggml-org/llama.cpp master tip, shallow-checkout it, and apply the full paged
  series 0001-0030 in order with the build's own git-apply method via the new
  shared helper .github/scripts/paged-canary-apply.sh. Red on any apply break.
- compile job (needs apply-check): on the exact tip it validated, build the
  paged backend (cublas) inside the same base-grpc-cuda-12 toolchain and the
  same `make grpc-server` target the shipped build uses, so a red means upstream
  drift, not toolchain noise. nvcc compiles the kernels with no GPU present.

Red here = run a PIN_SYNC (rebase + bit-exact gate + re-export), then bump the
paged Makefile pin. The canary is signal-only: it opens no PR and never moves
the pin, so the shipped build and the dep-bump PRs stay green regardless. It is
fully separate from bump_deps.

The lone pre-existing quirk in the series (patch 0019 carries a stray modify
hunk against the dev-only doc SSM_DECODE_FIX_RESULTS.md, absent from any clean
upstream checkout; git apply is atomic so it rejects the whole patch and
cascades to 0021/0022/0026/0028) is handled path-scoped: the helper excludes
only that dev-doc and still applies 0019's real code hunks atomically, mirroring
prepare.sh's tolerance, so the quirk never false-positives the canary but a
genuine code break in 0019 still turns it red.

Point the existing pin comments in backend/cpp/llama-cpp-localai-paged/Makefile
and .github/workflows/bump_deps.yaml at this canary as the drift signal, and
document it in the PIN_SYNC doc: canary red -> do a pin-sync.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 08:29:09 +00:00
Ettore Di Giacinto
e160041f05 chore(paged): decouple paged llama.cpp pin from the nightly auto-bumper
The llama-cpp-localai-paged backend reused backend/cpp/llama-cpp's LLAMA_VERSION,
which .github/workflows/bump_deps.yaml auto-bumps nightly to the latest
ggml-org/llama.cpp master tip. The stock backend is patch-free so that bump is
safe, but the paged backend applies a vendored patch series
(backend/cpp/llama-cpp/patches/paged/) hand-verified bit-exact against ONE
specific tip. A naive bump moves the tip out from under the patches and breaks
'git apply' at build time - a dep-bump PR would go red (or, worse, the break
surfaces later in a release build).

Mirror the turboquant precedent: give the paged wrapper its OWN LLAMA_VERSION
pin (the verified 9d5d882d) and force it into every copied build via
LLAMA_VERSION=$(LLAMA_VERSION), so the nightly stock bump no longer drags the
paged build to an unverified tip. Unlike turboquant (whose fork branch carries
the patches and is safe to auto-bump), the paged series is vendored, so it gets
NO bump_deps.yaml entry: it is advanced only by the manual PIN_SYNC process.
Add cross-referencing comments in both Makefiles and bump_deps.yaml.

Also add PIN_BUMP_APPLY_CHECK.md: an apply-feasibility report for the latest tip
(c299a92c, 23 commits ahead). The full series applies CLEAN under 'git apply'
with only benign line offsets and zero conflicts; the lone failure (0019) is a
pre-existing stray dev-doc hunk, identical on the current pin, not a bump
regression.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 08:02:37 +00:00
Ettore Di Giacinto
400930db19 Merge remote-tracking branch 'origin/master' into worktree-feat+paged-attention
# Conflicts:
#	gallery/index.yaml
2026-06-27 07:48:49 +00:00
Ettore Di Giacinto
202a29f980 feat(paged): Metal/darwin build availability for llama-cpp-localai-paged
Close the single build-targeting gap the cross-arch audit (ARCH_GENERALITY_AUDIT.md
section 6, item 2) flagged: the paged backend had no Metal/darwin variant and no
metal: capability key, so a Mac user selecting llama-cpp-localai-paged fell back to
default=cpu (a Linux image) that does not run, with no fallthrough to stock llama-cpp.

Mirror exactly how stock llama-cpp does darwin:

- .github/backend-matrix.yml: add the includeDarwin row
  (-metal-darwin-arm64-llama-cpp-localai-paged, arch arm64, lang go) next to the
  stock llama-cpp darwin row.
- backend/index.yaml: add the metal: capability key to the
  llama-cpp-localai-paged meta-backend plus the metal-llama-cpp-localai-paged and
  -development variant entries (URIs match the matrix tag-suffix); add Metal to tags.
- scripts/build/llama-cpp-localai-paged-darwin.sh: new bespoke darwin build,
  a line-for-line mirror of llama-cpp-darwin.sh swapping the paged wrapper dir,
  binary names, ggml-shared-libs dir and output tar. Same CPU_ALL_VARIANTS + Metal
  path (GGML_METAL=ON via the reused llama-cpp Makefile when OS=Darwin; --target ggml
  pulls in ggml-metal via add_dependencies) with LLAMA_PAGED=on.
- Makefile: add backends/llama-cpp-localai-paged-darwin target (+ .NOTPARALLEL).
- .github/workflows/backend_build_darwin.yml: give the paged backend the same
  bespoke darwin build step as stock llama-cpp, share the llama ccache restore (save
  stays stock-only to avoid a same-run key collision), and exclude it from the
  generic build-darwin-go-backend step.
- scripts/changed-backends.js: comment-only - the paged darwin path mapping was
  already present (forward-looking); update the stale "if a metal row is ever added"
  note now that the row exists.

Metal delivers paged-KV only (NVFP4 FP4-MMA is CUDA/Blackwell-only); the GDN/conv
fused ops have no Metal kernel, so a gated-DeltaNet (qwen35) model falls back to the
CPU reference op at runtime - made SAFE by the fused-op backend gate (patch 0030).
This is config; the Metal build runs in CI on the next push and is runtime-tested on
the M4 Mac.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 07:42:08 +00:00
Ettore Di Giacinto
621a20d2b5 feat(paged): backend-gate fused GDN/discriminated SSM_CONV emission (patch 0030)
Closes audit RISKY-1 (the one latent silent-miscompute hazard). The fused/in-place
Gated Delta Net op (0018/0019/0026) and the discriminated SSM_CONV decode op
(0021/0028, which REUSE GGML_OP_SSM_CONV / GGML_OP_GATED_DELTA_NET via a non-null
src[3]/src[4] discriminator) are CUDA+CPU-only but were emitted DEFAULT-ON
(cparams.fused_gdn_ar/ch=true, auto_fgdn=true) with no backend guard. A backend
that supports plain SSM_CONV but ignores the discriminator (Vulkan/SYCL/Metal)
would run the wrong plain conv => silent corruption.

Fix: in llama_context::sched_reserve(), before the auto_fgdn resolution, force
fused_gdn_ar = fused_gdn_ch = auto_fgdn = false when any non-CPU compute backend
is not CUDA-family (reg name not "CUDA"/"ROCm"/"MUSA"). Every emission site keys
off these flags, so the graph falls back to the upstream non-fused plain
ggml_ssm_conv + ggml_silu path that every backend handles. On CUDA the reg name is
"CUDA", the flags are left untouched, and the decode graph is byte-identical.

Mirror of DGX paged patch 0030; adds FUSED_OP_BACKEND_GATE_RESULTS.md.

Verified GPU-free: reconstructed pin 9d5d882d + paged 0001-0029 + 0030, CPU-only
build (GGML_CUDA=OFF) of libllama + test-backend-ops links with 0 errors; 0030
applies cleanly via git apply and patch -p1. test-backend-ops correctness for
SSM_CONV/SSM_CONV_UPDATE(_IDS)/GATED_DELTA_NET is CUDA0-vs-CPU (pending DGX,
tunnel offline this session); registered test cases will exercise it.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 07:32:49 +00:00
Ettore Di Giacinto
2332587fdc fix(gallery): scope NVFP4-paged entries to Blackwell + consistent tags
The six LocalAI-paged NVFP4 entries advertised GB10 throughput figures with
no machine-readable hardware signal, and the four qwopus/MTP entries lacked
the nvfp4 tag entirely (not discoverable as NVFP4). Per the cross-arch audit
(ARCH_GENERALITY_AUDIT.md section gallery-targeting), NVFP4 GGUFs run
everywhere via dequant (never fail), so the gap is performance-expectation,
not correctness; the only available lever is description + tags.

- Add the nvfp4 tag to the four qwopus/MTP entries that lacked it; the two
  base qwen3.6 entries already had it.
- Add a blackwell tag to all six (precedent: the nvidia hardware tag is
  already used on many gallery entries as a filter chip).
- Lead each of the six descriptions with a one-line Blackwell-recommended /
  runs-slower-off-Blackwell caveat.
- Scope the qwen3.6-27b 90-117% of vLLM claim explicitly to GB10 / DGX Spark
  (consumer Blackwell) so it is not read as a universal figure.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 07:19:42 +00:00
Ettore Di Giacinto
af6e133759 docs(paged): cross-arch synthesis - ship verdict + minimum non-Blackwell fixes
Synthesizes the four ARCH_GENERALITY_AUDIT sections (build-matrix,
gguf-gallery-targeting, optimization-generality, patch-arch-safety) into a
single cross-arch ship decision: build-safety table per target, every
patch bucketed (SAFE-EVERYWHERE / BLACKWELL-ONLY-clean-fallback / GB10-TUNED
/ RISKY), the NVFP4 gallery recommendation, a per-arch roadmap ranked by
value/effort, the empirical-verification matrix (GB10 + M4 cover all but
non-Blackwell NVIDIA), and the ship verdict.

Verdict: SAFE to ship as Blackwell/Linux today; the build is arch-general
(no GB10 pin; FP4 code is default-off + #if-guarded) and NVFP4 GGUFs run
everywhere via dequant. The one hard prerequisite before extending paged to
Metal/Vulkan/SYCL is closing the backend-ungated, default-on fused GDN/conv
op emission (discriminated GGML_OP_SSM_CONV via non-null src[3], CUDA+CPU
only, no supports_op guard) - latent on current Linux targets, silent
miscompute on a future non-CUDA paged build of a gated-DeltaNet model.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 07:07:20 +00:00
Ettore Di Giacinto
87cfd1fadb docs(paged): quant-generality audit - SSM/serving opts are quant-agnostic (0013-0029)
Source-verify each paged decode optimization as quant-agnostic (operates on the
f32 gated-DeltaNet/conv recurrent state, the paged serving host path, or the
generic MMQ/CUDA-graph routing) vs NVFP4-specific (only fires inside the
use_native_fp4 / GGML_TYPE_NVFP4 branch).

Findings: 14 of 16 landed patches are quant-agnostic (0013/0014/0015/0016/0018/
0019/0020/0021/0022/0024/0025/0026/0028/0029). Only 0023 (MoE FP4 act-quant
de-dup, inside use_native_fp4) is NVFP4-specific; 0017 is NVFP4-only but
default-off/inert (kill-gate, no win).

Corrects the hypothesis on 0025: the actual patch is the MUL_MAT_ID CUDA-graph
guard relaxation gated on ggml_is_quantized + ggml_cuda_should_use_mmq (the
generic quantized grouped-MMQ path), NOT NVFP4. The NVFP4-specific act-quant /
quantize_mmq_nvfp4 work is LEVER 3, which was a measurement STOP and never
landed (no patch); LEVER 4 (NVFP4 projections) KL-failed and never shipped.

Adds the relative-impact-by-quant estimate (fixed f32-recurrence/host ms is the
largest step fraction at NVFP4, shrinks at Q8/bf16 as the weight read grows) and
the A/B plan to prove generality on a Q4_K_M requant of the same Qwen3.6 (build
the control first, md5/KLD bit-exact gate per path, decode_agg npl 32/128, with
0023 as the NVFP4-only negative control).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 07:06:14 +00:00
Ettore Di Giacinto
2a2de1d6c1 docs(paged): patch-arch-safety classification for patches 0018-0029
Build-break / miscompile audit of the paged patch series. Classifies each
patch general/Blackwell-gated/risky, records the only conditional arch surface
(0017, fully #if-gated + default-off), and gives the per-target build-safety
verdict (sm_80-90 CUDA / sm_100 / Metal-not-a-target / CPU / ROCm-SYCL-Vulkan).
Flags the one latent silent-correctness hazard: fused GDN/conv ops reuse
GGML_OP_SSM_CONV via a src discriminator with CUDA+CPU-only kernels and
backend-ungated emission.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 07:04:05 +00:00
Ettore Di Giacinto
5667dfe461 docs(paged): arch-generality audit - optimization classification (0017-0029)
Classify the paged-attention optimizations as arch-GENERAL (ship everywhere),
GB10-TUNED (per-arch retune), or Blackwell-precision-specific; add the per-arch
expected story (sm_100/Hopper/Ada/Metal/CPU) and the SAFETY gap (fused GDN/conv
ops are CUDA+CPU-only with backend-ungated emission). Extends the prior
build/gallery-targeting audit in the same file.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 07:02:54 +00:00
Ettore Di Giacinto
34abf392fc docs(paged): ARCH audit - NVFP4 GGUF off-Blackwell portability + gallery targeting gap
NVFP4 (GGML_TYPE_NVFP4=40 / MOSTLY_NVFP4) GGUFs are portable: full CPU/CUDA-DP4A/
generic-MMA/Vulkan dequant coverage. FP4-MMA is a runtime Blackwell-only speed
tier (mmq.cu use_native_fp4 flag), not a load/run gate. Off-Blackwell = runs via
dequant, correct-but-slow, never fail/garbage. Gallery has no microarch-gating
primitive (tags are search-only, capabilities map is family-level nvidia/amd/
metal, model struct has no hardware field), so the 6 -paged entries can only
express Blackwell-targeting via description prose + tags.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 07:00:34 +00:00
Ettore Di Giacinto
683e22500f docs(paged): arch-generality audit - build-targeting (CUDA arch fan + variants + Metal gap)
The llama-cpp-localai-paged backend sets NO explicit CUDA arch list anywhere
(CUDA_DOCKER_ARCH empty in every matrix row; compile.sh only injects
-DCMAKE_CUDA_ARCHITECTURES when non-empty), so it compiles the full upstream
ggml default arch fan - bit-identical targeting to stock llama-cpp, NOT
Blackwell-only. NVFP4 FP4-MMA is gated inside the kernel by
BLACKWELL_MMA_AVAILABLE, not by the build matrix, so the binary is arch-portable.

Variants: CUDA 12/13 + l4t arm64, ROCm, SYCL f32/f16, Vulkan amd64/arm64, CPU
amd64/arm64 (CPU_ALL_VARIANTS) - same Linux set as stock llama-cpp, not CUDA-only.

Single gap vs stock: NO Metal/Darwin row in includeDarwin and NO metal:
capability key in the meta-backend. macOS hosts fall back to the default cpu
(Linux) image, which will not run, and do not auto-fall to stock llama-cpp.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 06:59:36 +00:00
Ettore Di Giacinto
db6ebc53b2 feat(paged): block-table within-step host cache (patch 0029)
Mirror of paged-dev commit e2acb3b (lever 5). get_block_table() is recomputed
once per full-attention layer per decode step, but the KV cell layout is fixed
for the whole step (it only changes in apply()). This caches the table the first
time it is built in a step and memcpy-reuses the identical bytes for the rest,
invalidating in apply(). Bit-exact; toggle off with LLAMA_PAGED_NO_BT_CACHE=1.

Host-side get_block_table time (llama-batched-bench, npp128 ntg128 npl128,
cache OFF -> ON): MoE 112.94 -> 14.82 ms (-87%), dense 193.78 -> 16.90 ms (-91%).
Dense decode is partly host-bound and gains (TG 364.8 -> 374.7 t/s, ~96% of the
vLLM 391 t/s @npl128 reference); MoE decode is compute-bound (FP4 GEMM) so the
saved host time is off the critical path and MoE TG is flat. Details in
LEVER5_HOSTPIPE_RESULTS.md.

Also records the per-path bit-exactness gate (PAGED_BITEXACT_NOTE.md): the
paged-MoE greedy md5 (8cb0ce23) differs from the non-paged md5 (07db32c2) by a
benign FP-accumulation-order difference of the paged attention reduction, not a
bug. KL-validated vs the f16 reference (16 chunks, c512): KLD(paged||f16) =
0.13600 <= KLD(nonpaged||f16) = 0.13660, PPL(paged) = 7.4009 ~ PPL(nonpaged) =
7.3896 (within +/- 0.29). Canonical references are now per path: non-paged MoE
07db32c2 and paged MoE 8cb0ce23; dense is bit-exact across paths (5951a5b4).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 01:47:08 +00:00
Ettore Di Giacinto
9b0e4e544c docs(paged): residual-assess FINAL - MoE at bit-exact ceiling, hunt DONE
Conclude the MoE-parity hunt. The two remaining sub-levers in the
20.3-vs-13.8 ms projection bucket are both bit-changing or at the BW floor:

- convert-glue (3.24 ms/step, measured: 1.73 input f32->bf16 + 1.52 output
  bf16->f32): NOT bit-exact eliminable. ggml-cuda.cu:1663-1690 rounds the f32
  GEMM accumulator to bf16 (CUDA_R_16BF dst) then widens to f32; that
  bf16-rounded value is load-bearing for the shipped md5. Removing the
  round-trip (f32-direct output, bf16 residual stream, or NVFP4 weights) all
  rebaseline md5. A precision boundary, like lever 4.
- bf16 projection GEMM (17.27 ms/step): BW-bound at the LPDDR5x floor
  (~4.7 GB/step at 273 GB/s; M=128 -> 128 FLOP/byte vs >900 ridge). nvjet
  already TMA-streams the weights; cutlass reads the same bytes. No kernel
  lever; only fewer bytes (quantize) helps - rejected on quality.

Corrects the body premise that vLLM runs these projections as NVFP4-Marlin:
vLLM runs the same nvidia-modelopt checkpoint that keeps them BF16, so the
projection bucket is a matched-precision gap, not a quant gap.

Realistic bit-exact MoE ceiling ~86-88% of vLLM; shipped lever 1 (86.3%) is
at it. No one-more-lever for MoE. Only clean win left is DENSE (+0.41% lever 5),
gated behind resolving the paged-MoE baseline md5 drift.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-27 01:05:06 +00:00
Ettore Di Giacinto
e3f8149f3b docs(paged): lever-4 KL-gate FAIL - NVFP4 MoE projections cost ~6% PPL, no-ship
Re-quantizing the MoE GGUF's bf16 GDN/attn projections to NVFP4 (the lever-4
scope hypothesis) fails the KL gate on every axis vs the shipping NVFP4 baseline:
PPL +6.51% (FULL) / +6.15% (CONS) against a <1% gate, mean KLD-to-f16 0.164/0.172
vs baseline 0.137, top-1 argmax agreement down ~2.2-2.6 points. Both projq
variants rejected; in_proj_ba being kept bf16 (CONS) recovered almost nothing, so
the damage is in the bulk attn/GDN projections.

Root cause: the bf16 projections are a deliberate modelopt precision choice, not a
provenance accident. vLLM runs the same modelopt checkpoint, so it keeps these
projections bf16 too - the baseline GGUF already matches vLLM. The ~20.3ms
projection-GEMM bucket is the price of high-precision projections that vLLM also
pays; it is not the llama-vs-vLLM lever it appeared to be. The speed win is only
purchasable with a 6% PPL regression. MoE stays at 86.3% of vLLM @ npl128.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 23:36:38 +00:00
Ettore Di Giacinto
9a1be79f04 docs(paged): lever-4 scope - NVFP4 the still-bf16 MoE GDN/attn projections (+6.5ms bucket)
Scopes lever 4 (read-only, no GPU) on top of the flat levers 2+3. Root cause: the
MoE GGUF (nvidia modelopt, 241 NVFP4 tensors) quantized only the experts and left the
GDN/attn linear projections in BF16, while the dense GGUF (unsloth, 304 NVFP4 tensors)
already has them NVFP4 (proven: dense ssm_out runs FP4 MMQ; dense decode at 96.6% of
vLLM). Lever 4 = re-quantize the MoE GGUF's bf16 GDN/attn projections to NVFP4, the same
move vLLM makes on the identical weights - the +6.5ms projections bucket, the largest
single banked MoE gain available.

Path: offline re-quantize to a new GGUF variant (expanded --tensor-type); zero kernel
code - the loader sidecar-scale path + tuned mul_mat_q<NVFP4> are already in tree and
proven by the dense GGUF. Bit-changing => KL-gate, not md5. KL expected to pass (per-step
non-accumulating weight quant, unlike the failed bf16-state; experts already W4A4-clean);
lm_head is the one risky tensor (gate on argmax-agreement). Expected ~+4-6.5ms => MoE
86.3% -> ~88-91% of vLLM. Recommend a separate OPT-IN gallery variant (preserve the
bit-exact default; promote to default only if the KL gate is clean).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 22:57:05 +00:00
Ettore Di Giacinto
62c407ed55 docs(paged): lever1 gather-fusion bench landed - checkpoint + attribution (patch 0028)
Anchors the rigorous same-session A/B validation of patch 0028 (residual conv-state
tap k_get_rows fusion) on this worktree branch with sign-off attribution. The
regenerated 0028 patch + bench-updated LEVER1_GATHER_RESULTS.md first landed via a
concurrent origin/master merge (c1f1d1e8e) that swept the staged files; this records
the provenance and the bench summary in the checkpoint.

Gate (bit-exact, greedy --temp 0 --seed 1 -n 48): dense q36-27b-nvfp4
5951a5b4d624ce891e22ab5fca9bc439, MoE q36-35b-a3b-nvfp4 07db32c2bcb78d17a43ed18bc22705cd
(both == baseline; base == lever1). decode_agg npl128: dense 369.95 -> 377.83 t/s
(+2.13%, 96.6% of vLLM), MoE 763.47 -> 777.95 t/s (+1.90%, 86.3% of vLLM). nsys MoE
decode: k_get_rows_float 17334 -> 15414 inst (-1920), 358.37 -> 133.52 ms, step -3.13 ms.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 21:41:45 +00:00
Ettore Di Giacinto
c1f1d1e8ea Merge remote-tracking branch 'origin/master' into worktree-feat+paged-attention
# Conflicts:
#	gallery/index.yaml
2026-06-26 21:38:56 +00:00
Ettore Di Giacinto
6dd8a3d895 docs(gallery): NVFP4 GGUFs published to mudler/ - update header note
The dense + MoE base NVFP4 GGUFs are live (huggingface.co/mudler/Qwen3.6-27B-NVFP4-GGUF
and .../Qwen3.6-35B-A3B-NVFP4-GGUF), sha256 verified vs the Hub LFS hash, uris resolve.
Replaces the placeholder/not-yet-published TODO.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 21:31:16 +00:00
Ettore Di Giacinto
79edfd26a3 feat(gallery): -paged suffix rename + qwopus NVFP4-MTP paged variants
Rename the two base NVFP4 entries to a consistent -paged suffix
(qwen3.6-27b-nvfp4 -> qwen3.6-27b-nvfp4-paged, qwen3.6-35b-a3b-nvfp4 ->
qwen3.6-35b-a3b-nvfp4-paged) so all four base/MTP paged entries share the
naming convention. Update the two matching examples in the backend plan doc.

Add qwopus3.6-27b-v2-mtp-nvfp4-paged and qwopus3.6-27b-coder-mtp-nvfp4-paged:
verbatim copies of the stock qwopus NVFP4-MTP entries (same GGUF uri/sha256,
sampling, template, tags, function block) rewired onto the LocalAI
paged-attention stack (backend llama-cpp-localai-paged; f16, flash_attention,
131072 context, 99 gpu_layers, batch 512; paged_kv + max_batch_tokens:512 +
kv_unified:false + parallel:128). The stock entries are left untouched.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 21:26:14 +00:00
Ettore Di Giacinto
bf9b4fafa8 feat(gallery): NVFP4-MTP Qwen3.6 entries for the LocalAI paged backend
Add qwen3.6-27b-nvfp4-mtp-paged and qwen3.6-35b-a3b-nvfp4-mtp-paged: the
existing michaelw9999 NVFP4-MTP GGUFs (same uri/sha256/filename and the
recommended Qwen3.6 sampling defaults) wired to backend
llama-cpp-localai-paged with our optimized paged options (f16, flash
attention, 128k context, gpu_layers 99, batch 512, paged_kv, decode-first
max_batch_tokens, kv_unified:false, parallel:128).

These coexist with the stock llama-cpp *-nvfp4-mtp entries (distinct
-paged names) so the four LocalAI-paged NVFP4 entries sit together at the
top of the gallery.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 21:19:52 +00:00
Ettore Di Giacinto
b1667b48ea feat(paged): qwen35 recurrent-state gather fusion (patch 0028)
Fuse the residual k_get_rows_float in the gated-DeltaNet decode path (the biggest
single kernel vLLM lacks per MOE_GAP_VS_VLLM.md, ~5.2 ms/step MoE). 0019 fused the
SSM-state gather, 0021 fused the conv compute but kept a build_rs gather for the
conv taps; nsys located that conv-state tap gather (n_embd_r=24576 floats x 128
seqs, ~720 x ~115 us per 24-step window) as the last k_get_rows in the GDN path.

New op ggml_ssm_conv_update_inplace_ids reads each sequence's prior conv taps from
cache[ids[s]] in-kernel (identity in place from the write slot, non-identity via a
disjoint scratch), mirroring the 0019 in-place + ids fusion. Bit-exact: read VALUES
unchanged, only the read path changes. Helps both dense and MoE (shared GDN conv).

GATE test-backend-ops (CUDA0 2/2): SSM_CONV_UPDATE_IDS, SSM_CONV_UPDATE, SSM_CONV,
GATED_DELTA_NET, GET_ROWS all PASS. GATE greedy md5 (-temp 0 -seed 1 -n 48)
BYTE-IDENTICAL both models: q36-27b-nvfp4 5951a5b4..., q36-35b-a3b-nvfp4 07db32c2...
nsys: k_get_rows<float,float> 10174 -> 9454 instances, 186.3 -> 102.8 ms (720 conv
gathers eliminated, replaced by a ~1.1 us no-op gather).

Built and gated on the DGX llama tree (branch paged, commit 944636c, f32 default).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 20:59:59 +00:00
Ettore Di Giacinto
6c6a925213 docs(paged): MoE-vs-vLLM DECIDE synthesis - reject W4A16 Marlin, the GEMM is a llama win
Cross-agent synthesis on top of the both-engine nsys decomposition (3b5957157):
settle the user's "can we do what vLLM does on MoE?" question with the three
converging investigations (groundtruth measurement + vllm-marlin source-read +
marlin-port feasibility).

Verdict: vLLM's ~15% MoE-decode lead is NOT the Marlin GEMM (that bucket is a
-1.7 ms llama WIN: native FP4-MMA W4A4 47.3 vs Marlin W4A16 50.0 at the ragged
tiny-M decode shape, both at the LPDDR5x BW floor). The gap is bf16
dense-projection bandwidth (+6.5), recurrence state-gather plumbing (+6.6, led
by k_get_rows 5.2), graph/stream-overlap overhead (~+7), W4A4 act-quant tax
(+3.3), and router/glue (+5.4).

A W4A16/Marlin grouped MoE GEMM is REJECTED (default and opt-in): it would
regress the 27% GEMM bucket to half-rate bf16 MMA, re-enter the GB10 occupancy
wall the dense scaffold already STOPPED at, and its entire intrinsic upside is
the ~2% act-quant tax - smaller than the bit-exact +1.9% the 0025 re-graph
already banked, and closeable bit-exactly by fusing the act-quant.

Recommended build (none a new MoE GEMM): (1) fuse the k_get_rows SSM-state
gather (bit-exact, ~+5, biggest single-kernel win); (2) extend CUDA-graph
coverage + stream overlap (bit-exact, ~+7); (3) fuse the W4A4 act-quant into
RMSNorm/SiLU (bit-exact, +3.3); (4) NVFP4-quantize the still-bf16 GDN/attn
projections + lm_head (bit-changing, +6.5, the same NVFP4-dense-quant move vLLM
makes). Bit-exact levers alone reach ~94% of vLLM; with the projection quant
~96-97%, parity-or-better physically in reach since both heaviest kernels
(SSM core, MoE GEMM) are already llama wins.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 20:14:30 +00:00
Ettore Di Giacinto
3b59571579 docs(paged): both-engine MoE decode decomposition - the 15% is NOT the Marlin GEMM
Ground-truth side-by-side per-kernel ms/step of the MoE decode gap on DGX GB10.
llama (752 t/s, step 169.8ms) vs vLLM graphs-on (901-equiv, step 142.0ms): 27.8ms gap.

Headline: the grouped MoE-expert GEMM is a llama WIN - native FP4-MMA W4A4 47.3ms
vs vLLM Marlin W4A16 50.0ms at the tiny-M decode shape. A Marlin-style W4A16 MoE
GEMM would be slower; it is not the lever (extends the w4a16-marlin DENSE verdict).

The 15% lives elsewhere: bf16 projections + convert glue (+6.5ms), recurrence
state-gather plumbing (+6.6ms, led by k_get_rows 5.2ms), graph coverage + stream
overlap (~+7ms), W4A4 act-quant tax (+3.3ms), router/glue (+5.4ms).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 20:11:40 +00:00
Ettore Di Giacinto
b3d3323105 feat(paged): wire ssm_bf16_tau model option for hybrid SSM-state fast mode
Patch 0026 added the hybrid per-head bf16 SSM-state opt-in as the
ssm_hybrid_tau_thresh cparam + the --ssm-bf16-tau CLI flag (default 0 =
bit-exact f32). Expose it per-model via the LocalAI gallery/model YAML
`options:` list, mirroring the paged_kv / max_batch_tokens setenv hooks.

- grpc-server.cpp: new `ssm_bf16_tau` (alias `ssm_hybrid_tau`) option ->
  setenv(LLAMA_SSM_BF16_TAU) when the value parses to a positive float. It
  does NOT reference the paged-only common_params field, so the turboquant
  fork (which lacks patch 0026) stays byte-clean.
- patch 0026 (common.cpp common_context_params_to_llama): getenv fallback
  feeds cparams.ssm_hybrid_tau_thresh from LLAMA_SSM_BF16_TAU only when the
  --ssm-bf16-tau CLI flag is unset (0). Absent/non-positive env => untouched,
  so stock stays bit-exact; the CLI flag takes precedence when set.
- docs: backend/index.yaml note, docs backends.md, gallery header NOTE
  (referencing A_HYBRID_SSM_RESULTS.md; the 2 NVFP4 entries stay bit-exact).

Byte-safe when unset: with no ssm_bf16_tau option the env is never touched
and the default f32 bit-exact recurrence is preserved. Verified the parse +
consume code paths with a standalone compile-and-run (option string ->
LLAMA_SSM_BF16_TAU -> tau, plus 0 / garbage / CLI-precedence / unset cases).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 19:51:00 +00:00
Ettore Di Giacinto
9c1c2a6a16 docs(paged): B-3 mmq_y-down warp-remap NEGATIVE - bit-exact MoE ceiling ~85% of vLLM
B-3 (the 0017-deferred mmq_y-down warp-remap of the NVFP4 grouped FP4-MMA
mul_mat_q) was built bit-exact on the clean 0025 base and measured: the
grouped GEMM kernel itself runs -1.3% (occupancy did rise via the nwarps=4
warp-remap / 128 threads-per-CTA), but end-to-end MoE decode is FLAT
(npl128 +0.4%, npl32 +0.3%, within noise) because the stream-k fixup grows
+42% (mmq_y=64 doubles the row-tiles) and the step is SSM/BW-bound. md5 PASS
both models, test-backend-ops MUL_MAT 1146/1146 + MUL_MAT_ID 806/806 PASS.
No patch 0028; DGX dev tree reverted to pristine 0025.

Assessment: the bit-exact MoE GEMM/launch track is exhausted (B-1 re-graph
banked ~82->85%; B-2 and B-3 are 0). Honest bit-exact MoE ceiling = ~85% of
vLLM @npl128. The residual is the structural Marlin-NvFp4 grouped-GEMM gap
that no bit-exact lever closes. Recommend shipping the ~85% bit-exact default
and exposing the held 0026 bf16-SSM as a default-off opt-in (it reaches ~95%
but is non-bit-exact and fails the MoE KL gate).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 19:10:24 +00:00
Ettore Di Giacinto
1f857f179e docs(paged): B-2 down_proj act-quant retune RESULT - negative (no headroom)
B-2 / M1 (SPEEDUP_HUNT rank #2): bit-exact block/grid/occupancy retune of
quantize_mmq_nvfp4 (the MoE down_proj activation-quant, ~2% of the MoE decode
step). Built+measured on a clean 0025 base (DGX GB10 sm_121), then reverted -
it does not lift.

Finding: the existing blockDim.x=128 is ALREADY the kernel-level optimum for
quantize_mmq_nvfp4 on GB10. nsys (8193 invocations): block=128 total 117.4M ns
is the fastest; 64 +8.7%, 192 +9.9%, 256 +6.9%. End-to-end MoE decode_agg is
flat within 0.4% noise across all block sizes {32..256} (npl32 ~438, npl128
~751 t/s). The act-quant is ~2% of a BW-bound step, so even a perfect kernel
caps the win at ~2%, and 128 is already optimal => measured 0%. Same outcome as
patch 0015 (M-tile) and 0017 (MINBLOCKS): no occupancy headroom on this
256-tiny-expert BW-bound model.

Bit-exactness proven: md5 identical at block 64/128/256 for both models (the
per-thread quant body is untouched; thread->output map is invariant to
blockDim.x). Gate at default: dense 5951a5b4 == ref, MoE 07db32c2 == ref,
MUL_MAT 1146/1146, MUL_MAT_ID 806/806 PASS.

MoE stays ~85% of vLLM @npl128 / ~87% @npl32 - still well below vLLM, so the
remaining MoE lever is B-3 (mmq_y-down warp-remap on the grouped FP4 GEMM).
No patch 0027; dev tree reverted to pristine 0025. Full data in B_MOE_RESULTS.md.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 18:31:51 +00:00
Ettore Di Giacinto
33dfe7fd41 feat(paged): qwen35 hybrid per-head f32/bf16 SSM state - carry fix + gate sweep (patch 0026)
Regenerate patch 0026 with the hybrid-decode carry fix and record the
KL/throughput gate-sweep results.

Fix: clear(data=true) zeroes the whole recurrent buffer including the head_slot
maps, which were uploaded only once at construction; after the post-warmup
reset every head read head_slot==0 (f32-local-0), collapsing the split and
producing incoherent decode. Persist head_slot_host and re-upload via
upload_head_slots() after every buffer clear. Hybrid decode is now coherent and
the cross-op state carry is byte-exact (write==read, both partitions).

Gate result: de-risk PASS (test-backend-ops 84/84; T=0 md5 == 0023 baseline,
both models). Ship gate FAILS - no T_thresh meets MeanKLD<1e-3 AND
same-top-p>=99.5% with a meaningful speedup. The premise that the bf16 error
concentrates in long-memory heads is refuted: KL scales with the bf16 head
count and saturates ~0.06/~91% (MoE saturates at the minimal split). The carry
is byte-exact, so this is genuine bf16 sensitivity, not a bug. The byte-saving
lever is real (dense +12.4%, MoE +11.5% decode @npl128 at T=128) but cannot
meet the strict KL bar. Shipped default-off (f32, bit-exact opt-out); hybrid is
opt-in only and not recommended in the gallery config. Full tables in
A_HYBRID_SSM_RESULTS.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 17:44:05 +00:00
Ettore Di Giacinto
fe5bd3f53d feat(paged): qwen35 hybrid per-head f32/bf16 SSM state (patch 0026)
Lever A patch + build/de-risk results. Splits the persisted gated-DeltaNet
recurrent state per head: f32 on long-memory heads (where bf16 rounding does not
contract and the KL error concentrates), bf16 on fast-decaying heads, classified
at model load by tau_h = 1/(|ssm_a|*softplus(ssm_dt)). Default ssm_hybrid_tau_thresh
= 0.0 keeps every head f32 (bit-exact opt-out).

De-risk gates BOTH PASS: test-backend-ops GATED_DELTA_NET CUDA0 OK (incl 32 hybrid
mixed CUDA-vs-CPU cases); default all-f32 greedy md5 == 0023 baseline both models
(dense 5951a5b4d624ce891e22ab5fca9bc439, MoE 07db32c2bcb78d17a43ed18bc22705cd).

Known open issue (opt-in hybrid only; default unaffected): hybrid-ON model decode
(ids in-place path) is incoherent; classifier/cache/kernel-params verified correct,
bug isolated to the ids in-place cross-step state path. See A_HYBRID_SSM_RESULTS.md.
Not ready for the GateSweep until fixed.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]
2026-06-26 16:21:33 +00:00
Ettore Di Giacinto
6bfca146d6 docs(paged): speedup-hunt C section + final RANK + PLAN synthesis
Append lever C (structural dense residual: lm_head + scheduling) findings
and the master RANK + PLAN section to SPEEDUP_HUNT.md. Per-lever scorecard
(gain x tractability x gate), ranked build order, the concrete A build plan
for the hybrid per-head f32/bf16 SSM state cache, and the ordered B/C/D queue
with each one's build trigger.

Verdict: ship the MoE re-graph (patch 0025, measured +1.9-4.4%, both gates
PASSED) now; build A as the lead (only lever ABOVE vLLM on dense, KL-gated,
~430-454 t/s = 103-108% of vLLM); bank B-2/B-3 on MoE; C last (<1% bit-exact,
dead-end); D opt-in-only and dense-only behind the same KL gate bf16-SSM failed.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 14:56:53 +00:00
Ettore Di Giacinto
4d3fecd524 docs(paged): MoE decode re-graph lever (patch 0025) + speedup-hunt B findings
Mirror of llama.cpp dev-tree patch 0025 (qwen35moe NVFP4 MoE-decode re-graph) and the GPU-agent B
findings in SPEEDUP_HUNT.md: re-confirmed MoE decode decomposition @npl128, the measured re-graph
lever (+4.4%/+2.9%/+1.9% decode_agg at npl 32/64/128; bit-exact: test-backend-ops MUL_MAT_ID 806/806
+ parallel-greedy np16 byte-identical ON==OFF), grouped-GEMM occupancy headroom (exhausted on this
bandwidth-bound model), and the W4A16 assessment (rejected: non-bit-exact, slower BF16 MMA).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 14:53:14 +00:00
Ettore Di Giacinto
ec7c1b1f68 feat(paged): pin-sync patchset to llama.cpp 9d5d882d (re-export 4 patches)
The worktree merge bumped LLAMA_VERSION 8be759e6 -> 9d5d882d. This re-syncs the
paged patch-stack (0001-0024) to the new tip: the stack was rebased onto
9d5d882d on the DGX dev tree, rebuilt clean (CUDA sm_121), and re-validated
bit-exact before re-exporting the LocalAI .patch files.

Re-exporting each shipped patch from its rebased commit and diffing body-to-body
against the committed files identifies exactly 4 that changed and no longer
git-apply to 9d5d882d:

- 0008 cross-request prefix share: re-anchored the [paged 0008] commit block to
  the refactored update_slots() lambda (continue->return, batch.n_tokens->
  batch.size()); identical env-guarded logic.
- 0013 static prefill budget: budget var-block / while-gate / admission-break
  re-expressed against the refactored loop (add_ok=false idiom).
- 0015 expert-density MoE token-tile auto-select: pure context re-anchor; upstream
  inserted a test_mul_mat_id case at the hunk anchor in test-backend-ops.cpp. The
  inserted lines are unchanged. (This one rebased cleanly via 3-way but its
  committed .patch no longer applies with plain git apply, so it is caught by the
  per-patch apply-check, not by the rebase conflict count.)
- 0016 dynamic decode-first budget: dynamic budget block + n_decode_in_batch =
  batch.size() + add_ok=false against the refactored loop.

All four are byte-faithful format-patch exports of the gate-green rebased commits.
Applying the full corrected series to a fresh 9d5d882d reproduces the gate-green
tree byte-for-byte across every code file.

The other 7 touched patches (0009/0017/0018/0019/0020/0021/0024) are LINENUM-only
(hunk bodies byte-identical, only @@ line-numbers shifted) and still apply
cleanly, so they are left unchanged. The remaining patches are identical.

Validation on the rebased build (NVFP4 Qwen3.6, GB10 sm_121):
- test-backend-ops CUDA0: GATED_DELTA_NET 36/36, SSM_CONV 45/45, MUL_MAT
  1146/1146, MUL_MAT_ID 806/806 all OK.
- greedy md5 (-fa on -n 48 --temp 0 --seed 1): dense q36-27b-nvfp4
  5951a5b4d624ce891e22ab5fca9bc439 and MoE q36-35b-a3b-nvfp4
  07db32c2bcb78d17a43ed18bc22705cd, both == baseline.
- decode S_TG @npl128: dense 366.41 t/s (ref 373.2, -1.8%), MoE 751.11 t/s
  (ref 745.7, +0.7%), both within noise.

Details in backend/cpp/llama-cpp/patches/paged/PIN_SYNC_9d5d882d.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 14:12:36 +00:00
Ettore Di Giacinto
30a2b590d9 Merge branch 'master' into worktree-feat+paged-attention (llama.cpp pin -> 9d5d882d)
Sync to master (12 commits) + the llama.cpp pin bump 8be759e6 -> 9d5d882d.
Conflicts resolved:
- Makefile .NOTPARALLEL: union (keep both backends/llama-cpp-localai-paged and
  master's backends/privacy-filter-darwin).
- gallery/index.yaml: our 2 base NVFP4 entries (qwen3.6-27b-nvfp4, qwen3.6-35b-a3b-nvfp4)
  for the paged backend prepended to master's full list; master keeps its own
  *-nvfp4-mtp variants (distinct entries). Go build + YAML validated; the 8 duplicate
  gallery names are pre-existing in master, not introduced here.

The patchset still needs re-verification against the new tip (pin-sync, next step).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 13:16:13 +00:00
Ettore Di Giacinto
167768cac3 feat(backend): llama-cpp-localai-paged variant + NVFP4 Qwen3.6 gallery
New backend = stock llama-cpp grpc-server + the paged patchset (forces LLAMA_PAGED=on),
shipped as its own meta-backend (mirrors turboquant, simpler: no fork pin, no
grpc-server patching - the paged runtime hooks already exist in grpc-server.cpp).
Stock llama-cpp untouched (LLAMA_PAGED?=on retained; the de-risk flip deferred for
sign-off). Gallery: qwen3.6-27b-nvfp4 (dense) + qwen3.6-35b-a3b-nvfp4 (MoE) with the
benchmark run config (paged_kv, max_batch_tokens, parallel, flash_attention, f16),
mudler/ GGUF uris (sha256 TODO until publish). Importer dropdown entry + tests.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 12:58:56 +00:00
Ettore Di Giacinto
125d10a782 feat(paged): paged-pool burst-reclaim (truncate + defrag + slot release) (patch 0024)
Fixes the paged-pool burst-degradation bug (OTHER_PATHS_INVESTIGATION.md section C
Part 2): on a long-lived llama-server with LLAMA_KV_PAGED=1, a high-fan-out prefill
burst strands KV blocks in the host-side paged pool, so a later lower-npl prefill
draws from a depleted/fragmented pool and its throughput collapses (the benchmark's
"restart per npl" crutch). Decode is unaffected. The fix changes only host-side
block accounting and placement, never KV values or compute, and is gated behind
LLAMA_KV_PAGED (LLAMA_PAGED_NO_RECLAIM=1 restores the pre-fix behavior).

Fix-1 reclaim trailing blocks: PagedKVManager::truncate(seq, n_keep) frees every
block beyond ceil(n_keep/bs) (ref-counted); called from llama_kv_cache::seq_rm for
the p1==MAX && p0>0 partial-tail case so the manager tracks the kv-cache exactly.
Fix-2 defrag on empty: when the pool is fully idle, defrag_free_pool() relinks the
free queue into ascending block-id order (FreeBlockQueue::rebuild), preserving
content-cache hashes.
Fix-3 release on slot completion: server_slot::release() issues prompt_clear()
under the paged engine so a finished-idle slot returns its blocks promptly.

Validation (DGX GB10, q36-27b-nvfp4 = qwen35 hybrid; HEAD f7409c2 = patch 0023):
- Bit-exact: greedy md5 identical across paged off / paged on / paged on+NO_RECLAIM
  (5951a5b4d624ce891e22ab5fca9bc439), == the 0023 baseline. test-backend-ops
  unaffected (no ggml op touched).
- Host unit test: truncate reclaims exactly 16 trailing blocks; defrag restores
  ascending popleft order. UNIT PASS.
- Model A/B (one binary, NO_RECLAIM): fragmentation prefill ratio 0.944 -> 0.998;
  64 idle slots strand 2048 blocks, reclaim returns the pool to fresh (2527).
- Server A/B (FRESH-npl8 -> BURST-npl64 -> POST-npl8): POST-npl8 prefill collapses
  488 -> 44 t/s with NO_RECLAIM (the bug; investigation saw 507 -> 65), restored to
  532 t/s (fresh 525, within 1%) with the fix. Paged release-log count 17 -> 96
  (Fix-3 fires per slot completion). Canary tokens identical fresh-vs-post in both
  arms (bit-exact serving).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 10:44:33 +00:00
Ettore Di Giacinto
b061e4aef0 docs(paged): OTHER_PATHS investigation - rank 4 post-0023 paths, pick paged-pool burst bug as first build target
Synthesis of the four read-only/GPU investigations (A MoE grouped-GEMM,
B cublas lm_head, C TTFT/paged-pool burst, D dense CUDA-graph):

- A: llama already has the sorted-grouped-FP4-MMA GEMM (higher tier than
  vLLM's GB10 W4A16 Marlin fallback); standalone bit-exact kernel win is
  bounded on this bandwidth-bound a3b model. Keep down_proj quantize
  retune (M1) as a cheap bank-shot; fold the decode-graph (M2) into a
  later shared GDN+MoE decode-graph project.
- B: lm_head is BF16 (not FP4), nvjet already ~72% of peak HBM; bit-exact
  ceiling <1%, the only big win (NVFP4 head) is non-bit-exact and unfair
  vs vLLM. Dead end. Rank last.
- C: paged-pool burst-degradation BUG (Part 2) is a true correctness
  defect (prefill collapses 507->65 t/s after a burst, restart cures it):
  reclamation gap on partial seq_rm + free-queue fragmentation. Plus the
  static decode-first budget (Part 1) explains 903s/213s burst TTFT and
  the chunked-interleave fix.
- D: f32 dense CUDA-graph is STABLE (<1%, no bimodality); the brief's
  bimodality was the shelved BF16 SSM path. Closed.

First build target: the paged-pool burst-degradation bug fix (Fix-1
truncate-on-partial-seq_rm + Fix-2 defrag-on-empty + Fix-3 release-on-slot-
completion). Small, localized, default-off byte-identical, crisp repro
(npl64 burst then npl8: prefill within 10% of fresh + num_free restored).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 09:42:55 +00:00
Ettore Di Giacinto
89e62fc74f docs(paged): finalize f16 glue probe - cost analysis + build verdict
Synthesize the GPU kernel-budget probe with the read-only glue source
map. Add (4) the implementation cost - llama has no model-compute-dtype
knob, the residual stream is F32 by construction (ggml_mul_mat hardcodes
F32 output), so f16 glue is not a flag but an opt-in multi-file change
(norm.cu f16 kernels + f16 residual stream). Add the final verdict:
precision is not the dominant cause of the 8% residual (83% of the step
is already f32/W4A4-matched), f16 recovers only 40-60% of the gap and is
non-bit-exact, so do not build it as the default; ship the 95%-bit-exact
f32 plateau and target the structural cublas/graph-launch ~3-4% instead.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 09:12:55 +00:00
Ettore Di Giacinto
001d833426 docs(paged): f16/bf16 glue probe - dense decode residual ceiling
Empirical probe on q36-27b-nvfp4 @npl128 (build f7409c2, patch 0023):
- attention KV cache default is ALREADY f16 (K/V f16) -> --cache-type f16 is a
  no-op; q8_0 within noise -> KV dtype is not a decode lever
- nsys node-trace decode budget: f32-glue (norms/elementwise/activations/attn,
  excl. SSM recurrence + NVFP4 GEMM) = 28.7 ms = 8.4% of step (40.9 ms = 12%
  incl. the non-FP4 cublas GEMM)
- f16 realistically recovers ~11-16 ms of the ~27 ms/step gap = ~40-60% of the
  8.2% residual -> ~95-96% parity, not a full close; non-bit-exact opt-in only

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 09:11:21 +00:00
Ettore Di Giacinto
00f92659f8 docs(paged): correct vLLM recurrent-state precision (f32, not bf16)
Earlier text claimed bf16 = vLLM's own precision; that was a refuted byte-gate
draft re-surfacing. The settled finding (BITEXACT_VS_VLLM.md, proven 3 ways) is
that vLLM keeps the gated-DeltaNet TEMPORAL state in f32 (only its conv state is
bf16). So bf16 temporal is BELOW vLLM's recurrent precision, not a match; and at
equal f32 precision llama's recurrence already beats vLLM (84.6% vs 82.4% peak).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 06:22:08 +00:00
Ettore Di Giacinto
7dd3431040 docs(paged): promote TTFT/prefill + paged-pool burst-degradation bug (benchmark finding)
The final benchmark exposed TTFT as the weakest number (dense npl128 903s vs vLLM
6-18s, decode-first budget throttling burst-prefill) plus a concrete paged-pool
burst-degradation bug (post-burst low-npl prefill collapses 507->65 t/s; decode
unaffected). Highest-value serving fix; decode + memory already strong.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 03:54:27 +00:00
Ettore Di Giacinto
ae0042f214 docs(paged): publish NVFP4 decode benchmark - plot-ready CSV + decode-vs-npl plots
Public deliverable for the patch-0018..0023 f32 bit-exact paged-attention ship:
the apples-to-apples NVFP4 decode benchmark (llama.cpp paged 0023 vs vLLM 0.23.0
on GB10 / DGX Spark, matched weights, CUDA graphs ON both sides).

- final_benchmark.csv: clean 8-column plot-ready schema
  (model,engine,npl,decode_agg_tps,decode_perseq_tps,prefill_tps,ttft_mean_ms,peak_gb),
  16 rows (2 models x 2 engines x npl 8/32/64/128).
- QWEN36_NVFP4_BENCH.md: embed the two decode-vs-npl plots; add the
  internal-consistency note (decode_agg vs perseq*npl is TTFT-governed, holds on
  both engines, no stale-baseline carry-over).
- decode-vs-npl PNGs (one per model), llama vs vLLM, per-point llama-%-of-vLLM labels.

Headline (measured, nothing pre-assumed): dense llama 90-117% of vLLM decode
(ahead at npl8), MoE 77-83%, at higher precision (f32 GDN state + q8 act vs vLLM
bf16 GDN + w4a4) and 1.5-3x lower unified memory (on-demand paged KV vs vLLM's
flat ~107 GB pool).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 03:51:35 +00:00
Ettore Di Giacinto
aaaa90ae4b bench(paged): final apples-to-apples NVFP4 decode benchmark (0023 vs vLLM 0.23.0, GB10)
Publishable, plot-ready head-to-head on GB10 / DGX Spark with matched NVFP4 weights,
both engines at their best realistic config (CUDA graphs ON both sides; vLLM util 0.85
max-model-len 4096 max-num-seqs 256; llama -c 131072 --parallel 128 LLAMA_KV_PAGED=1
LLAMA_MAX_BATCH_TOKENS=512). Identical async client: 512-tok unique-nonce prompt
(fresh full prefill), max_tokens=256, temp 0, ignore_eos, stream+usage; npl 8/32/64/128.

llama = clean patch 0023 (dev tree f7409c2, bf16 GDN-state work reverted, build-cuda
rebuilt). llama runs at HIGHER precision (f32 GDN state + q8 act) than vLLM (bf16 + w4a4).

decode_agg t/s, llama as % of vLLM:
  DENSE q36-27b-nvfp4:  npl8 117%  npl32 91%  npl64 90%  npl128 92%
  MoE   q36-35b-a3b:    npl8  83%  npl32 78%  npl64 77%  npl128 82%
memory: llama on-demand paged KV 50-90 GB (dense) / 36-58 GB (MoE) vs vLLM fixed ~107 GB
pool at all npl (1.5-3x lower). TTFT: vLLM wins under synchronized burst (llama
decode-first budget trades burst-prefill for decode; decode + memory unaffected).

Outputs: final_benchmark.csv (16 rows, 5 metrics each), refreshed QWEN36_NVFP4_BENCH.md
(FINAL section), BENCHMARK_PROGRESS.md (per-row checkpoint log). Methodology notes:
per-npl llama server restart (paged-pool degrades after high-npl bursts; decode robust),
vLLM npl8 re-check confirms no degradation; clean env (service containers stopped for the
run, restored after).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 03:47:24 +00:00
Ettore Di Giacinto
7c45447c9e docs(paged): FUTURE_LEVERS - parked decode-parity exploration trail
Ranked pick-up points after the 95%-bit-exact plateau: hybrid-precision SSM state
(per-head f32/bf16 split - the bf16 error is concentrated in long-memory heads, so
a split could capture most of the +25-31% while passing the f32 KL gate), dense
CUDA-graph instability, the rms_norm->fp4 fold (flat-risk), datacenter Blackwell
sm_100 (no LPDDR5x floor), adaptive prefill budget, MoE-specific recurrence tuning.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 00:53:09 +00:00
Ettore Di Giacinto
24833f0966 docs(paged): bf16 SSM-state NO-SHIP - fails f32 KL gate (= vLLM's own precision)
De-risk passed (test-backend-ops 52/52 bf16, f32 default byte-identical to 0023),
and the throughput lever is real (recurrence -49%/call, dense ~490 t/s = 125% of
vLLM clean). But bf16-vs-f32 KLD is 0.06-0.17 at >=1024 ctx (threshold 1e-3) with
~90% top-token agreement: intrinsic bf16 error over gated-DeltaNet long-memory
heads, not a bug. That is exactly vLLM's own bf16 GDN precision. Shelved; ship the
95% bit-exact f32 plateau (0018-0023). bf16 work backed up on DGX (BF16_SSM_STATE.diff).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-26 00:49:49 +00:00
Ettore Di Giacinto
634c0e5a0f docs(paged): rms_norm->fp4 fold analysis - bit-exact decode ceiling at 95% of vLLM
The standalone quantize fold is empirically flat (Lever-2 precedent) with the
worst gain/plumbing ratio; no bit-exact lever remains. Dense 371.81 t/s @npl128
= 95.0% of vLLM 391, recurrence past vLLM at the LPDDR5x DRAM floor, all
byte-identical to llama f32. Only bf16 state (shelved) goes further.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 22:42:08 +00:00
Ettore Di Giacinto
64766ecc85 Merge branch 'master' into worktree-feat+paged-attention
Resolve pkg/xsysinfo/gpu.go: keep master's NVIDIAComputeCapability +
parseComputeCap (the #10485 multi-GPU work); re-express our IsNVIDIABlackwell
as a thin wrapper over NVIDIAComputeCapability instead of a duplicate
nvidia-smi probe.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 21:56:35 +00:00
Ettore Di Giacinto
02cbae5ea9 feat(paged): qwen35moe NVFP4 activation-quantize de-dup (patch 0023)
Mirror patch 0023 + results into the paged series. Bit-exact MoE decode/prefill
lever: ggml mul_mat_id re-quantizes each token's activation once per expert for
the broadcast up/gate proj (ne11==1); quantize_mmq_nvfp4 has no cross-thread
reduction, so the gathered blocks are byte-identical across experts. The lever
quantizes the ne12 unique tokens once and gathers the block_fp4_mmq rows into the
expert-gathered layout with a coalesced uint4 copy (144 B = 9 uint4); the GEMM is
untouched and down_proj keeps the stock path.

Measured (DGX GB10, on top of patch 0022, q36-35b-a3b-nvfp4): decode S_TG npl128
745.2 -> 758.1 t/s (+1.73%), npl32 +0.6%, prefill T_PP -4%; dense q36-27b-nvfp4
byte-flat. nsys: quantize_mmq_nvfp4 868 -> 457 ms, gather +32 ms (net -379 ms).
Bit-exact: q36-27b 5951a5b4..., q36-35b-a3b 07db32c2... (on == off == 0022);
test-backend-ops MUL_MAT 1115/1115, MUL_MAT_ID 805/805. On by default;
GGML_CUDA_MOE_QUANT_DEDUP=0 restores stock.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 21:49:15 +00:00
Ettore Di Giacinto
3c1ed67b4b feat(paged): qwen35 gated-DeltaNet decode occupancy/coalescing retune (patch 0022)
Bit-exact occupancy retune of gated_delta_net_cuda, the B=128 decode recurrence
kernel, carried as paged patch 0022. After the f32 verdict (vLLM carries the
gated-DeltaNet temporal state in float32 and moves the same ~805 MB/call as llama;
the gap was pure DRAM bandwidth efficiency on equal bytes - llama 73.4% vs vLLM
82.4% of the 273 GB/s GB10 peak), the lever is a latency-coverage retune that keeps
the per-column f32 reduction/FMA order byte-identical (md5-gateable). The
bf16-state plan stays shelved.

Column folding: each warp owns COLS_PER_WARP columns of the 128x128 recurrent state
instead of 1, looping the existing per-column body over col, col+NUM_WARPS, ...
within a per-block column tile; grid.z = S_v / (NUM_WARPS*COLS_PER_WARP). The
per-lane strided row sharding and the warp_reduce butterfly are unchanged, so only
the (warp,block)->column assignment differs and the result is bit-identical;
per-warp memory-level parallelism rises ~COLS_PER_WARP-fold, covering more DRAM
latency on this bandwidth-bound kernel. Default tile is the measured GB10 winner
(NUM_WARPS=16, COLS_PER_WARP=8), env-selectable via GDN_NW / GDN_CPW.

GB10: gated_delta_net decode 4.02 -> 3.49 ms/call, 73.4% -> 84.6% of peak (above
vLLM's 82.4%; 102.6% of vLLM recurrence BW). decode S_TG t/s: dense 27b npl128
335.9 -> 373.2 (+11.1%), MoE 35b-a3b npl128 688.4 -> 745.7 (+8.3%). Greedy md5
byte-identical to the 0021 baseline on both q36-27b-nvfp4 and q36-35b-a3b-nvfp4;
test-backend-ops -o GATED_DELTA_NET 36/36 PASS. Bench/method in
OCCUPANCY_RETUNE_RESULTS.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 18:34:17 +00:00
Ettore Di Giacinto
8f8777e0f4 feat(paged): qwen35 decode conv-state in-place fusion (patch 0021)
The no-regret bit-exact conv-state cleanup from the GDN recurrence byte-gate
design (point 3). After the recurrence verdict (NO-BUILD: the gated-DeltaNet
recurrence is already single-pass at the f32 byte floor), the decode conv path
was the only remaining bit-exact lever.

New fused op ggml_ssm_conv_update_inplace (reuses GGML_OP_SSM_CONV, discriminated
by a non-null src[3]). On the single-token decode path it replaces the four-op
conv chain - qkv transpose + ggml_concat (concat_cont) + ggml_ssm_conv + ggml_silu
+ ggml_cpy of the shifted ring state (cpy_scalar) - with one kernel that, per
(channel, sequence), assembles the width-K window in registers from the K-1 cached
taps plus the current qkv_mixed token, computes the depthwise conv with the SAME
ascending-tap FMA order as ssm_conv_f32 at i==0, folds silu, writes the conv
output, and writes the 1-token-shifted ring state back IN PLACE into the conv
cache slot at kv_head. This is vLLM causal_conv1d_update; it mirrors the 0018
in-place write-back and 0019 patterns. Read source (the build_rs tap gather) and
write target (the cache view) are disjoint buffers, so it is race-free by
construction with no ids/identity logic.

- ggml.h/ggml.c: builder (src0=conv_states [K-1,ch,n_seqs], src1=conv_kernel,
  src2=x_cur [ch,1,n_seqs], src3=conv_state_dst [(K-1)*ch,n_seqs] in-place ring;
  op_params[0]=fuse_silu)
- ggml-cuda/ssm-conv.cu: ssm_conv_update_f32<apply_silu,d_conv> kernel +
  ggml_cuda_op_ssm_conv_update + src[3]-discriminated branch in ggml_cuda_op_ssm_conv
- ggml-cpu/ops.cpp: ggml_compute_forward_ssm_conv_update_f32 (threads over channels)
  + branch in ggml_compute_forward_ssm_conv
- delta-net-base.cpp/models.h: build_conv_state_fused (keeps the cheap build_rs
  conv-tap gather; fuses conv+silu+shifted write-back)
- qwen35.cpp, qwen35moe.cpp, qwen3next.cpp: route the single-token decode path
  (n_seq_tokens==1 && n_rs_seq==0 && fused_gdn_ar); prefill/chunked/rollback keep
  the original chain
- tests/test-backend-ops.cpp: test_ssm_conv_update (16 cases) vs the CPU reference

test-backend-ops: SSM_CONV 45/45, SSM_CONV_UPDATE 16/16, SSM_CONV_BIAS_SILU 90/90.

Greedy (--temp 0 --seed 1 --ignore-eos -n 256) byte-identical to the Lever-1
(0019/0020) baseline: q36-27b-nvfp4 md5 675cd522..., q36-35b-a3b-nvfp4 md5
ac163882... both BYTE-IDENTICAL.

decode_agg S_TG (npp128 ntg128, -fa on, CUDA-graph), same session:
  dense q36-27b-nvfp4 : npl 32  199.76 -> 202.99 (+1.6%)
                        npl 128 336.35 -> 347.14 (+3.2%, 86.0 -> 88.8 percent of vLLM 391)
  MoE   q36-35b-a3b   : npl 32  421.72 -> 432.39 (+2.5%)
                        npl 128 689.74 -> 713.54 (+3.5%)
Lift holds in eager too (dense npl128 333.62 -> 342.97). Step -11.9 ms/step
(dense npl128: 380.6 -> 368.7). nsys eager decode: concat_cont (1152 calls) and the
decode cpy_scalar GONE; ssm_conv_f32 at decode replaced by ssm_conv_update (1152);
conv-path ~20.9 -> ~7.6 ms/step. Bit-exact, no regression, de-risks the bf16-state
conv-cache plumbing.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 16:56:35 +00:00
Ettore Di Giacinto
5cec1a6a21 docs(paged): bitexact-vs-vLLM verdict + verified f32 GDN-state correction
Synthesize the cross-engine bit-exactness and f32-preserving-parity study.
Resolve the contradiction between sub-agents (one f32, two bf16) by reading
every link of vLLM's state-dtype chain on live source:

- config.json text_config.mamba_ssm_dtype = "float32" (both served models)
- cache.py default mamba_ssm_cache_dtype = "auto"; bench passes no override
- vllm.py __post_init__ -> try_verify_and_update_config (config finalize)
- Qwen3_5ForConditionalGenerationConfig override copies "float32" into
  mamba_ssm_cache_dtype before state-dtype resolution
- mamba_utils._mamba_state_dtype -> temporal = torch.float32 (conv = bf16)
- qwen_gdn_linear_attn allocates the temporal cache at f32

Verdicts: B1 TRUE (sub-claim 'more efficient than vLLM' refuted); B2 REFUTED
(equal f32 bytes both sides, ~10pct efficiency gap not 2x width); B3 REFUTED
(vLLM hits throughput with f32 state; a bit-exact occupancy/coalescing retune
of gated_delta_net_cuda 74->81pct peak is the f32-preserving parity lever);
B4 CONFIRMED (bit-exact-vs-vLLM impossible: A1 FP4 GEMM 8/4/16-bit operand
gap + A2 recurrence g.Sigma vs Sigma.g reassociation on different reduction
trees, plus general FP non-associativity). bf16 temporal state degrades BELOW
vLLM's f32 recurrent precision -> an over-clock, not a parity requirement.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 16:55:25 +00:00
Ettore Di Giacinto
17855735c7 docs(paged): bf16 SSM-state build plan (PART C synthesis: edits, KL gate, bench, risks)
Synthesizes the bf16 SSM recurrent-state-cache plan into a build-agent brief:
ordered file-by-file edit list (kernel/op dtype-generic first, then cparams
default flip, gRPC/YAML, back-compat), the KL<1e-3 + PPL-delta + coherence +
long-context-drift acceptance gate that REPLACES the bit-exact md5 gate (bf16 is
intentionally non-bit-exact, equal precision to vLLM), bench targets (recurrence
3.98->2-3 ms/call, step 384->289-339 ms, 360-443 tok/s dense) + nsys check, the
default-bf16/f32-opt-out semantics + state-file back-compat, the risk register,
and the single biggest risk (silent corruption on the prefill/keep_rs_t/gather
paths) with the de-risk-first test-backend-ops step. Conv state stays f32 in v1.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 16:46:59 +00:00
Ettore Di Giacinto
2a8103c419 docs(paged): FINAL DECISION - NO-BUILD fused recurrence, BUILD conv fusion + bf16 state
Synthesis of the byte-gate workflow (ncu-byte-gate measurement +
vllm-fused-recurrence-study + llama-fused-recurrence-design + conv-fusion-design).

Verdict closes all five decision points:
(1) Byte ratio: llama re-stream ~1.0x (cap <=1.33x); recurrence at 74% GB10 peak,
    MORE BW-efficient than vLLM packed_decode at 41%. The 2x DRAM gap is 100%
    f32-vs-bf16 state-cache width, not extra passes.
(2) Fused single-pass recurrence: NO-BUILD - already one R + one W of f32 state,
    gate ops touch tiny q/k/g/beta not the 805 MB state -> recovers ~0 bytes.
(3) Conv-state in-place fusion: GO - bit-exact, no-regret, +12-14 ms/step (~+3%),
    eliminates concat_cont + cpy_scalar + folds silu.
(4) bf16 SSM state: BUILD (KL<1e-3 gated product call) - only lever on the dominant
    50% recurrence term, +45-95 ms/step -> step 289-339 ms = parity-to-ahead of vLLM.
    Bit-exact parity unreachable on this term (f32 bytes irreducible); bf16 = equal
    precision to vLLM, which is itself bf16.
(5) Build order: conv fusion next (no-regret, bit-exact), then bf16 state (highest
    value, gated). Confirming measurements stated per step.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 15:27:04 +00:00
Ettore Di Giacinto
fd4332e8f0 docs(paged): GDN recurrence byte-gate SETTLED - re-stream ~1.0x, build bf16 state not fused kernel
Decisive measurement (ncu-byte-gate agent, DGX GB10). ncu HW DRAM counters were
blocked (ERR_NVGPUCTRPERM, root-only NVreg param; no passwordless sudo), so the
byte ratio was settled via CUPTI kernel timing + exact byte geometry: bytes moved
<= peak_BW x duration caps the re-stream factor.

llama gated_delta_net_cuda decode (B=128, f32 state): 3.98 ms/call, 805 MB R+W,
202 GB/s = 74% of GB10 peak. vLLM fused_recurrent_packed_decode (B=128, bf16 state):
3.62 ms/call, 402 MB R+W, 111 GB/s = 41% peak. Both single-pass (load-once/store-once,
verified in source). llama re-stream factor ~1.0x (hard cap <=1.33x; >=1.5x needs
>peak BW = impossible).

VERDICT: NO-BUILD the fused single-pass recurrence - the kernel is already single-pass,
coalesced, and MORE bandwidth-efficient than vLLM's triton kernel; the gate ops touch
the tiny q/k/g/beta projections, not the 805 MB state, so fusion recovers ~0 state bytes.
The entire 2x DRAM gap vs vLLM is f32 (llama) vs bf16 (vLLM) state-cache width. BUILD
bf16 SSM state instead: halves 805->413 MB, ~45-95 ms/step, step 384 -> 289-339 ms =
parity-to-ahead of vLLM 327 (non-bit-exact vs f32 but equal to vLLM's own bf16 precision).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 15:24:49 +00:00
Ettore Di Giacinto
5825b073a5 docs(paged): SYNTHESIS - validated decode-parity picture, ranked plan, verdict
Final synthesis of the critical-path gap analysis: the decode step is
99.94% GPU-busy single-stream (idle 0.225ms = 0.06%), so the 14% gap to
vLLM is kernel GPU-time dominated by the bandwidth-bound gated_delta_net
recurrence (196.37ms = 51.6%), not launch bubbles. Claims A/B/C all
REFUTED as worded; the single residual is the unmeasured DRAM byte ratio
of llama's recurrence vs vLLM's fused kernel. Ranked plan: single-pass
fused GDN recurrence (gap-closer, gate on ncu byte-ratio test) + conv-state
concat fusion (no-regret +2-3%, bit-exact); gate-fold alone tops out at
~89% of vLLM; bf16 state is the only floor-mover but breaks bit-exactness.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 15:03:18 +00:00
Ettore Di Giacinto
a72385257a docs(paged): decisive node-level decode timeline gap - bubbles refuted
Fresh nsys --cuda-graph-trace=node capture of one steady decode step on
q36-27b-nvfp4 dense at npl128 (clean Lever-1 build-cuda-base). The decode step
is a single CUDA graph; node-level expansion shows it is 99.94% GPU-busy on a
single stream with 0.225 ms/step inter-kernel idle (0.06%, zero gaps >5us).

This refutes the "~60% idle bubbles / 57 ms = 100% bubble" hypothesis and
confirms the cudagraph-coverage source verdict. Real decode mix: gated_delta_net
196 ms = 51.6% of the step (4.08 ms/call x48; the prior 1.47 ms/call "near-vLLM"
was a prefill-contaminated eager average), FP4 GEMM+quantize 29%, gating glue
(Lever 3 target) only 3.35%, gdn_gather 0.06 ms. By roofline-decode's own sizing
test (idle < 57 ms => gap is elsewhere) the 14% gap to vLLM lives in kernel
GPU-time, dominated by the bandwidth-bound GDN recurrence, not in bubbles; Lever
3 fusion is resized to ~3% and reframed as byte-reduction, not bubble removal.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 14:57:37 +00:00
Ettore Di Giacinto
2b57997df0 docs(paged): cudagraph-coverage - GDN serial chain IS graph-covered at B=128
Determine whether the ggml CUDA graph covers the gated-DeltaNet serial chain
at batch=128. It does: nothing in the GDN region forces graph-disable
(check_compability lists only split-buffers and large-batch MUL_MAT_ID), and
the recurrent head is constant for a steady 128-seq batch so the inplace_ids
state_dst offset + rs_head op_param + SSM input shapes are stable across steps.
The fused op does no host-sync / capture-time cudaMalloc. The only re-warm is
the per-256-token full-attention block-table cadence (not a GDN op). The
~40% util is bandwidth-roofline (SSM state traffic 66% of step bytes), not
launch-gap idle - so no GDN graph-safe lever; the only non-covered idle is the
~0.4% between-step host cgraph rebuild.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 14:45:51 +00:00
Ettore Di Giacinto
e597a8ac78 docs(paged): vLLM GDN decode = 2 fused kernels under CUDA graph vs llama ~8 ops
Read-only source comparison of the gated-DeltaNet decode region. vLLM folds
conv-silu, q/k l2norm, scale, softplus+A_log gate, sigmoid-beta, the delta-rule
recurrence and the SSM state write-back into ONE Triton kernel
(fused_recurrent_gated_delta_rule_packed_decode), with the output gate fused into
a gated rms_norm, and captures the whole decode forward in a full CUDA graph
(GDNAttentionMetadata UNIFORM_BATCH, decode-only full cudagraph). llama runs the
same region as ~8 separate host-launched, serially-dependent ggml nodes. That
launch/bubble delta - not GEMM throughput - is the candidate 62%-vs-40% busy gap.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 14:43:01 +00:00
Ettore Di Giacinto
b895f4dff8 feat(paged): qwen35 gated-DeltaNet o_proj MMVQ->MMQ reshape (patch 0020)
Lever 1, the single biggest decode-parity lever for the Qwen3.6 hybrid-SSM
models (arch qwen35: 48 gated-DeltaNet + 16 full-attention layers). Post-SSM
(patches 0018 + 0019) dense decode sat at 255 t/s = 65% of vLLM 391; profiling
both engines pinned the largest llama-specific overage to the gated-DeltaNet
output projection (ssm_out).

The GDN op left its output in SSM layout and the graph reshaped it to 3D
[value_dim, n_seq_tokens=1, n_seqs=128] before the ssm_out matmul, so
src1->ne[1]=1. That trips the ggml-cuda MMVQ dispatch (ne[1] <= 8) with the 128
sequences stuck in ne[2]; MMVQ is built for batch <= 8 and does not amortize the
ssm_out weight read across the 128 sequences. vLLM packs the same projection into
one M=128 GEMM. The in-projection was already 2D -> MMQ; only the output was 3D.

The fix collapses the GDN output to 2D [value_dim, n_seq_tokens * n_seqs]
(= [6144, 128] at decode) before the ssm_out ggml_mul_mat, so src1->ne[1]=128
routes to the MMQ M=128 tensor-core GEMM. The result is then already 2D, so the
redundant post-matmul reshape_2d is dropped. Same contiguous data, just a 2D vs
3D view: bit-identical. Gated to the gated-DeltaNet path (qwen35 / qwen35moe /
qwen3next); other archs untouched.

Bit-identical greedy (--temp 0 --seed 1) vs the post-SSM baseline on both
q36-27b-nvfp4 (dense) and q36-35b-a3b-nvfp4 (MoE), byte/md5-identical.
test-backend-ops MUL_MAT and MUL_MAT_ID OK.

decode_agg S_TG (llama-batched-bench, -fa on, npp128 ntg128, npl 32/128):
  dense q36-27b:     170.52 / 254.92 -> 200.00 / 335.80 t/s (+17.3% / +31.7%)
  MoE   q36-35b-a3b: 373.28 / 560.66 -> 420.77 / 691.24 t/s (+12.7% / +23.3%)
Dense @128 = 335.80 t/s = 85.9% of vLLM 391 (up from 65%; target 82-85% hit).

nsys: the o_proj mul_mat_vec_q<NVFP4,m=1> bucket (132.8 ms / 48 inst) collapses
to zero; mul_mat_q<NVFP4,m=128> absorbs it (+1200 inst, +363 ms) at a LOWER
per-call average (620.8 -> 582.7 us). Realized o_proj-as-MMQ cost ~0.30 ms/call
vs 2.77 ms/call for the old GEMV.

Mirrors DGX dev-tree commit df1cc97.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 10:41:38 +00:00
Ettore Di Giacinto
c0e0ed3865 docs(paged): synthesize decode-parity exploration - the o_proj MMVQ lever
Cross-check the adversarial validation against the profiler ground-truth and
finalize DECODE_PARITY_EXPLORE.md. The post-SSM 254->391 decode gap is one
llama-specific defect: the gated-DeltaNet output projection (ssm_out) runs as
an FP4 GEMV (mul_mat_vec_q, 132 ms/step = 26% of decode) at batch 128 instead
of a tensor-core MMQ GEMM. Mechanism confirmed at source: final_output is 3D
[6144,1,n_seqs] so src1->ne[1]=1 trips the MMVQ dispatch (<=8), with the 128
sequences in ne[2]. vLLM packs the same projection into a cutlass M=128 GEMM.

GDN recurrence is only +11%/call (not the lever); P2a optimized the wrong FP4
kernel (the 17% MMQ, not the 26% MMVQ); CUDA graphs, host loop, and DRAM bytes
are all ruled out. Decode parity is reachable in software (not a hardware
floor): identical bytes/floor, vLLM hits 62% util vs llama 40% on the same
GB10. Highest-value next step (~free, bit-exact): collapse final_output to 2D
before ssm_out so M=128 routes to MMQ. Ranked levers + cumulative ceilings
toward 391 documented.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 09:06:50 +00:00
Ettore Di Giacinto
ee13fd18ce docs(paged): profile-both-engines post-SSM ground-truth decode decomposition
Fresh post-SSM nsys of llama (build-cuda-base, patch 0019) AND vLLM 0.23.0 at
npl128 decode. Reproduces the 391 reference (vLLM 394 t/s eager / 420 graphs,
graphs +6% only) and confirms llama 245 t/s. Both ~98% GPU-busy; the gap is
GPU kernel-time, not idle/host/graphs. GDN compute comparable (llama 4.03 vs
vLLM 3.62 ms/call, +11%). bytes/step: llama not higher (131 vs 85 MB memcpy;
SSM-fix 18GB/step DtoD removal confirmed in-trace). Single biggest llama-specific
overage = FP4 matmul path 236 vs 117 ms/step (+119 ms = 64% of the gap),
dominated by mul_mat_vec_q (FP4 GEMV at batch 128, 132 ms/step, 26%, one per
GDN layer). Track B optimized the wrong FP4 kernel (mul_mat_q, not the GEMV).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 08:56:37 +00:00
Ettore Di Giacinto
6f0792c3be feat(paged): qwen35 SSM decode fused recurrent-state gather (patch 0019)
Mirror of the llama-paged-dev patch 0019 engine change plus the measured
results. Step 2 of the SSM decode work: after Step 1 (in-place state write-back,
patch 0018) the largest non-GEMM decode bucket was the recurrent-state get_rows
gather (18.8 percent of decode GPU time). This removes that materialization,
mirroring ggml_ssm_scan's ids source: ggml_gated_delta_net_inplace_ids reads each
sequence's prior state directly from cache[ids[seq]] (src[5] = full cache,
src[7] = ids), so combined with Step 1's in-place write the op reads AND writes
the cache directly with no state materialization at all.

Race-free by construction: identity sequences (ids[seq] == rs_head + seq, the
whole AR decode path) read s0 in place from the destination slot; non-identity
sequences (reorder / rs_zero, e.g. multi-new-seq prefill) read from a disjoint
scratch a small gather kernel populates first. ids stays a device pointer.
Bit-identical to the get_rows path. Gated to qwen35 + qwen35moe; qwen3next,
kimi-linear, the non-fused and rollback paths are unchanged.

Measured (decode_agg S_TG, npp128 ntg128, -fa on, paged on, fusion off):
  q36-27b-nvfp4 dense: npl32 137.64 -> 170.68 (+24.0 percent),
    npl128 186.25 -> 256.57 (+37.8 percent, 47.6 -> 65.6 percent of vLLM 391).
  q36-35b-a3b-nvfp4 MoE: npl32 299.68 -> 366.69 (+22.4 percent),
    npl128 409.30 -> 553.63 (+35.3 percent).
Greedy (--temp 0 --seed 1) llama-completion bit-identical vs the Step-1 build
(dense + MoE). nsys k_get_rows_float bucket 18.8 -> 0.7 percent. The residual
decode gap to vLLM is now the FP4 GEMM (~48 percent of decode). See
SSM_DECODE_FIX_RESULTS.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 23:47:51 +00:00
Ettore Di Giacinto
5ce2f1df51 feat(paged): qwen35 gated-DeltaNet in-place SSM state write-back (patch 0018)
Mirror of the llama-paged-dev patch 0018 engine change plus the measured
results. Per SSM layer per step decode no longer D2D-copies the full ~225 MB
recurrent state into the cache: the fused gated_delta_net op writes the final
state in place at the active sequences cache slot (new
ggml_gated_delta_net_inplace, src[6] = state_dst), mirroring vLLM
fused_recurrent_gated_delta_rule. SSM math unchanged (bit-identical greedy).

Measured (decode_agg S_TG, npp128 ntg128, -fa on, paged on):
  q36-27b-nvfp4 dense: npl32 113.74 -> 136.39 (+19.9 percent),
    npl128 146.23 -> 180.53 (+23.5 percent, = predicted copy-removal ceiling).
  q36-35b-a3b-nvfp4 MoE: npl128 313.36 -> 372.62 (+18.9 percent).
nsys D2D memcpy bucket 18.9 -> 0.23 percent (356 -> 2.93 GB). vLLM share
(391 @128) 37.4 -> 46.2 percent. See SSM_DECODE_FIX_RESULTS.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 22:45:49 +00:00
Ettore Di Giacinto
34cadb64af docs(paged): A.2 final synthesis - CUDA-graph decode verdict
Append the four-point synthesis to A2_CUDAGRAPH_DECODE.md: measured
CUDA-graph lever size (<1%, not the guessed 10-20%), the corrected
'eager' premise (default paged decode already captures), the unchanged
37-38% of vLLM at npl128, and the honest verdict that A.2 closes none of
the 2.6x gap because paged attention touches ~0.4% of decode on this
hybrid-SSM model. Residual lever is the qwen35 gated-DeltaNet SSM path
(state D2D copy + get_rows gather), orthogonal to paged attention.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 21:45:42 +00:00
Ettore Di Giacinto
2dd5d68e6d docs(paged): A.2 Phase 2 - locate the real decode lever (gated-DeltaNet SSM path)
Phase 1 ruled out CUDA graphs as the paged-decode lever (GPU 99.4% busy,
decode_agg flat graphs on-vs-off) and attributed the 2.6x gap to vLLM to the
per-step GPU kernel work (FP4 GEMM + attention at batch 128). Phase 2 decomposed
that kernel work directly on the Phase-1 nsys reps and corrects the attribution.

Findings (q36-27b-nvfp4 = gguf arch qwen35, a 48:16 hybrid gated-DeltaNet
linear-attention + full-attention model; DGX GB10 sm_121, fusion off):
- Graphs re-confirmed not the lever: fresh paged graphs-ON 146.03 vs OFF 144.90
  t/s (+0.78%, noise); the captured rep is 99.5% busy with the same ~3267ms
  memcpy (graphs capture memcpy nodes too).
- The 99.4% busy is real but ~19% of it is D2D memcpy, not compute: an
  overlap-correct interval-union sweep gives kernels-only 80.2% busy, the gap
  filled by 1584 D2D copies/run (~80/step, ~230MB each = the gated-DeltaNet
  recurrent state). Phase 1's cuda_gpu_trace lumped this into compute.
- Decode GPU-time decomposition (% of kernel+memcpy busy): gated_delta_net 23.4%,
  get_rows 21.9%, D2D state copy 18.9%, FP4 GEMV 15.5%, FP4 GEMM 10.4%,
  full attention 0.4%. Grouped: SSM/gated-DeltaNet machinery ~67%, FP4 matmul
  ~28%, full attention (all paged-attn optimizes) ~0.4%.

Verdict: not graphs, not the host loop, not primarily FP4 GEMM, not attention.
Paged attention touches ~0.4% of decode on this model, so no paged/graph/
block-table change can move decode_agg. The lever is the ggml qwen35
gated-DeltaNet decode: kill the per-layer recurrent-state D2D copy and fuse the
get_rows gather into the recurrence (vLLM's fused_recurrent_gated_delta_rule
keeps state in place). Ceiling: -copy ~146->180; -copy-and-gather ~146->247 t/s.

No code patch (the lever is an SSM-path rewrite, orthogonal to paged attention);
patches/paged/0018 stays free.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 21:44:22 +00:00
Ettore Di Giacinto
da67fd87e2 docs(paged): A.2 CUDA-graph decode lever measurement and gap diagnosis
Phase 1 measures the CUDA-graph lever on the paged decode (q36-27b-nvfp4
dense, GB10 sm_121, fusion off). The 4-cell decode_agg {stock,paged} x
{graphs on,off} is flat within ~1%: the graphs-on win is +0.13% at npl128
and +1.1% at npl32 (both within run noise). The default paged decode is not
eager: it captures and replays graphs with a 256-token reset cadence
identical to stock non-paged (block-table ne0 = GGML_PAD(n_gather,256) only
steps at 256-token boundaries); only the gather fallback grows n_gather every
step and runs pure eager. 'graphs reused=0' was a uid fast-path false negative
(llama rebuilds the cgraph each step, so the reuse log never fires while the
graph still replays via the instance path).

nsys (reliable eager trace, plus the captured trace re-run with
--cuda-graph-trace=node to defeat nsys omitting graph-internal kernels, an
artifact that otherwise reads 0.3% busy) shows the steady decode is 99.4-99.5%
GPU-busy. Idle is ~0.6% of the step: 0.37% within-step launch gaps (the only
thing graphs remove, cut to 0.11% when captured) plus a 0.24% between-step
host gap (~2ms per step). Throughput is identical on/off.

Verdict: CUDA-graphing the paged decode is not a throughput lever; the decode
is GPU-compute-bound and the 2.6x gap to vLLM (148 vs 391) is in the per-step
GPU kernel work (FP4 GEMM + attention at batch 128), not launch overhead or
the host loop.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 21:26:16 +00:00
Ettore Di Giacinto
40f019e761 docs(paged): mirror FP4 decode-GEMM track-B P0 gate + P1 kill-gate results (patch 0017)
Mirror of llama.cpp dev-tree commit 089f78d. Track B P0 (bit-exact NVFP4 dense decode-shape
MUL_MAT parity gate) + P1 (default-off occupancy levers) for the GB10 dense FP4 weight GEMM.

P1 kill-gate TRIPPED: the cheap host/occupancy levers do not lift decode_agg on GB10 (sm_121).
DENSE q36-27b-nvfp4 @npl128 149.5 -> minblocks2 147.9 (-1.1%) -> dense mmq_x=64 144.3 (-3.5%);
MoE q36-35b-a3b mmq_x-down regresses (TILE16 -3.7%, TILE8 -5.9%, reproduces patch 0015). nsys:
the FP4 GEMM mul_mat_q<NVFP4,128,0> went 2.782s->3.025s (+8.7% slower) under register-capping
(spilling). The dense M=128 tile is already weight-read/one-read-optimal; the only untested lever
is the structural mmq_y-down (nwarps=4 warp-remap, blocked by nwarps*tile_C::I==mmq_y), deferred
to P2. All levers default-off => default build byte-identical to stock. See THROUGHPUT_B_P1_RESULTS.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 17:58:00 +00:00
Ettore Di Giacinto
39e16cc2c4 docs(paged): adversarial review of track-B FP4-GEMM parity go/no-go
Append section 9 (skeptical staff-CUDA-engineer review) to FP4_GEMM_SCOPE_B.md,
stress-testing the dense/MoE parity verdict against the committed grounding.

Key findings:
- Not the W4A16 wall: the npl-sweep (dense 99/56/46/41% of vLLM at npl 8/32/64/128)
  shows llama's FP4-MMA kernel HITS the weight-read floor at M=8 and FALLS OFF it as
  M grows, while vLLM HOLDS it. Working-path tune, dual existence proof (M=8 + vLLM
  M=128), not a greenfield build. Same binding constraint as W4A16 though (hide
  LPDDR5x latency at the larger tile on an occupancy-dominated part).
- The dense gap is ~82-87% GEMM, ~13-18% non-GEMM (467 ms total = 383-405 GEMM +
  62-84 non-GEMM). B alone caps ~80%; track A is what tips dense over the parity line.
- Sharpest omission: vLLM's M=128 floor is reached via cutlass TMA + deep pipeline -
  the technique the doc forbids on GB10. TMA != manual cp.async (lower occupancy cost);
  it must be an in-scope P2 fallback, not categorically banned.
- Honest landing: dense ~80-90% (parity the optimistic tail, contingent on B+A+floor),
  MoE ~55-65% (parity not reachable from B). Low-regret: even a tripped P2 kill-gate
  lands B+A ~89%, doubling today's 41%.
- Sequencing fix: land A first (defines B's interface + baseline + kill-gate), then
  run B's P2 against the post-A number.

Verdict: DENSE conditional GO (scope as GEMM-gap-closing, not true parity; A-first,
gate at P2, add TMA); MoE NO-GO for parity from B (do the cheap mmq_x-down win as a
1.7-1.85x, not parity).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 14:31:35 +00:00
Ettore Di Giacinto
7434d64c75 docs(paged): build-ready track-B FP4-GEMM scope - kernel decision + per-phase decode_agg
Rewrite the track-B scope into the definitive build-ready plan for the
NVFP4 FP4-MMA decode GEMM toward vLLM GB10 parity. Source-read of the
mmq.cuh/mma.cuh/quantize.cu FP4 path on the dgx paged dev tree settles two
load-bearing facts the prior draft got partly wrong:

  - llama's dense path is already TRUE W4A4 (block_fp4_mmq packs 256 e2m1
    values + ue4m3 scales; the MMA is kind::mxf4nvf4 e2m1.e2m1...ue4m3), so
    there is no activation-bit-width work to do; the whole dense deficit is
    scheduling/occupancy.
  - the mmq_x selector minimizes ntiles_x, which PINS dense decode at
    mmq_x=128 (weights read once). Shrinking mmq_x re-reads the 18 GB
    weights, so the dense occupancy lever is mmq_y-down (BW-neutral), NOT
    mmq_x-down; MoE's free lever is the per-expert mmq_x-down (patch 0015).

Adds the explicit kernel-approach decision (tune the existing FP4-MMA
mul_mat_q; reject the cutlass-SM120 rewrite, dead on GB10 and broken on
sm_121; reject the BF16-Marlin descent), the concrete build-ready changes
(mmq_y/granularity/stream-k knobs, FP4-MMA fragment invariants, the
ue4m3 scale path, and the block_fp4_mmq y-tile ABI contract for the
track-A act-quant fusion handoff), the GB10-fit rules, the bit-exact
test-backend-ops gate with decode-shape + ragged-M cases, and per-phase
expected decode_agg tables.

Verdict (honest, roofline-grounded): the decode GEMM is bandwidth-bound on
the hardware roofline (M=128 << crossover 611; weight-read floors 4-6x
above vLLM) but compute-bound in practice at ~3% FP4 eff, so 273 GB/s is
not the wall. DENSE: GO (conditional) - B+A reaches 376-394 tok/s =
90-103% of vLLM 391, gated by a P2 occupancy kill-gate (<15% FP4 eff ->
parity off). MoE: PARTIAL/NO-GO - ceiling ~76% of 811 (618) from the GEMM
alone; full MoE parity needs the non-GEMM tracks too.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 14:21:48 +00:00
Ettore Di Giacinto
c1d7f336cb docs(paged): enrich track-B scope with code-level FP4-GEMM inefficiencies
Add the source-read kernel-mechanism map (no cp.async weight pipeline,
mmq_x tile-maximizing selector vs GB10 occupancy, MoE per-expert M-tile
waste, iter_k=512 coupling, ruled-out non-levers) and strip the stray
trailing tags from the prior write.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 14:11:41 +00:00
Ettore Di Giacinto
ea634ee958 docs(paged): scope track B - FP4-MMA decode-GEMM roofline + parity go/no-go
Roofline at the decode batch shape (M=128, NVFP4 weights) on GB10 (sm_121):
the dense weight-read floor (~1,940 tok/s) and MoE floor (~1,590 tok/s) sit
4-6x above vLLM's 391/811, so 273 GB/s is NOT the wall. At FP4 peak the GEMM is
bandwidth-bound (crossover M*~611 >> 128); at the kernel's ~3% achieved FP4
efficiency it is compute-bound by its own inefficiency (471 ms vs a 66 ms floor).

Verdict: dense decode parity is plausibly reachable via a tuned FP4-MMA decode
M-tile (track B) + fused act-quant (track A), landing 376-394 tok/s = 90-103% of
vLLM 391, but only at the top of the demonstrated GB10 FP4 envelope (~17-21%) and
with no margin (occupancy wall is the binding constraint, not bandwidth). MoE
parity is NOT reachable from the GEMM alone (ceiling ~60-76% of 811): its floor
is the hardest grouped-GEMM regime and ~24% of its step is non-GEMM work outside
track B. GO (conditional) for dense, PARTIAL for MoE. Build-ready phased plan
included; tune the existing block_fp4_mmq path, not a W4A16 rewrite.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 14:09:41 +00:00
Ettore Di Giacinto
e4c63179e0 docs(paged): verify llama.cpp GDN decode is O(1)-in-context, not a 2.4x lever
Closes lever 5 of VLLM_DECODE_GROUNDING.md. GGUF metadata + source reading on
the paged dev tree plus nsys decode traces on Qwen3.6-27B NVFP4 (GB10 sm_121)
confirm the Gated-Delta-Net linear-attention layers decode as a fused single
CUDA kernel (gated_delta_net.cu) updating a fixed-size cached recurrent state:
no context-length parameter, no KV re-scan. Matched-batch context-scaling
control (npl4, pure decode) shows the GDN kernel flat (10.3 -> 8.0 us/launch)
across 4x context while full-attention grows 3.1x (27 -> 85 us). GDN is a small,
context-flat share (~0.4-10%% by batch); the FP4 weight GEMM dominates (~67%).
Verdict: GDN decode is efficient, not the cheap model-specific fix; the 2.4x is
the general GEMM + full-attention kernel work, as the grounding concluded.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 11:21:44 +00:00
Ettore Di Giacinto
f7500df64e docs(paged): staggered-arrival evaluation of patch 0016 dynamic budget
The prior all-at-once BURST H2H is adversarial to any prefill budget (TTFT is
prefill-rate-bound, a cap only slows the drain) and showed 0016 ~= 0013. Run a
STAGGERED-arrival benchmark on the GB10 DGX (patch 0016 built @253cbae): a
steady-rate client that keeps a mix of in-flight decoders + newly-arriving
prefills, capturing per-request TTFT and the full inter-token-latency series.

Append the metrics (in-flight decode protection + new-request TTFT, per arm) and
an honest verdict to P1_DYNAMIC_BUDGET_RESULTS.md. On staggered traffic stock's
in-flight decoders freeze multi-second on every prefill admission while both
budget arms keep ITL flat; 0016 (mbt512) sits at a strictly better point on the
protection/TTFT frontier than 0013-256 (equal spike-free protection, materially
lower TTFT/throughput/wall) and adds a decode-adaptive single-T knob. It does not
strictly dominate stock (Pareto tradeoff: smoothness vs raw TTFT). Verdict: 0016
earns its keep over 0013 on staggered traffic; recommend LLAMA_MAX_BATCH_TOKENS=512.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 10:56:13 +00:00
Ettore Di Giacinto
24ce7d0823 feat(llama-cpp/paged): dynamic decode-first prefill budget (patch 0016, continuous-batch P1)
Mirror the P1 engine change of CONTINUOUS_BATCH_SCHEDULER_SCOPE.md into the
vendored paged patch series and surface it as a LocalAI model option.

- patches/paged/0016-paged-dynamic-prefill-budget-continuous-batch.patch:
  supersede patch 0013's STATIC per-step prefill cap with a DYNAMIC,
  decode-first token budget in update_slots(). At the budget seam (already
  after Phase 1's decode fill, so batch.n_tokens == D is known) compute
  T = clamp(LLAMA_MAX_BATCH_TOKENS ?: n_batch, n_ubatch, n_batch),
  prefill_budget_step = max(n_ubatch, T - D), and a per-slot prompt-chunk
  cap prefill_cap_per_slot; bound the Phase-2 prompt-fill loop and outer
  admission break by these instead of 0013's constant. Policy-only change,
  no new slot states, no batch-formation rewrite, zero libllama changes.
  Decode is structurally claimed first (Phase 1) so the decode-first
  guarantee is free. As decode load D rises the leftover auto-shrinks, so
  the budget self-tunes across npl 8..128 and dense vs MoE and holds the
  GB10 decode ceiling tuning-free (vs 0013's hand-picked 256). The legacy
  LLAMA_PREFILL_BUDGET path is preserved (honoured only when the dynamic
  knob is unset), so 0013 is cleanly subsumed. DEFAULT-OFF byte-identical:
  all-knobs-unset and the degenerate T == n_batch case are bit-identical to
  stock by construction (the n_batch hard ceiling is kept and the dynamic
  bounds reach it at the same point for every D). Orthogonal to
  LLAMA_KV_PAGED.

- grpc-server.cpp: wire the new knob as model options max_batch_tokens / mbt
  (-> LLAMA_MAX_BATCH_TOKENS) and prefill_cap (-> LLAMA_PREFILL_CAP), beside
  the existing max_prefill_tokens / mpt seam; default-off, takes precedence
  over the legacy static budget when set.

- patches/paged/P1_DYNAMIC_BUDGET_RESULTS.md: design, the byte-identical
  determinism analysis (verified by construction), the local patch-apply
  verification, and the gate + A/B bench methodology.

Validation status: the patch applies cleanly on top of LLAMA_VERSION
(f3e1828) + paged 0001-0015, and the off-path / T==n_batch determinism is
proven by construction. The GB10 sm_121 build, the four runtime gates, and
the dense+MoE A/B sweep are PENDING a DGX run (the dev box was unreachable
this session) and are documented as such in P1_DYNAMIC_BUDGET_RESULTS.md; do
not sell the quantitative TTFT payoff until that re-run lands.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 07:48:20 +00:00
Ettore Di Giacinto
fccbb4082d docs(paged): ground vLLM 0.23.0 eager-decode architecture vs llama.cpp
Decompose vLLM's enforce_eager decode step (attention / weight GEMM /
sampling / host loop) on GB10 (DGX Spark, sm_121) and attribute the
measured ~2.4x NVFP4 decode-throughput gap to its parts, from source
reading plus the existing nsys decode trace and H2H bench logs.

Key finding: the gap is dominantly a KERNEL-efficiency gap (~80-90%),
not a host-overhead gap. llama's GPU is already ~94.6% busy during
steady decode, so a CUDA-graphed decode is a minority lever (~10-20%
of the gap, bounded by the GPU-idle bubble), not the silver bullet.
vLLM's wins: in-kernel paged-decode read (no gather tax), faster
long-context attention, fused native-FP4 / grouped-Marlin GEMM, and
O(1)-in-ctx GDN linear-attention layers on these Qwen3.6 hybrids.
vLLM achieved 2.4x with synchronous scheduling and no CUDA graphs.

Evidence: vllm 0.23.0 source (gpu_model_runner, flash_attn/gdn
backends, modelopt/marlin GEMM, v1/sample), reproduced nsys kernel
categorization (cat2.py), and QWEN36_NVFP4_BENCH / DECODE_GAP_STUDY /
CONTINUOUS_BATCH_SCHEDULER_SCOPE.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-24 07:44:07 +00:00
Ettore Di Giacinto
5a38dd3f09 docs(paged): adversarial review of the continuous-batch scheduler scope
Append a source-verified Review / risk section to
CONTINUOUS_BATCH_SCHEDULER_SCOPE.md. Verdict: scope is sound, GO on P0 ->
P1, conditional P2, separate-track P3.

Key checks against HEAD 151343b:
- Tractability: zero libllama changes. The mixed per-seq prefill+decode
  ubatch is the existing shipping path (common_batch_add per-token pos/seq,
  init_batch split, paged_alloc is hooks on the same llama_kv_cache class,
  not a new class). The new scheduler changes only the prefill token count,
  never the batch structure.
- The real serving config is kv_unified=false (-> n_stream=n_seq_max=128),
  so the split path is split_equal(sequential=true), not the contiguous
  split_simple the pseudocode implies. Fold into P0 ubatch-shape and
  determinism analysis; lock the split path in the A/B.
- CUDA graphs ruled out: both NVFP4 H2H vLLM servers ran --enforce-eager
  (cudagraph_mode=NONE), so the npl128 2.4x decode gap is genuine
  eager-kernel + per-step host overhead. Scheduler cannot close it; the
  157/333 ceiling stands.
- TTFT root quantified: prefill_tps collapses with concurrency for llama
  (dense 1117->125) while vLLM holds flat ~1420. The dynamic T-D budget
  attacks this directly and can sustain prefill_tps >= vLLM during the
  drain, so burst-TTFT parity is mechanically plausible, but it couples to
  a decode-ITL knob (T) that MUST be co-reported with TTFT.

Two calibration fixes required before P1: co-report drain-phase decode-ITL
with TTFT (stop charging/selling the steady-state decode_agg number), and
acknowledge the split_equal/n_stream=128 path. Neither changes the go
decision. P1 is the minimal high-ROI step (handful of line edits at named
seams); gate P2 on P1 metrics; P3 (kernel/CUDA-graph) owns the 2.4x
residual independent of the scheduler.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 22:48:31 +00:00
Ettore Di Giacinto
ed17fc804e docs(paged): scope token-granular continuous-batch scheduler for llama-server
Build-ready plan (not implemented) for a vLLM-v1-style token-granular
continuous-batch scheduler in tools/server/server-context.cpp update_slots(),
the last lever after patch 0013 on the GB10 NVFP4 llama-vs-vLLM gap.

Key findings that shape the scope:
- The unified mixed batch already exists: Phase 1 (2604-2719) claims every
  ready decode token unconditionally, Phase 2 (2753-3330) fills prefill into
  the same llama_batch. Decode-first is structural, not a thing to build.
- The chunked-prefill slot state already persists across steps (a
  PROCESSING_PROMPT slot with prompt.n_tokens() < task->n_tokens() resumes).
  No slot-state rewrite is needed - the feared big risk does not materialize.
- The only missing piece is the budget POLICY: convert 0013's static per-step
  prefill cap into a dynamic, decode-first, per-slot-fair token budget (one
  total T, decode claims D, prefill gets leftover T-D, capped per slot).
- Honest ceiling: the residual ~2.4x decode gap is a decode-KERNEL batch
  scaling ceiling (~157-161 dense / ~333 MoE @npl128), NOT a scheduler defect.
  The scheduler closes the 12x TTFT gap and holds that ceiling tuning-free;
  the throughput residual is a separate, named decode-kernel lever (P3).

Phased P0-P3 with per-phase payoff, files, risks, and GB10 considerations.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 22:36:15 +00:00
Ettore Di Giacinto
362eea90ff docs(paged): fair re-run verdict - synthesize NVFP4 llama vs vLLM scorecard
Phase 3 synthesis of the max_prefill_tokens (patch 0013) fair re-run:
how much of the gap was prefill starvation, the genuine remaining gap
to vLLM, and where par-or-beat stands per concurrency/model.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 21:39:22 +00:00
Ettore Di Giacinto
c7075fb796 docs(paged): MoE 35B-A3B NVFP4 fair re-run with max_prefill_tokens budget
Budget 256/512 sweep on the A3B MoE under patch 0013. Mirror image of the
dense case: stock MoE was never prefill-starved (3B active, TTFT 84.8s @npl128),
so the budget is a decode-throughput lever paid for in TTFT, not a TTFT fix.
Budget 256 lifts decode_agg +14% (292->333.5 tok/s) and restores monotonic
decode scaling (kills the stock +7.4% plateau, now +20% into npl128), moving
llama 36.0%->41.1% of vLLM decode. Gap not closed: vLLM still ~2.4x decode and
~12x lower TTFT @npl128.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 21:38:08 +00:00
Ettore Di Giacinto
c8b1f16507 docs(paged): dense NVFP4 fair re-run with max_prefill_tokens budget sweep
Re-run the dense Qwen3.6-27B NVFP4 vs vLLM A/B with patch 0013's QoS
prefill budget enabled (LLAMA_PREFILL_BUDGET swept over 256/512/1024),
fixing the prior run that left prefill unbounded and let high-concurrency
prefills starve each other.

At the saturated npl128 point budget=256 is the best lever: decode_agg
134.6 -> 161.2 tok/s (+19.8%) and TTFT 491.2 s -> 305.4 s (-37.8%) vs the
starved stock run, moving llama from 34.5% to 41.3% of vLLM decode. Larger
budgets help less; at light/moderate concurrency the budget is net-negative
for TTFT because this all-at-once workload has no in-flight decode to protect
at t=0. Documented honestly: a real but narrow high-concurrency lever, not a
gap-closer (vLLM still ~2.4x decode / ~12x lower TTFT at npl128).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 21:22:07 +00:00
Ettore Di Giacinto
2975a74fb4 docs(paged): Qwen3.6 NVFP4 apples-to-apples scorecard (llama vs vLLM, dense + MoE)
Full 4-way sweep (npl 8/32/64/128): dense Qwen3.6-27B (clean W4A4) + MoE
Qwen3.6-35B-A3B (vLLM Marlin NvFp4). Parity at npl8; vLLM scales ~2.8-2.9x ahead
on decode at npl128. llama TTFT explodes at high concurrency - run WITHOUT
max_prefill_tokens (0013), the prefill-starvation also drags decode_agg; fair
re-run with the QoS budget pending. llama wins on on-demand memory (paged).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 20:21:50 +00:00
Ettore Di Giacinto
ee78ae4a11 docs(paged): Qwen3.6 NVFP4 h2h bench doc - MoE llama.cpp table
First crash-resilient slab of the apples-to-apples NVFP4-vs-NVFP4
llama.cpp-vs-vLLM benchmark on GB10. MoE Qwen3.6-35B-A3B paged
llama.cpp (patch 0015) decode/prefill/TTFT/VRAM at npl 8/32/64/128.
vLLM and dense tables append as the sweeps land.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 19:43:55 +00:00
Ettore Di Giacinto
acb22a66ed feat(paged): mirror MoE token-tile density-aware auto-select (patch 0015)
Mirror of llama-paged-dev commit 151343b into the pinned paged patch series.
The durable, default-on follow-up to patch 0014's opt-in LLAMA_MOE_MMQ_X global
cap: a host-side density-aware mmq_x auto-select in mul_mat_q_case that caps the
MUL_MAT_ID grouped FP4-MMA token-tile only at low per-expert density (decode) and
keeps the 128 tile at high density (prefill), so it is prefill-safe by construction
(removes 0014's ~1.3% prefill cost). No new kernel.

density_max default = 8 (not tile/4 = 16): 16 equals the 256-expert prefill-ubatch
density and regressed S_PP ~2% on Qwen3.6-35B-A3B NVFP4; 8 sits between decode and
prefill density for n_experts in [128,511] at n_ubatch=512.

Honest result on the mission's MoE target (Qwen3.6-35B-A3B NVFP4, 256 experts +
GDN/SSM linear attention, GB10 sm_121, median of 5 reps): NEUTRAL. Decode S_TG is
within run-to-run noise (npl128 +0.36%) and prefill S_PP neutral (within +/-0.7%).
This model is bound by the SSM recurrence and 256-tiny-expert weight bandwidth, not
the MoE col-tile occupancy, so the col-tile lever has nothing to bite on; a npl128
tile sweep confirms 64 is the only useful width (TILE8 -6.3% ... TILE96 -0.8%). The
lever's real win lives on col-tile-bound MoE (Qwen3-Coder-30B, +4.8% @npl128 per
patch 0014), which the auto-select reproduces at npl128 by construction at zero
prefill cost. Shipped default-on because it is prefill-safe, decode-neutral here,
and correctness-gated.

LLAMA_MOE_MMQ_X (0014) kept as a manual override; LLAMA_MOE_AUTO_TILE=0 restores
exact stock selection. P0 gate: test-backend-ops test_mul_mat_id ragged small-M
NVFP4/MXFP4 MoE decode-density shapes pass CUDA-vs-CPU on GB10 both default-on and
stock. Full rationale and tables in patches/paged/MOE_DENSITY_AUTO_TILE.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 19:04:55 +00:00
Ettore Di Giacinto
010067d900 feat(paged): mirror patch 0014 - expert-aware MoE token-tile cap
Mirror of the dev-tree engine patch (ggml mmq.cuh) into the paged patch set,
plus its measurement writeup. Adds LLAMA_MOE_MMQ_X, an opt-in env cap on the MoE
grouped-GEMM token-tile (mmq_x) for the MUL_MAT_ID path; default-off =
byte-identical to stock.

Honest result of the MoE near-term lever: the npl128 decode cliff does NOT exist
on current HEAD (stock decode is monotonic 85/282/629/935/1295/1779 t/s at npl
1/8/32/64/128/256; the old cliff was fixed upstream by the sorted grouped
FP4-MMA GEMM + MoE stream-k). The cap is therefore not a cliff fix but a modest
high-batch decode micro-optimization: cap64 gives +4.8% decode at npl128 and
+2.3% at npl256 (reproducible, neutral at npl<=64) for a ~1.3% prefill cost;
cap16/cap32 are net-negative (prefill -41% / -17%). Full tables in
MOE_TOKEN_TILE_CAP.md; durable density-aware follow-up in
MOE_GROUPED_GEMM_SCOPE.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 13:49:15 +00:00
Ettore Di Giacinto
8925c009b7 docs(paged): scope durable grouped FP4-MMA MoE GEMM port for GB10
Build-ready plan (not implemented) for matching/beating vLLM MoE
grouped-GEMM efficiency on GB10 sm_121 for Qwen3-30B-A3B mxfp4.

Honest reframe: the grouped GEMM the mission scoped to build already
exists upstream and runs on GB10 for mxfp4 - should_use_mmq() routes
MUL_MAT_ID to the grouped mmq path, which already contains both vLLM
building blocks (mm_ids_helper moe_align/scatter + a persistent stream-k
FP4-MMA grouped GEMM). The npl128 cliff was a since-fixed regression, not
a batched-bench artifact; re-measured decode is monotonic 85->1771 t/s.

The one structural gap is M-tile sizing: ggml maximizes mmq_x over the
aggregate token count while vLLM uses a small per-expert BLOCK_SIZE_M, so
each tiny per-expert M-tile is 3-6% filled at decode density. Scope is a
surgical two-step delta (expert-aware mmq_x selection; block-padded
moe_align), the parity gate (test_mul_mat_id bit-exact + ragged small-M),
and a phased plan gated behind the GB10 W4A16 occupancy wall.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 13:17:03 +00:00
Ettore Di Giacinto
a3abd60ae0 docs(paged): GB10 head-to-head server sweep (llama-server vs vLLM)
Same-day steady-state aggregate-decode sweep at npl 8/32/64/128 for three
model classes, replacing the stale ~75-80%-of-vLLM carried figure with a
full concurrency curve.

Findings:
- Dense 32B (NVFP4 vs NVFP4A16): parity at batch-8 (97%), 72-86% mid/high.
- Small 0.6B: parity at batch-8 (99%), 49-67% at high concurrency
  (llama plateaus ~2.0k, vLLM scales to 4.2k; runtime/scheduler-bound).
- MoE 30B-A3B: llama-only at 290-1041 tok/s. vLLM cannot serve it on GB10
  (bf16 hangs at MoE warmup and reboots the box, twice; mxfp4 GGUF expert
  tensors unmappable by vLLM 0.23.0).

Batch-8 anomaly resolved: clean isolated dense batch-8 decode is ~88-90
tok/s (~89 ms/step) across paged-vs-stock (within 2%, paged slightly
faster) and ctx 65536-vs-163840 (within 1%). The prior 471 ms/step was a
mixed-load decode/prefill contention artifact, not paged overhead, ctx
allocation, or NVFP4 cost - the case patch 0013 LLAMA_PREFILL_BUDGET bounds.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 12:22:15 +00:00
Ettore Di Giacinto
dd6a4425e0 feat(llama-cpp): per-model max_prefill_tokens option (chunked-prefill QoS budget)
Surface patch 0013's decoupled per-step prefill-token budget as a per-model
grpc-server option, mirroring the existing kv_paged option. When
max_prefill_tokens (aliases: mpt, prefill_budget) is set to a positive integer,
params_parse setenv's LLAMA_PREFILL_BUDGET before context creation so the
vendored update_slots() scheduler latches it; unset or non-positive leaves the
env untouched, preserving stock unbounded-prefill behaviour (an externally
exported LLAMA_PREFILL_BUDGET still works as an escape hatch).

This bounds the head-of-line decode stall a large prompt inflicts on the
in-flight decoders co-batched with it, with no steady-state throughput cost.

Verified on GB10 (sm_121), dense Qwen3-32B-NVFP4, paged build, 8-slot
continuous batching, one ~6k-token prefill injected mid-stream; same binary,
only the budget differs:

  budget      worst decode gap   prefill wall
  unset           2.462 s          6.672 s
  512             0.669 s (3.7x)   7.516 s
  256             0.398 s (6.2x)   8.854 s

Monotonic: a smaller budget cuts the decode stall further at a modest TTFT
cost, the classic chunked-prefill trade-off. grpc-server.cpp compiles cleanly
against the paged build tree.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 11:25:44 +00:00
Ettore Di Giacinto
4bc2b4a9b2 feat(paged): add patch 0013 decoupled per-step prefill-token budget
Mirror of the dev-tree paged scheduler patch into the llama.cpp backend's
vendored patch series. Adds LLAMA_PREFILL_BUDGET, a per-step prefill-token
budget for the inherited update_slots() scheduler, decoupled from n_batch
(the analogue of vLLM's --max-num-batched-tokens). It caps how many prompt
tokens a single update_slots() step ingests, splitting a long prefill across
more steps so co-batched decode keeps advancing instead of freezing for the
duration of one fat ~n_batch prefill chunk. Default (env unset or <= 0) =
disabled, so stock behaviour is byte-identical; orthogonal to LLAMA_KV_PAGED.

Measured on GB10 (dense Qwen3-32B-NVFP4, 8 steady decoders + one injected
6000-token prefill, same binary, only the env differs): worst decode freeze
3380 -> 482 ms (7.0x) and decode_stall 3285 -> 387 ms (8.5x) at budget=256,
for a +20% TTFT on the long request; budget=512 gives 4.8x at ~no TTFT cost.
This is a latency/fairness lever, not an aggregate-throughput lever (steady
decode is NVFP4 weight-read-bound on GB10, which the scheduler cannot lift).

Correctness: budget unset or >= n_batch is byte-identical to stock; budget=N
is byte-identical to stock -bN while preserving n_batch for decode width; the
only deviation on long prompts is intrinsic flash-attn chunk-size FP grouping
that pure stock -b exhibits too. Verified applying on the pinned llama.cpp
f3e1828 after patch 0008.

Productisation follow-up: surface as a grpc-server.cpp options knob
(max_prefill_tokens) per CHUNKED_PREFILL_PLAN Phase B.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 09:55:32 +00:00
Ettore Di Giacinto
ba6bd94976 feat(paged): assert mask-pad invariant for the paged tile route (patch 0012)
Patch 0012 of the paged-attention series. Adds a defensive GGML_ASSERT in
src/paged-attn.cpp so the now-default paged decode route (GQA-grouped
fattn-tile kernel) cannot silently start leaking past-end KV rows.

The route stays correct only because the compacted mask/block-table length
n_view = GGML_PAD(n_gather, 256) is a whole number of flash-attn KV tiles
(nbatch_fa = 64 for head_dim 128 divides 256), so the last tile sits entirely
inside the -inf pad window. The assert (n_view % 64 == 0) pins that implicit
invariant: a future pad < 256 or tile > 256 that broke it now aborts instead
of leaking. Additive only, no behaviour change.

Verified on the DGX dev tree: build-cpu compiles and the paged CPU byte gate
(LLAMA_KV_PAGED off vs on, Qwen3-0.6B-Q8_0, greedy) stays byte-identical with
the assert silent.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-23 09:13:08 +00:00
Ettore Di Giacinto
e983919516 feat(paged): route GQA-grouped tile kernel by default for paged decode (patch 0011)
Increment 3 attention lever. In the paged in-kernel decode dispatch, route the
common grouped-query F16 case to the tile kernel and keep the inc-1 vec kernel
for everything else. Tile groups the q-heads that share a kv-head (ncols2) so
each K/V row is loaded once per group instead of once per q-head, and runs at
higher occupancy (108-128 regs vs vec 168 -> 25%). On GB10 (Qwen3-32B NVFP4,
F16 cache, gqa 8, batch 32, 1024 ctx, same build, env-toggled) this cuts the
decode step from 186.3 to 177.9 ms/step (-4.5%), within 1.8% of stock (174.8).
The win grows with context (tile vs vec decode step, npl=8): 1024 -2.3%, 4096
-3.3%, 8192 -4.1%, 16384 -6.1%, as attention takes a larger share of the step.

Routing guard: tile has no K/V type template (loads half2), so a non-F16 cache
would be converted to a contiguous F16 copy by launch_fattn, breaking the
in-kernel block-table read. So tile is correct only for an F16 cache, and the
grouping only helps at gqa>=2. tile is used only for {F16 K and V, gqa_ratio>=2};
everything else falls back to the inc-1 vec path, exactly as before this change.
LLAMA_KV_PAGED_VEC=1 forces vec for A/B. The inc-2 phys(j) tile read (patch 0010)
was already plumbed; this only adds the default route. (Paged decode currently
needs an F16 cache; quantized + paged is a pre-existing limitation unaffected by
this change: stock+q8_0 works, paged+q8_0 aborts both before and after.)

Split-K was ruled out: the vec decode grid is already block-saturated (~43 waves
over 144 resident on 48 SM), so more parallel_blocks adds no SM fill; the
under-saturation is intra-SM occupancy + 8x KV re-streaming, which GQA grouping
attacks directly.

Validated (greedy): CPU plumbing gate (0.6B, build-cpu, paged-on vs off)
byte-identical; GPU 0.6B gqa=2 tile token-coherent with the inc-1 vec path
(7/8 sequences identical, 8th in the same kernel-noise band where vec also
drifts from stock); 32B gqa=8 tile tracks stock at least as well as vec. Stock
(no block table) is byte-identical: the dispatch guard only diverts on src[5].
Full rationale and numbers in the patch header.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]
2026-06-22 22:38:28 +00:00
Ettore Di Giacinto
2c5adda28c feat(paged): tile in-kernel decode read + dispatch guard (patch 0010)
Increment 2 (robustness): graft the patch-0009 phys(j) block-table read into
the CUDA tile kernel (mirror of fattn-vec.cuh) and add a dispatch guard so a
present block table (src[5]) routes ONLY to the vec or tile kernel, never to
mma/wmma (which ignore the table and would silently read the wrong physical
cells). Default route stays vec, the inc-1 byte-validated path.

Gates: CPU byte-identical paged-on vs off (Qwen3-0.6B) PASS; GPU vec-paged ==
stock at -s 1 PASS; the real Qwen3-32B NVFP4 batch decode confirmed dispatching
to vec (Q ne=[128,1,64,N]). The tile graft is plumbed for the increment-3 GQA
head-group reuse but is EXPERIMENTAL/not byte-validated (LLAMA_KV_PAGED_TILE=1):
the GQA-grouped ncols2>1 tile path reads a full nbatch_fa tile unbounded while
the compacted paged mask is not padded to cover it. Bounding that path is
increment-3 work; the default vec route is unaffected.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 20:37:12 +00:00
Ettore Di Giacinto
ee13a94a8c paged: in-kernel decode read patch 0009 (kill the gather regression)
Mirror patch 0009 for the paged llama.cpp engine. It removes the patch-0003
per-layer per-step gather (ggml_get_rows of K/V to a contiguous buffer) on the
decode step and instead reads paged blocks in-kernel: build_attn passes the
physical K/V views plus a position-ordered block table (src[5] of
ggml_flash_attn_ext, padded to FATTN_KQ_STRIDE), and the CUDA fattn vec kernel
plus the CPU reference map each logical KV index to its physical cell and read
in place. KV_max / parallel_blocks / stream_k split-K are unchanged; a nullptr
block table is the stock contiguous read (byte-identical, gated by
LLAMA_KV_PAGED).

Verified on GB10 (sm_121, Qwen3-32B NVFP4, batch 32 / 1024 ctx): the decode
step drops from 1279 ms (paged-gather) to 696 ms in-kernel (-46%), reaching
stock parity (647 ms). CPU paged vs stock is bit-for-bit identical; GPU stays
within the documented batch-shape non-determinism band.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 18:04:09 +00:00
Ettore Di Giacinto
4dcbcfcf92 docs(paged): decode-step gap study vs vLLM on GB10
Profiling decomposition of the llama-server batch-32 / 1024-ctx decode step
vs vLLM on a DGX Spark (GB10, sm_121). Findings: decode is GPU-bound (~95%
busy, sampling/loop fully hidden); at 1024 ctx the step is ~84% KV/attention
and ~16% weight GEMM; the paged KV engine is a ~1.85x decode regression vs
stock (per-layer gather-to-contiguous); even stock is ~4-5x slower than vLLM,
gated by the long-context decode-attention and thin-batch FP4 GEMM kernels,
not by the serving loop. Ranked closable-vs-structural levers included.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 15:44:24 +00:00
Ettore Di Giacinto
80e0c1ac6b feat(paged): wire cross-request prefix share into llama-server (patch 0008)
Ship patch 0008 of the paged-attention series: wire the paged cross-request
prefix recompute-skip (patch 0007's paged_prefix_api::share/commit engine seam)
into the llama-server continuous-batching loop so CONCURRENT requests sharing a
long prefix reuse one committed copy of the prefix blocks and prefill ONLY their
divergent suffix. The server's native prompt cache only reuses a slot's own prior
prompt; it does not share across distinct concurrent slots. 0008 adds that
cross-slot share, fully gated behind LLAMA_KV_PAGED (stock byte-identical).

The hook lives in tools/server/server-context.cpp update_slots (the only place
with the slot prompt-processing loop; grpc-server.cpp includes it), ~50 gated
lines: a fresh-slot share() that advances n_past past the committed prefix, and a
commit() at the prefill->generation transition. The n_past<block gate guarantees
every positive share is adopted so the engine reservation matches the suffix-only
batch (no stale paged blocks).

Verified in-server (32B NVFP4, CUDA, --kv-unified) with a live prefix holder:
K=16/32 concurrent shared-prefix requests prefill only their ~27-token suffix
instead of the ~1003-token prefix (36x fewer prefill tokens; K=16 23.9s->1.5s,
K=32 57.9s->2.3s), engine logs 'shares ... prefix blocks - NOT recomputed'
(ref_cnt>1), greedy output within the documented CUDA batch-shape
non-determinism band.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 15:03:16 +00:00
Ettore Di Giacinto
52f0f7b8cf docs(paged): apples-to-apples paged llama.cpp vs vLLM (batched+NVFP4+prefix cache)
Matched comparison on DGX Spark (GB10, sm_121): batched llama-server with NVFP4
GGUF and the paged engine vs batched vLLM 0.23.0 NVFP4A16 with APC, both eager,
both prefix-cache on. Two findings: (1) the paged cross-request prefix
recompute-skip (patch 0007) does NOT engage in llama-server - it is only reachable
via paged_prefix_api::share/commit, which the server never calls; the server
engages only physical paged block placement plus its own native prompt cache. (2)
With every confounder removed, vLLM is ~6x faster end-to-end (K=16: 8.6s vs 50.7s;
K=32: 8.9s vs 58.3s), decode-bound not prefill-bound: llama ~828ms/decode-step at
batch 32 vs vLLM ~185ms; CUDA graphs are not the differentiator (both eager).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 14:16:52 +00:00
Ettore Di Giacinto
f347f7ca1d docs(paged): stock GPU batch-shape determinism + vLLM shared-prefix comparison
Two closing measurements on DGX Spark (GB10, sm_121):

1. Stock GPU determinism (no paging): with LLAMA_KV_PAGED unset, stock
   llama.cpp produces a different greedy token stream when the same prompt
   is decoded in a full-prefill batch vs a split (prefix-then-suffix) batch.
   At G=24 the generated stream diverges 1/5 prompts on CPU and 2/5 on CUDA
   (and earlier on CUDA). This confirms the patch-0007 GPU byte-identity
   failure is stock floating-point batch-shape non-determinism, not a paged
   bug. CPU exhibits it too, just less often, which is why 0007's short CPU
   scenarios passed 16/16 while the CUDA run flipped.

2. vLLM vs llama.cpp+paged on a shared-prefix fan-out (K reqs share a
   1024-tok prefix + unique 32-tok suffix, gen 64). llama.cpp+paged prefix
   cache gives 7.15x (K=16) / 10.3x (K=32) prefill reduction vs its no-share
   baseline - the same cross-request prefix-skip vLLM's APC provides (97%
   hit rate confirmed). Head-to-head on cached prefill vLLM is ~5x faster
   (Q4_K_M vs nvfp4a16 quant, vLLM on FP4 emulation + eager), and wider
   end-to-end due to continuous batched decode. Competitive in kind, behind
   in absolute terms on this hardware.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 13:48:01 +00:00
Ettore Di Giacinto
0dd45f0da5 docs(llama-cpp/paged): GPU 0007 re-run + shared-prefix benchmark results
Record the belt-and-suspenders GPU run of the 0007 prefix-engine driver and a
shared-prefix throughput benchmark. The committed CPU driver passes ALL PASS;
the CUDA build fails only the strict greedy-token-equality assertions (the same
binary fails them at ngl=0 too), which is CUDA float-kernel non-determinism, not
a paged-logic defect - every structural KV-reuse invariant passes on GPU.

The shared-prefix benchmark shows a real, K-scaling win: prefill wall time drops
7.2x (32B K=16) to 10.3x (32B K=32) when the shared prefix is computed once and
reused via the paged cross-request prefix cache.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 12:59:09 +00:00
Ettore Di Giacinto
9537726649 fix(llama-cpp/paged): stop double-applying the paged patches in prepare.sh
The Makefile llama.cpp target git-applies the paged series at checkout; prepare.sh
then re-applied with patch, fuzzily duplicating hunks (redefinition errors -> the
grpc-server CUDA build failed under LLAMA_PAGED=on). Guard prepare.sh's apply with a
sentinel (skip when llama.cpp/src/paged-kv-manager.cpp already exists) + -N/-r flags,
so it only does work against an unpatched checkout. Found by the GPU/full-build
verification (PAGED_GPU_VERIFY.md).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 11:54:51 +00:00
Ettore Di Giacinto
d1ba327843 docs(paged): record GPU correctness + CUDA backend-build verification
GPU (DGX Spark, GB10/sm_121, CUDA 13.0) verification of the paged-KV series:
core token-identical gate and 4-stream multiseq are byte-identical stock-vs-paged
at -ngl 99, the device gather is confirmed firing, and a 32B paged run is coherent.
Full backend: patches/paged apply clean to the pin and grpc-server compiles+links
under CUDA sm_121. Notes also flag a double patch-application in the LLAMA_PAGED=on
make flow (git apply + prepare.sh) and a token divergence in the unshipped
prefix-recompute-skip dev driver (same on CPU and GPU).

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 11:50:01 +00:00
Ettore Di Giacinto
ecffd4b097 feat(llama-cpp/paged): engine-level prefix recompute-skip (patch 0007)
Mirror patch 0007 of the paged-attention series into the vendored llama.cpp
patch set. It wires the host-side cross-request prefix cache (0006) into the
engine so a new sequence physically shares the cached prefix blocks (ref-counted)
and decodes only the divergent suffix - the shared prefix KV is never recomputed.

paged-alloc becomes one persistent caching PagedKVManager per (kv-cache, stream)
keyed by the real seq_id (per-sequence ref-counted free); two gated
llama_kv_cache methods (paged_prefix_share / paged_prefix_commit) mark the shared
physical cells' seq-membership so the engine attention mask covers the
already-computed prefix; find_slot anchors placement on each sequence's ubatch.pos.
Existing-file core touch is llama-kv-cache.{cpp,h} (+71 -3); everything else is
additive vendored units. Gated behind LLAMA_KV_PAGED, default off, stock
byte-identical.

Verified on Qwen3-0.6B-Q8_0 (CPU, unified cache): greedy byte-identity vs decode
from scratch at a block boundary and mid-block, prefill computing only the suffix
(32 prefix tokens skipped), and ref-counted free safety (2->1 on one sharer's
removal, survivor intact and re-shareable, pool restored when all freed). The
0004 serving gate stays byte-identical stock vs paged in unified and non-unified
mode.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 10:47:10 +00:00
Ettore Di Giacinto
67c6208b3a feat(llama-cpp/paged): cross-request prefix caching patch 0006
Mirror patch 0006 of the paged-attention series into the vendored llama.cpp
patch set. Extends the vendored PagedKVManager (src/paged-kv-manager) with
host-side cross-request prefix sharing: place_with_prefix reuses cached
physical blocks for a new sequence shared prefix (ref_cnt++) and allocates
only the divergent suffix; cow_block copy-on-writes a still-shared (ref>1)
block before a divergent write so co-owners stay byte-correct; ref-counted
free releases a shared block only at ref 0. Core kv-cache files untouched;
gated behind LLAMA_KV_PAGED, default off.

Gate 0 verified on the dev tree (CPU, Qwen3-0.6B-Q8_0): shared-prefix
greedy tokens byte-identical to the unshared baseline at both a block boundary
and mid-block, measured 2-block reuse (ref_cnt==2, only the suffix allocated),
and copy-on-write + seq_rm ref-count safety with no use-after-free.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 10:14:27 +00:00
Ettore Di Giacinto
667a21c119 feat(llama-cpp): expose paged KV cache as a per-server option (patch 0005)
Wire the continuous-batching serving path (update_slots) to the on-demand
paged KV-cache engine (patches 0001-0004). update_slots already drives the
engine transparently through the existing kv-cache seams: each slot's
sequence allocates paged blocks on arrival (find_slot placement) and returns
them on slot release (the seq_rm free seam). No serving-loop change is
needed for correctness.

This patch only exposes the enable cleanly: instead of forcing operators to
export the process-wide LLAMA_KV_PAGED env, add `kv_paged` (aliases
`paged_kv` / `paged_attention`) and `kv_paged_debug` model options that set
the env before the model/context is created. Default off; when the option is
absent nothing is touched, so an externally exported env still works and
stock behaviour is unchanged.

Verified on a dynamic continuous-batching harness (NP physical slots reused
across M>NP queued prompts, single mixed llama_decode per step, greedy):
12 dynamically-arriving sequences over 4 slots are token-identical to the
stock single-slot serial baseline under both the unified and per-sequence
caches. The debug trace confirms per-slot [paged-alloc] grow on arrival and
per-stream release on seq_rm. The per-slot allocate/free capacity benefit
only materialises under a per-sequence cache (kv_unified:false), since paged
block ownership is keyed by stream; the unified cache collapses every slot
onto one stream and the run stays correct but degenerates to a single
bounded, stock-recycled pool. We do not flip kv_unified here, to keep the
default serving behaviour and idle-slot prompt cache unchanged.

No core llama.cpp patch: no engine bug was found under dynamic slot churn.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 09:33:32 +00:00
Ettore Di Giacinto
04e3d04ab8 build(llama-cpp): isolate paged patches in patches/paged/ behind LLAMA_PAGED flag (default on)
Move the paged-attention patch series (0001-0004 + docs) into patches/paged/,
applied behind a new LLAMA_PAGED build flag (default on). The base patches/ dir is
now clean, so a dep-bump that breaks a paged hook can be unblocked with
LLAMA_PAGED=off (clean-against-upstream build) and the paged carry fixed
independently - decoupling the paged-KV maintenance from routine bumps without a
separate backend. Both apply paths wired (Makefile git-apply + prepare.sh re-apply,
flag passed through). Runtime stays gated by LLAMA_KV_PAGED env, so an on build is
byte-identical to stock until that env is set. Glob/flag logic verified in bash.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 09:22:36 +00:00
Ettore Di Giacinto
4968cd8a94 paged-attn 0004: on-demand KV block allocation
Wire the paged placement in find_slot through the vendored PagedKVManager
(0001) instead of a fixed full-pool permutation. Blocks are popped from a free
pool on demand as a sequence crosses block boundaries, and returned on sequence
end (full seq_rm / clear). One manager per (kv-cache, stream); all state lives
in a new src/paged-alloc unit keyed by a static registry, so the core kv-cache
struct is untouched (find_slot/clear/seq_rm gain only a gated call). Default
off; stock path byte-identical.

Gate 0 (CPU, Qwen3-0.6B-Q8_0), LLAMA_KV_PAGED=1 token-identical vs stock:
- single-stream llama-simple, 48 tok: identical
- multi-stream driver, 3 seqs x 40 tok: identical
Demand-driven confirmed via debug log: blocks grow 0->1->2->3->4 at logical
positions 16/32/48 (peak 4 blocks vs 16-block budget), per stream independently.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 08:50:57 +00:00
Ettore Di Giacinto
37e0e1ef55 paged-attn 0003: lift gather-read to multi-stream
The 0003 gather-read was single-stream only (GGML_ASSERT k->ne[3]==1). Lift it
to N streams: one index column per stream over the unified batch, gathered with
a single ggml_get_rows along the stream axis. Each column is position-sorted
(preserving the flash-attn online-softmax reduction order that makes the read
byte-identical) and padded to the max non-empty count across streams with a
masked (empty) cell, which contributes exp(-inf)=0.

Core touch stays additive: the one-line build_attn hook is unchanged; only the
two kv-cache gather helpers (now per-stream) and src/paged-attn.cpp grow.

Gate 0 (CPU, Qwen3-0.6B-Q8_0): a multi-sequence greedy driver (non-unified KV,
k->ne[3]>1) is token-identical between stock (env unset) and LLAMA_KV_PAGED=1:
3 seqs x 40 tok, 2 seqs x 32 tok, 5 seqs x 32 tok all identical; single-stream
llama-simple unchanged. Debug log confirms n_stream=3 engaged the multi path.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 08:46:12 +00:00
Ettore Di Giacinto
d9d846e04b feat(paged): patch 0003 gather-read - Gate 0 green, token-identical, additive
Implements the paged-attention gather-read (the real engine compute): attention
reads ONLY a sequence's used cells by gathering K, V and the kq_mask by the
non-empty-cell index list before build_attn_mha. Verified token-identical to stock
greedy generation, 9/9 across 3 prompts x {32,96,128} tokens on Qwen3-0.6B, with
n_gather=71 < n_kv=256 confirming real compaction (not an identity no-op).

Built in the additive "hook, don't edit" form: all logic in new src/paged-attn.{h,cpp}
(an llm_graph_input_i gather-index subclass + the K/V/mask gather), hooked by one line
in build_attn + two thin accessors on llama_kv_cache_context + one CMake line. No edit
to llm_graph_input_attn_kv or llama-graph.h. 216 insertions; default-off behind
LLAMA_KV_PAGED so stock path stays byte-identical.

Key correctness finding: get_gather_idxs emits cells sorted by token position. CPU
flash-attn's online softmax reduces cells in physical-array order and is FP-order-
sensitive, so 0002's scattered placement alone (full-window read) diverges from stock
past the first block; the position-sorted gather reproduces stock's exact reduction
order -> bit-identical. So 0003 is what makes paged placement token-identical under
flash-attn.

Verified on a dev tree at the pin (0001+0002+0003 on branch paged); not pushed.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 08:26:46 +00:00
Ettore Di Giacinto
84d59e659b docs(paged): additive "hook, don't edit" layout for the patch series
Maintainers rejected PR #22569 (the upstream paged draft) as "slop" - it rewrites
core attention and is unvendorable. Our own series must be additive so it survives
llama.cpp pin bumps. This documents the rule and the per-patch core-touch budget:
every change is either new code in a new vendored src/ file, or a single env-gated
hook at one call site that delegates to it - no logic in core files, no core struct
edits.

Grounds it in the pinned source: llm_graph_input_i is pure-virtual and
res->add_input() lets a new file register a graph input, so paged behavior plugs in
without editing core graph types. Redesigns 0003 (gather-read) from the old 4-file
surgery to one build_attn hook + a new paged-attn.{h,cpp} (a gather-input subclass)
+ two thin cache accessors (~8 core lines vs a core-struct rewrite). 0005 lands
entirely in LocalAI's grpc-server.cpp (no core patch).

Dev tree at the pin with 0001+0002 applied is set up; 0003 implementation is the
next focused token-identical Gate-0 block.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 07:28:44 +00:00
Ettore Di Giacinto
931793aa24 feat(paged): target-readiness for 2xH200 - correctness PASS, load-gen harness, projection
Deliverables for pushing paged KV toward the real target (2xH200), since GB10 is
only the test box and its "no win" result is a low-bandwidth artifact:

1. Correctness verified. test-paged-kv-e2e is greedy-equivalent to the contiguous
   reference (top-5 argmax ref=paged=3743, overlap 5/5). Found + fixed the blocking
   bug: common_fit_paged_kv_blocks over-reports free VRAM on GB10's unified device
   and tried 245GB of KV on a 119GB box, OOM-aborting context creation. Patch in
   patches/0002; durable fix (clamp to free_vram, honor --fit off) noted.

2. paged-loadgen.cpp: a dynamic-load benchmark that actually exercises where paging
   wins - variable prompt/gen lengths, continuous arrival, shared prefix - and
   reports the capacity ratio (contiguous reserve / paged peak KV). The stock tools
   run fixed-length all-at-once load, which is why they never show a paged win.

3. Projection to 2xH200, grounded in measured GB10 plateaus. Decode is bandwidth-
   bound, so the ceiling (~16k t/s for 32B) needs ~3,800 concurrent seqs, but
   contiguous KV fits only ~490 in HBM at 2k ctx - so KV memory IS the binding
   constraint on the target (unlike GB10), and paged KV's ~5-10x capacity (no
   over-reservation + prefix sharing) is what reaches the ceiling. The thesis holds
   on the target; remaining work is hardening/finishing the paged op (PR22569 was
   12-13% slower and lacks prefix sharing).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 23:16:28 +00:00
Ettore Di Giacinto
0337505dc8 docs(paged): measure paged KV at high concurrency (LLAMA_MAX_SEQ=2048) - no single-GB10 win
Closes the open question from PR22569_EVAL: that eval was blocked by the 256-seq
compile cap and used a compute-bound 32B. Recompiled LLAMA_MAX_SEQ=2048 and swept a
bandwidth-bound model (Qwen3-1.7B) to npl=2048, both KV layouts.

Result: aggregate decode plateaus at the hardware ceiling for BOTH layouts - 1.7B
flattens ~3200-3700 t/s by npl=512 (contiguous and paged alike), 32B-dense ~540 by
npl=128. Pushing concurrency past the plateau collapses per-seq tps (23->1.9) and
explodes TTFT (0.6s->64s) with no aggregate gain. Paged KV is a memory-capacity /
anti-fragmentation / prefix-sharing feature, not a single-node throughput lever; the
24k aggregate is a fleet-level (multi-GPU) result, unreachable on one GB10 regardless
of KV layout.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 22:47:20 +00:00
Ettore Di Giacinto
faeb5b457c analysis: NVFP4 closes the decode gap too (547->619, ~93% of vLLM)
Measured npl=128 cold A/B: NVFP4 decode 619 vs Q4_K 547 (+13%), closing the gap to
vLLM (667) from ~22% to ~7%. NVFP4's FP4-MMA kernel is more bandwidth-efficient at
the thin n=128 decode shape than Q4_K int8-MMQ (which ran 2.1x above the floor), so
it IS the better int4 decode GEMM the diagnosis called for - no multi-day
Marlin-for-K-quants needed. With NVFP4, llama.cpp on GB10 is ahead on prefill
(1209 vs 800) and within ~7% on decode. Remaining 7% = optional FP4 kernel tuning.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 21:42:17 +00:00
Ettore Di Giacinto
6e0b910210 analysis: decode gap is GPU/kernel-bound, NOT host overhead (corrects premise)
Rigorous re-measurement on pr24423: concurrent decode is GPU-compute-bound (~96%
util, sampled), CUDA graphs ARE enabled at npl=128 (94/98 calls replay a captured
graph; n_kv padded to 256 keeps topology stable), and graphs ON vs OFF is only
+1.5% at npl=128. The earlier '20% GPU util / 170ms host' read was a windowing
error (whole-run nsys vs decode-windowed). So no host/graph patch helps. The real
547->667 gap is the quantized DECODE GEMM: mul_mat_q (Q4_K/Q6_K) is ~68% of decode
GPU time and runs ~2.1x above the GB10 bandwidth floor (poorly tuned for the thin
n=128 shape); vLLM's Marlin int4 runs closer. Lever = a Marlin-style int4 decode
kernel for K-quants (or a Marlin-friendly int4 serving format), not host work.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 21:32:58 +00:00
Ettore Di Giacinto
aaf7b4112e test(llama-cpp): NVFP4-dense FP4 quality+speed eval on GB10
NVFP4-dense is producible via --tensor-type attn=nvfp4 --tensor-type ffn=nvfp4
(GGML_TYPE_NVFP4 has a full quantize path; no top-level ftype needed). Clean-from-BF16
4B PPL: NVFP4 14.31 vs Q4_K 13.66 vs MXFP4 17.42 vs BF16 13.32 - Q4_K-class, not
MXFP4-class. Prefill routes onto the FP4 MMA kernel (~1.29x Q4_K on 4B, within 5% of
MXFP4). It is the quality-preserving FP4 win MXFP4 was not.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 18:44:57 +00:00
Ettore Di Giacinto
037ad82b7c docs(paged): MXFP4-dense vs Q4_K quality gate on GB10 (do not recommend)
Fair clean-source perplexity check on DGX Spark (GB10): quantize Qwen3-4B
from one BF16 source to both Q4_K_M and MXFP4 (no imatrix, identical recipe).
Q4_K_M is +2.6% PPL vs BF16; MXFP4-dense is +30.8% (+27.5% worse than Q4_K).
The existing 32B MXFP4 was confirmed double-quant (Q4_K_M -> MXFP4 via
--allow-requantize), but the clean 4B test shows the gap is intrinsic to the
format, not the double-quant. Output stays coherent. Verdict: the ~1.58x
prefill / ~1.2x decode win does not justify a Blackwell MXFP4-dense quality
recommendation; keep Q4_K_M the dense default, pursue NVFP4 instead.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 17:25:14 +00:00
Ettore Di Giacinto
1887385b79 analysis: MXFP4-dense fails quality check (~27% worse PPL than Q4_K) - do not recommend
Clean fair comparison (Qwen3-4B, all from same BF16 source, wikitext PPL): BF16
13.32, Q4_K_M 13.66 (+2.6%, near-lossless), MXFP4 17.42 (+30.8%). MXFP4 is ~27%
worse than Q4_K even clean from BF16 (32B double-quant cross-check: 7.39 vs 8.46,
+14.6%, same direction). MXFP4_MOE is built for MoE expert tensors; on dense
attn/ffn it is far lossier than Q4_K's 6-bit superblock structure. The ~1.58x
prefill is not worth ~27% PPL - Q4_K stays the dense default; FP4 only where the
model is trained for it (MoE). Verdict: do NOT ship a Blackwell MXFP4-dense rec.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 17:24:24 +00:00
Ettore Di Giacinto
40ee9cdd13 docs(paged): evaluate llama.cpp PR #17004 (GPU/backend sampling) on GB10
PR #17004 is merged and already present in our pinned llama.cpp f3e1828.
Measured on DGX Spark (GB10, sm_121, Qwen3-32B-Q4_K_M):

- llama-batched-bench does no sampling (random tokens), so it cannot test
  the fix; its ~540 t/s plateau is not sampling-bound.
- Real-sampling A/B via llama-batched (CPU vs -bs GPU sampler): +25% at
  np=32, +3% at np=64, GGML_ASSERT(obj_new) graph-alloc crash at np>=128.
- nsys at np=64: GPU-busy time and kernel mix unchanged (392 vs 404 t/s);
  sampling kernels negligible. GPU utilization did not rise.

Clean negative: the fix does not break the plateau toward the ~2700 ceiling
or past vLLM 667, and is unusable at the multi-user parallelism in question.

Adoption: code arrives via LLAMA_VERSION bump (prepare.sh vendors the
modified upstream server-context.cpp), but grpc-server must set
params.sampling.backend_sampling to enable it; grammar/tool-call/logprobs
requests fall back to CPU. Defer adoption until #18547/#18550 stabilise it.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 15:44:21 +00:00
Ettore Di Giacinto
d6c91b7d62 analysis: finalize PR #22569 paged-KV eval (full detail + compute-bound note)
Agent-finalized eval: builds (1-line Qwen3 reshape fix), but on GB10+32B paged is
~12% slower than contiguous and both cap at LLAMA_MAX_SEQ=256 (not OOM; 16GiB/119).
Agent argues 32B is compute-bound + plateaus by npl=128 so raising the cap won't
help - but 540 t/s << ~1900 bandwidth ceiling, so the plateau cause is unconfirmed
(attention-over-KV or CPU sampling, not matmul saturation). Next: raise the cap +
remeasure to settle it. Verdict: do not adopt #22569; paged KV not a GB10 lever.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 14:35:02 +00:00
Ettore Di Giacinto
92e93dfc34 analysis: paged KV gives ZERO benefit on GB10 (measured) - not the lever
Full sweep, Qwen3-32B: contiguous decode 537/541 t/s at npl=128/256 (plateau);
paged (#22569) 477/471 - SLOWER at matched concurrency. Both FAIL at npl=512/1024
with n_seq_max<=256 - paged does NOT bypass the LLAMA_MAX_SEQ=256 compile cap, its
whole purpose. GB10's limit is the 256-seq cap + the ~540 decode plateau (flat by
npl=128), NOT KV capacity/fragmentation (122 GB unified). Paged KV solves a problem
GB10 doesn't have; it remains valid for memory-constrained datacenter GPUs (24-48GB)
but must be validated there, not GB10. Do not adopt #22569; do not build paged KV
for GB10. Real GB10 questions: the 256 cap (cheap) + the 540 plateau (vs vLLM 667).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 13:31:33 +00:00
Ettore Di Giacinto
fdb7f56bb7 docs(llama-cpp): scope chunked prefill + n_batch/n_ubatch decouple
Add CHUNKED_PREFILL_PLAN.md for the llama.cpp backend. Key finding: the
vendored llama.cpp server scheduler (update_slots) already implements
chunked prefill with prefill/decode interleaving on the pinned version -
decode tokens are seated first each iteration, prefill fills the leftover
n_batch budget, both share one llama_decode. The draft upstream PR #10718
goal is already absorbed; no re-implementation needed.

The real LocalAI gap is the n_batch/n_ubatch coupling at grpc-server.cpp
(both set to nbatch()), which pins the logical scheduling window to the
physical ubatch width. The plan scopes the decouple (C++ option + proto
NUBatch + options.go), an optional decode-headroom prefill cap as a
vendored patch, a token-identical verification harness, and keeps the
work orthogonal to paged KV.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 12:54:22 +00:00
Ettore Di Giacinto
07985ba45b analysis: measured llama.cpp aggregate vs vLLM - already ~75-80% at npl<=128
llama-batched-bench Qwen3-32B-Q4_K_M: aggregate decode 235/391/540 t/s at
npl=32/64/128 vs vLLM 328/569/667 = 72/69/81%, multiplier 53x (vLLM 56x), still
climbing at 128. The 30x headline is wrong at realistic concurrency: llama.cpp is
ahead single-stream (MXFP4 1153 > 800) and ~75-80% aggregate. Aggregate prefill is
flat ~760 but GB10-compute-capped (vLLM ~800 too), so chunked prefill is a
latency/TTFT win not throughput; paged KV is the high-concurrency (thousands-seqs)
lever for vLLM's 24k regime. ROI: MXFP4 ship -> chunked prefill -> paged KV.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 11:32:40 +00:00
Ettore Di Giacinto
fc589b3fad analysis: vLLM GB10 advantage is the SCHEDULER, not the kernel (pivot)
Code-grounded vLLM v0.23.0 analysis + DGX measurement: vLLM single-stream W4A16
prefill ~800 t/s (~52 TFLOPS) is TIED with llama.cpp MMQ (718/47), using the exact
XOR-swizzle + 4-stage cp.async Marlin we proved collapses GB10 occupancy. vLLM has
no FP4 cubins on sm_121 (forced W4A16 fallback), so llama.cpp MXFP4 (1153) already
beats vLLM single-stream. vLLM's ~24k headline is the aggregate decode multiplier
(~56x) from paged KV + chunked prefill + continuous batching - a scheduler win.
llama.cpp lacks paged KV + chunked prefill. Kernel work (W4A16 178 t/s, FP4-MMA)
banked as not-the-lever; effort pivots to the scheduler. Detail in
VLLM_DECOMPOSITION.md; W4A16 plan marked STOPPED.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 07:09:42 +00:00
Ettore Di Giacinto
2b79083b71 feat(w4a16): grow tile to BN128/16w (q4_K +17%, pp512 148->178)
P3b-2 for the Blackwell W4A16 Marlin GEMM. The q4_K dequant wall is partly
cross-N-block-redundant: every N-block re-decodes the same weight strip, so
halving the N-block count (BN 64->128) halves that redundant 6-bit superblock
decode. A BN sweep showed this only pays off when BN is spread across more
warps (16 warps, 8 m16n8 C-tiles/warp) rather than more fragments-per-warp -
the FN=8 / FM=4 variants (16 C-tiles/warp) regressed to ~6.6 TFLOPS on
register pressure. Shipping tile is now WM=4,WN=4,FM=2,FN=4 -> BM=128, BN=128,
16 warps.

Thermally-bracketed cold A/B (q4_K n=512 / q4_0 n=512 via test-backend-ops
perf; pp512/pp2048 via llama-bench Qwen3-32B-Q4_K_M):
  BN64/8w  (prev): 8.50 / 10.56 TFLOPS, measured 8.45/10.51 again (bracket)
  BN128/16w (this): 9.92 / 11.68 TFLOPS, pp512 177.6, pp2048 185.0
  -> +17% q4_K, +11% q4_0, +20% pp512 vs the previous commit; +49% pp512 vs
     the original block-tiled kernel (119).

Parity gate GGML_CUDA_W4A16=1 test-backend-ops MUL_MAT = 1103/1103, flag set
and unset (byte-identical when unset). Still ~4.7x under MMQ (47 TFLOPS) and
does NOT beat MMQ; BN growth divides the redundant decode but cannot remove
the per-k-step decode itself - the offline weight prepack remains the next
unlock for q4_K. Plan doc P3 table + bottleneck notes updated.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 02:01:12 +00:00
Ettore Di Giacinto
2f648dc6a0 feat(w4a16): conflict-free skew-pad ldmatrix + BM128/8w tile (q4_K +28%, q4_0 +40%)
P3b for the Blackwell (sm_120/121) W4A16 Marlin GEMM. Two combined changes
over the prior block-tiled kernel, both verified by a thermally-bracketed
cold A/B (committed measured identically before and after):

- Skew-padded shared layout: store the staged weight/activation rows at a
  padded stride of 12 bf162 (8 data + 4 pad) and feed the tensor cores with
  ldmatrix.x4 (A) / ldmatrix.x2 (B). ldmatrix's per-lane address is
  row*stride; the natural stride 8 divides the 32-bank cycle and collides
  rows 0,4,8,12 (2-way bank conflict). Skewing to 12 (still 16-byte aligned)
  spreads {r*12 mod 32} across 8 distinct bank-quads, so both ldmatrix halves
  are conflict-free at only +50% on the ~6 KB staged tile - unlike a 128-byte
  -row XOR swizzle, which is conflict-free but needs 16 KB shared and
  collapses occupancy on GB10 (measured 2.84 TFLOPS, worse than baseline).
- Larger tile: BM=128, BN=64, 8 warps (WM=4,WN=2,FM=2,FN=4), which cuts the
  redundant per-M-block activation re-reads.

Cold A/B (q4_K n=512 / q4_0 n=512 via test-backend-ops perf; pp512/pp2048 via
llama-bench Qwen3-32B-Q4_K_M):
  committed: 6.63 / 7.53 TFLOPS, pp512 119
  this:      8.52 / 10.49 TFLOPS, pp512 148.5, pp2048 153.9  (+28% / +40% / +25%)

Parity gate GGML_CUDA_W4A16=1 test-backend-ops MUL_MAT = 1103/1103, flag set
and unset (byte-identical when unset). Still ~5.5x under MMQ (47 TFLOPS) and
does NOT beat MMQ yet; the q4_K limiter has now moved from the mma feed to the
per-element 6-bit superblock dequant (q4_0 scales to 15.8 TFLOPS with more
warps while q4_K stays ~8.5), so the offline weight prepack is the next unlock.
Plan doc P3 section updated with the sweep data and the corrected bottleneck.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-21 01:15:07 +00:00
Ettore Di Giacinto
9973fa995a feat(w4a16): P3 step 1 - block-tiled multi-warp Marlin GEMM (GB10)
Replace the P2 1-warp-per-16x8 W4A16 kernel with a block-tiled multi-warp
kernel: blockDim=(32, WM*WN) so threadIdx.x is the warp lane (required by
mma.cuh get_i/get_j) and threadIdx.y is the warp index. WM*WN warps compute a
BM(=WM*FM*16) x BN(=WN*FN*8) output tile, each warp owning an FM x FN grid of
m16n8k16 BF16 mma fragments accumulated in F32. The BM x 16 dequantized Q4
weight strip is staged once per k-step in a small (~4 KB) shared buffer and
reused across the block's whole BN span. Shipping config WM=2,WN=2,FM=2,FN=4.

The P2 launch put all threads on threadIdx.x; with >1 warp that drove the mma
tile get_j past the shared bound (out-of-bounds shared read, caught by
compute-sanitizer). The new (32, nwarps) layout matches mmf.cu and fixes it.

Parity gate holds 1103/1103 (test-backend-ops MUL_MAT CUDA0), flag set and
unset (byte-identical when GGML_CUDA_W4A16 is unset; the seam returns false).

Perf (q4_K m=4096 k=14336 n=512): ~2 TFLOPS (P2) -> ~7-9 TFLOPS (thermal
dependent); llama-bench Qwen3-32B-Q4_K_M pp512 31.75 -> ~118-142 t/s. Still
below the MMQ baseline (47 TFLOPS / 718 t/s): a tile sweep stayed flat and
q4_0 vs q4_K differ by only ~12%, so dequant compute is not the limiter - the
shared-load / mma-feed is. A naive double-buffered cp.async pipeline (32 KB
shared) regressed via occupancy collapse and an ldmatrix swap was neutral
(unswizzled layout bank-conflicts), both reverted. The path to >=150 TFLOPS is
the full Marlin machinery (XOR-swizzled shared layout + offline weight reshuffle
+ tuned async pipeline + Stream-K), deferred to P3 step 4. See
W4A16_MARLIN_KERNEL_PLAN.md for the per-step table and dead-end notes.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 23:36:58 +00:00
Ettore Di Giacinto
4de0c3b1b2 feat(cuda): W4A16 P2 correctness-first BF16 GEMM kernel
Replace the P1 dispatch-seam TODO in marlin-w4a16.cu with a real W4A16
GEMM for consumer Blackwell (sm_120/121). In-kernel dequant of Q4 weights
to BF16, mma.sync m16n8k16 f32.bf16.bf16.f32 tensor-core multiply against
BF16-converted f32 activations, f32 accumulate and write, reusing ggml's
mma.cuh tile abstractions.

Handles the contiguous 2D GEMM prefill path for Q4_0 and Q4_K (f32
activations, ne2==ne3==1); batched, broadcast, permuted, non-contiguous
and f16-activation cases return false and fall back to MMQ so the gate
stays green. M/N boundaries are zero-padded in-kernel.

Parity gate (GGML_CUDA_W4A16=1 test-backend-ops MUL_MAT on GB10):
1103/1103 passed; default flag-off build stays byte-identical 1103/1103.
Model sanity: Qwen3-32B-Q4_K_M llama-bench pp512 31.75 t/s (slow is
expected for P2 - the naive single-warp kernel is the correctness
checkpoint; P3 adds the cp.async pipeline and weight reshuffle).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 22:09:12 +00:00
Ettore Di Giacinto
9a71e81fc4 kernel: written subagent dispatch briefs for P3/P4/P5
Same strategy as P2: one fresh Opus-4.8 subagent per phase, each handed a
complete zero-context brief, dispatched sequentially as each predecessor lands
(P3 pipeline needs P2's correct kernel, P4 tune needs P3, P5 enable needs P4).
Shared DGX/harness/commit boilerplate factored into a COMMON section; each phase
brief carries its goal, incremental steps, acceptance gate, and a splice note for
the prior phase's actual deliverable.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 22:01:18 +00:00
Ettore Di Giacinto
718b31d063 kernel(P1): W4A16 dispatch seam (gated, byte-identical fallback to MMQ)
marlin-w4a16.{cuh,cu} + a gated hook in ggml_cuda_mul_mat (dense path), behind
GGML_CUDA_W4A16 + sm_120/121 + Q4_0/Q4_K + f32. Returns false -> MMQ, so the
default build is byte-identical. Verified on GB10: clean build, test-backend-ops
MUL_MAT 1103/1103, llama-bench pp512 unchanged (717.77 default / 718.26 flagged),
and GGML_CUDA_W4A16=1 reaches the seam ([w4a16] P1 warning) before falling back.
Source + apply steps under kernel/w4a16/ (DGX checkout is volatile). The frame the
P2 correctness kernel + P3 Marlin pipeline fill.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 21:46:38 +00:00
Ettore Di Giacinto
d291e15114 kernel(P0): record precise op-level baseline (q4_K n=512 = 47 TFLOPS, ~22% of ceiling)
test-backend-ops perf MUL_MAT m=4096 k=14336: q4_K prefill (n=512) = 47.1 TFLOPS,
q4_0 = 49.5; decode (n=1) = 761/817 GFLOPS (memory-bound). The prefill GEMM target
is 47 -> ~213 TFLOPS (~4.5x). Cleaner per-shape target than end-to-end for kernel
iteration.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 21:33:50 +00:00
Ettore Di Giacinto
dae2679c3b kernel(P0): parity harness established + baseline (test-backend-ops 1103/1103 green)
P0 done: test-backend-ops MUL_MAT on CUDA0 = 1103/1103 (CUDA vs CPU ref, covers
Q4_0/Q4_K at m=4096,k=14336,n=1..512) - the correctness gate the W4A16 kernel must
keep green. Baseline llama-bench dense Q4 prefill ~750 t/s (~46 TFLOP/s, ~21% of
the 213 BF16 ceiling) - the number to beat toward ~3300. Reusable harness at
~/p0harness.sh (needed -DLLAMA_BUILD_TESTS=ON).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 21:29:21 +00:00
Ettore Di Giacinto
13e6ee89c7 kernel: validate cuBLAS dead-end (sm_80 fallback) + W4A16 Marlin impl plan
Decisive DGX experiment: rebuilt with -DGGML_CUDA_FORCE_CUBLAS (it's a compile
#ifdef, not the runtime env we'd been setting - so prior 'cuBLAS no-op' tests
never engaged it). Real result: cuBLAS is SLOWER than MMQ for dense Q4 (pp2048
690 vs 750) and runs an Ampere cutlass_80_tensorop kernel - CUDA-13 has no sm_121
GEMM, falls back to sm_80. So both MMQ and cuBLAS sit at ~46 TFLOP/s; no library
shortcut to the 213 ceiling on GB10. Confirms a hand-tuned sm_120a kernel is
required. Added the phased W4A16 Marlin-style implementation plan (P0 harness ->
P5 enable) as the committed multi-week build; corrected the cuBLAS note.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 21:16:13 +00:00
Ettore Di Giacinto
76cc0b6abc docs(paged): phased plan to make llama.cpp a viable vLLM alternative
Phase 1 (config, PR #10411, DONE): VRAM-scaled n_parallel + Blackwell batch.
Phase 2: paged KV (PR #22569, ~9.5x concurrency). Phase 3: chunked prefill +
n_batch/ubatch split. Phase 4: batched-GEMM kernel tuning. Phase 5: backend
sampling. Cross-cutting: spec-dec for dense.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 09:35:53 +00:00
Ettore Di Giacinto
122df1c620 analysis: vLLM throughput gap decomposed - spec-dec is the per-user lever
Per-user decode is at parity without spec-dec (10.2 vs 11.7, bandwidth-bound).
vLLM's per-user speed = speculative decoding (lossless, target-verified). GB10 is
best-case (bandwidth-bound + idle compute); llama.cpp spec-dec measured 2.9x on
dense Qwen2.5-32B. Qwen3-32B has no native MTP - use Qwen3-1.7B draft or EAGLE3
head. Recommendation: make spec-dec easy for dense >=14B on Blackwell (keeps
Q4_K_M quality, no kernel). Prefill-kernel + continuous-batching are separate
(TTFT / aggregate). Our own DGX run pending (box rebooted, llama-cli hangs).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 08:40:20 +00:00
Ettore Di Giacinto
14e3da25b6 kernel: dense MXFP4 test = free 1.44x (765->1153) but FP4-MMA untuned (~17% of ceiling)
MXFP4 dense moves prefill off int8-MMQ onto the FP4-MMA path (existing kernel) for
a free 1.44x - shippable as a Blackwell dense-quant recommendation. But it's ~17%
of the FP4 roofline, so the FP4-MMA kernel is itself untuned: ~4-6x still in the
kernel. Sharpens the target to TUNING the FP4-MMA (serves dense+MoE, only path to
beat vLLM). Marlin-style W4A16 BF16 is the alt to match on the BF16 ceiling.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 07:48:29 +00:00
Ettore Di Giacinto
f5e9caece1 kernel: reframed Blackwell kernel-gap map (research + profiles)
Key corrections: (1) vLLM 24k is AGGREGATE; single-stream roofline ~3300 t/s
(BF16) / 6600 (FP4). (2) GB10 is 1:1:2 BF16:INT8:FP4 - INT8 == BF16, only FP4 is
2x. (3) Measured: dense int8-MMQ at 21% of ceiling, MoE FP4-MMQ at ~5% - both
EXIST, just untuned for Blackwell. Strategy: to MATCH vLLM, tune MMQ or build a
Marlin-style W4A16 BF16 GEMM (FP4 NOT required); to BEAT, fix the existing FP4
MMA on sm_121 (build/miscompile, not greenfield). Dropped the tcgen05 grouped
GEMM rewrite. Cheap next test: dense MXFP4 quant + existing FP4-MMA.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 07:21:56 +00:00
Ettore Di Giacinto
d2651c86d9 bench(dense): root-cause the W4A4 NVFP4 hang; W4A16 vs Q4 is the headline
Researched: W4A4 hangs on GB10 because FlashInfer ships no FP4 cubins for
sm_120/121 (all datacenter Sm100a); dense mm_fp4 is gated-off/returns-zeros on
consumer Blackwell, and the FlashInfer FP4 autotuner spins on the first forward
pass. Not a misconfig - dense W4A4 inference isn't validated on sm_121. W4A16
(4-bit weight / 16-bit act, Marlin) vs llama Q4_K_M is the correct apples-to-
apples (same quant class) AND the fast path. Removed the misleading 'W4A4 would
be faster / lower bound' framing. Sources: vllm #30163/#26381, flashinfer
#2577/#3294, cutlass #3096.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 06:59:50 +00:00
Ettore Di Giacinto
19742aee64 bench(dense): FORCE_CUBLAS no-op for dense too (720.8 vs 721.8) - every flag lever exhausted
Confirms parity (dense+MoE, both phases) is strictly the FP4 tensor-core kernel;
no config/flag shortcut remains.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 03:59:27 +00:00
Ettore Di Giacinto
ce60737fc5 kernel(doc): dense scope resolved - two FP4 kernels (dense first, then grouped)
Benchmark confirms dense prefill 7.6-32x behind too, so the kernel track needs a
non-grouped FP4 dense GEMM (simpler, land first) + the MoE grouped GEMM. Both
share the e2m1 block-scaled collective; dense is grouped-with-one-group.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-20 03:56:33 +00:00
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
Ettore Di Giacinto
b7b2e8291c kernel(fp4-grouped-moe): scaffold the FP4 grouped-GEMM MoE dispatch (Lever 3)
The only work that closes the vLLM gap on Blackwell: mul_mat_q<MXFP4> is 37%
prefill + 54.6% decode-B64 GPU time; paged attention can't touch it (proven).
Scaffold (builds clean on GB10, default byte-identical): fp4-grouped-moe.{cuh,cu}
entry + gated hook in ggml_cuda_mul_mat_id (env GGML_CUDA_FP4_GROUPED), always
falls back to MMQ for now. Design doc has the CUTLASS/tcgen05 implementation
phases + parity harness + the dense-path follow-up (#28).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 23:44:31 +00:00
Ettore Di Giacinto
cb28deda6b bench(paged): decode profile overturns 'engine-addressable' - decode is 54.6% MoE GEMM too
Decode-dominated B=64 nsys: mul_mat_q<MXFP4> 54.6%, attention only 19.8%. Both
phases are FP4-MoE-kernel-bound (Lever 3). The paged series cannot close the vLLM
gap in either phase; its real value is capacity + prefix-sharing, not tok/s parity.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 23:27:35 +00:00
Ettore Di Giacinto
2a500c371f bench(paged): fresh GB10 head-to-head vs vLLM - two distinct gaps
Prefill 6-48x behind and does NOT scale with B (kernel-bound, paging can't fix).
Decode: we win at B=1; 2.5-3.7x behind at B>=8 - THAT concurrency gap is the
engine's domain (0004 pool + 0005 continuous batching target it). Baseline for
the series to improve on.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 23:20:22 +00:00
Ettore Di Giacinto
48fbb9384f docs(paged): refine 0003 plan - used-cell gather, per-ubatch rebuild, single-stream first
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 23:14:25 +00:00
Ettore Di Giacinto
145e45b6f2 docs(paged): exact executable plan for 0003 gather-read
Every edit mapped (gather-index graph input mirroring k_idxs; gather K/V/mask by
one aligned index; n_kv compaction; gated so stock stays byte-identical) with
the token-identical gate and the known risks (mask transpose layout, v_trans).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 23:12:18 +00:00
Ettore Di Giacinto
c4b4f3a3e4 docs(paged): series status 0001/0002 done+verified; honest parity note
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 23:05:14 +00:00
Ettore Di Giacinto
61ff738177 patch(paged) 0002: LLAMA_KV_PAGED block placement, Gate 0 token-identical
find_slot places a sequence's tokens at permuted non-contiguous blocks; greedy
generation is token-identical to stock (verified on Qwen3-0.6B at the pin),
branch confirmed firing. Default off. The placement substrate for the gather-read.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 23:04:28 +00:00
Ettore Di Giacinto
ce48cc0751 patch(paged) 0001: vendor PagedKVManager into llama.cpp src
First patch of the stacking series. Adds src/paged-kv-manager.{h,cpp} (the
CPU-verified vLLM-parity block manager) + CMake entry. No behavior change.
Generated against the pinned LLAMA_VERSION; applies clean.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 22:55:22 +00:00
Ettore Di Giacinto
ba3fa5a633 build(paged): stacking patch-series scaffolding for llama.cpp paged attention
Numbered patches under backend/cpp/llama-cpp/patches/ applied in order against
the pinned LLAMA_VERSION (build hook in the llama.cpp: target). Each phase is one
small, independently-buildable patch so the work rebases cleanly across llama.cpp
bumps (anti-drift). README defines the series (0001 vendor manager -> 0006 prefix
caching) + the regen workflow.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 22:53:20 +00:00
Ettore Di Giacinto
62f0ae17e3 docs(paged): upstream survey - no FP4 MoE GEMM to patch in; phase 3 is from-scratch
No tcgen05/CUTLASS grouped-GEMM MoE kernel exists upstream (merged/in-flight/
draft); CUTLASS not a dep; no fork has one; activation-quant gather already
fused. Matching vLLM needs a from-scratch tcgen05 grouped GEMM (months,
maintainers deferring to cuTile). No tractable patch closes the 27x.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 22:44:11 +00:00
Ettore Di Giacinto
b14214620c docs(paged): Lever-3 phase-1 nwarps tweak = dead end (constants coupled)
static_assert(nwarps*tile_C::I == mmq_y) locks nwarps=8 for mmq_y=128; can't
raise occupancy without co-scaling mmq_y (blows Blackwell smem). MMQ kernel is
not freely tunable -> parity needs the tcgen05/CUTLASS rewrite, not knobs.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 22:32:02 +00:00
Ettore Di Giacinto
1449b806ab docs(paged): Lever-3 + paged-attention implementation plans + upstream ggml issue draft
Plan A (Lever 3): phased path to FP4 MoE GEMM parity — cheap tweaks, act-quant
fusion, then the real lever (tcgen05/CUTLASS grouped GEMM), full-model FP4.
Plan B (paged attention): on-demand pool, gather-read + Gate 0, continuous
batching, prefix sharing; benchmark in memory-pressured/mixed-length regimes.
Upstream issue draft: GB10 numbers, nsys profile, ruled-out config knobs,
tcgen05 proposal.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 22:28:28 +00:00
Ettore Di Giacinto
9f16a907be docs(paged): Lever 3 profiled + Q4/MXFP4 findings, auto-ubatch shipped
Prefill doesn't scale with bigger single prompts (attention O(N^2)); real gap
is batched MoE prefill (B=32: 27x vs vLLM, ~22 effective TFLOP/s). nsys pins
Lever 3 target: mul_mat_q<MXFP4> MoE GEMM 37% + un-fused act-quant 8%; native
FP4 MMA already engaged, inefficiency is the per-expert thin-tile scheduler.
Q4_K_M matches MXFP4 on decode (decode win is generic 4-bit); MXFP4's only edge
is prefill. Auto-ubatch=2048 on Blackwell shipped (PR #10411).

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 20:56:46 +00:00
Ettore Di Giacinto
aba0bfd24f feat(backend): auto-default physical batch to 2048 on Blackwell GPUs
On NVIDIA Blackwell consumer GPUs (sm_120/121, incl. GB10/DGX Spark) a larger
physical batch (n_ubatch) materially lifts MoE prefill throughput - measured on
a GB10 with Qwen3-30B-A3B to lift the prefill ceiling and saturate at ~2048.

When a model config leaves `batch:` unset, EffectiveBatchSize now picks 2048 on
Blackwell instead of 512; explicit `batch:` always overrides. Detection is a
shared, cached Go helper (xsysinfo.IsNVIDIABlackwell, nvidia-smi compute_cap
>= 12). Logic is isolated in core/backend/hardware_defaults.go and applied at
the common ModelOptions builder, so it covers the C++ llama.cpp backend too.

Measured (GB10, Qwen3-Coder-30B-A3B MXFP4): prefill ub512 2994 -> ub2048 3316
t/s; saturates past 2048. Also recorded in the DGX gap plan: 4-bit quant alone
captures the decode win (Q4_K_M 93.5 >= MXFP4 86.4 t/s), MXFP4's only edge is
prefill via Blackwell FP4 tensor cores.

Tests: hardware_defaults_internal_test.go; existing NBatch specs pinned to the
no-Blackwell branch for determinism.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 20:46:45 +00:00
Ettore Di Giacinto
7aa61d4c32 docs(paged): DGX Blackwell gap analysis + lever plan (living doc)
Captures the full dgx.casa investigation: Q8/F16/vLLM baselines, concurrency
sweeps, paged-patch (no concurrency effect), nsys+code root-cause (MoE int8
MMQ on Ampere-class tensor cores = 74.5% compute, no FP8 path), and the
lever plan.

Measured wins:
- Lever 1 (MXFP4 / Blackwell FP4 path): decode +50-66% over Q8, prefill
  plateau +66% (2200->3650). MXFP4 decode beats vLLM FP8 at B=1 (83 vs 48),
  near-parity B=8. Prefill still plateaus (fused-MoE-GEMM gap).
- Lever 2 (ubatch): saturates at 2048; ceiling is the kernel, not batch.

Designed (not built): Lever 3 fused FP4/FP8 MoE grouped GEMM, Lever 4 FP8
GEMM (needs ggml_mul_mat_ext scale plumbing), Lever 5 tcgen05 kernels, and
the complete paged attention (on-demand alloc + gather-read + continuous
batching + prefix sharing). Honest scope: each is multi-week kernel/systems
work.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 20:15:14 +00:00
Ettore Di Giacinto
bbc84a9889 feat(paged): Gate 0 in-model - token-identical generation with paged KV placement
Wire paged, non-contiguous fixed-size BLOCK placement into the real
llama.cpp KV cache (find_slot), behind env LLAMA_KV_PAGED, and validate
Gate 0 on a real GGUF: Qwen3-0.6B greedy generation is TOKEN-IDENTICAL to
the contiguous cache while its KV is physically scattered across permuted
blocks (cells 0-15, 144-159, 32-47, ...). Proven non-contiguous via
LLAMA_KV_PAGED_DEBUG, not a silent fallback.

This retires the correctness premise of paged attention IN THE MODEL (not
just at the ggml-op level): attention is invariant to physical KV placement,
because reads use per-cell pos/seq metadata for masking. The patch lives at
patches/0001-paged-kv-block-placement.patch (against llama.cpp 0253fb21f).

Scope: storage/placement layer, single sequence. Remaining (P4): the
gather-read compute path (attend only a seq's own blocks) for the throughput
win, and the multi-sequence driver. README updated with repro + status.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 08:51:42 +00:00
Ettore Di Giacinto
3ed3279739 docs(paged): status + integration map for in-model Gate 0
Capture verified state (P0 manager parity, P1 ggml write/gather, P2 attention
numerics 7.5e-08, P3 capacity 9.2x + prefix-sharing 11.3x) and the exact
remaining work: wire build_attn_paged into llama-graph.cpp and validate
token-identical generation on Qwen3-0.6B (Gate 0), then win-2 throughput.

Records the integration seams (create_memory, find_slot, get_k/get_v,
build_attn, mask) and the honest caveats (unified cache already shares a
pool; vLLM's classic kernel is deprecated) so the next session starts warm.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 08:45:51 +00:00
Ettore Di Giacinto
ddace5fb6a feat(paged): paged-bench - measure capacity & prefix-sharing wins
Quantify the two multi-tenant wins that are properties of the host-side
block model (vLLM-parity), independent of the in-model compute path:

  WIN 1 concurrency capacity @ 512-block budget
    contiguous (reserve n_ctx/seq): 4 sequences
    paged (on-demand blocks):       37 sequences
    --> 9.2x more concurrent sequences

  WIN 3 cross-tenant prefix sharing (32 tenants, 1024-tok shared prefix)
    prefix-cache OFF: 2176 physical blocks
    prefix-cache ON:  192 physical blocks
    --> 11.3x less KV memory

WIN 2 (throughput) is deliberately reported as PENDING: it requires the
paged gather-read path wired into llama-graph.cpp (Gate 0) and is not
measurable at the allocation layer. The win-1 baseline is per-sequence
n_ctx reservation (stream mode); llama.cpp's unified cache already shares
one pool, so the honest win there is on-demand sizing + prefix dedup.

Phase 3 (partial) of docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 08:44:41 +00:00
Ettore Di Giacinto
5a5d3df8c8 feat(paged): Phase 2 core - attention over paged KV matches reference
Retire the central numeric risk from the design: feeding gather-to-scratch
KV (a sequence whose blocks are non-contiguous in the shared pool, [2,1,5])
into ggml's standard attention ops produces correct attention.

Path under test: set_rows write -> get_rows gather (K and V) ->
mul_mat(K,Q) -> soft_max_ext -> mul_mat(V^T, probs). Result is compared
against an independent host-computed softmax attention over the same K/V/Q.
Max abs error ~7.5e-08 (n_kv=48, d=8, n_q=4).

This proves the paged read path is numerically sound on CPU with no new
ggml op. Remaining: wire build_attn_paged into llama-graph.cpp and validate
Gate 0 (token-identical greedy generation in a real model).

Phase 2 (core) of docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 08:35:35 +00:00
Ettore Di Giacinto
c6698dd4bf feat(paged): Phase 1 - ggml paged write/gather mechanism (CPU)
Validate the paged KV read/write path at the ggml-op level, driven by
PagedKVManager:

- write: ggml_set_rows(pool, k_src, slot_mapping)  scatter K rows by slot
- read:  ggml_get_rows(pool, gather_idx)           gather a seq's slots into
         contiguous scratch (the tensor an attention kernel consumes)

The test forces a non-contiguous, out-of-order physical block layout
(allocate seqA+seqB, free seqA, reallocate seqC -> blocks [2,1,5]) and
proves gather(write(x)) == x plus cross-sequence isolation in the shared
pool. This de-risks the central question (does slot-addressed paged storage
round-trip correctly through ggml) before the llama-graph integration.

Pool is statically allocated via ggml_backend_alloc_ctx_tensors, mirroring
how llama.cpp allocates its KV cache. CPU backend, no new ggml op.
Built against ggml from the vendored llama.cpp checkout.

Phase 1 of docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 08:33:26 +00:00
Ettore Di Giacinto
edb1a11abc feat(paged): vLLM-parity KV block manager (Phase 0, CPU-first prototype)
Host-side paged-attention block manager ported faithfully from vLLM V1
(block_pool.py, kv_cache_utils.py, single_type_kv_cache_manager.py):

- KVCacheBlock + intrusive LRU FreeBlockQueue (O(1) middle removal)
- BlockPool: get_new_blocks / touch / free_blocks eviction ordering /
  cache_full_blocks / lazy eviction on reuse
- PagedKVManager: on-demand allocate, block_table, slot arithmetic
  (slot = block_id*block_size + offset), free
- Prefix caching: chained block hashing + find_longest_cache_hit
  (first-miss stop), enabling automatic cross-tenant prefix sharing

Pure C++17, zero ggml/llama.cpp dependency, unit-tested to vLLM behavioral
parity (4/4 suites green). Parity is on algorithm/behavior, not hash bytes.

Phase 0 of docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md.
Phases 1-5 (ggml storage, gather-to-scratch read path, Gate 0 correctness,
benchmark wins, prefix-share serving) follow.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-19 08:26:31 +00:00
1585 changed files with 63210 additions and 151308 deletions

View File

@@ -34,7 +34,7 @@ The build matrix is data-only YAML at `.github/backend-matrix.yml` (not inside `
**Without an entry here no image is ever built or pushed, and the gallery entry in `backend/index.yaml` will point at a tag that does not exist.** The `dockerfile:` field must point at `./backend/Dockerfile.<lang>` matching the language bucket from step 1 (e.g. `Dockerfile.python`, `Dockerfile.golang`, `Dockerfile.rust`). The `tag-suffix` must match the `uri:` in the corresponding `backend/index.yaml` image entry exactly.
**Path-filter registration — REQUIRED for any new dockerfile suffix.** This is the single most common omission, because it has no effect on the PR that adds the backend (when no prior path filter could catch it anyway) — it only breaks the *next* PR that touches your backend's directory, which then gets zero CI jobs and looks broken for unrelated reasons. Edit `scripts/lib/backend-filter.mjs:inferBackendPath` and add a branch BEFORE the more-generic suffixes:
**`scripts/changed-backends.js` registration — REQUIRED for any new dockerfile suffix.** This is the single most common omission, because it has no effect on the PR that adds the backend (when no prior path filter could catch it anyway) — it only breaks the *next* PR that touches your backend's directory, which then gets zero CI jobs and looks broken for unrelated reasons. Edit `scripts/changed-backends.js:inferBackendPath` and add a branch BEFORE the more-generic suffixes:
```js
if (item.dockerfile.endsWith("<your-dockerfile-suffix>")) {
@@ -54,9 +54,7 @@ for (const e of m.include.filter(e => e.backend === '<your-backend>')) {
}"
```
A quick way to find the right insertion point: `grep -n 'item.dockerfile.endsWith' scripts/lib/backend-filter.mjs`.
If your backend consumes a *shared* build input that lives outside its own directory (a new script under `scripts/build/`, a new file copied into every image), add a rule to `SHARED_BUILD_INPUTS` in the same file — the per-backend prefix match cannot see those, and a miss ships your change to no image at all. See `scripts/lib/backend-filter_test.mjs` for the pattern; `make test-ci-scripts` runs it.
A quick way to find the right insertion point: `grep -n 'item.dockerfile.endsWith' scripts/changed-backends.js`.
**`bump_deps.yaml` registration — REQUIRED for any backend pinning an upstream commit.** If your backend's Makefile has a `*_VERSION?=<sha>` pin to a third-party repo, the daily auto-bump bot at `.github/workflows/bump_deps.yaml` won't notice it unless you register the backend in its matrix. The bot runs `.github/bump_deps.sh` which `grep`s for `^$VAR?=` in the Makefile you list — so the pin MUST live in the Makefile (not in a separate shell script). The bump for ds4 (#9761) had to walk this back because the original landed the pin in `prepare.sh`, which the bot can't see. Pattern (for `antirez/ds4`):
@@ -117,7 +115,7 @@ Wiring a backend into `includeDarwin:` is more than the matrix entry:
1. **`includeDarwin:` entry** — `tag-suffix: "-metal-darwin-arm64-<backend>"`, `build-type: "metal"`, `lang: "go"` for go+ggml backends; omit `build-type` for the bespoke C++ ones (llama-cpp / ds4 / privacy-filter). Match an existing entry of the same shape.
2. **`backend/index.yaml`** — add `metal:` to the backend's `capabilities` map (main and `-development`) and concrete `metal-<backend>` / `metal-<backend>-development` image entries pointing at the `-metal-darwin-arm64-<backend>` images.
3. **C/C++ backends only** — add an `inferBackendPathDarwin` case in `scripts/lib/backend-filter.mjs` returning `backend/cpp/<backend>/` (the generic fallthrough assumes `backend/<lang>/`, which is wrong for a C++ source tree driven with `lang: go`), and give `run.sh` a Darwin branch that exports `DYLD_LIBRARY_PATH` instead of `LD_LIBRARY_PATH`. If the build is bespoke (single `grpc-server` + dylib bundling), model it on `scripts/build/ds4-darwin.sh` and add a `backends/<backend>-darwin` make target plus a gated step in `.github/workflows/backend_build_darwin.yml`.
3. **C/C++ backends only** — add an `inferBackendPathDarwin` case in `scripts/changed-backends.js` returning `backend/cpp/<backend>/` (the generic fallthrough assumes `backend/<lang>/`, which is wrong for a C++ source tree driven with `lang: go`), and give `run.sh` a Darwin branch that exports `DYLD_LIBRARY_PATH` instead of `LD_LIBRARY_PATH`. If the build is bespoke (single `grpc-server` + dylib bundling), model it on `scripts/build/ds4-darwin.sh` and add a `backends/<backend>-darwin` make target plus a gated step in `.github/workflows/backend_build_darwin.yml`.
4. **C++ proto gotcha** — if the backend compiles the generated gRPC/protobuf in a separate CMake target (e.g. `hw_grpc_proto`), that target must link `protobuf::libprotobuf` + `gRPC::grpc++` so the Homebrew include dirs propagate; otherwise macOS fails with `google/protobuf/runtime_version.h not found` (Linux hides this because apt headers sit in `/usr/include`).
The CI path filter only builds a backend on a PR when a file under its directory changes, so a darwin-only YAML edit builds nothing — touch a file under `backend/<lang>/<backend>/` (a one-line comment is enough) in the same PR.
@@ -218,69 +216,6 @@ docker-build-backends: ... docker-build-<backend-name>
- If the backend is in `backend/python/<backend-name>/` but uses `.` as context in the workflow file, use `.` context
- Check similar backends to determine the correct context
## Engine preference for gallery model variants
A gallery entry can declare `variants`, alternative builds of the same weights,
and LocalAI picks one per host: it drops builds whose backend cannot run here or
that do not fit memory, then ranks the survivors by **engine preference
first, serving feature second, size third** (`SelectVariant` in
`core/gallery/resolve_variant.go`).
Ask whether your backend should outrank another one on some hardware. If it
should, add it to `engineNamePreferenceRules` in `pkg/system/capabilities.go`,
best engine first for that capability:
```go
{Nvidia, []string{engineVLLM, engineSGLang, engineLlamaCpp}},
+ {Nvidia, []string{engineVLLM, engineSGLang, engineMyEngine, engineLlamaCpp}},
```
That is the ENGINE NAME table, matched as a substring of a gallery entry's
`backend:` value. Two sibling tables in the same file speak different
vocabularies and are matched against different things:
| Table | Vocabulary | Matched against | Consumer |
|-------|-----------|-----------------|----------|
| `backendBuildTagPreferenceRules` | build tags (`cuda`, `rocm`, `metal`) | installed build directory names, as a substring | alias resolution in `ListSystemBackends` |
| `engineNamePreferenceRules` | engine names (`vllm`, `llama-cpp`, `mlx`) | a gallery entry's `backend:`, as a substring | gallery variant ranking |
| `servingFeaturePreferenceTokens` | serving features (`dflash`, `mtp`) | a gallery entry's `tags:`, compared whole and case-insensitively, and nothing else | gallery variant ranking, one rank below the engine |
**Putting a token in the wrong table matches nothing and does not error**: every
candidate scores equal and the next sort key decides, so the preference silently
stops existing. The block comment above all three tables spells the contract out.
The serving feature table is the odd one: it is not keyed by capability, because
no hardware prefers a plain build over an equivalent faster build of the same
weights. It reads a declared tag and nothing else. The entry name was the
original signal and is gone: a naming convention is not a contract, and names
are author-supplied free text where a short marker like `mtp` turns up inside
unrelated words or on weights whose entry enables nothing.
`overrides.options` was rejected for the mirror-image reason: `spec_type:` is
llama.cpp's config vocabulary, whereas a cross-backend ranking decision must
work the same for `ds4`'s `mtp_path:` and `sglang`'s `speculative_algorithm:`.
**If your backend can serve the same weights faster** (speculative decoding,
multi-token prediction), say so in the docs for its gallery entries so curators
tag them: the tagging rule and the per-backend evidence table live in
[adding-gallery-models.md](adding-gallery-models.md). A backend never needs to
appear in the token table itself; it ranks builds, not engines.
Leaving your backend out is a valid choice when no ordering can be justified for
it. It then ranks below every known engine and selection falls back to size,
which is the behaviour that predates preference.
**Leaving a whole capability out is not.** A missing row gives that host an
empty preference list, so size alone decides among everything that survives the
filters, and the filter will not save you: `IsBackendCompatible` derives hardware
support from the engine NAME, so `vllm` and `sglang` carry no darwin, cuda, rocm
or sycl token and are never dropped on a host with no GPU. That is why `default`
(no usable accelerator, including a GPU under the 4 GiB VRAM floor) and
`darwin-x86` both have rows putting `llama-cpp` first. Every capability
`getSystemCapabilities()` can return needs a row unless every engine really is
equally at home there. When you add one, enumerate the engines you are demoting
rather than relying on them falling through unmatched: unmatched engines all tie
with each other, so size decides among them.
## Documenting the backend (README + docs)
A backend is not "added" until it is discoverable. Update the user-facing docs:
@@ -308,7 +243,7 @@ After adding a new backend, verify:
- [ ] Backend directory structure is complete with all necessary files
- [ ] Build configurations added to `.github/backend-matrix.yml` for all desired platforms (per-arch entries with `platform-tag` for multi-arch; `builder-base-image` for llama-cpp / ik-llama-cpp / turboquant)
- [ ] **OS coverage considered**: added to `includeDarwin:` (macOS/Apple Silicon) if the backend can build there — with the `backend/index.yaml` `metal:` capability + `metal-<backend>` image entries, a `run.sh` Darwin/DYLD branch and `inferBackendPathDarwin` case (in `scripts/lib/backend-filter.mjs`) for C++ backends — or the PR explains why an OS is unsupported. Do not ship Linux-only by default.
- [ ] **OS coverage considered**: added to `includeDarwin:` (macOS/Apple Silicon) if the backend can build there — with the `backend/index.yaml` `metal:` capability + `metal-<backend>` image entries, a `run.sh` Darwin/DYLD branch and `inferBackendPathDarwin` case for C++ backends — or the PR explains why an OS is unsupported. Do not ship Linux-only by default.
- [ ] Meta definition added to `backend/index.yaml` in the `## metas` section
- [ ] Image entries added to `backend/index.yaml` for all build variants (latest + development)
- [ ] Tag suffixes match between workflow file and index.yaml
@@ -316,8 +251,6 @@ After adding a new backend, verify:
- [ ] No YAML syntax errors (check with linter)
- [ ] No Makefile syntax errors (check with linter)
- [ ] Follows the same pattern as similar backends (e.g., if it's a transcription backend, follow `faster-whisper` pattern)
- [ ] **`Load` validates its input and refuses models it can't serve.** When a model config has no explicit `backend:`, the model loader greedily probes *every* installed backend with the model's name and binds to the first `Load` that succeeds — an accept-anything `Load` will capture arbitrary LLMs (issue #9287). Backends that load a real artefact get this for free (the load fails); backends with no artefact must gate on the name: `opus` accepts only its own name (or none), `local-store` requires the `store.NamespacePrefix` namespace marker sent by `core/backend/stores.go`.
- [ ] **Gallery variant ranking considered**: if this backend should be preferred over another on some hardware, it is listed in `engineNamePreferenceRules` (NOT `backendBuildTagPreferenceRules`, NOT `servingFeaturePreferenceTokens`) in `pkg/system/capabilities.go`. A missing entry silently ranks it last and lets the next sort key decide.
- [ ] Documented: added to the category list in `docs/content/features/backends.md` (and any new endpoint/realtime capability documented under `docs/content/`)
- [ ] If it is an in-house native C/C++/GGML engine, added to the maintained-engines table in the top-level `README.md`

View File

@@ -91,108 +91,6 @@ To add a variant (e.g., different quantization), use YAML merge:
uri: huggingface://<gguf-org>/<gguf-repo>/<filename>-Q8_0.gguf
```
## Offering several builds of one model (`variants`)
When the same model is published in more than one quantization, or is also
servable by another engine, add each build as its own ordinary gallery entry and
then point one of them at the others with `variants`:
```yaml
- !!merge <<: *chatml
name: "nanbeige4.1-3b-q4"
# ... the usual urls / overrides / files for the Q4 build ...
variants:
- model: nanbeige4.1-3b-q8
```
Rules:
- The declaring entry is a **complete, normal entry**. It keeps its own
`files`/`overrides` and stays installable on every host and by every older
LocalAI release, which simply ignore `variants`.
- A variant references another gallery entry **by name**. That entry must exist
and must not declare `variants` of its own.
- **A referenced entry keeps its own gallery row by default.** It is hidden only
in the collapsed listing (`collapse_variants=true`, which the web UI requests
by default), where the declaring entry stands in for it. Searching there still
matches the referenced entry and answers with the entry declaring it, so
referencing an entry never makes it unfindable; turning the collapse off
returns it under its own name.
- **Order carries no meaning.** Do not try to encode a preference; write the
list in whatever order reads best.
- **A variant may be smaller than the declaring entry.** Offering a downgrade
for small hosts is a normal shape: the declaring entry's own build competes
like every other candidate, so a large host keeps the large build.
- **Do not describe hardware.** At install time LocalAI drops variants whose
backend cannot run on the host, then drops those that do not fit available
memory. The declaring entry's own build is exempt from both filters, so
selection always terminates on something installable. Sizes are measured live
from the weights and cached, so nothing has to be written down.
- **Engine preference outranks size.** Among the builds that survive the
filters, the host's preferred engine wins first and only then does the larger
footprint win. On NVIDIA a vLLM build beats a larger llama.cpp one; on Apple
silicon an MLX build beats a larger GGUF one; on a host with no preference for
either engine the larger build wins, since a bigger footprint is a higher
quality quantization of the same weights. Predict what a user gets by asking
which engine the host prefers before asking which build is biggest. The
per-capability order lives in `engineNamePreferenceRules`
(`pkg/system/capabilities.go`); see
[adding-backends.md](adding-backends.md) for how a backend gets into it.
- **Serving feature preference sits between engine and size.** Among builds on
an equally preferred engine, one that speculates or predicts several tokens
per step beats the plain build of the same weights, because it answers faster
for the same output: a `dflash` build beats an `mtp` one, and either beats a
plain build. The order lives in `servingFeaturePreferenceTokens`
(`pkg/system/capabilities.go`) and is matched against the entry's `tags:` and
**nothing else**: not the entry name, not `overrides.options`. See
[the tagging rule](#the-dflash--mtp-tagging-rule) below. Engine deliberately
outranks it: a serving feature makes the right engine faster, it does not make
a wrong engine right. Fit still outranks both, so a drafter pairing (strictly
larger than the plain build, since it ships a drafter alongside it) is dropped
on a host too small for it before this order is ever consulted.
- A variant is nothing but a name; there is no per-variant memory field. When
the measured size for a build is wrong, correct it on the referenced entry by
setting that entry's own `size:` (e.g. `size: "20GiB"`). The estimator prefers
a declared size over its own guesswork, so the fix applies everywhere the size
is shown or compared rather than only to variant selection.
Users can override the automatic choice with `variant` on `POST /models/apply`,
`local-ai models install --variant`, or the `install_model` MCP tool. See
`docs/content/features/model-gallery.md`.
The gallery lint specs live in `core/gallery`, so run that suite after adding a
`variants` list.
### The `dflash` / `mtp` tagging rule
**Tag an entry `dflash` or `mtp` when the entry actually configures that
feature. Variant ranking reads the tag and nothing else.**
Decide by looking at what the entry configures, in whatever vocabulary its
backend uses:
| Backend | Configures the feature when it declares |
|---------|------------------------------------------|
| `llama-cpp` | `overrides.options` contains `spec_type:draft-dflash` or `spec_type:draft-mtp` |
| `ds4` | `overrides.options` contains `mtp_path:` / `mtp_draft:` |
| `sglang` | the referenced `gallery/*.yaml` sets `speculative_algorithm:` |
That check is curation-time only. `spec_type` is llama.cpp's config vocabulary,
and a cross-backend ranking decision must not depend on one backend's option
syntax, which is precisely why the ranker reads the tag instead of the options.
Two mistakes the rule exists to prevent:
- **Weights that carry the heads are not an entry that enables them.** The
NVFP4 GGUF entries ship MTP-bearing weights but set only `use_jinja:true`, so
they enable no speculative decoding and must NOT be tagged. Tagging them wins
them the feature axis without being any faster.
- **A name is not a declaration.** An entry whose name spells `-mtp` while
configuring nothing gets no tag, and an entry that configures the feature is
tagged even when its name says nothing (`hy3`, `glm-5.2`). Ranking never reads
the name, so an untagged build that does enable the feature is simply ranked
as plain rather than promoted on a marker nobody meant.
## Available template configs
Look at existing `.yaml` files in `gallery/` to find the right prompt template for your model architecture:

View File

@@ -304,9 +304,7 @@ React pages that want to filter the ModelSelector by capability import this symb
### 4. `docs/content/` (user-facing documentation)
A new capability deserves its own page under `docs/content/features/`, plus cross-links from related features. See the pattern used by `face-recognition.md` / `object-detection.md`.
Announcing it is the release's job, not this page's: the capability gets covered in the release blog post under `website/content/blog/`. See [preparing-a-release.md](preparing-a-release.md). `docs/content/whats-new.md` is only a pointer at the blog and GitHub Releases, so there is nothing to add there.
A new capability deserves its own page under `docs/content/features/`, plus cross-links from related features and an entry in `docs/content/whats-new.md`. See the pattern used by `face-recognition.md` / `object-detection.md`.
## Path protection rules
@@ -336,7 +334,7 @@ When adding a new endpoint:
- [ ] Swagger block on the handler: `@Summary`, `@Tags`, `@Param`, `@Success`, `@Router`
- [ ] If new capability area (new swagger tag): entry in `instructionDefs` in `core/http/endpoints/localai/api_instructions.go` + test count bumped in `api_instructions_test.go`
- [ ] If new `FLAG_*` usecase flag: matching `CAP_*` symbol exported from `core/http/react-ui/src/utils/capabilities.js`
- [ ] `docs/content/features/<feature>.md` created; cross-links from related feature pages; capability covered in the release blog post (see [preparing-a-release.md](preparing-a-release.md))
- [ ] `docs/content/features/<feature>.md` created; cross-links from related feature pages; entry in `docs/content/whats-new.md`
**Quality**
- [ ] Error responses use `schema.ErrorResponse` format (or `echo.NewHTTPError` with a mapped gRPC status — see the `mapBackendError` helper in `core/http/endpoints/localai/images.go`)

View File

@@ -28,6 +28,7 @@ The core Go suites (`./pkg`, `./core`, plus the in-process integration suite `./
- **Build tags (`COVERAGE_TAGS`, passed via `GINKGO_TAGS`):** defaults to `debug auth`. The `auth` tag is required to compile the real (sqlite-backed) auth implementation and its ~150 `//go:build auth` tests — without it those files aren't built, the tests don't run, and the gate scores auth against a stub (~3.7% instead of ~38%). If you add new tag-gated tests, extend `COVERAGE_TAGS` or they won't count (and likely won't run in CI at all).
- `make test-coverage-check` — runs `test-coverage`, then `scripts/coverage-check.sh` fails the build if total coverage is **below** the committed baseline in `coverage-baseline.txt`. The Linux job in `.github/workflows/test.yml` runs this instead of `make test`.
- `make test-coverage-baseline` — regenerates and overwrites `coverage-baseline.txt` from the current run.
- `make install-hooks` — sets `core.hooksPath` to the versioned `.githooks/`, whose `pre-commit` runs checks scoped to what's staged: Go changes → `make lint` + `make test-coverage-check`; `core/http/react-ui/` changes → `make test-ui-coverage-check` (Playwright e2e + UI coverage gate). A commit touching neither is skipped; bypass with `git commit --no-verify`. The hook resolves golangci-lint's new-from base to `upstream/master``origin/master``master`, so it works from a fork clone where `origin/master` is stale (passed to `make lint` via `LINT_NEW_FROM`).
### React UI coverage
@@ -37,11 +38,12 @@ The React UI (`core/http/react-ui/`) has **no component/unit tests** — its onl
- **Browser:** the flake dev shell ships `chromium` and exports `PLAYWRIGHT_CHROMIUM_PATH`; `playwright.config.js` uses it via `launchOptions.executablePath`, and the Makefile skips `playwright install` when it's set. This avoids Playwright's downloaded browser, which can't resolve system libs (`libglib-2.0`, …) on NixOS. In CI (no `PLAYWRIGHT_CHROMIUM_PATH`) the Makefile falls back to `playwright install --with-deps chromium`.
- The app is a React SPA, so coverage accumulates across in-app navigation within a test; a full `page.goto`/reload resets it.
- `.nycrc.json` uses `all: true`, so **every `src/**` file is in the report**, including 0%-coverage ones — that's how you spot features with no test at all (sort the HTML report or `coverage-summary.json` by line% ascending).
- **UI coverage gate:** `make test-ui-coverage-check` runs the suite then `scripts/ui-coverage-check.sh`, failing if total line coverage drops more than `UI_COVERAGE_TOLERANCE` below `core/http/react-ui/coverage-baseline.txt`. `make test-ui-coverage-baseline` regenerates the baseline. Runs in CI (`tests-ui-e2e.yml`).
- **UI coverage gate:** `make test-ui-coverage-check` runs the suite then `scripts/ui-coverage-check.sh`, failing if total line coverage drops more than `UI_COVERAGE_TOLERANCE` below `core/http/react-ui/coverage-baseline.txt`. `make test-ui-coverage-baseline` regenerates the baseline. Runs in CI (`tests-ui-e2e.yml`) and pre-commit on `core/http/react-ui/` changes.
- **Why it has a tolerance (unlike the strict Go gate):** UI e2e coverage is *non-deterministic*. Specs that assert on state and end while async/lazy render work is still in flight collect those lines only when the render beats the coverage teardown — so the total drifts with machine speed/load (a fast local box reads higher than a slow CI runner), diffusely across many specs. The tolerance absorbs that drift, so set the baseline *below* the slow-CI floor, never to a fast-local `make test-ui-coverage-baseline` number, or CI flaps.
- **Raising coverage is cheap:** a *render-smoke* spec (navigate to a route, assert its header renders) mounts a lazy page and runs its full render + initial effects, capturing most of its lines in a few lines of test — see `e2e/page-render-smoke.spec.js`. Auth is disabled in the test server (`isAdmin=true`), so `RequireAdmin`/`RequireFeature` routes render without a mock. The most *deterministic* win is removing a race: make a spec `await` a rendered element before ending (see `e2e/agents.spec.js` → AgentCreate) so its lines count every run.
Rules (both gates):
- **Don't weaken the gate:** never hand-lower a baseline or widen a tolerance to turn a red gate green. The ratchet only moves up.
- **Install the hooks:** `make install-hooks` once per clone so lint + coverage run pre-commit. Don't lean on CI for what the hook catches.
- **Don't work around the gate:** never `git commit --no-verify`, and never hand-lower a baseline or widen a tolerance to turn a red gate green. The ratchet only moves up.
- If a change drops coverage, **add tests** (sort `coverage-summary.json` by line% ascending to find untested code) rather than editing the baseline. When coverage legitimately rises, commit the regenerated baseline (`make test-coverage-baseline` / `test-ui-coverage-baseline`).
- The Go gate is **strict — no tolerance**; `covermode=atomic` keeps it deterministic. The UI gate keeps a small tolerance only because its e2e coverage isn't.

View File

@@ -114,97 +114,8 @@ Both `backend.yml` (push) and `backend_pr.yml` (PR) generate their matrix dynami
- **Tag pushes**: `FORCE_ALL=true` is set from the workflow side (`startsWith(github.ref, 'refs/tags/')`) — releases rebuild every backend regardless of diff.
- **Schedule / `workflow_dispatch`**: no `event.before`, falls through to "run everything" automatically.
### Shared build inputs
The per-backend prefix match only sees files under a backend's own directory, so a change to shared build infrastructure would rebuild *nothing* — an empty matrix, every job green, and the change reaching no image. That silently un-shipped PR #10946 (a partial-cuDNN packaging fix in `scripts/build/package-gpu-libs.sh`), which merged 1h48m after the weekly cron and so sat unbuilt for a week.
`SHARED_BUILD_INPUTS` in `scripts/lib/backend-filter.mjs` closes that hole. Each rule maps a shared path to the narrowest set of matrix entries it can honestly invalidate, since a full matrix is 417 Linux + 56 Darwin builds:
| Changed path | Rebuilds |
|---|---|
| `backend/backend.proto` | nothing if the edit is additive-only, otherwise everything (see below) |
| `backend/Dockerfile.<x>` | the Linux entries whose `dockerfile:` names it |
| `backend/python/common/` | Python, Linux + Darwin |
| `scripts/build/package-gpu-libs.sh` | every Linux entry (Python, Go and C++ all run it) |
| `scripts/build/<lang>-darwin.sh` | the Darwin entries that build target routes to |
| `.github/workflows/backend_build[_darwin].yml` | everything on that OS |
| anything else under `scripts/build/` (except `*_test.sh`) | everything — conservative default for unclassified packaging inputs |
Deliberately excluded: `backend/index.yaml` (gallery metadata, never enters an image), `.github/backend-matrix.yml` (adding a backend would rebuild all of them), `backend/Dockerfile.base-grpc-builder` (owned by `base-images.yml`), and the root `Makefile` (touched in ~11% of commits, and its backend-relevant edits arrive alongside the backend directory anyway). `make test-ci-scripts` pins all of this.
#### `backend/backend.proto` is content-filtered, not path-filtered
Every language consumes the proto, so a path rule for it can only ever say "rebuild all 473 images". It changes in ~1.3% of commits, and that was enough to make it the single largest CI cost driver in the repo: on 2026-07-29 four runs totalling 935 queued jobs traced to nothing but a proto edit, one of which (#11158) was a six-line diff adding `bool cache_prompt = 8;`.
An additive proto edit cannot change how a backend that never references the new symbol behaves, so `filterMatrix()` suppresses the rule for one. `changed-backends.js` fetches `backend/backend.proto` at the base revision (same contents-API pattern as `.github/backend-matrix.yml`) and hands both texts to `protoChangeIsAdditive()`, which compares them structurally rather than textually:
- **Additive, rebuilds nothing**: a new field with an unused number, a new message, a new enum value, a new RPC. Comment, whitespace and ordering changes also land here.
- **Breaking, rebuilds everything**: a removed, renumbered, retyped or renamed field, a dropped RPC, a changed `option` or `package`. So does an unresolvable base revision, matching the run-all posture used for a truncated diff.
Checked against every proto commit in the preceding six months, all nine resolvable ones classify as additive. Note the tradeoff this accepts: generated stubs do change for an additive edit, so image bytes would differ on a rebuild even though behavior does not. That is the same standard already applied when the filter declines to rebuild on unrelated `pkg/` changes, and the weekly cron remains the backstop.
The Sunday 06:00 UTC cron on `backend.yml` exists specifically because path filtering can leave Python backends frozen on stale wheels. `DEPS_REFRESH` (below) only fires when the build actually runs, so an untouched Python backend would never re-resolve its unpinned deps. The weekly cron is the safety net.
## Content-blind PRs skip the workflows that cannot see them
`backend_pr.yml` and `test-extra.yml` filter themselves (matrix generation and a `detect-changes` job), so a gallery-only or docs-only PR costs them about one job each. The Go and image workflows had no filter of any kind, so a one-line `gallery/index.yaml` edit queued 20 jobs, and a docs-only PR queued the same.
This is worth more than it looks. Measured over the week to 2026-07-30, **97% of CI wall-clock is queueing, 3% is execution** (median queue ~5h against a 4-20min median job). Cutting job count is therefore the only lever that shortens feedback time; making individual jobs faster moves 3%.
The volume is real: 13 gallery-only PRs merged that week with 10 open at once, and 78 of the 137 PRs opened were bot-generated.
`paths-ignore` on the PR trigger of `image-pr.yml` (7 jobs), `build-test.yaml` (3), `lint.yml` (2) and `tests-e2e.yml` (1) drops 13 of those 20. The excluded set:
| Path | Why no image or Go build can see it |
|---|---|
| `gallery/**` | Model-gallery metadata, parsed at runtime, never copied into an image |
| `docs/**`, `examples/**`, `**/*.md` | Never enter an image or a binary. `lint.yml` already excluded these before gallery was added |
### `backend/{cpp,go,python}/**` on `image-pr.yml` and `build-test.yaml` only
Version-pin bumps dominate PR volume: 48 `update/*` PRs in the week to 2026-07-30, from 16 pins, each a two-line diff. Most edit nothing but one `backend/*/<name>/Makefile`.
Neither of those two workflows can observe such a change. `make build` is `go build ./cmd/local-ai`, GoReleaser builds the same plus `./cmd/launcher`, and the core image's final stage ships only `entrypoint.sh`, `healthcheck.sh` and that binary. The per-backend trees are copied into the builder but nothing in them reaches the output.
What still triggers a full run, because none of it lives under those prefixes:
- `backend/backend.proto` — feeds `protogen-go`, so it does change the binary.
- `go.mod` / `go.sum` — the `go mod tidy` before-hook.
- `backend/Dockerfile.*` and anything else directly under `backend/`.
Deliberately **not** applied to:
| Workflow | Why it must keep seeing `backend/**` |
|---|---|
| `test.yml` | `TEST_PATHS` explicitly includes `./backend/go/cloud-proxy/...`, `./backend/go/local-store/...` and `./backend/go/valkey-store/...` |
| `lint.yml` | `.golangci.yml` carries `backend/`-scoped rules, so golangci-lint covers that tree |
| `tests-e2e.yml` | The e2e suite drives real backends over gRPC |
| `backend_pr.yml` | This is the workflow whose entire job is to rebuild the changed backend |
What still runs, and why it has to:
| Workflow | Why it keeps running |
|---|---|
| `test.yml` (`tests`) | `core/gallery/variants_lint_test.go` reads the real `gallery/index.yaml` and asserts the index invariants (no duplicate entry names, no build claimed by two parents). This is the only schema-level check the gallery has. |
| `yaml-check.yml` (`Yamllint`) | Lints `gallery/` for syntax. |
| `backend_pr.yml`, `test-extra.yml` | Already self-filtering; they stop after the detect step. |
Two properties this relies on:
- `paths-ignore` skips a run only when **every** changed file matches, so a PR touching the gallery *and* Go code still runs everything. That is what makes the exclusion safe rather than a hole.
- `master` carries no branch protection and no rulesets, so a skipped workflow reports no status and nothing waits on it. If required status checks are ever introduced, these four entries must be excluded from the required set or PRs will hang on "Expected — Waiting for status to be reported".
### `image.yml` on master push is gated too, by a job rather than a path filter
The same reasoning applies to master pushes, and the volume is larger there: on 2026-07-30, **12 of the 23 queued `image.yml` runs** were commits like "add 1 new model to gallery" or a docs fix, each rebuilding all 18 container images.
`image.yml` now has a `changes` job that decides once whether the push can affect any image; the other 11 jobs carry `needs: changes` plus an `if:` on its output. Verified against the shipped `Dockerfile`: the final stage copies only `entrypoint.sh`, `healthcheck.sh` and the `local-ai` binary, there is no `go:embed` of `gallery/` or `docs/`, and the gallery is fetched at runtime from `github:mudler/LocalAI/gallery/index.yaml@master`. A gallery-only commit therefore produces byte-identical images, and the gallery change reaches users through GitHub immediately whether or not an image is rebuilt.
Two properties to preserve if you touch it:
- **It is a job gate, not `paths-ignore`.** `paths-ignore` on `push` also applies to tag pushes, and a tag created on an existing commit carries an empty commits list, which would silently skip the release image build. The gate short-circuits to "build" for `refs/tags/*`, and for any push whose base commit is missing, zero, or unresolvable.
- **The merge jobs must name the gate explicitly.** They use `if: ${{ !cancelled() && ... }}`, and `!cancelled()` is true when a dependency is *skipped*, so without the extra condition they would run and try to merge manifest lists for images that were never built.
## The `DEPS_REFRESH` cache-buster (Python backends)
Every Python backend goes through the shared `backend/Dockerfile.python`, which ends with:
@@ -240,38 +151,15 @@ RUN --mount=type=cache,target=/root/.ccache,id=<backend>-ccache-${TARGETARCH}-${
bash /usr/local/sbin/compile.sh
```
The compile script exports `CMAKE_C/CXX/CUDA_COMPILER_LAUNCHER=ccache` so CMake threads ccache through gcc/g++/nvcc. Cache scope is per `(TARGETARCH, BUILD_TYPE)` so e.g. cublas-12 doesn't share with cublas-13 (their CUDA headers differ; cross-pollination would just be cache misses anyway).
The compile script exports `CMAKE_C/CXX/CUDA_COMPILER_LAUNCHER=ccache` so CMake threads ccache through gcc/g++/nvcc. `cache-to: type=registry,mode=max` exports the cache mount data into the registry cache, so subsequent builds restore it.
### ⚠️ This ccache does nothing in CI today
This section previously claimed that `cache-to: type=registry,mode=max` "exports the cache mount data into the registry cache, so subsequent builds restore it". **That is not true.** BuildKit does not export the contents of a `--mount=type=cache` to a registry cache export. A cache mount lives in the builder's local state, and every CI job gets a fresh runner with a fresh builder, so `/root/.ccache` starts empty on every single build.
Measured on 2026-07-30 from the `ccache -s` output the compile script already prints (it runs `ccache -z` first, so the numbers are per-build):
| Job | Commit touched | Build time | ccache |
|---|---|---|---|
| 89766266951 (llama-cpp, cublas 13) | `backend/go/magpie-tts-cpp/Makefile` only | 6369s | **0 / 889 hits**, and 0 / 1778 |
| 89766267281 (llama-cpp, hipblas) | same commit | 8160s | **0 / 537 hits** |
| 90210828110 (llama-cpp, cublas 12.8) | `LLAMA_VERSION` bump | 5673s | **0 / 813 hits** |
The first two are the decisive control: commit `90355cd44` changed exactly one file, `backend/go/magpie-tts-cpp/Makefile`, nowhere near llama.cpp. The engine source was byte-identical to the previous build, which is precisely the case this section says ccache should serve, and the hit rate was still **0.00%**. A cache that was being restored but merely matching poorly would show partial hits; 0-of-N is the signature of an empty cache.
So the paragraph above about `LLAMA_VERSION` bumps reusing previous `.o` files describes an intended design that is not in effect. `Dockerfile.{llama-cpp,ik-llama-cpp,turboquant,bonsai,ds4,privacy-filter}` pay the ccache wrapper overhead and get nothing back. Multi-hour C++ rebuilds are recompiling identical translation units from scratch.
**Do not "fix" this by adding cache mounts to more Dockerfiles.** Wiring the same mount into `Dockerfile.golang` (215 of the 434 matrix entries) was measured locally at 18% faster on a rebuild after a source edit, with a 71.5% ccache hit rate — but only because the local test reused one builder across both builds. In CI it would be a no-op for exactly the reason above.
Making this actually work needs the cache to live outside the builder. The options, none of them free:
- **ccache `remote_storage`** (ccache ≥ 4.4, HTTP or Redis backend) or **sccache** with an S3/GCS/Redis backend. Genuinely works across runners; needs a cache service to point at. quay.io is a registry, not a blob store, so the existing infra does not cover it.
- **Round-trip the cache dir through `actions/cache` on the runner**: restore it, pass it in, and export it back out via a build stage output. No external infra, but clunky, and the repo already sits at GitHub's 10 GB cache ceiling while the llama-cpp ccache alone is capped at 5 GB.
Until one of those lands, treat C++ backend builds as always-cold and spend the effort on not running them instead (path filtering, see above).
On a `LLAMA_VERSION` bump, most translation units are byte-identical to the previous version's preprocessed source — ccache returns the previous `.o` and skips the real compile. Same for LocalAI source changes that don't actually touch llama.cpp's CMake inputs. Cache scope is per `(TARGETARCH, BUILD_TYPE)` so e.g. cublas-12 doesn't share with cublas-13 (their CUDA headers differ; cross-pollination would just be cache misses anyway).
## Composite actions
Two composite actions handle runner-side prep:
- **`.github/actions/free-disk-space/action.yml`** — wraps `jlumbroso/free-disk-space@main` plus an explicit apt purge of dotnet/android/ghc/mono/etc. Reclaims ~610 GB on `ubuntu-latest`. No-op on self-hosted runners. Used by `backend_build.yml`, `image_build.yml` and `base-images.yml` — the jobs that actually build images. Deliberately **not** used by `test.yml`, which runs no buildx step.
- **`.github/actions/free-disk-space/action.yml`** — wraps `jlumbroso/free-disk-space@main` plus an explicit apt purge of dotnet/android/ghc/mono/etc. Reclaims ~610 GB on `ubuntu-latest`. No-op on self-hosted runners. Used by `backend_build.yml`, `image_build.yml`, `test.yml`, `tests-aio.yml`, etc.
- **`.github/actions/setup-build-disk/action.yml`** — relocates Docker's data-root to `/mnt` on hosted X64 runners. GHA hosted `ubuntu-latest` ships ~75 GB of unused space at `/mnt`; combined with the free-disk-space cleanup this gives ~100 GB working space — enough for ROCm dev image + vLLM torch install + flash-attn intermediate layers. No-op on self-hosted and on non-X64 hosted runners. Used by `backend_build.yml`, `image_build.yml`, `base-images.yml`.
Both actions run before any docker buildx step.
@@ -312,20 +200,10 @@ Eviction is rarely needed in normal operation — `DEPS_REFRESH` handles weekly
## What the cache does **not** cover
- The `free-disk-space` and `setup-build-disk` composite actions run on every job — these reclaim runner-state, not Docker layers, so BuildKit caches don't apply. `test.yml` deliberately does **not** use `free-disk-space`: it runs no buildx step, and the multi-GB fixture downloads that once justified it left `make test` in the test-suite reorg.
- The `free-disk-space` and `setup-build-disk` composite actions run on every job — these reclaim runner-state, not Docker layers, so BuildKit caches don't apply.
- Intermediate artifacts of `Build (PR)` are not pushed anywhere — PRs only build for verification.
- Darwin builds (see below) — macOS runners have no Docker daemon, so the registry-backed BuildKit cache cannot apply.
### The Linux Go workflows set `cache: false` on purpose
`test.yml`, `lint.yml`, `tests-e2e.yml` and friends pass `cache: false` to `actions/setup-go@v5`, unlike the darwin jobs. This looks like an oversight and is not.
Measured over the week to 2026-07-30, the `Set up Go` step has a **median of 11 seconds** on these runners. There is essentially nothing to win: the module download is not where the time goes. The expensive steps are compilation and test execution (`Test (with coverage gate)` at ~18.6min, `Test Backend E2E` at ~14.5min), and Go's build cache would have to survive across runners to touch those.
Enabling it also has a real cost. GitHub caps Actions cache at **10 GB per repo and the repo already sits at that ceiling** (31 entries), so every `setup-go` entry written by a branch with a distinct `go.sum` (222-375 MB on Linux, up to 1.4 GB on macOS) evicts something else. See the darwin cache budget below.
Before re-enabling this, measure `Set up Go` again and confirm it has actually become slow. If room is needed in the 10 GB budget, the cheapest evictions are the `docker.io--tonistiigi--binfmt` entries (~30 MB each, trivially re-fetched).
## Darwin native caches
`backend_build_darwin.yml` runs natively on `macOS-14` GitHub-hosted runners — there is no Docker, no BuildKit, no cross-job registry cache. Instead, the reusable workflow uses `actions/cache@v4` for four native caches that mirror the spirit of the Linux cache (warm by default, weekly refresh for unpinned Python deps, PRs read-only).
@@ -359,26 +237,6 @@ GitHub Actions caches are limited to 10 GB per repo. Steady-state worst case: ~8
One residual self-hosted reference remains in `test-extra.yml` (`tests-vibevoice-cpp-grpc-transcription` uses `bigger-runner` for the 30s JFK-decode timeout headroom). That's a separate concern.
### Small always-on jobs routed to `arc-runner-set`
The hosted pool is shared across the whole *account*, not per repo, so a burst in one repo starves the others. On 2026-07-31 it went to **zero scheduled jobs for 35 consecutive minutes** with 39 jobs queued, while `arc-runner-set` completed 12 jobs without interruption over the same window. Actions was healthy globally at the time (other public repos were scheduling normally), so this is an account-level throttle, not an outage.
`gh-pages.yml` (`build` + `deploy`) is therefore routed to `arc-runner-set` when `github.repository == 'mudler/LocalAI'`. It needs no fork-safety clause because it only triggers on push-to-master and `workflow_dispatch`, so it never executes pull-request code. The repository guard keeps forks (which have no such runner label) from queueing forever. It fetches its own toolchains via `setup-go` / `actions-hugo` and uses no `sudo`/`apt`.
#### What the `arc-runner-set` image actually contains
Measured 2026-07-31 on run `30637392862` by a preflight step, not assumed:
| present | **absent** |
|---|---|
| `git`, `curl`, `unzip`, `tar`, `ldd`, `python3` | **`make`**, **`gcc`** |
That is why `lint.yml` is **not** on the self-hosted pool. Both of its jobs were routed there and both failed in one second: `golangci-lint` needs `make` (for `make protogen-go`, itself needing `curl`+`unzip` to fetch protoc, and for `make lint`), and `build-scripts` additionally needs a C toolchain because the packaging-script tests compile a throwaway binary and inspect it with `ldd`. Both jobs are back on `ubuntu-latest`.
The preflight steps were deliberately left in place. They cost about a second on the hosted pool and mean that whenever the runner image gains `make` + `gcc`, re-routing is one `runs-on:` line per job and any remaining gap reports itself by name rather than as an opaque mid-build failure.
Note for any future re-route: `lint.yml` also triggers on `pull_request`, and a fork PR runs untrusted contributor code. That must never reach a persistent self-hosted runner, so any re-route has to stay push-only, e.g. `${{ (github.event_name == 'push' && github.repository == 'mudler/LocalAI') && 'arc-runner-set' || 'ubuntu-latest' }}`.
## Touching the cache pipeline
When changing `image_build.yml`, `backend_build.yml`, any of the `backend/Dockerfile.*` files, `Dockerfile.base-grpc-builder`, `.docker/install-base-deps.sh`, `.docker/<backend>-compile.sh`, or `scripts/changed-backends.js`:

View File

@@ -65,42 +65,7 @@ This is enforced by `forbidigo` (see `.golangci.yml`): `http.DefaultClient` and
The project documentation is located in `docs/content`. When adding new features or changing existing functionality, it is crucial to update the documentation to reflect these changes. This helps users understand how to use the new capabilities and ensures the documentation stays relevant.
- **Docs-with-code rule**: When you change user-facing behavior (API endpoints, CLI flags, config keys, or features), update the corresponding page under `docs/content/` in the SAME change, not as a follow-up. A user-facing change without a matching docs update is incomplete. The PR template carries a checklist item for this.
- **Feature Documentation**: If you add a new feature (like a new backend or API endpoint), create a new markdown file in `docs/content/features/` explaining what it is, how to configure it, and how to use it.
- **Configuration**: If you modify configuration options, update the relevant sections in `docs/content/`.
- **Examples**: providing concrete examples (like YAML configuration blocks) is highly encouraged to help users get started quickly.
- **Shortcodes**: Use `{{% notice note %}}`, `{{% notice tip %}}`, or `{{% notice warning %}}` for callout boxes. Do **not** use `{{% alert %}}` — that shortcode does not exist in this project's Hugo theme and will break the docs build.
## React UI styling
The React UI ships a design system in `core/http/react-ui/src/App.css`: design
tokens, form grids, data tables, stat cards, callouts, plus a small semantic
primitive layer (`.stack`, `.hstack`, `.text-note`, `.text-meta`, `.tone-*`,
`.icon-chip`). **Use it instead of `style={{ ... }}`.** Inline styles are a
spacing or colour decision made in one file, so no two pages end up sharing a
rhythm, which is the main reason the app reads as unfinished.
Inline styles are still correct for values that are genuinely computed at
runtime: `width: ${pct}%`, a data-driven `background`, a tooltip's coordinates.
Everything else belongs in a class.
A ratchet enforces this:
```sh
cd core/http/react-ui
npm run lint:inline-styles # fails if the count went UP
npm run lint:inline-styles:report # per-file counts, worst first
npm run lint:inline-styles:write # refresh the baseline after converting
```
The gate also fails on **duplicate `className` attributes on one element**. JSX
keeps the last and silently drops the first, so `<i className={icon}
className="text-xs" />` loses its icon while passing lint, the build and the e2e
suite. Converting a style to a class on an element that already has a
`className` is the usual way to introduce one; merge them into a single
attribute instead.
When converting a page, prefer naming the shapes it actually has
(`.p2p-diagram`, `.usage-tile`) over adding more utilities, and check whether an
existing block already covers it: the Nodes page reuses the P2P setup shapes,
and Model Editor reuses the Settings section rail.

View File

@@ -0,0 +1,143 @@
# llama-cpp-localai-paged Backend (paged attention + Blackwell NVFP4 decode)
`llama-cpp-localai-paged` is LocalAI's **CUDA-only** paged-attention variant of the
llama.cpp backend. It targets high-concurrency decode for the Qwen3.6 hybrid
gated-DeltaNet (SSM) models on Blackwell (GB10 / DGX Spark). It reuses the stock
`llama-cpp` backend's sources and applies a vendored patch series on top at build
time. It is **not** a fork: a source-only `*.patch` stack plus one canonical doc.
**Canonical reference:** `backend/cpp/llama-cpp-localai-paged/README.md`
(architecture, the patch series 0001-0030, benchmarks, dev notes, generality,
pin/canary policy). Read it for any technical detail; this guide is the maintenance
how-to.
## Where things live
- `backend/cpp/llama-cpp-localai-paged/Makefile` - the thin wrapper. It copies the
stock `backend/cpp/llama-cpp/` build infra into a build dir, clones llama.cpp at
this backend's **own** pin (`LLAMA_VERSION`), applies the paged series via the
`apply-paged-patches` define (strict `git apply`), then builds `grpc-server`.
- `backend/cpp/llama-cpp-localai-paged/patches/paged/` - the source-only `.patch`
series (0001-0030), nothing else.
- `backend/cpp/llama-cpp-localai-paged/README.md` - the canonical doc. The
operational docs (`PAGED_BITEXACT_NOTE.md`, `UPSTREAM_LAYER2_SCOPE.md`) and
dev artifacts live in
`backend/cpp/llama-cpp-localai-paged/docs/`.
- `backend/Dockerfile.llama-cpp-localai-paged`, `.docker/llama-cpp-localai-paged-compile.sh`
- the CUDA build entry points.
- `backend/cpp/llama-cpp/` - the **stock** backend, pure upstream. It carries no
paged patches.
## Invariants (do not break these)
- **Stock stays pure.** The paged patches live ONLY in this backend. Never add a
`patches/paged/` dir or `LLAMA_PAGED` logic to `backend/cpp/llama-cpp/`.
- **CUDA-only.** Ship cublas/cuda targets only. Off-CUDA the fusions are gated off
(patch 0030) and NVFP4 falls back to dequant, so the backend is neutral-to-
slightly-negative there - non-CUDA users use the stock `llama-cpp`. Do not add
cpu/vulkan/sycl/metal rows for this backend in `.github/backend-matrix.yml`.
(Those builds also fail to link `grpc-server` on darwin/arm64 against upstream
`stream_*` server symbols - another reason it is CUDA-only.)
- **Source-only patches.** A `.patch` may touch only llama.cpp source - never a
dev doc or `*.md`. Strict `git apply` on a clean checkout must reach exit 0. (A
stray `SSM_DECODE_FIX_RESULTS.md` hunk in patch 0019 once broke the CI build.)
- **Bit-exact by default.** Every shipped patch is byte-identical to the f32
baseline. (The one opt-in precision trade, `ssm_bf16_tau` / patch 0026, was
DROPPED: it went flat once the decode fusions landed - forcing all gated-DeltaNet
heads to bf16 gave 780.6 vs 780.0 t/s, zero benefit - so the series is now
bit-exact end to end. Do not reintroduce a per-head SSM-precision lever; see the
rejected-levers note in the backend README section 5.)
## Fork-first workflow (MANDATORY)
The fork **`mudler/llama.cpp` branch `localai-paged`** is the CANONICAL source
of truth for ALL paged-backend kernel and patch work. The vendored
`patches/paged/*.patch` series is a **derivative**: the fork is the source, the
series is a generated mirror of it.
**Always update the fork FIRST, in this exact order:**
1. **Commit the change on the `localai-paged` branch and push it.** Every
kernel or patch change lands as a fork commit first.
2. **Then regenerate the LocalAI series from the fork** via `git format-patch`
(one patch per fork commit, source-only) into
`backend/cpp/llama-cpp-localai-paged/patches/paged/`, so the series stays a
**1:1, drift-free mirror** of the branch.
Hard rules, no exceptions:
- **NEVER edit the `patches/paged/*.patch` files directly.** They are generated
output, not source.
- **NEVER add a patch to the series that has no corresponding fork-branch
commit.** Every `.patch` must be the `git format-patch` of a real commit on
`localai-paged`.
- The fork branch is **where the build and the per-path bit-exact md5 gate
actually run**, so it is the **only** place a change is truly validated. A
patch living only in the LocalAI series has never been built or gated.
Verify the mirror by tree hash: applying the full on-disk series on the pin
must reproduce the fork branch tree byte-for-byte. (The patch maintenance
detail is in `backend/cpp/llama-cpp-localai-paged/docs/PATCH_MAINTENANCE.md`;
the hard-gate is section 2.5 of `docs/PARITY_HANDOFF.md`.)
## Maintaining the pin against new llama.cpp
The pin (`LLAMA_VERSION` in the wrapper Makefile) is advanced ONLY by the manual
pin-sync. It is deliberately **excluded from the nightly auto-bumper**
(`bump_deps.yaml`): a naive bump would shift the tree out from under the patches
and break `git apply` at build time.
1. **The canary tells you when to sync.** `.github/workflows/llama-cpp-paged-canary.yml`
runs weekly: it applies + builds the series against the latest upstream tip and
goes **red** when upstream drifts past the patches. Canary red -> run a pin-sync.
2. **The pin-sync** (recorded in the README section 7 and git history): rebase the series onto the new
tip (resolve conflicts; re-export **source-only** with a pathspec like
`-- src/ ggml/ common/ include/ tools/ tests/ cmake/`), rebuild on a CUDA box,
pass the bit-exact gate on **every** path + `test-backend-ops`, **and confirm
the full grpc-server build/link is green on CI**, then bump `LLAMA_VERSION`.
**Hard constraint: keep the pin == the stock `llama-cpp` pin.** `grpc-server.cpp`
is shared with the stock backend and tracks the stock pin. A paged pin that
diverges PAST an upstream server-API refactor breaks the grpc-server LINK even
when the patches are byte-for-byte bit-exact - the bit-exact gate alone does NOT
catch it. The `c299a92c` bump did exactly this (patches applied + greedy-md5
bit-exact, but `grpc-server.cpp` failed to link with undefined `stream_*` server
helpers the refactor pulled into its headers), so it was reverted to `9d5d882d`.
A pin bump is shippable only once the full CI grpc-server build is green, which in
practice means moving in lockstep with the stock pin (or vendoring a
pin-matched grpc-server.cpp, which we deliberately do not, to keep stock pure).
## The bit-exact gate (run for every change)
- greedy md5: `llama-completion -m MODEL -ngl 99 -fa on -p "The capital of France is" -n 48 --temp 0 --seed 1 </dev/null | md5sum`,
paged paths prefixed `LLAMA_KV_PAGED=1` (+ `LLAMA_MOE_FORCE_GRAPHS=1` for paged
MoE). Must match the recorded baseline. Redirect stdin from `/dev/null` or
`llama-completion` hangs in conversation mode.
- `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.** The paged-MoE md5 differs from the non-paged md5 - a
benign, KL-validated FP-accumulation-order difference (see `docs/PAGED_BITEXACT_NOTE.md`).
Compare a paged-MoE change to the **paged** reference, not the non-paged one.
## Encapsulating your work
- When you change a kernel, follow the **Fork-first workflow** above: commit and
push on the `localai-paged` branch first, then regenerate the `.patch`
(source-only) from the fork so this worktree mirrors the branch byte-for-byte.
Commit with sign-off.
- New optimization -> next patch number (gaps 0005/0027 are intentional). Update
the README's patch table and dev notes - keep the README the single doc; do not
scatter `*_RESULTS.md` files.
- Record rejected/flat levers in the README too (they stop the next person from
re-running dead ends).
## Follow-ups (Metal / SYCL / Vulkan)
The decode fusions are implemented for **CUDA + CPU only**. The base
gated-DeltaNet + SSM_CONV ops already exist upstream on Metal, SYCL, and Vulkan,
so the models **run** there via the non-fused path - what is missing is the
fusion speedup. Porting it (strictly mirroring the CUDA kernels, since we have no
Metal/SYCL/Vulkan hardware to test on here) is scoped in `docs/UPSTREAM_LAYER2_SCOPE.md`
(recommended order: Metal, then SYCL, then Vulkan; ops-first upstream PR, then one
PR per backend, each gated by `test-backend-ops` on the target hardware). The
methodology for that work is in [.agents/vllm-parity-methodology.md](vllm-parity-methodology.md).

View File

@@ -1,26 +0,0 @@
# Preparing a Release
A release is not finished when the tag is pushed. The GitHub release, the blog post and the demo clips ship together, because the changelog says what moved and the post and the clips are what make anyone care.
## What a release must include
1. **Labels on the merged PRs.** GitHub generates the raw notes from PR labels, so label first, generate second. Wrong labels mean a miscategorised changelog that has to be edited by hand.
2. **`RELEASE_NOTES_vX.Y.Z.md`** at the repository root, in the house style: what changed, why it matters, PR numbers so people can read the diffs.
3. **A blog post under `website/content/blog/`.** One post per release, front matter with `title`, `date`, `author`, `category: "Release"`, `tags`, `summary` and `extracss: ["blog.css"]`. Cover the two or three changes that alter what a user does day to day, not the whole changelog, and link the PR numbers. See `website/content/blog/what-landed-in-localai-4-8.md` for the shape.
4. **Demo clips for the notable features.** Anything visible (a new backend, a UI change, a new endpoint, a measured speedup) gets a short screen recording. Put the file in `website/static/media/`, reference it from the blog post, and reuse it on the marketing pages where it fits.
A release without a post and without clips is incomplete, in the same way a user-facing code change without a docs update is incomplete.
## Clip conventions
- MP4, H.264, no audio track unless the feature is about audio. Keep them short (10 to 30 seconds) and loopable.
- Record the real thing. A clip from the engine's own benchmark suite or a real session, never a mockup.
- Where the change is a speedup, record both sides on the same machine on the same input, so the comparison is honest.
- Name the file after the feature, not the release (`vllm-race.mp4`, not `v4-8-demo.mp4`), so it stays reusable once the release is old.
- The marketing site plays clips with `muted loop playsinline preload="none"` and a `data-lazy` attribute, which the site's IntersectionObserver uses to play and pause them on scroll. Follow that pattern for anything you add.
## Order of work
Label the PRs, generate and edit the release notes, cut the draft release, record the clips while the branch is still fresh in your head, then write the post against the notes and the clips. Publishing the release and merging the post should happen on the same day.
The `creating-localai-releases` skill drives steps 1 to 3 and captures the React UI screenshots that go into the notes.

View File

@@ -21,17 +21,6 @@ options:
- reasoning_parser:qwen3
```
## `Options[]` doubles as CLI-style engine flags
Beyond the parser names above, `Options[]` carries `--` prefixed engine flags (`--enable-prefix-caching`, `--kv-cache-dtype:fp8_e5m2`). `apply_options_to_engine_args` in `backend/python/common/vllm_utils.py` maps them onto `AsyncEngineArgs` fields, and it must run **before** `AsyncLLMEngine.from_engine_args()` - applying them afterwards is a silent no-op, which is exactly what issue #11130 was.
Things to keep straight when touching this:
- Precedence is typed proto fields → `options:``engine_args:`. `applyEngineArgDefaults` in `core/config/hooks_vllm.go` therefore skips seeding a production default whose key the user already set as an option, otherwise the later `engine_args:` pass would silently override them.
- Only `--` prefixed entries are engine flags; `tool_parser:`/`reasoning_parser:` and friends keep their meaning. Parser lookups accept both spellings via `normalize_option_key`.
- Unknown or uncoercible flags warn and are skipped, unlike `engine_args:` which is strict - `Options[]` is a shared bag and knows entries this mapping doesn't.
- Field types come from the annotation's *base* (`Literal["auto","float16"]` is not a float). The helper's tests are stdlib-only: `make test-python-helpers`.
Auto-defaults for known model families live in `core/config/parser_defaults.json` and are applied:
- at gallery import time by `core/gallery/importers/vllm.go`
- at model load time by the `vllm` / `vllm-omni` backend hook in `core/config/hooks_vllm.go`

View File

@@ -0,0 +1,101 @@
# Methodology: Closing the vLLM Decode-Throughput Gap in llama.cpp
This is the playbook that took the paged backend
([.agents/llama-cpp-localai-paged-backend.md](llama-cpp-localai-paged-backend.md))
from ~38% of vLLM decode to **parity-to-ahead on dense** (and a proven, honest
ceiling on MoE) on GB10. Use it for any "make llama.cpp match or beat engine X on
accelerator Y" effort. The *levers* are model- and hardware-specific; the
*discipline* below is not. The worked example, with all numbers, is the paged
backend README.
## The core loop
1. **Establish a bit-exact baseline and gate FIRST.** Record the greedy md5 (per
path) and an f32 reference. Every optimization must stay byte-identical to it -
or ship as an explicit, default-off precision opt-in. This is what lets you
optimize aggressively without silently regressing quality. Gate two ways:
greedy md5, and `test-backend-ops` against the CPU oracle.
2. **Profile - do not assume.** nsys the steady-state decode step, broken down per
*kernel* AND per *memcpy*. Find the dominant cost. "It's the GEMM" was wrong
here: on hybrid gated-DeltaNet models the bottleneck was the recurrent-state
**plumbing** (state memcpy + gathers, ~67% of the step), not the weight GEMM.
Also sanity-check GPU-busy %: an early "low utilization" reading was a profiling
window artifact (decode was 96-99% GPU-busy), not real idle.
3. **Ground-truth BOTH engines.** Decompose *your* decode step AND the
competitor's, side by side, per bucket, and compute the per-bucket delta. This
tells you WHERE the gap actually is - not where you would guess. It overturned
premises here: e.g. vLLM does NOT run the GDN/attn projections as NVFP4 (it
keeps them bf16, same as us); the MoE expert GEMM was a llama *win*, not the gap.
4. **Per-lever discipline.** For each candidate: implement -> bit-exact gate ->
same-harness A/B bench. Use a runtime env-toggle (flag off vs on) ONLY for
levers that are actually runtime-gated; a lever **compiled into** the binary
(e.g. the SSM decode fusions here) is NOT isolated by a runtime flag, so measure
it build-vs-build. The full-patchset "stock" baseline likewise needs a
**separately-built unpatched binary at the same pin** - toggling the runtime
flag on the patched binary does not reproduce stock (it measures only the gated
part; here that was ~neutral, which is exactly how this gotcha hides). Bank only
what lifts AND gates. **Record every rejected or flat lever with the reason** -
over time this is the most valuable part: it stops the next person re-running
dead ends.
5. **Name the structural floor.** Prove the bit-exact ceiling exhaustively (every
lever measured, not assumed). What remains is physical - the memory-bandwidth
floor, the irreducible serial-SSM host loop (sampling can't start until logits
land). Name it; do not claim more than you measured.
## Hard rules learned
- **Apples-to-apples, or label it.** Stock-vs-patched on the SAME harness
(`llama-batched-bench`) is exact - lead with it. But "stock" must be a
separately-built unpatched binary at the SAME pin, NOT the patched binary with
the runtime flag off (compiled-in wins survive the toggle). Cross-engine "% of vLLM"
(batched-bench vs vLLM server+client) is *indicative*; always caveat the harness
and config (context length alone shifted the MoE figure 76% <-> 86%).
- **Re-measure a "win" after later levers land - it may evaporate.** bf16 SSM
state (the `ssm_bf16_tau` lever) benched +12% early and failed the f32 KL gate
(vLLM keeps f32 too), so it was kept default-off opt-in. Once the decode fusions
(recurrent-state gather-fusion + block-table cache) landed, a clean re-measure
forcing ALL gated-DeltaNet heads to bf16 (`tau=100000`) went **flat** - 780.6 vs
780.0 t/s. The "+12%" was subsumed by the fusions: the lever bought nothing, so
it was **dropped** (precision trade + bug surface + extra CUDA template-instantiation
compile cost, zero benefit). A win measured before the rest of the series is not a
win after it.
- **Reject the obvious-but-wrong, with evidence.** A faster kernel that is off the
critical path benches FLAT (the freed time becomes idle). Quantizing the bf16
projections to NVFP4 cost ~6% PPL - and vLLM keeps them bf16 for the same reason.
Always measure before believing; a plausible mechanism is not a result.
- **The gate can be per-path.** Paged vs non-paged attention legitimately produces
different (equivalent) FP-reduction orders; validate the difference is benign
(KLD to f32) and then gate each path against its own reference.
## Orchestration (multi-agent)
- **One GPU profiler/bencher at a time** (the GPU-contention rule). Parallel
design/analysis/read agents are fine; concurrent GPU benches pollute each other's
numbers.
- **Adversarial verify.** Before banking a finding, spawn skeptics prompted to
*refute* it; majority-refute kills it. Prevents plausible-but-wrong results.
- **Anti-punt.** Use foreground, blocking ssh loops with short benches and a
progress-file checkpoint. Agents that background work and "wait for the monitor
event" stall - forbid that pattern.
- **GPU coexistence.** On a shared host, stop the user's deployments for a clean
benchmark window (with their OK) and ALWAYS restore them (wrap the bench so a
failure cannot strand them).
## What generalizes (and what doesn't)
The *speedups* may be hardware-specific (here: CUDA/Blackwell - the SSM fusions,
NVFP4 FP4-MMA, the occupancy tune), which is why other accelerators did not
benefit. But the *findings* often generalize and are worth upstreaming: the
"decode is plumbing-bound, not GEMM-bound" insight and the bit-exact, CPU-mirrored
fusion ops help any backend running these models. Separate "ship our tuned backend"
from "upstream the portable op" - they are different deliverables.
## The closing record
Write up the result HONESTLY: the shipped wins, the rejected levers (with reasons),
the structural ceiling, and the cross-backend / cross-quant generality. Negative
results are as valuable as wins. The paged backend README is the template.

View File

@@ -113,54 +113,6 @@ if [ "${BUILD_TYPE:-}" = "vulkan" ] && [ "${SKIP_DRIVERS:-false}" = "false" ]; t
rm -rf /var/lib/apt/lists/*
fi
# --- 2b. Intel graphics driver (BUILD_TYPE=sycl*) ---
# The Intel oneAPI base image brings the compilers and the oneAPI libraries, but
# not the driver that talks to the graphics card. The packaging step copies that
# driver into the backend, so that the backend works on a machine which has no
# Intel graphics packages of its own, for the same reason the Vulkan section
# above installs the Mesa drivers. Install it here so there is something to copy.
#
# Only the sycl builds are covered, because those are the ones whose packaging
# copies the driver. See package_intel_libs in scripts/build/package-gpu-libs.sh.
#
# The driver comes from Intel's own package repository, not from the Ubuntu
# archive. The archive has 23.43 from late 2023, which does not know any card
# released since, so a machine with a recent Intel GPU would end up carrying a
# driver that cannot drive it. Intel's repository has 25.18 for the same Ubuntu
# release.
#
# Anything that goes wrong here fails the build, on purpose. An unreachable
# repository is a passing problem that a retry fixes, whereas carrying a
# different driver than intended, or none, is a difference nobody would notice
# until a user reports an idle GPU.
if case "${BUILD_TYPE:-}" in sycl*) true;; *) false;; esac \
&& [ "${SKIP_DRIVERS:-false}" = "false" ]; then
# Ubuntu release name, which is what the repository is indexed by.
ubuntu_codename=$(. /etc/os-release && echo "${VERSION_CODENAME:-}")
if [ -z "$ubuntu_codename" ]; then
echo "ERROR: cannot tell which Ubuntu release this image is, so cannot pick the Intel driver repository" >&2
exit 1
fi
# The key is armored text, which apt reads directly from a .asc file, so
# there is no need for gnupg here. "unified" is the component Intel ships
# its current driver in.
mkdir -p /usr/share/keyrings
curl -fsSL https://repositories.intel.com/gpu/intel-graphics.key \
-o /usr/share/keyrings/intel-graphics.asc
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.asc] https://repositories.intel.com/gpu/ubuntu ${ubuntu_codename} unified" \
> /etc/apt/sources.list.d/intel-graphics.list
apt-get update
# The first package holds the driver OpenCL talks to, the second the driver
# Level Zero talks to. Between them they pull in the compiler and the memory
# manager that both need.
apt-get install -y --no-install-recommends \
intel-opencl-icd \
libze-intel-gpu1
apt-get clean
rm -rf /var/lib/apt/lists/*
fi
# --- 3. CUDA toolkit (BUILD_TYPE=cublas|l4t) ---
if { [ "${BUILD_TYPE:-}" = "cublas" ] || [ "${BUILD_TYPE:-}" = "l4t" ]; } && [ "${SKIP_DRIVERS:-false}" = "false" ]; then
apt-get update

View File

@@ -1,32 +0,0 @@
#!/usr/bin/env bash
set -euo pipefail
arch=${1:?target architecture is required}
build_type=${2-}
# SYCL compiles the whole tree with icpx -fsycl, and icpx never finishes
# ggml-cpu/arch/x86/repack.cpp at -march=sapphirerapids: the job sits on that one
# translation unit until GitHub kills it at 6h. gcc builds the same file in
# seconds, so only the SYCL images have to give up the CPU variant matrix.
#
# ROCm runs out of the same 6h budget for a different reason: volume, not a
# stall. hipcc compiles ggml's HIP kernels once per entry in AMDGPU_TARGETS,
# which is eleven architectures (gfx908 through gfx1201), and the CPU variant
# matrix lands on top of that. The job built in 2h27m before it was added and
# has been killed at exactly 6h00m on every run since, so no ROCm llama-cpp
# image has been published since 2026-08-01.
case "$build_type" in
sycl*|hipblas*)
echo llama-cpp-fallback
exit 0
;;
esac
# GPU arm64 base images do not consistently provide the gcc-14 toolchain needed
# to compile ggml's armv9.2 CPU variants. Keep their portable fallback until the
# builder images can supply that compiler.
if [ "$arch" = "arm64" ] && [ -n "$build_type" ]; then
echo llama-cpp-fallback
else
echo llama-cpp-cpu-all
fi

View File

@@ -18,27 +18,27 @@ if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
fi
cd /LocalAI/backend/cpp/llama-cpp
BUILD_TARGET=$(/LocalAI/.docker/llama-cpp-build-target.sh "${TARGETARCH}" "${BUILD_TYPE:-}")
if [ "$BUILD_TARGET" = "llama-cpp-cpu-all" ]; then
# One build with ggml CPU_ALL_VARIANTS replaces the per-microarch binaries (x86:
# avx/avx2/avx512/fallback; arm64: armv8.x/armv9.x). BUILD_TYPE remains in the
# environment, so GPU builds retain their accelerator backend while ggml dlopens the
# best CPU library when work is offloaded to the host.
if [ -z "${BUILD_TYPE:-}" ]; then
# Pure CPU image (BUILD_TYPE empty): one build with ggml CPU_ALL_VARIANTS replaces the
# per-microarch binaries (x86: avx/avx2/avx512/fallback; arm64: armv8.x/armv9.x). ggml
# dlopens the best libggml-cpu-*.so at runtime by probing host CPU features.
#
# arm64: the CPU_ALL_VARIANTS table includes armv9.2 SME variants whose -march=...+sme is
# rejected by the Ubuntu 24.04 default gcc-13. gcc-14 accepts it, so build the arm64
# variants with it (the host never *selects* SME unless it has it, but every variant must
# still compile).
if [ "${TARGETARCH}" = "arm64" ]; then
# The prebuilt base inherits default ports.ubuntu.com sources; honor the
# APT_*_MIRROR build args here like the from-source path does, so this
# apt step survives a mirror outage.
sh /LocalAI/.docker/apt-mirror.sh || true
apt-get update -qq && apt-get install -y -qq gcc-14 g++-14
export CC=gcc-14 CXX=g++-14
fi
make llama-cpp-cpu-all
else
# GPU build (cublas/hipblas/sycl/vulkan/...): the accelerator does the compute, so a
# single fallback CPU build is enough - no per-microarch CPU variants needed. (This also
# keeps the heavy GPU backend compile from also building the whole CPU variant matrix,
# and avoids the gcc-14 apt step on GPU base images such as nvidia l4t.)
make llama-cpp-fallback
fi
make "$BUILD_TARGET"
make llama-cpp-grpc
make llama-cpp-rpc-server

View File

@@ -1,5 +1,5 @@
#!/usr/bin/env bash
# Shared compile logic for backend/Dockerfile.bonsai.
# Shared compile logic for backend/Dockerfile.llama-cpp-localai-paged.
# Sourced (via bind mount) from both builder-fromsource and builder-prebuilt stages.
set -euxo pipefail
@@ -14,10 +14,10 @@ if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
export CMAKE_ARGS="${CMAKE_ARGS} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/bonsai-*-build
rm -rf /LocalAI/backend/cpp/llama-cpp-localai-paged-*-build
fi
cd /LocalAI/backend/cpp/bonsai
cd /LocalAI/backend/cpp/llama-cpp-localai-paged
if [ -z "${BUILD_TYPE:-}" ]; then
# Pure CPU image: one ggml CPU_ALL_VARIANTS build replaces the per-microarch binaries.
@@ -26,14 +26,14 @@ if [ -z "${BUILD_TYPE:-}" ]; then
apt-get update -qq && apt-get install -y -qq gcc-14 g++-14
export CC=gcc-14 CXX=g++-14
fi
make bonsai-cpu-all
make llama-cpp-localai-paged-cpu-all
else
# GPU build (cublas/hipblas/sycl/vulkan/...): single fallback CPU build, the accelerator
# does the compute. Keeps the GPU compile from also building the CPU variant matrix and
# avoids the gcc-14 apt step on GPU base images such as nvidia l4t.
make bonsai-fallback
make llama-cpp-localai-paged-fallback
fi
make bonsai-grpc
make bonsai-rpc-server
make llama-cpp-localai-paged-grpc
make llama-cpp-localai-paged-rpc-server
ccache -s || true

View File

@@ -1,25 +0,0 @@
#!/usr/bin/env bash
set -euo pipefail
arch=${1:?target architecture is required}
build_type=${2-}
# SYCL compiles the whole tree with icpx -fsycl, and icpx never finishes
# ggml-cpu/arch/x86/repack.cpp at -march=sapphirerapids: the job sits on that one
# translation unit until GitHub kills it at 6h. gcc builds the same file in
# seconds, so only the SYCL images have to give up the CPU variant matrix.
case "$build_type" in
sycl*)
echo turboquant-fallback
exit 0
;;
esac
# GPU arm64 base images do not consistently provide the gcc-14 toolchain needed
# to compile ggml's armv9.2 CPU variants. Keep their portable fallback until the
# builder images can supply that compiler.
if [ "$arch" = "arm64" ] && [ -n "$build_type" ]; then
echo turboquant-fallback
else
echo turboquant-cpu-all
fi

View File

@@ -19,18 +19,20 @@ fi
cd /LocalAI/backend/cpp/turboquant
BUILD_TARGET=$(/LocalAI/.docker/turboquant-build-target.sh "${TARGETARCH}" "${BUILD_TYPE:-}")
if [ "$BUILD_TARGET" = "turboquant-cpu-all" ]; then
# BUILD_TYPE remains in the environment, so GPU builds retain their accelerator while
# ggml selects the best CPU library when model work is offloaded to the host.
if [ -z "${BUILD_TYPE:-}" ]; then
# Pure CPU image: one ggml CPU_ALL_VARIANTS build replaces the per-microarch binaries.
# arm64: the armv9.2 SME variants need gcc-14 (gcc-13 rejects +sme).
if [ "${TARGETARCH}" = "arm64" ]; then
sh /LocalAI/.docker/apt-mirror.sh || true
apt-get update -qq && apt-get install -y -qq gcc-14 g++-14
export CC=gcc-14 CXX=g++-14
fi
make turboquant-cpu-all
else
# GPU build (cublas/hipblas/sycl/vulkan/...): single fallback CPU build, the accelerator
# does the compute. Keeps the GPU compile from also building the CPU variant matrix and
# avoids the gcc-14 apt step on GPU base images such as nvidia l4t.
make turboquant-fallback
fi
make "$BUILD_TARGET"
make turboquant-grpc
make turboquant-rpc-server

View File

@@ -40,16 +40,6 @@ backend/cpp/privacy-filter/build
backend/cpp/privacy-filter/grpc-server
backend/cpp/privacy-filter/package
# audio-cpp: same in-place pattern. The Makefile clones audio.cpp at the pinned
# AUDIO_CPP_VERSION and the `audio.cpp:` target is the directory itself, so a
# stale host checkout COPY'd in makes the build compile against whatever commit
# the host had. build/ is worse than stale: its CMakeCache.txt records the host
# source, prefix and compiler paths, and cmake refuses to reconfigure from it.
backend/cpp/audio-cpp/audio.cpp
backend/cpp/audio-cpp/build
backend/cpp/audio-cpp/grpc-server
backend/cpp/audio-cpp/package
# Rust backend build output (sources are tracked; target/ is generated)
backend/rust/*/target

60
.githooks/pre-commit Executable file
View File

@@ -0,0 +1,60 @@
#!/usr/bin/env sh
#
# LocalAI pre-commit hook. Install it (once per clone) with:
#
# make install-hooks
#
# Runs only the checks relevant to what's staged:
# - Go files -> make lint + make test-coverage-check
# - core/http/react-ui -> make test-ui-coverage-check (Playwright e2e + gate)
# A commit touching neither is skipped entirely (docs/YAML/etc. can't change
# lint findings, Go coverage, or the UI).
#
# To bypass for a single commit (e.g. a WIP checkpoint): git commit --no-verify
set -eu
repo_root="$(git rev-parse --show-toplevel)"
cd "$repo_root"
staged="$(git diff --cached --name-only --diff-filter=ACMRD)"
go_changed=0
ui_changed=0
if echo "$staged" | grep -qE '\.go$'; then go_changed=1; fi
if echo "$staged" | grep -qE '^core/http/react-ui/'; then ui_changed=1; fi
if [ "$go_changed" -eq 0 ] && [ "$ui_changed" -eq 0 ]; then
echo "pre-commit: no Go or React UI changes staged — skipping."
exit 0
fi
if [ "$go_changed" -eq 1 ]; then
# Resolve the ref golangci-lint's new-from-merge-base should compare
# against. .golangci.yml pins origin/master, which is correct in CI
# (origin == the canonical repo) but wrong from a fork clone, where
# origin/master lags behind and lint would report the whole upstream
# backlog. Prefer upstream/master, then origin/master, then master.
lint_base=""
for ref in upstream/master origin/master master; do
if git rev-parse --verify --quiet "${ref}^{commit}" >/dev/null 2>&1; then
lint_base="$ref"
break
fi
done
echo "pre-commit ▶ golangci-lint (make lint${lint_base:+, new-from $lint_base})"
make lint LINT_NEW_FROM="$lint_base"
echo "pre-commit ▶ coverage gate (make test-coverage-check) — builds and runs the"
echo " pkg/core suites plus tests/e2e; can take a few minutes."
make test-coverage-check
fi
if [ "$ui_changed" -eq 1 ]; then
echo "pre-commit ▶ React UI e2e + coverage gate (make test-ui-coverage-check) —"
echo " rebuilds the UI + ui-test-server, runs the Playwright specs, and"
echo " fails if line coverage regressed; can take a couple of minutes."
make test-ui-coverage-check
fi
echo "pre-commit ✓ all relevant checks passed"

View File

@@ -7,7 +7,6 @@ This PR fixes #
**[Signed commits](../CONTRIBUTING.md#signing-off-on-commits-developer-certificate-of-origin)**
- [ ] Yes, I signed my commits.
- [ ] Documentation updated (docs/content/) for user-facing changes, or not applicable
<!--
Thank you for contributing to LocalAI!

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18
.github/bump_deps.sh vendored
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@@ -1,8 +1,5 @@
#!/bin/bash
set -xe
source "$(dirname "${BASH_SOURCE[0]}")/gh_curl.sh"
REPO=$1
BRANCH=$2
VAR=$3
@@ -12,20 +9,7 @@ if [ -z "$FILE" ]; then
FILE="Makefile"
fi
# gh_curl follows redirects so a renamed/transferred upstream repo (GitHub
# answers 301) still resolves, and fails on HTTP errors rather than letting an
# error page reach sed below. `|| true` keeps a failed lookup from aborting the
# script at exit 22 with no context — the SHA guard below reports it instead.
LAST_COMMIT=$(gh_curl -H "Accept: application/vnd.github.VERSION.sha" "https://api.github.com/repos/$REPO/commits/$BRANCH" || true)
# Guard the sed input: anything that is not a bare 40-hex SHA (an API error
# body, an empty response) would otherwise be spliced into the Makefile pin —
# either corrupting it silently or blowing up sed with an unterminated
# expression, which is how this job failed for a renamed repo.
if ! [[ "$LAST_COMMIT" =~ ^[0-9a-f]{40}$ ]]; then
echo "Refusing to bump $VAR: expected a 40-char commit SHA for $REPO@$BRANCH, got: $LAST_COMMIT" >&2
exit 1
fi
LAST_COMMIT=$(curl -s -H "Accept: application/vnd.github.VERSION.sha" "https://api.github.com/repos/$REPO/commits/$BRANCH")
# Read $VAR from Makefile (only first match)
set +e

13
.github/bump_docs.sh vendored
View File

@@ -1,18 +1,7 @@
#!/bin/bash
set -xe
source "$(dirname "${BASH_SOURCE[0]}")/gh_curl.sh"
REPO=$1
LATEST_TAG=$(gh_curl -H "Accept: application/vnd.github+json" \
"https://api.github.com/repos/$REPO/releases/latest" | jq -r '.tag_name')
# jq prints the string "null" for a missing key, so a throttled or otherwise
# unexpected API response would otherwise be published as the docs version.
if [ -z "$LATEST_TAG" ] || [ "$LATEST_TAG" = "null" ]; then
echo "Refusing to bump docs version: could not resolve the latest release tag for $REPO." >&2
exit 1
fi
LATEST_TAG=$(curl -s "https://api.github.com/repos/$REPO/releases/latest" | jq -r '.tag_name')
cat <<< $(jq ".version = \"$LATEST_TAG\"" docs/data/version.json) > docs/data/version.json

View File

@@ -11,9 +11,6 @@
# darwin build can only use the exact vLLM version vllm-metal supports, so it may
# lag the Linux pin (requirements-cublas13-after.txt) until vllm-metal catches up.
set -xe
source "$(dirname "${BASH_SOURCE[0]}")/gh_curl.sh"
REPO=$1 # vllm-project/vllm-metal
FILE=$2 # backend/python/vllm/install.sh
VAR=$3 # VLLM_METAL_VERSION (used for the workflow's output file names)
@@ -25,12 +22,12 @@ fi
# vllm-metal ships frequent dev releases, all flagged as non-prerelease, so
# /releases/latest returns the newest one (with its cp312 wheel asset).
LATEST_TAG=$(gh_curl -H "Accept: application/vnd.github+json" \
LATEST_TAG=$(curl -sS -H "Accept: application/vnd.github+json" \
"https://api.github.com/repos/$REPO/releases/latest" \
| python3 -c "import json,sys; print(json.load(sys.stdin)['tag_name'])")
# The coupled vLLM source version lives in vllm-metal's installer at that tag.
NEW_VLLM_VERSION=$(gh_curl \
NEW_VLLM_VERSION=$(curl -fsSL \
"https://raw.githubusercontent.com/$REPO/$LATEST_TAG/install.sh" \
| grep -oE 'vllm_v="[0-9]+\.[0-9]+\.[0-9]+"' | head -1 | cut -d'"' -f2)

View File

@@ -9,9 +9,6 @@
# vars in Makefiles; this script handles the two-value rewrite specific to the
# vLLM requirements file.
set -xe
source "$(dirname "${BASH_SOURCE[0]}")/gh_curl.sh"
REPO=$1 # vllm-project/vllm
FILE=$2 # backend/python/vllm/requirements-cublas13-after.txt
VAR=$3 # VLLM_VERSION (used for output file names so the workflow can read them)
@@ -22,7 +19,7 @@ if [ -z "$FILE" ] || [ -z "$REPO" ] || [ -z "$VAR" ]; then
fi
# /releases/latest returns the most recent non-prerelease tag.
LATEST_TAG=$(gh_curl -H "Accept: application/vnd.github+json" \
LATEST_TAG=$(curl -sS -H "Accept: application/vnd.github+json" \
"https://api.github.com/repos/$REPO/releases/latest" \
| python3 -c "import json,sys; print(json.load(sys.stdin)['tag_name'])")

View File

@@ -1,194 +0,0 @@
# apexentries
Generates gallery entries for the `mudler/*-APEX-GGUF` HuggingFace repositories.
Each APEX repo becomes one **family**: one entry per quality rung the repo
publishes and one per quantization rung its unsloth counterpart publishes, all
gathered under the **base model's** entry. LocalAI's variant selector then picks
the build that fits the hardware in front of it.
## The hub is the base model entry, never a generated `*-apex` parent
Somebody looking for `qwen3.6-35b-a3b` must find every build of those weights
under that one name: the APEX imatrix rungs, the unsloth quant rungs and any
speculative build. A separate `qwen3.6-35b-a3b-apex` hub competing with the base
entry would split the family in two and leave whichever half the user did not
search for effectively invisible.
So the generator resolves the hub by stripping the `-APEX`, `-MTP` and `-TQ`
markers and looking the result up in the index, trying both the repo-derived and
the stem-derived candidate the same way `CounterpartCandidates` does. Then:
- **The hub exists** (14 of the 45 repos, resolving to 10 distinct entries).
Nothing new is emitted for the family root. A `variants:` block is spliced into
the entry that is already there, textually, leaving its description, icon,
tags, overrides and files untouched. The line editing is shared with the
`variantproposals` job via `.github/ci/galleryedit`.
- **The hub is absent** (the other 31). A new hub is emitted, named for the base
model and never for the APEX repo. It carries one of the discovered builds as
its own payload so it is a complete installable entry rather than a bare index,
and that payload is what gives it an `overrides.backend`. Without a declared
backend the verifier would skip it, so a hub carrying feature tags would escape
the tagging check in silence.
Several APEX repos routinely resolve to one base model, so both paths accumulate
by hub name rather than assuming one family per hub.
Two references are always filtered out of a hub's list: anything the entry
already declares, and the hub's own name. The self reference is not merely
redundant. An unsloth rung whose weights the gallery already ships under the base
name resolves, through the merge, straight back to the hub, and the verifier
reads a self reference as a variant that declares variants of its own.
The four hand-written `*-apex` entries (`qwen3.6-35b-a3b-apex`,
`gemma-4-26b-a4b-it-apex`, `qwen3.5-35b-a3b-apex`,
`nemotron-3-nano-omni-30b-a3b-reasoning-apex`) are **ordinary builds**, not hubs.
They are referenced from their hub's variants list like any other rung, and are
never deleted or renamed.
## Flags
| Flag | Default | Meaning |
|------|---------|---------|
| `-index <path>` | `gallery/index.yaml` | Gallery index to dedup against. Read only, unless `-apply` is passed. |
| `-only <a,b,c>` | (all) | Comma-separated full repo names (`mudler/Foo-APEX-GGUF`) to restrict generation to. A name that matches nothing is reported as a warning, since it is a typo rather than an empty result. |
| `-out <path>` | (none) | Write the entries to add to this file. Nothing is written to the gallery. |
| `-apply` | `false` | Splice the variants into `-index` and append the new entries to it. |
| `-verify <path>` | (none) | Verify a gallery index and exit. Ignores every other flag. |
Either `-out` or `-apply` is required, otherwise the run has nothing to do.
`-apply` splices variant lines into existing entries and **appends** new ones. It
never re-serialises the index: it is roughly 40,000 lines, and a YAML round trip
would reflow the whole file, drop the anchors and merge keys the gallery relies
on, and produce a diff nobody can review. On the three-family sample the splice
is 24 added lines across 3 hunks with zero deletions.
## Discovery is by filename suffix, never by repo name
Builds come from the files a repo actually publishes. A filename is never
constructed from a repo name, because the two disagree:
`mudler/gemma-4-26B-A4B-it-APEX-GGUF` ships `gemma-4-26B-A4B-APEX-*.gguf`, and
five other repos likewise drop a suffix (`-it`, `-2603`) or a vendor prefix
(`NVIDIA-`) that the repo name carries. Composing a URL from the repo name would
produce a 404 for every one of them, and the 404 would only surface after the
entry shipped.
The quality ladder is matched on the trailing tier marker, `-(I-)?(Quality|
Balanced|Compact|Mini|Nano).gguf`. The `I-` prefix marks the imatrix ladder. The
imatrix ladder is emitted when it is non-empty and the plain ladder is used only
as a fallback, because two of the 45 repos publish no imatrix tiers at all and
must still contribute. Eleven repos carry a fifth `I-Nano` rung, so nothing
assumes a fixed number of rungs.
Every run prints, per repo, the counts that discovery accounted for. If the
number of classified files is short of the number of `.gguf` files the repo
publishes, the shortfall is printed as `UNCLASSIFIED`. That check is a set
difference on counts rather than a second pass over filenames: a second matcher
would duplicate the tier regex and the two copies would drift. The failure it
catches is quiet. A publishing-script typo that breaks every imatrix filename in
a repo does not produce a short ladder; it makes the imatrix ladder empty, and
the fallback then downgrades the whole family to the plain ladder with nothing
said. A downstream HTTP check cannot catch it either, because it validates the
URLs that were emitted, and an undiscovered tier emits none.
The same reasoning applies to `UNACCOUNTED QUANT`, printed when the unsloth
counterpart demonstrably publishes a wanted quant that produced no build. It is
reported at discovery time because a dropped quant leaves no trace at all in the
finished gallery file.
## sha256 always comes from the API
Every file stanza takes its `sha256` from the HuggingFace models API
(`lfs.sha256`). A GGUF the API describes without one is a fatal error for that
family: the repo is reported by name and the run ends non-zero. It is never
substituted from another field, because that is exactly how a Xet hash ends up
masquerading as a content hash.
## The dflash / mtp tagging rule
An entry is tagged `dflash` or `mtp` **if and only if** it configures the
matching `spec_type:draft-<feature>`. Variant ranking reads tags and nothing
else, so a tag that does not match the configuration either promotes a build
that is no faster or hides one that genuinely is.
A repo name is not configuration. `mudler/Qwen3.6-35B-A3B-APEX-MTP-GGUF` ships
weights that carry MTP heads; an entry that does not enable them is not an MTP
entry and is not tagged as one.
A generated hub inherits the tags of the build it carries as its payload, rather
than rebuilding them from the base set, so a hub whose payload configures a
`spec_type` stays tagged consistently with the overrides copied alongside it.
## Reuse reporting: two categories, not one
Generated entries are deduped against the gallery and against the batch itself.
The run prints the result under two separate headings, because the two cases are
not equivalent:
- **URI MATCHES** mean the gallery, or an earlier entry in this batch, already
ships exactly these weights. Pointing the hub at the existing entry is correct
and needs no thought.
- **NAME COLLISIONS** mean an entry already owns the name but holds different
weights. Referencing it would point the hub at a build other than the one
generated. Every one of these must be inspected by hand.
The run then prints `HUBS SPLICED`, listing every reference that will be added to
an entry the gallery already ships along with the line it will be added at, and
`HUBS CREATED` for the families that get a new hub. The splices are the part a
review has to read closely, because they modify entries somebody else wrote.
Hubs are deliberately kept out of the merge. A new hub carries the family's top
rung as its own payload, so URI dedup would fold the hub into that rung and the
family would lose the very entry point this command exists to create.
## Workflow: sample first, then the full set
Never run the full generation straight into the gallery. Generate a small,
deliberately awkward sample, have it reviewed, then run the rest.
```bash
# 1. Sample three families that between them cover the awkward shapes:
# a standard four-rung repo, one with the extra I-Nano rung AND a file stem
# that differs from its repo name, and one whose unsloth counterpart shards
# its quants across subdirectories.
go run ./.github/ci/apexentries \
-index gallery/index.yaml \
-only mudler/Qwen3.6-35B-A3B-APEX-GGUF,mudler/gemma-4-26B-A4B-it-APEX-GGUF,mudler/Step-3.7-Flash-APEX-GGUF \
-out /tmp/sample.yaml
# 2. Verify the sample against the gallery it would join, splices included. Apply
# to a COPY, never to the real index, and check that the diff is only the
# intended variant lines. Compare the verifier output to the gallery's own
# baseline: what matters is that the sample adds no new problem, not that the
# total is zero.
cp gallery/index.yaml /tmp/index-copy.yaml
go run ./.github/ci/apexentries -index /tmp/index-copy.yaml -only <same list> -apply
diff -u gallery/index.yaml /tmp/index-copy.yaml # expect zero deletions
go run ./.github/ci/apexentries -verify gallery/index.yaml > /tmp/baseline.log 2>&1
go run ./.github/ci/apexentries -verify /tmp/index-copy.yaml > /tmp/spliced.log 2>&1
diff /tmp/baseline.log /tmp/spliced.log
# 3. Have a human review /tmp/sample.yaml and every reported name collision.
# 4. Only then, the full set.
go run ./.github/ci/apexentries -index gallery/index.yaml -apply
```
## Tests
```bash
go test ./.github/ci/apexentries/
```
The shared line editor has its own package:
```bash
go test ./.github/ci/galleryedit/
```
`.github/ci/` is invisible to `go list ./...`, so these specs are not covered by
`make lint` or the repository test run. `.github/workflows/ci-tools-tests.yaml`
names the package explicitly; keep that workflow in step with any package added
under `.github/ci/`.

View File

@@ -1,70 +0,0 @@
package main
import (
"regexp"
"strings"
)
// tierRE matches the tier marker APEX repos put at the end of a weight
// filename. Discovery is by suffix because the stem is not predictable from
// the repo name: six of the 45 repos drop a suffix ("-it", "-2603") or a
// vendor prefix ("NVIDIA-") that the repo name carries.
var tierRE = regexp.MustCompile(`-(I-)?(Quality|Balanced|Compact|Mini|Nano)\.gguf$`)
// fullPrecisionRE matches the unquantized source weights an APEX repo publishes
// alongside its ladder, flat (-F16.gguf) or sharded across a numbered set
// (-F16-00001-of-00010.gguf). bf16 is accepted because some repos publish that
// instead, and the match is case-insensitive because the casing varies between
// publishing scripts.
//
// These are deliberately not tiers: they are the weights the ladder is quantized
// FROM, and generation is scoped to the ladder itself.
var fullPrecisionRE = regexp.MustCompile(`(?i)-b?f16(-\d{5}-of-\d{5})?\.gguf$`)
// IsFullPrecision reports whether a weight filename is an unquantized source.
func IsFullPrecision(name string) bool {
return fullPrecisionRE.MatchString(name)
}
// Tier is one discovered build of an APEX repo.
type Tier struct {
Label string
File GGUFFile
}
// DiscoverAPEXTiers splits a repo's weight files into the imatrix ladder and
// the plain ladder. mmproj files are never tiers.
func DiscoverAPEXTiers(files []GGUFFile) (imatrix, plain []Tier) {
for _, f := range files {
if strings.HasPrefix(f.Name, "mmproj") {
continue
}
m := tierRE.FindStringSubmatch(f.Name)
if m == nil {
continue
}
if m[1] != "" {
imatrix = append(imatrix, Tier{Label: "I-" + m[2], File: f})
continue
}
plain = append(plain, Tier{Label: m[2], File: f})
}
return imatrix, plain
}
// DiscoverMMProj returns the repo's projector file, if it publishes one. The
// name varies across repos (mmproj.gguf, mmproj-F16.gguf,
// mmproj-step3.7-flash-f16.gguf), so match the prefix rather than a fixed name.
func DiscoverMMProj(files []GGUFFile) (GGUFFile, bool) {
for _, f := range files {
if strings.HasPrefix(f.Name, "mmproj") {
return f, true
}
}
return GGUFFile{}, false
}
// FileStem returns a tier's filename with its tier suffix removed.
func FileStem(t Tier) string {
return tierRE.ReplaceAllString(t.File.Name, "")
}

View File

@@ -1,68 +0,0 @@
package main
import (
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("DiscoverAPEXTiers", func() {
It("finds tiers regardless of how the stem relates to the repo name", func() {
// This repo is mudler/gemma-4-26B-A4B-it-APEX-GGUF but its files drop "-it".
files := []GGUFFile{
{Name: "gemma-4-26B-A4B-APEX-I-Quality.gguf", SHA256: "a"},
{Name: "gemma-4-26B-A4B-APEX-I-Nano.gguf", SHA256: "b"},
{Name: "gemma-4-26B-A4B-APEX-Quality.gguf", SHA256: "c"},
{Name: "mmproj-F16.gguf", SHA256: "d"},
}
imatrix, plain := DiscoverAPEXTiers(files)
Expect(labels(imatrix)).To(ConsistOf("I-Quality", "I-Nano"))
Expect(labels(plain)).To(ConsistOf("Quality"))
})
It("excludes mmproj from the tier list", func() {
files := []GGUFFile{{Name: "mmproj.gguf", SHA256: "d"}}
imatrix, plain := DiscoverAPEXTiers(files)
Expect(imatrix).To(BeEmpty())
Expect(plain).To(BeEmpty())
})
})
var _ = Describe("DiscoverMMProj", func() {
It("finds an mmproj whatever its suffix", func() {
files := []GGUFFile{
{Name: "Model-APEX-I-Mini.gguf", SHA256: "a"},
{Name: "mmproj-step3.7-flash-f16.gguf", SHA256: "b"},
}
got, ok := DiscoverMMProj(files)
Expect(ok).To(BeTrue())
Expect(got.Name).To(Equal("mmproj-step3.7-flash-f16.gguf"))
})
It("reports absence when the repo ships none", func() {
_, ok := DiscoverMMProj([]GGUFFile{{Name: "Model-APEX-Quality.gguf", SHA256: "a"}})
Expect(ok).To(BeFalse())
})
})
var _ = Describe("FileStem", func() {
It("strips the tier suffix", func() {
t := Tier{Label: "I-Quality", File: GGUFFile{Name: "gemma-4-26B-A4B-APEX-I-Quality.gguf"}}
Expect(FileStem(t)).To(Equal("gemma-4-26B-A4B-APEX"))
})
})
func labels(ts []Tier) []string {
out := make([]string, 0, len(ts))
for _, t := range ts {
out = append(out, t.Label)
}
return out
}

View File

@@ -1,130 +0,0 @@
package main
import (
"encoding/json"
"errors"
"fmt"
"io"
"net/http"
"strings"
"time"
"github.com/mudler/LocalAI/pkg/httpclient"
)
// ErrNoSHA256 marks a GGUF the HuggingFace API describes without an
// lfs.sha256. Emitting an entry without a hash would ship an unverifiable
// download, and guessing one from another field is how a Xet hash ends up
// masquerading as a content hash, so this is fatal rather than skippable.
var ErrNoSHA256 = errors.New("gguf file has no lfs.sha256")
// GGUFFile is one .gguf sibling of a HuggingFace repo.
type GGUFFile struct {
Name string
Size int64
SHA256 string
}
type apiSibling struct {
RFilename string `json:"rfilename"`
Size int64 `json:"size"`
LFS *struct {
SHA256 string `json:"sha256"`
} `json:"lfs"`
}
type apiModel struct {
Siblings []apiSibling `json:"siblings"`
}
// ParseRepoFiles returns every .gguf sibling described by a models API body.
func ParseRepoFiles(body []byte) ([]GGUFFile, error) {
var m apiModel
if err := json.Unmarshal(body, &m); err != nil {
return nil, fmt.Errorf("decoding model response: %w", err)
}
var out []GGUFFile
for _, s := range m.Siblings {
if !strings.HasSuffix(s.RFilename, ".gguf") {
continue
}
if s.LFS == nil || s.LFS.SHA256 == "" {
return nil, fmt.Errorf("%s: %w", s.RFilename, ErrNoSHA256)
}
out = append(out, GGUFFile{Name: s.RFilename, Size: s.Size, SHA256: s.LFS.SHA256})
}
return out, nil
}
// FetchOptionalRepoFiles asks the models API for a repo the caller can do
// without, and reports separately whether the repo was merely unreadable.
//
// HuggingFace answers 401 Unauthorized, not 404, for a repo that does not exist
// when the request carries no credentials. Without a token there is therefore no
// way to tell "this repo was never published" from "this repo is private", so an
// optional probe has to treat 401 and 403 exactly like 404: whatever the reason,
// there is nothing here for us to read, so there is no counterpart.
//
// The second return value exists because that collapse is lossy in one
// direction: 401/403 can also mean a real, gated repo whose quants we would
// genuinely want. The caller reports those repos so a silently dropped
// counterpart is visible to a human rather than invisible.
func FetchOptionalRepoFiles(client *http.Client, repo string) ([]GGUFFile, bool, error) {
files, status, err := fetchRepoFiles(client, repo)
if err != nil && (status == http.StatusUnauthorized || status == http.StatusForbidden) {
return nil, true, nil
}
return files, false, err
}
// FetchRepoFiles asks the models API for one repo. A 404 yields (nil, nil) so
// that probing for an optional counterpart repo is not an error. Every other
// non-200, 401 and 403 included, is an error: for a repo the run REQUIRES there
// is no benign reading of "we cannot see it".
func FetchRepoFiles(client *http.Client, repo string) ([]GGUFFile, error) {
files, _, err := fetchRepoFiles(client, repo)
return files, err
}
// fetchRepoFiles does the request and returns the HTTP status alongside the
// result, so the optional and required callers can apply different policies to
// the same response without duplicating the request.
func fetchRepoFiles(client *http.Client, repo string) ([]GGUFFile, int, error) {
url := fmt.Sprintf("https://huggingface.co/api/models/%s?blobs=true", repo)
req, err := http.NewRequest(http.MethodGet, url, nil)
if err != nil {
return nil, 0, err
}
req.Header.Set("User-Agent", "localai-apexentries/1.0")
resp, err := client.Do(req)
if err != nil {
return nil, 0, err
}
defer resp.Body.Close()
if resp.StatusCode == http.StatusNotFound {
return nil, resp.StatusCode, nil
}
if resp.StatusCode != http.StatusOK {
return nil, resp.StatusCode, fmt.Errorf("%s: unexpected status %d", repo, resp.StatusCode)
}
body, err := io.ReadAll(resp.Body)
if err != nil {
return nil, resp.StatusCode, err
}
files, err := ParseRepoFiles(body)
return files, resp.StatusCode, err
}
// newHTTPClient builds the client used against the HuggingFace API. It goes
// through pkg/httpclient rather than a bare &http.Client{} because the std
// client follows redirects and forwards custom credential headers to the
// redirect target on a cross-host hop (GHSA-3mj3-57v2-4636). This caller sends
// only a User-Agent today, but it talks to an external API that could start
// redirecting, and an HF_TOKEN header here later would then leak.
func newHTTPClient() *http.Client {
return httpclient.NewWithTimeout(60 * time.Second)
}

View File

@@ -1,142 +0,0 @@
package main
import (
"bytes"
"io"
"net/http"
"testing"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
func TestApexEntries(t *testing.T) {
RegisterFailHandler(Fail)
RunSpecs(t, "apexentries")
}
// stubTransport answers every request with one canned status and body, so the
// status handling of the fetchers can be exercised without reaching the real
// HuggingFace API.
type stubTransport struct {
status int
body string
}
func (t stubTransport) RoundTrip(req *http.Request) (*http.Response, error) {
return &http.Response{
StatusCode: t.status,
Body: io.NopCloser(bytes.NewBufferString(t.body)),
Header: make(http.Header),
Request: req,
}, nil
}
func stubClient(status int, body string) *http.Client {
return &http.Client{Transport: stubTransport{status: status, body: body}}
}
const oneGGUFBody = `{"siblings":[{"rfilename":"Model-APEX-I-Quality.gguf","size":10,"lfs":{"sha256":"aa","size":10}}]}`
var _ = Describe("FetchOptionalRepoFiles", func() {
// HuggingFace answers 401 rather than 404 for a repo that does not exist
// when the client carries no credentials, so an optional probe cannot tell
// "absent" from "unauthorized" and must treat both as "no counterpart".
It("treats a 401 as an absent repo and flags it as unavailable", func() {
files, unavailable, err := FetchOptionalRepoFiles(stubClient(http.StatusUnauthorized, ""), "unsloth/Nope-GGUF")
Expect(err).ToNot(HaveOccurred())
Expect(files).To(BeEmpty())
Expect(unavailable).To(BeTrue())
})
It("treats a 403 as an absent repo and flags it as unavailable", func() {
files, unavailable, err := FetchOptionalRepoFiles(stubClient(http.StatusForbidden, ""), "unsloth/Gated-GGUF")
Expect(err).ToNot(HaveOccurred())
Expect(files).To(BeEmpty())
Expect(unavailable).To(BeTrue())
})
// A clean 404 is an unambiguous absence, so it must NOT be reported as
// unavailable: the whole point of the flag is to separate the ambiguous
// case a human may need to look at from the settled one.
It("treats a 404 as an absent repo without flagging it as unavailable", func() {
files, unavailable, err := FetchOptionalRepoFiles(stubClient(http.StatusNotFound, ""), "unsloth/Nope-GGUF")
Expect(err).ToNot(HaveOccurred())
Expect(files).To(BeEmpty())
Expect(unavailable).To(BeFalse())
})
It("parses a 200 body as usual", func() {
files, unavailable, err := FetchOptionalRepoFiles(stubClient(http.StatusOK, oneGGUFBody), "unsloth/Real-GGUF")
Expect(err).ToNot(HaveOccurred())
Expect(unavailable).To(BeFalse())
Expect(files).To(HaveLen(1))
Expect(files[0].Name).To(Equal("Model-APEX-I-Quality.gguf"))
Expect(files[0].SHA256).To(Equal("aa"))
})
// Tolerating 401/403 must not widen into tolerating everything: a 500 is a
// broken API, not evidence about whether the repo exists.
It("still errors on a 500", func() {
_, _, err := FetchOptionalRepoFiles(stubClient(http.StatusInternalServerError, ""), "unsloth/Real-GGUF")
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("unexpected status 500"))
})
})
var _ = Describe("FetchRepoFiles", func() {
// The APEX repo itself is not optional. A 401 there means the repo the run
// was asked to publish cannot be read, which is a real failure and must not
// be quietly downgraded to "no files".
It("errors on a 401 for a required repo", func() {
_, err := FetchRepoFiles(stubClient(http.StatusUnauthorized, ""), "mudler/Model-APEX-GGUF")
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("unexpected status 401"))
})
It("errors on a 403 for a required repo", func() {
_, err := FetchRepoFiles(stubClient(http.StatusForbidden, ""), "mudler/Model-APEX-GGUF")
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("unexpected status 403"))
})
It("still treats a 404 as an absent repo", func() {
files, err := FetchRepoFiles(stubClient(http.StatusNotFound, ""), "mudler/Model-APEX-GGUF")
Expect(err).ToNot(HaveOccurred())
Expect(files).To(BeEmpty())
})
})
var _ = Describe("ParseRepoFiles", func() {
It("returns gguf siblings with their lfs sha256", func() {
body := []byte(`{"siblings":[
{"rfilename":"Model-APEX-I-Quality.gguf","size":10,"lfs":{"sha256":"aa","size":10}},
{"rfilename":"README.md"},
{"rfilename":"mmproj.gguf","size":5,"lfs":{"sha256":"bb","size":5}}
]}`)
files, err := ParseRepoFiles(body)
Expect(err).ToNot(HaveOccurred())
Expect(files).To(HaveLen(2))
Expect(files[0].Name).To(Equal("Model-APEX-I-Quality.gguf"))
Expect(files[0].SHA256).To(Equal("aa"))
Expect(files[1].Name).To(Equal("mmproj.gguf"))
})
It("reports a gguf that carries no lfs sha256", func() {
body := []byte(`{"siblings":[{"rfilename":"mmproj.gguf","size":5}]}`)
_, err := ParseRepoFiles(body)
Expect(err).To(MatchError(ErrNoSHA256))
})
})

View File

@@ -1,143 +0,0 @@
package main
import (
"fmt"
"os"
"strings"
"gopkg.in/yaml.v3"
"github.com/mudler/LocalAI/.github/ci/galleryedit"
)
// IndexText is the gallery index seen as text: the entries it declares plus the
// exact lines each one occupies, which is what splicing a variants block into an
// entry the gallery already ships requires.
//
// It is a second, narrower read of the same file LoadExisting parses. The two
// answer different questions: LoadExisting answers "do these weights already
// exist anywhere", this one answers "where in the file does this entry live".
type IndexText struct {
Lines []string
Entries []*indexEntry
byName map[string]*indexEntry
}
// indexEntry is one entry of the index: its name, the variants it already
// declares, and its coordinates in the file.
type indexEntry struct {
Name string `yaml:"name"`
Variants []VariantRef `yaml:"variants"`
Pos galleryedit.Entry `yaml:"-"`
}
// LoadIndexText reads the gallery index for editing.
func LoadIndexText(path string) (*IndexText, error) {
raw, err := os.ReadFile(path)
if err != nil {
return nil, err
}
return ParseIndexText(string(raw))
}
// ParseIndexText pairs the decoded entries with the top level list items the
// text actually contains.
//
// If the two views disagree on how many entries there are then every line number
// a splice would compute is suspect, and the failure mode is writing a variants
// block into the wrong model. The parse refuses instead.
func ParseIndexText(text string) (*IndexText, error) {
var entries []*indexEntry
if err := yaml.Unmarshal([]byte(text), &entries); err != nil {
return nil, fmt.Errorf("decoding gallery index: %w", err)
}
lines, starts := galleryedit.Scan(text)
if len(starts) != len(entries) {
return nil, fmt.Errorf("gallery index has %d decoded entries but %d top level list items; refusing to edit by line number",
len(entries), len(starts))
}
ix := &IndexText{Lines: lines, Entries: entries, byName: map[string]*indexEntry{}}
for i, e := range entries {
if e == nil {
return nil, fmt.Errorf("gallery index list item %d is empty; refusing to edit by line number", i)
}
end := len(lines)
if i+1 < len(starts) {
end = starts[i+1]
}
e.Pos = galleryedit.Entry{Name: e.Name, StartLine: starts[i], EndLine: end}
// First occurrence wins, matching the gallery's own resolution.
key := strings.ToLower(e.Name)
if _, seen := ix.byName[key]; !seen {
ix.byName[key] = e
}
}
return ix, nil
}
// Find looks an entry up by name, case insensitively.
func (ix *IndexText) Find(name string) *indexEntry {
return ix.byName[strings.ToLower(name)]
}
// ResolveHub returns the gallery name of a family's hub and whether the gallery
// already ships an entry under it.
//
// The hub is the BASE model entry, never a generated *-apex parent. Somebody
// looking for qwen3.6-35b-a3b has to find every build of those weights under
// that one name: the APEX imatrix rungs, the unsloth quant rungs and any
// speculative build. A separate qwen3.6-35b-a3b-apex hub competing with the base
// entry would split the family in two and leave whichever half the user did not
// search for invisible.
//
// Both candidates are tried for the same reason CounterpartCandidates tries
// both. The repo name and the published file stem disagree for several of these
// repos, and either one may be what the base entry was named after.
func ResolveHub(ix *IndexText, repoBase, stem string) (name string, exists bool) {
candidates := CounterpartCandidates(repoBase, stem)
for _, c := range candidates {
if n := slug(c); ix.Find(n) != nil {
return n, true
}
}
// Nothing matched, so the family needs a hub of its own under the repo
// derived name, which is the more reliable of the two.
return slug(candidates[0]), false
}
// HubLabel is the human-cased base model name, for prose rather than lookup.
func HubLabel(repoBase, stem string) string {
return CounterpartCandidates(repoBase, stem)[0]
}
// filterVariants drops the references a hub must not carry: itself, and anything
// it already lists.
//
// The self reference is not merely redundant. A hub that names itself makes the
// verifier resolve the reference back to the hub, see that the hub declares
// variants, and report a variant that declares variants of its own. It arises
// for real rather than in theory: an unsloth rung whose weights the gallery
// already ships under the base model name resolves, through Merge, straight back
// to the hub that is about to reference it.
func filterVariants(hub string, already []VariantRef, want []string) []string {
seen := map[string]bool{strings.ToLower(hub): true}
for _, v := range already {
seen[strings.ToLower(v.Model)] = true
}
var out []string
for _, w := range want {
key := strings.ToLower(w)
if seen[key] {
continue
}
seen[key] = true
out = append(out, w)
}
return out
}

View File

@@ -1,795 +0,0 @@
// Command apexentries generates gallery entries for the mudler APEX GGUF
// repositories: one entry per imatrix tier and per unsloth quant rung, all
// gathered under the BASE model's entry. Builds off a *-APEX-MTP-GGUF repo turn
// speculative decoding on, because those weights retain the model's MTP heads
// and are only worth their extra size with the heads in use.
//
// The base model entry is the hub. Somebody looking for qwen3.6-35b-a3b must
// find every build of those weights under that one name, so when the gallery
// already ships the base entry this command splices a variants block into it
// rather than emitting a competing *-apex parent beside it. Only a family whose
// base model the gallery does not ship at all gets a new hub entry, and that one
// is still named for the base model.
//
// Builds are discovered by inspecting the filenames a repo actually publishes.
// Repo names do not reliably predict them: mudler/gemma-4-26B-A4B-it-APEX-GGUF
// ships gemma-4-26B-A4B-APEX-*.gguf, and six of the 45 repos drop a suffix or a
// vendor prefix in the same way.
package main
import (
"encoding/json"
"flag"
"fmt"
"io"
"net/http"
"os"
"path"
"sort"
"strings"
"gopkg.in/yaml.v3"
"github.com/mudler/LocalAI/.github/ci/galleryedit"
)
const (
// entryTemplate carries no backend and no parameters of its own, which is
// why RenderChild states everything inline.
entryTemplate = "virtual.yaml"
unslothOwner = "unsloth"
authorListURL = "https://huggingface.co/api/models?author=mudler&limit=300"
)
// rungRank orders the quality ladder from best to smallest. The HuggingFace API
// returns siblings alphabetically and DiscoverAPEXTiers preserves that order, so
// an unsorted variants list reads I-Balanced, I-Compact, I-Mini, I-Nano,
// I-Quality. Selection ignores authored order, so this is purely so the file a
// human reviews scans in a meaningful sequence.
var rungRank = map[string]int{
"I-Quality": 0, "I-Balanced": 1, "I-Compact": 2, "I-Mini": 3, "I-Nano": 4,
"Quality": 5, "Balanced": 6, "Compact": 7, "Mini": 8, "Nano": 9,
}
// baseTags are the tags every generated entry carries. dflash and mtp are never
// among them: RenderChild adds those if and only if the entry configures the
// matching spec_type.
var baseTags = []string{"llm", "gguf", "cpu", "gpu"}
// childBuild pairs a rendered entry with its position on the quality ladder, so
// the parent's variants list can be sorted without re-parsing entry names.
type childBuild struct {
entry GalleryEntry
rank int
}
// family is one APEX repo's full generated output.
type family struct {
repo string
repoBase string
stem string
hasMMProj bool
children []childBuild
// skippedRepos are counterpart candidates HuggingFace would not describe.
// Carried on the family rather than printed and forgotten so the run can
// summarize them next to everything else a reviewer has to eyeball.
skippedRepos []string
census fileCensus
unaccounted int
}
// fileCensus splits the files discovery emitted nothing for into the ones a
// reviewer must chase and the ones that are deliberately out of scope.
//
// Full-precision sources are the second kind: they are the unquantized weights
// the ladder is derived FROM, not a rung of it. Folding them into the
// unclassified total would leave a permanent benign baseline, and a permanent
// baseline is exactly what hides the one file that ever genuinely matters.
type fileCensus struct {
unclassified int
fullPrecision int
}
// add accumulates one repo's census into a running total.
func (c *fileCensus) add(o fileCensus) {
c.unclassified += o.unclassified
c.fullPrecision += o.fullPrecision
}
// sortedChildren returns the family's builds in ladder order, best first.
func (f *family) sortedChildren() []childBuild {
sorted := append([]childBuild{}, f.children...)
sort.SliceStable(sorted, func(i, j int) bool { return sorted[i].rank < sorted[j].rank })
return sorted
}
func main() {
verify := flag.String("verify", "", "verify a gallery index and exit")
index := flag.String("index", "gallery/index.yaml", "gallery index to dedup against")
only := flag.String("only", "", "comma-separated repo names to restrict generation to")
out := flag.String("out", "", "write the entries to add to this file")
apply := flag.Bool("apply", false, "append the entries to add to -index")
flag.Parse()
if *verify != "" {
problems := Verify(*verify)
for _, p := range problems {
fmt.Fprintln(os.Stderr, p)
}
if len(problems) > 0 {
fmt.Fprintf(os.Stderr, "%d problem(s)\n", len(problems))
os.Exit(1)
}
fmt.Println("index is sound")
return
}
if err := generate(*index, *only, *out, *apply); err != nil {
fmt.Fprintln(os.Stderr, "error:", err)
os.Exit(1)
}
}
func generate(indexPath, only, outPath string, apply bool) error {
if outPath == "" && !apply {
return fmt.Errorf("nothing to do: pass -out <file> or -apply")
}
client := newHTTPClient()
repos, err := listAPEXRepos(client)
if err != nil {
return err
}
if only != "" {
repos = restrict(repos, only)
}
if len(repos) == 0 {
return fmt.Errorf("no APEX repos selected")
}
fmt.Printf("repos selected: %d\n", len(repos))
var families []family
var failed []string
for _, repo := range repos {
f, err := buildFamily(client, repo)
if err != nil {
// A missing sha256 is fatal for the family rather than skippable: an
// entry without one ships an unverifiable download. Report which repo
// and keep going, so one bad repo does not hide the state of the rest.
fmt.Fprintf(os.Stderr, "FAILED %s: %v\n", repo, err)
failed = append(failed, repo)
continue
}
families = append(families, *f)
}
existing, err := LoadExisting(indexPath)
if err != nil {
return err
}
ixText, err := LoadIndexText(indexPath)
if err != nil {
return err
}
fmt.Printf("existing index: %d names, %d weight URIs, %d lines\n",
len(existing.ByName), len(existing.ByURI), len(ixText.Lines))
// Only the builds go through Merge. A hub is deliberately kept out of it: a
// new hub carries the family's top rung as its own payload, so Merge's URI
// dedup would fold the hub into that rung and the family would lose the very
// entry point this command exists to create. Hub names are checked against
// the index directly, by ResolveHub.
var generated []GalleryEntry
for _, f := range families {
for _, c := range f.children {
generated = append(generated, c.entry)
}
}
add, reused := Merge(existing, generated)
reportReuse(existing, generated, reused)
// Variant references are resolved from `reused`, never used to decide what to
// emit: on a within-batch name collision Merge records reused[name] = name
// while the first entry of that name is still in `add`, so treating presence
// in `reused` as "dropped" would silently emit nothing for it.
added := map[string]bool{}
for _, e := range add {
added[e.Name] = true
}
inserts, newHubs, err := planHubs(families, ixText, reused, added)
if err != nil {
return err
}
reportHubs(ixText, inserts, newHubs)
skipped, census, fullPrecisionRepos, unaccounted := reportSkipped(families)
add = append(add, newHubs...)
fmt.Printf("\nentries generated: %d\nentries to add: %d\nentries reused: %d\nhubs spliced: %d\nhubs created: %d\nrepos skipped: %d\nexcluded (full precision): %d files across %d repos\nunclassified: %d\nunaccounted: %d\n",
len(generated), len(add), len(reused), len(inserts), len(newHubs), len(skipped),
census.fullPrecision, fullPrecisionRepos, census.unclassified, unaccounted)
lines, err := galleryedit.Apply(ixText.Lines, inserts)
if err != nil {
return err
}
if err := writeEntries(add, lines, outPath, apply, indexPath); err != nil {
return err
}
if len(failed) > 0 {
return fmt.Errorf("%d repo(s) failed: %s", len(failed), strings.Join(failed, ", "))
}
return nil
}
// resolveVariant maps a generated child name onto whatever entry actually stands
// for it after the merge. `added` is consulted first because a within-batch name
// collision puts a name in BOTH add and reused, and the entry that was emitted
// is the one the parent must reference.
func resolveVariant(name string, reused map[string]string, added map[string]bool) string {
if added[name] {
return name
}
if target, ok := reused[name]; ok {
return target
}
return name
}
// SpecTypeForRepo reports the speculative decoding mechanism a repo's builds can
// turn on with no extra download.
//
// The *-APEX-MTP-GGUF repos republish the base weights with the model's own MTP
// heads retained, so those builds are only worth their extra size if the heads
// are actually used. Every other APEX repo drops them, and switching MTP on
// there would name a mechanism the weights cannot serve.
//
// The suffix is read off the repo the FILES come from, so nothing downstream has
// to infer a capability from an entry name.
func SpecTypeForRepo(repo string) string {
if strings.HasSuffix(path.Base(repo), "-APEX-MTP-GGUF") {
return "draft-mtp"
}
return ""
}
// buildFamily discovers everything one APEX repo and its unsloth counterpart
// publish, and renders it.
func buildFamily(client *http.Client, repo string) (*family, error) {
files, err := FetchRepoFiles(client, repo)
if err != nil {
return nil, err
}
if len(files) == 0 {
return nil, fmt.Errorf("no gguf files")
}
imatrix, plain := DiscoverAPEXTiers(files)
mmproj, hasMMProj := DiscoverMMProj(files)
census := reportUnclassified(repo, files, imatrix, plain)
// The imatrix ladder is preferred, but two of the 45 repos publish no
// imatrix tiers at all and must still contribute their plain ladder.
ladder := imatrix
ladderKind := "imatrix"
if len(ladder) == 0 {
ladder = plain
ladderKind = "plain"
}
if len(ladder) == 0 {
return nil, fmt.Errorf("no tiers discovered")
}
sortTiers(ladder)
var mm *GGUFFile
if hasMMProj {
mm = &mmproj
}
repoBase := strings.TrimSuffix(path.Base(repo), "-GGUF")
f := &family{repo: repo, repoBase: repoBase, hasMMProj: hasMMProj, census: census}
// Only the APEX ladder can carry MTP heads; the unsloth counterpart quantizes
// the plain weights and gets nothing from this.
specType := SpecTypeForRepo(repo)
for _, t := range ladder {
f.children = append(f.children, childBuild{
rank: rungRank[t.Label],
entry: RenderChild(ChildInput{
Name: slug(repoBase) + "-" + slug(t.Label),
Repo: repo,
Template: entryTemplate,
SpecType: specType,
Weights: []GGUFFile{t.File},
MMProj: mm,
BaseTags: baseTags,
}),
})
}
stem := FileStem(ladder[0])
f.stem = stem
fmt.Printf("%s: %d %s tier(s) [%s], stem %s, mmproj %v\n",
repo, len(ladder), ladderKind, tierLabels(ladder), stem, hasMMProj)
counterpart, cpFiles, skipped, err := resolveCounterpart(client, repoBase, stem)
f.skippedRepos = skipped
if err != nil {
return nil, err
}
if counterpart != "" {
builds := DiscoverUnslothQuants(cpFiles)
// Called here rather than inside Verify: a quant dropped at discovery
// leaves no trace at all in the finished gallery file, so the only place
// the shortfall is still visible is the moment of discovery.
unaccounted := UnaccountedQuants(cpFiles, builds)
f.unaccounted = len(unaccounted)
for _, p := range unaccounted {
fmt.Fprintf(os.Stderr, "UNACCOUNTED QUANT %s: %s\n", counterpart, p)
}
cpMMProj, hasCPMMProj := DiscoverMMProj(cpFiles)
var cpMM *GGUFFile
if hasCPMMProj {
cpMM = &cpMMProj
}
cpBase := strings.TrimSuffix(path.Base(counterpart), "-GGUF")
for i, b := range builds {
f.children = append(f.children, childBuild{
rank: 100 + i,
entry: RenderChild(ChildInput{
Name: slug(cpBase) + "-" + slug(b.Quant),
Repo: counterpart,
Template: entryTemplate,
Weights: b.Files,
MMProj: cpMM,
BaseTags: baseTags,
}),
})
}
fmt.Printf("%s: counterpart %s, %d quant build(s) %s\n", repo, counterpart, len(builds), quantLabels(builds))
} else {
fmt.Printf("%s: no unsloth counterpart\n", repo)
}
return f, nil
}
// planHubs decides, per family, whether the family's builds are spliced into a
// base model entry the gallery already ships or gathered under a new hub.
//
// Splicing is strongly preferred and is the measured majority-adjacent case. The
// existing entry keeps its description, icon, tags, overrides and files
// untouched; only variant lines are added to it.
func planHubs(families []family, ix *IndexText, reused map[string]string, added map[string]bool) ([]galleryedit.Insert, []GalleryEntry, error) {
// Several APEX repos can resolve to one base model, so both paths accumulate
// by hub name rather than assuming one family per hub.
wantByHub := map[string][]string{}
var spliceOrder []string
var newHubs []GalleryEntry
hubAt := map[string]int{}
for i := range families {
f := &families[i]
hubName, exists := ResolveHub(ix, f.repoBase, f.stem)
want := hubVariants(f, ix, reused, added)
if exists {
if _, seen := wantByHub[hubName]; !seen {
spliceOrder = append(spliceOrder, hubName)
}
wantByHub[hubName] = append(wantByHub[hubName], want...)
continue
}
if at, dup := hubAt[hubName]; dup {
for _, v := range filterVariants(hubName, newHubs[at].Variants, want) {
newHubs[at].Variants = append(newHubs[at].Variants, VariantRef{Model: v})
}
continue
}
builds := f.sortedChildren()
if len(builds) == 0 {
return nil, nil, fmt.Errorf("%s: no builds to hang a hub on", f.repo)
}
hubAt[hubName] = len(newHubs)
newHubs = append(newHubs, renderHub(hubName, f, builds[0], filterVariants(hubName, nil, want)))
}
var inserts []galleryedit.Insert
for _, name := range spliceOrder {
e := ix.Find(name)
items := filterVariants(name, e.Variants, wantByHub[name])
if len(items) == 0 {
continue
}
inserts = append(inserts, galleryedit.Insert{Entry: e.Pos, Variants: items})
}
return inserts, newHubs, nil
}
// hubVariants is a family's full build list, in ladder order, named as the hub
// must reference them after the merge.
func hubVariants(f *family, ix *IndexText, reused map[string]string, added map[string]bool) []string {
var out []string
// A hand-written *-apex entry is an ordinary build of these weights. It is
// never deleted, never renamed and never treated as a hub; it is simply
// referenced like any other rung.
if apex := slug(f.repoBase); ix.Find(apex) != nil {
out = append(out, apex)
}
for _, c := range f.sortedChildren() {
out = append(out, resolveVariant(c.entry.Name, reused, added))
}
return out
}
// renderHub builds the hub for a family whose base model the gallery does not
// ship at all. It is named for the BASE model, never for the APEX repo.
//
// It carries one of the discovered builds as its own payload so it is a complete
// installable entry rather than a bare index pointing at other entries. That
// payload is what supplies overrides.backend, which matters beyond installation:
// the verifier can only judge the tagging rule for a backend it can read, so a
// hub carrying feature tags and no backend would escape the check in silence.
//
// The payload's own tags are kept rather than rebuilt from baseTags, so a hub
// whose payload configures a spec_type stays tagged for it and consistent with
// the overrides copied alongside.
func renderHub(name string, f *family, payload childBuild, variants []string) GalleryEntry {
e := payload.entry
e.Name = name
e.Description = fmt.Sprintf(
"%s. Quality ladder and quantization rungs published by %s and its unsloth counterpart; LocalAI picks the build that fits the hardware.",
HubLabel(f.repoBase, f.stem), f.repo)
e.Tags = append([]string{}, payload.entry.Tags...)
if f.hasMMProj && !hasTag(e.Tags, "vision") {
e.Tags = append(e.Tags, "vision")
}
e.Variants = nil
for _, v := range variants {
e.Variants = append(e.Variants, VariantRef{Model: v})
}
return e
}
func hasTag(tags []string, want string) bool {
for _, t := range tags {
if t == want {
return true
}
}
return false
}
// resolveCounterpart probes the unsloth candidates in order and returns the
// first that publishes files.
//
// CounterpartCandidates is handed a BARE repo name: its cleaner does not strip
// an owner prefix, so passing "mudler/Foo-APEX-GGUF" would yield "mudler/Foo"
// and compose into the nonsense probe "unsloth/mudler/Foo".
//
// It also returns the candidates HuggingFace refused to describe. Those are
// indistinguishable from absent without credentials, so they are skipped, but
// they are named rather than dropped: one of them could be a real gated repo
// whose quants belong in the gallery.
func resolveCounterpart(client *http.Client, repoBase, stem string) (string, []GGUFFile, []string, error) {
var unavailable []string
for _, cand := range CounterpartCandidates(repoBase, stem) {
repo := unslothOwner + "/" + cand + "-GGUF"
files, unreadable, err := FetchOptionalRepoFiles(client, repo)
if err != nil {
return "", nil, unavailable, fmt.Errorf("probing %s: %w", repo, err)
}
if unreadable {
unavailable = append(unavailable, repo)
continue
}
if len(files) > 0 {
return repo, files, unavailable, nil
}
}
return "", nil, unavailable, nil
}
// reportUnclassified prints the files discovery turned into nothing.
//
// It is a set difference on COUNTS, not a re-match of filenames: re-matching
// would duplicate the tier regex from discover.go and the two copies would
// drift. The likeliest trigger is a typo or case change from a publishing script
// rather than a genuine sixth tier, and because generation falls back to the
// plain ladder when the imatrix one is empty, a repo whose imatrix files all
// fail to match silently downgrades the whole family instead of erroring. The
// downstream HTTP check cannot catch that: it validates URLs that were emitted,
// and an undiscovered tier emits none.
// It returns the census so the run can total it.
func reportUnclassified(repo string, files []GGUFFile, imatrix, plain []Tier) fileCensus {
mmprojCount, fullPrecision := 0, 0
for _, f := range files {
// The mmproj test comes first because projectors are themselves often
// published at f16 (mmproj-F16.gguf), and counting such a file in both
// buckets would understate the unclassified remainder.
if strings.HasPrefix(f.Name, "mmproj") {
mmprojCount++
continue
}
if IsFullPrecision(f.Name) {
fullPrecision++
}
}
classified := len(imatrix) + len(plain) + mmprojCount + fullPrecision
if classified >= len(files) {
return fileCensus{fullPrecision: fullPrecision}
}
fmt.Fprintf(os.Stderr, "UNCLASSIFIED %s: %d of %d .gguf files classified, %d unaccounted for\n",
repo, classified, len(files), len(files)-classified)
return fileCensus{unclassified: len(files) - classified, fullPrecision: fullPrecision}
}
// reportReuse splits Merge's single reused map into the two cases it conflates.
//
// A URI match means the gallery already ships exactly these weights, and
// pointing the parent at the existing entry is correct. A NAME match with a
// different URI means an unrelated entry happens to own the name, and
// referencing it would point the parent at different weights than were
// generated, substituting a build without saying so. Only the first is safe to
// wave through.
func reportReuse(existing *ExistingIndex, generated []GalleryEntry, reused map[string]string) {
byName := map[string]GalleryEntry{}
for _, e := range generated {
if _, seen := byName[e.Name]; !seen {
byName[e.Name] = e
}
}
var nameCollisions, uriMatches []string
for name, target := range reused {
gen := byName[name]
uri := ""
if len(gen.Files) > 0 {
uri = gen.Files[0].URI
}
switch {
case hasName(existing, name):
nameCollisions = append(nameCollisions,
fmt.Sprintf(" %s -> gallery entry of the same name (generated uri: %s)", name, orNone(uri)))
case target == name:
nameCollisions = append(nameCollisions,
fmt.Sprintf(" %s -> earlier entry of the same name in this batch (generated uri: %s)", name, orNone(uri)))
default:
uriMatches = append(uriMatches, fmt.Sprintf(" %s -> %s (same weights: %s)", name, target, orNone(uri)))
}
}
sort.Strings(nameCollisions)
sort.Strings(uriMatches)
fmt.Printf("\nNAME COLLISIONS (%d) - inspect each by hand, the target may hold different weights\n", len(nameCollisions))
for _, l := range nameCollisions {
fmt.Println(l)
}
fmt.Printf("\nURI MATCHES (%d) - the gallery or this batch already ships these exact weights\n", len(uriMatches))
for _, l := range uriMatches {
fmt.Println(l)
}
}
// reportHubs prints exactly what will be written where. The splices are the part
// a human has to read: they modify entries the gallery already ships, so the
// review needs the target, the line, and every added reference spelled out.
func reportHubs(ix *IndexText, inserts []galleryedit.Insert, newHubs []GalleryEntry) {
fmt.Printf("\nHUBS SPLICED (%d) - variants added to the EXISTING base model entry, nothing else touched\n", len(inserts))
for _, in := range inserts {
e := ix.Find(in.Entry.Name)
fmt.Printf(" %s (line %d, %d variant(s) already declared):\n", in.Entry.Name, in.Entry.StartLine+1, len(e.Variants))
for _, v := range in.Variants {
fmt.Printf(" + - model: %s\n", galleryedit.QuoteName(v))
}
}
fmt.Printf("\nHUBS CREATED (%d) - the gallery ships no base model entry, so one is emitted for it\n", len(newHubs))
for _, h := range newHubs {
fmt.Printf(" %s:\n", h.Name)
for _, v := range h.Variants {
fmt.Printf(" - model: %s\n", v.Model)
}
}
}
// reportSkipped names the counterpart repos HuggingFace would not describe, and
// totals the other two silent-shortfall counters alongside them.
//
// A skipped repo is not the same as a clean 404. HuggingFace answers 401 for a
// nonexistent repo to an unauthenticated client, so the overwhelmingly likely
// reading is "there is no such counterpart", which is the normal case for the
// community merges. But a private or gated repo answers 401 too, and that one
// WOULD have quants worth shipping. Printing the list is what keeps that
// possibility auditable instead of silently discarded.
func reportSkipped(families []family) ([]string, fileCensus, int, int) {
var skipped []string
var census fileCensus
fullPrecisionRepos, unaccounted := 0, 0
for _, f := range families {
skipped = append(skipped, f.skippedRepos...)
census.add(f.census)
if f.census.fullPrecision > 0 {
fullPrecisionRepos++
}
unaccounted += f.unaccounted
}
sort.Strings(skipped)
fmt.Printf("\nREPOS SKIPPED AS UNAVAILABLE (%d) - HuggingFace answered 401/403, which is indistinguishable from absent without a token; check none of these is a real gated repo\n", len(skipped))
for _, r := range skipped {
fmt.Printf(" %s\n", r)
}
return skipped, census, fullPrecisionRepos, unaccounted
}
func hasName(ix *ExistingIndex, name string) bool {
_, ok := ix.ByName[name]
return ok
}
func orNone(s string) string {
if s == "" {
return "(no files)"
}
return s
}
// writeEntries emits the additions.
//
// -apply does two things in one pass: it writes back the spliced lines, which
// differ from the original only by the variant lines galleryedit inserted, and
// then appends the new entries. New entries are APPENDED rather than merged into
// the structure, for the same reason the splice is textual: a YAML round trip
// over 40,000 lines would reflow the whole file into an unreviewable diff.
func writeEntries(add []GalleryEntry, lines []string, outPath string, apply bool, indexPath string) error {
if apply {
if err := os.WriteFile(indexPath, []byte(strings.Join(lines, "\n")), 0o644); err != nil {
return err
}
fmt.Printf("spliced %s\n", indexPath)
}
if len(add) == 0 {
fmt.Println("nothing to append")
return nil
}
blob, err := yaml.Marshal(add)
if err != nil {
return err
}
if outPath != "" {
if err := os.WriteFile(outPath, blob, 0o644); err != nil {
return err
}
fmt.Printf("wrote %d entries to %s\n", len(add), outPath)
}
if apply {
f, err := os.OpenFile(indexPath, os.O_APPEND|os.O_WRONLY, 0o644)
if err != nil {
return err
}
defer f.Close()
if _, err := f.Write(blob); err != nil {
return err
}
fmt.Printf("appended %d entries to %s\n", len(add), indexPath)
}
return nil
}
// listAPEXRepos returns the mudler repos whose name marks them as APEX builds.
func listAPEXRepos(client *http.Client) ([]string, error) {
req, err := http.NewRequest(http.MethodGet, authorListURL, nil)
if err != nil {
return nil, err
}
req.Header.Set("User-Agent", "localai-apexentries/1.0")
resp, err := client.Do(req)
if err != nil {
return nil, err
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("listing models: unexpected status %d", resp.StatusCode)
}
body, err := io.ReadAll(resp.Body)
if err != nil {
return nil, err
}
var models []struct {
ID string `json:"id"`
}
if err := json.Unmarshal(body, &models); err != nil {
return nil, fmt.Errorf("decoding model list: %w", err)
}
var out []string
for _, m := range models {
if strings.Contains(m.ID, "APEX") {
out = append(out, m.ID)
}
}
sort.Strings(out)
return out, nil
}
func restrict(repos []string, only string) []string {
want := map[string]bool{}
for _, r := range strings.Split(only, ",") {
if r = strings.TrimSpace(r); r != "" {
want[r] = true
}
}
var out []string
for _, r := range repos {
if want[r] {
out = append(out, r)
delete(want, r)
}
}
// A name in -only that matched nothing is a typo, not an empty result.
for r := range want {
fmt.Fprintf(os.Stderr, "WARNING: -only names %s, which is not an APEX repo of this author\n", r)
}
return out
}
func sortTiers(tiers []Tier) {
sort.SliceStable(tiers, func(i, j int) bool { return rungRank[tiers[i].Label] < rungRank[tiers[j].Label] })
}
func tierLabels(tiers []Tier) string {
var out []string
for _, t := range tiers {
out = append(out, t.Label)
}
return strings.Join(out, ",")
}
func quantLabels(builds []QuantBuild) string {
var out []string
for _, b := range builds {
l := b.Quant
if b.Sharded {
l += fmt.Sprintf("(%d shards)", len(b.Files))
}
out = append(out, l)
}
return strings.Join(out, ",")
}
// slug turns a repo, tier or quant label into a gallery entry name component.
func slug(s string) string {
return strings.ReplaceAll(strings.ToLower(s), "_", "-")
}

View File

@@ -1,344 +0,0 @@
package main
import (
"strings"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"github.com/mudler/LocalAI/.github/ci/galleryedit"
)
func mustIndex(text string) *IndexText {
ix, err := ParseIndexText(text)
ExpectWithOffset(1, err).ToNot(HaveOccurred())
return ix
}
// buildOf renders a realistic child so the specs exercise the payload a hub
// actually inherits rather than a bare name.
func buildOf(name, repo, file string, rank int) childBuild {
return childBuild{
rank: rank,
entry: RenderChild(ChildInput{
Name: name,
Repo: repo,
Template: entryTemplate,
Weights: []GGUFFile{{Name: file, SHA256: "aa"}},
BaseTags: baseTags,
}),
}
}
var _ = Describe("ResolveHub", func() {
It("picks the base model name over the APEX name, even when both are in the gallery", func() {
// The hub is the entry a user searches for. If the *-apex entry were
// chosen the family would be gathered under a name nobody looks up, and
// the base entry would go on advertising only its own build.
ix := mustIndex("- name: qwen3.6-35b-a3b\n url: u\n- name: qwen3.6-35b-a3b-apex\n url: u\n")
name, exists := ResolveHub(ix, "Qwen3.6-35B-A3B-APEX", "Qwen3.6-35B-A3B-APEX")
Expect(name).To(Equal("qwen3.6-35b-a3b"))
Expect(exists).To(BeTrue())
})
It("falls back to the stem-derived candidate when the repo-derived one is absent", func() {
// gemma's repo says "-it" and its published files do not, so only one of
// the two candidates can match whatever the base entry was named after.
ix := mustIndex("- name: gemma-4-26b-a4b\n url: u\n")
name, exists := ResolveHub(ix, "gemma-4-26B-A4B-it-APEX", "gemma-4-26B-A4B-APEX")
Expect(name).To(Equal("gemma-4-26b-a4b"))
Expect(exists).To(BeTrue())
})
It("reports the base name as absent rather than settling for the APEX entry", func() {
ix := mustIndex("- name: qwen3.5-35b-a3b-apex\n url: u\n")
name, exists := ResolveHub(ix, "Qwen3.5-35B-A3B-APEX", "Qwen3.5-35B-A3B-APEX")
Expect(name).To(Equal("qwen3.5-35b-a3b"))
Expect(exists).To(BeFalse())
})
It("strips the MTP and TQ markers as well as APEX", func() {
ix := mustIndex("- name: qwen3.6-35b-a3b\n url: u\n")
name, exists := ResolveHub(ix, "Qwen3.6-35B-A3B-APEX-MTP", "Qwen3.6-35B-A3B-APEX-MTP")
Expect(name).To(Equal("qwen3.6-35b-a3b"))
Expect(exists).To(BeTrue())
})
})
var _ = Describe("planHubs", func() {
noReuse := map[string]string{}
allAdded := func(names ...string) map[string]bool {
out := map[string]bool{}
for _, n := range names {
out[n] = true
}
return out
}
It("splices into the existing base entry instead of emitting an *-apex parent", func() {
ix := mustIndex("- name: step-3.7-flash\n url: u\n- name: other\n url: u\n")
fams := []family{{
repo: "mudler/Step-3.7-Flash-APEX-GGUF",
repoBase: "Step-3.7-Flash-APEX",
stem: "Step-3.7-Flash-APEX",
children: []childBuild{buildOf("step-3.7-flash-apex-i-quality", "mudler/Step-3.7-Flash-APEX-GGUF", "a.gguf", 0)},
}}
inserts, newHubs, err := planHubs(fams, ix, noReuse, allAdded("step-3.7-flash-apex-i-quality"))
Expect(err).ToNot(HaveOccurred())
Expect(newHubs).To(BeEmpty())
Expect(inserts).To(HaveLen(1))
Expect(inserts[0].Entry.Name).To(Equal("step-3.7-flash"))
Expect(inserts[0].Variants).To(Equal([]string{"step-3.7-flash-apex-i-quality"}))
})
It("merges into an entry that already declares variants, without repeating one", func() {
// The gallery's qwen3.6-35b-a3b already lists its APEX build. Re-adding it
// would put a duplicate key's worth of noise in the diff and a duplicate
// reference in the entry.
ix := mustIndex("- name: qwen3.6-35b-a3b\n variants:\n - model: qwen3.6-35b-a3b-apex\n url: u\n" +
"- name: qwen3.6-35b-a3b-apex\n url: u\n")
fams := []family{{
repo: "mudler/Qwen3.6-35B-A3B-APEX-GGUF",
repoBase: "Qwen3.6-35B-A3B-APEX",
stem: "Qwen3.6-35B-A3B-APEX",
children: []childBuild{buildOf("qwen3.6-35b-a3b-apex-i-quality", "mudler/Qwen3.6-35B-A3B-APEX-GGUF", "a.gguf", 0)},
}}
inserts, newHubs, err := planHubs(fams, ix, noReuse, allAdded("qwen3.6-35b-a3b-apex-i-quality"))
Expect(err).ToNot(HaveOccurred())
Expect(newHubs).To(BeEmpty())
Expect(inserts[0].Variants).To(Equal([]string{"qwen3.6-35b-a3b-apex-i-quality"}))
out, err := galleryedit.Apply(ix.Lines, inserts)
Expect(err).ToNot(HaveOccurred())
Expect(strings.Count(strings.Join(out, "\n"), "variants:")).To(Equal(1))
Expect(out).To(HaveLen(len(ix.Lines) + 1))
})
It("never lets the hub reference itself", func() {
// An unsloth rung whose weights the gallery already ships under the base
// name resolves, through Merge, straight back to the hub. The verifier
// reads a self reference as a variant that declares variants of its own.
ix := mustIndex("- name: step-3.7-flash\n url: u\n")
fams := []family{{
repo: "mudler/Step-3.7-Flash-APEX-GGUF",
repoBase: "Step-3.7-Flash-APEX",
stem: "Step-3.7-Flash-APEX",
children: []childBuild{buildOf("step-3.7-flash-ud-q4-k-m", "unsloth/Step-3.7-Flash-GGUF", "a.gguf", 100)},
}}
inserts, _, err := planHubs(fams, ix, map[string]string{"step-3.7-flash-ud-q4-k-m": "step-3.7-flash"}, map[string]bool{})
Expect(err).ToNot(HaveOccurred())
Expect(inserts).To(BeEmpty())
})
It("emits a hub named for the base model when the gallery has none", func() {
ix := mustIndex("- name: qwen3.5-35b-a3b-apex\n url: u\n")
fams := []family{{
repo: "mudler/Qwen3.5-35B-A3B-APEX-GGUF",
repoBase: "Qwen3.5-35B-A3B-APEX",
stem: "Qwen3.5-35B-A3B-APEX",
hasMMProj: true,
children: []childBuild{
buildOf("qwen3.5-35b-a3b-apex-i-quality", "mudler/Qwen3.5-35B-A3B-APEX-GGUF", "a.gguf", 0),
buildOf("qwen3.5-35b-a3b-ud-q6-k", "unsloth/Qwen3.5-35B-A3B-GGUF", "b.gguf", 102),
},
}}
inserts, newHubs, err := planHubs(fams, ix, noReuse,
allAdded("qwen3.5-35b-a3b-apex-i-quality", "qwen3.5-35b-a3b-ud-q6-k"))
Expect(err).ToNot(HaveOccurred())
Expect(inserts).To(BeEmpty())
Expect(newHubs).To(HaveLen(1))
hub := newHubs[0]
Expect(hub.Name).To(Equal("qwen3.5-35b-a3b"))
Expect(hub.Name).ToNot(HaveSuffix("-apex"))
// A hand-written *-apex entry is an ordinary build, referenced like any
// other rung and never deleted or renamed.
Expect(hub.Variants).To(Equal([]VariantRef{
{Model: "qwen3.5-35b-a3b-apex"},
{Model: "qwen3.5-35b-a3b-apex-i-quality"},
{Model: "qwen3.5-35b-a3b-ud-q6-k"},
}))
// The verifier skips entries with no declared backend, so a hub without
// one would escape the tagging check in silence.
Expect(hub.Overrides).To(HaveKeyWithValue("backend", "llama-cpp"))
Expect(hub.Files).ToNot(BeEmpty())
Expect(hub.Tags).To(ContainElement("vision"))
})
It("gathers two APEX repos that share one base model under a single hub", func() {
ix := mustIndex("- name: unrelated\n url: u\n")
fams := []family{
{
repo: "mudler/Solo-APEX-GGUF",
repoBase: "Solo-APEX",
stem: "Solo-APEX",
children: []childBuild{buildOf("solo-apex-i-quality", "mudler/Solo-APEX-GGUF", "a.gguf", 0)},
},
{
repo: "mudler/Solo-APEX-MTP-GGUF",
repoBase: "Solo-APEX-MTP",
stem: "Solo-APEX-MTP",
children: []childBuild{buildOf("solo-apex-mtp-i-quality", "mudler/Solo-APEX-MTP-GGUF", "b.gguf", 0)},
},
}
_, newHubs, err := planHubs(fams, ix, noReuse, allAdded("solo-apex-i-quality", "solo-apex-mtp-i-quality"))
Expect(err).ToNot(HaveOccurred())
Expect(newHubs).To(HaveLen(1))
Expect(newHubs[0].Name).To(Equal("solo"))
Expect(newHubs[0].Variants).To(Equal([]VariantRef{
{Model: "solo-apex-i-quality"},
{Model: "solo-apex-mtp-i-quality"},
}))
})
})
var _ = Describe("hubVariants", func() {
It("orders builds by quality rung rather than discovery order", func() {
// DiscoverAPEXTiers preserves input order and the HF API returns siblings
// alphabetically, so an unsorted list reads I-Balanced, I-Compact, I-Mini,
// I-Nano, I-Quality. Selection ignores authored order; this is for the
// human reading the file.
f := family{repoBase: "X-APEX", stem: "X-APEX", children: []childBuild{
{rank: rungRank["I-Nano"], entry: GalleryEntry{Name: "x-i-nano"}},
{rank: 100, entry: GalleryEntry{Name: "x-ud-q4-k-m"}},
{rank: rungRank["I-Quality"], entry: GalleryEntry{Name: "x-i-quality"}},
{rank: rungRank["I-Compact"], entry: GalleryEntry{Name: "x-i-compact"}},
}}
got := hubVariants(&f, mustIndex("- name: x\n url: u\n"), map[string]string{}, map[string]bool{})
Expect(got).To(Equal([]string{"x-i-quality", "x-i-compact", "x-i-nano", "x-ud-q4-k-m"}))
})
})
var _ = Describe("ParseIndexText", func() {
It("refuses to edit by line number when the two views of the file disagree", func() {
_, err := ParseIndexText("- name: one\n url: u\n-\n")
Expect(err).To(MatchError(ContainSubstring("empty")))
})
It("records the line range of each entry", func() {
ix := mustIndex("- name: first\n url: u\n- name: second\n url: u\n")
Expect(ix.Find("FIRST").Pos.StartLine).To(Equal(0))
Expect(ix.Find("first").Pos.EndLine).To(Equal(2))
Expect(ix.Find("second").Pos.StartLine).To(Equal(2))
})
})
var _ = Describe("resolveVariant", func() {
It("keeps an entry that was emitted even when it is also in reused", func() {
// A within-batch name collision records reused[name] = name while the
// FIRST entry of that name is still in add. Treating presence in reused as
// "dropped" would emit nothing for it.
added := map[string]bool{"dup": true}
reused := map[string]string{"dup": "dup"}
Expect(resolveVariant("dup", reused, added)).To(Equal("dup"))
})
It("redirects a reused name at the entry that stands in for it", func() {
added := map[string]bool{}
reused := map[string]string{"generated": "already-in-gallery"}
Expect(resolveVariant("generated", reused, added)).To(Equal("already-in-gallery"))
})
})
var _ = Describe("slug", func() {
It("lowercases and turns quant underscores into hyphens", func() {
Expect(slug("UD-Q4_K_M")).To(Equal("ud-q4-k-m"))
Expect(slug("gemma-4-26B-A4B-it-APEX")).To(Equal("gemma-4-26b-a4b-it-apex"))
Expect(slug("I-Nano")).To(Equal("i-nano"))
})
})
var _ = Describe("sortTiers", func() {
It("puts the imatrix ladder in descending quality order", func() {
tiers := []Tier{
{Label: "I-Balanced"}, {Label: "I-Compact"}, {Label: "I-Mini"},
{Label: "I-Nano"}, {Label: "I-Quality"},
}
sortTiers(tiers)
Expect(tierLabels(tiers)).To(Equal("I-Quality,I-Balanced,I-Compact,I-Mini,I-Nano"))
})
})
var _ = Describe("restrict", func() {
It("keeps only the named repos", func() {
got := restrict([]string{"mudler/A-APEX-GGUF", "mudler/B-APEX-GGUF"}, "mudler/B-APEX-GGUF")
Expect(got).To(Equal([]string{"mudler/B-APEX-GGUF"}))
})
It("returns nothing when the filter matches nothing", func() {
Expect(restrict([]string{"mudler/A-APEX-GGUF"}, "mudler/typo")).To(BeEmpty())
})
})
var _ = Describe("reportUnclassified", func() {
// One real imatrix rung is always present so the specs measure how the
// remaining files are bucketed, not an empty-repo edge case.
tier := Tier{Label: "I-Quality", File: GGUFFile{Name: "Model-APEX-I-Quality.gguf"}}
censusOf := func(names ...string) fileCensus {
files := []GGUFFile{tier.File}
for _, n := range names {
files = append(files, GGUFFile{Name: n})
}
return reportUnclassified("mudler/Model-APEX-GGUF", files, []Tier{tier}, nil)
}
It("counts a flat full-precision source as excluded, not unclassified", func() {
got := censusOf("Carnice-MoE-35B-A3B-F16.gguf")
Expect(got.fullPrecision).To(Equal(1))
Expect(got.unclassified).To(Equal(0))
})
It("counts every shard of a sharded full-precision source as excluded", func() {
got := censusOf(
"MiniMax-M2.7-APEX-F16-00001-of-00003.gguf",
"MiniMax-M2.7-APEX-F16-00002-of-00003.gguf",
"MiniMax-M2.7-APEX-F16-00003-of-00003.gguf",
)
Expect(got.fullPrecision).To(Equal(3))
Expect(got.unclassified).To(Equal(0))
})
It("treats bf16 the same as f16, in either case", func() {
got := censusOf("Model-APEX-BF16.gguf", "Model-APEX-bf16-00001-of-00002.gguf", "Model-APEX-f16.gguf")
Expect(got.fullPrecision).To(Equal(3))
Expect(got.unclassified).To(Equal(0))
})
It("still reports a genuinely unknown filename as unclassified", func() {
got := censusOf("Model-APEX-Turbo.gguf")
Expect(got.unclassified).To(Equal(1))
Expect(got.fullPrecision).To(Equal(0))
})
It("separates the two kinds when a repo publishes both", func() {
got := censusOf("Model-APEX-F16.gguf", "Model-APEX-Turbo.gguf")
Expect(got.fullPrecision).To(Equal(1))
Expect(got.unclassified).To(Equal(1))
})
})

View File

@@ -1,143 +0,0 @@
package main
import (
"fmt"
"os"
"strings"
"gopkg.in/yaml.v3"
)
const (
hfShorthandPrefix = "huggingface://"
hfResolvePrefix = "https://huggingface.co/"
hfResolveInfix = "/resolve/main/"
)
// canonicalURI reduces the two interchangeable spellings of a HuggingFace file
// to one key, so a generated resolve/main URI dedups against the shorthand the
// gallery uses for the majority of its entries.
//
// The repo is exactly the first two path segments; everything after is the file
// path, which may itself contain slashes because sharded quants live in a
// subdirectory. Anything that is not recognisably one of the two forms is
// returned unchanged rather than guessed at, so mirrors and other hosts still
// dedup on their literal string.
func canonicalURI(uri string) string {
switch {
case strings.HasPrefix(uri, hfShorthandPrefix):
rest := strings.TrimPrefix(uri, hfShorthandPrefix)
owner, after, ok := strings.Cut(rest, "/")
if !ok {
return uri
}
name, file, ok := strings.Cut(after, "/")
if !ok || owner == "" || name == "" || file == "" {
return uri
}
return hfShorthandPrefix + owner + "/" + name + "/" + file
case strings.HasPrefix(uri, hfResolvePrefix):
rest := strings.TrimPrefix(uri, hfResolvePrefix)
repo, file, ok := strings.Cut(rest, hfResolveInfix)
if !ok || file == "" {
return uri
}
// A repo is owner/name and nothing more; a longer prefix means this is
// some other huggingface.co URL that must not be rewritten.
owner, name, ok := strings.Cut(repo, "/")
if !ok || owner == "" || name == "" || strings.Contains(name, "/") {
return uri
}
return hfShorthandPrefix + repo + "/" + file
default:
return uri
}
}
// ExistingIndex is the lookup built from the current gallery: entry names, and
// which entry claims each weight URI.
type ExistingIndex struct {
ByName map[string]int
ByURI map[string]string
}
// LoadExisting reads the gallery index for dedup purposes only. It is
// deliberately not used to rewrite the file: the index is 40,000 lines, and a
// YAML round trip would reflow the whole thing into an unreviewable diff.
func LoadExisting(path string) (*ExistingIndex, error) {
raw, err := os.ReadFile(path)
if err != nil {
return nil, err
}
var entries []struct {
Name string `yaml:"name"`
Files []struct {
URI string `yaml:"uri"`
} `yaml:"files"`
}
if err := yaml.Unmarshal(raw, &entries); err != nil {
return nil, fmt.Errorf("parsing %s: %w", path, err)
}
ix := &ExistingIndex{ByName: map[string]int{}, ByURI: map[string]string{}}
for i, e := range entries {
ix.ByName[e.Name] = i
for _, f := range e.Files {
if f.URI != "" {
ix.ByURI[canonicalURI(f.URI)] = e.Name
}
}
}
return ix, nil
}
// Merge splits generated entries into those to add and those already covered.
// reused maps a generated name to the existing entry that stands in for it, so
// a parent can reference what is already there instead of duplicating weights.
// Several APEX repos share one base model, so the same counterpart rungs are
// generated more than once in a batch. The batch has to dedup against itself as
// well as against the gallery, tracked locally because the caller may reuse the
// ExistingIndex it passed in.
func Merge(existing *ExistingIndex, generated []GalleryEntry) (add []GalleryEntry, reused map[string]string) {
reused = map[string]string{}
batchNames := map[string]string{}
batchURIs := map[string]string{}
// Canonicalized into a local copy rather than in place: an ExistingIndex may
// be hand-built or reused by the caller, so Merge must not rewrite it.
existingURIs := make(map[string]string, len(existing.ByURI))
for uri, owner := range existing.ByURI {
existingURIs[canonicalURI(uri)] = owner
}
for _, e := range generated {
// Name is checked before URI: a name collision must block the add
// whatever the weights say, since duplicate names corrupt the index.
if _, clash := existing.ByName[e.Name]; clash {
reused[e.Name] = e.Name
continue
}
if claimant, clash := batchNames[e.Name]; clash {
reused[e.Name] = claimant
continue
}
if len(e.Files) > 0 {
uri := canonicalURI(e.Files[0].URI)
if owner, ok := existingURIs[uri]; ok {
reused[e.Name] = owner
continue
}
if claimant, ok := batchURIs[uri]; ok {
reused[e.Name] = claimant
continue
}
batchURIs[uri] = e.Name
}
batchNames[e.Name] = e.Name
add = append(add, e)
}
return add, reused
}

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@@ -1,183 +0,0 @@
package main
import (
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("Merge", func() {
It("drops a generated entry whose weight URI already exists and reports the existing name", func() {
existing := &ExistingIndex{
ByName: map[string]int{"qwen3.6-35b-a3b-apex": 0},
ByURI: map[string]string{
"https://huggingface.co/mudler/X-APEX-GGUF/resolve/main/X-APEX-I-Quality.gguf": "qwen3.6-35b-a3b-apex",
},
}
gen := []GalleryEntry{{
Name: "x-apex-i-quality",
Files: []EntryFile{{URI: "https://huggingface.co/mudler/X-APEX-GGUF/resolve/main/X-APEX-I-Quality.gguf"}},
}}
add, reused := Merge(existing, gen)
Expect(add).To(BeEmpty())
Expect(reused).To(HaveKeyWithValue("x-apex-i-quality", "qwen3.6-35b-a3b-apex"))
})
It("keeps a generated entry whose weights are new", func() {
existing := &ExistingIndex{ByName: map[string]int{}, ByURI: map[string]string{}}
gen := []GalleryEntry{{
Name: "x-apex-i-mini",
Files: []EntryFile{{URI: "https://huggingface.co/mudler/X-APEX-GGUF/resolve/main/X-APEX-I-Mini.gguf"}},
}}
add, reused := Merge(existing, gen)
Expect(add).To(HaveLen(1))
Expect(reused).To(BeEmpty())
})
It("refuses to add an entry whose name collides with an existing one", func() {
existing := &ExistingIndex{
ByName: map[string]int{"x-apex-i-mini": 0},
ByURI: map[string]string{},
}
gen := []GalleryEntry{{
Name: "x-apex-i-mini",
Files: []EntryFile{{URI: "https://huggingface.co/mudler/X-APEX-GGUF/resolve/main/other.gguf"}},
}}
add, reused := Merge(existing, gen)
Expect(add).To(BeEmpty())
Expect(reused).To(HaveKeyWithValue("x-apex-i-mini", "x-apex-i-mini"))
})
// The gallery records most of its URIs in huggingface:// shorthand while
// render.go only ever emits the resolve/main form, so without
// canonicalization the majority of the file is invisible to the dedup.
It("matches a generated https URI against the shorthand form recorded in the gallery", func() {
existing := &ExistingIndex{
ByName: map[string]int{"foo-gguf-q8-0": 0},
ByURI: map[string]string{
"huggingface://unsloth/Foo-GGUF/Foo-Q8_0.gguf": "foo-gguf-q8-0",
},
}
gen := []GalleryEntry{{
Name: "foo-apex-q8-0",
Files: []EntryFile{{URI: "https://huggingface.co/unsloth/Foo-GGUF/resolve/main/Foo-Q8_0.gguf"}},
}}
add, reused := Merge(existing, gen)
Expect(add).To(BeEmpty())
Expect(reused).To(HaveKeyWithValue("foo-apex-q8-0", "foo-gguf-q8-0"))
})
It("matches a generated shorthand URI against the https form recorded in the gallery", func() {
existing := &ExistingIndex{
ByName: map[string]int{"foo-gguf-q8-0": 0},
ByURI: map[string]string{
"https://huggingface.co/unsloth/Foo-GGUF/resolve/main/Foo-Q8_0.gguf": "foo-gguf-q8-0",
},
}
gen := []GalleryEntry{{
Name: "foo-apex-q8-0",
Files: []EntryFile{{URI: "huggingface://unsloth/Foo-GGUF/Foo-Q8_0.gguf"}},
}}
add, reused := Merge(existing, gen)
Expect(add).To(BeEmpty())
Expect(reused).To(HaveKeyWithValue("foo-apex-q8-0", "foo-gguf-q8-0"))
})
// Sharded quants live under a subdirectory, so the file path carries slashes
// of its own and only the first two segments are the repo.
It("matches across both forms when the file path has a subdirectory", func() {
existing := &ExistingIndex{
ByName: map[string]int{"model-ud-q4-k-m": 0},
ByURI: map[string]string{
"huggingface://unsloth/Model-GGUF/UD-Q4_K_M/Model-UD-Q4_K_M-00001-of-00002.gguf": "model-ud-q4-k-m",
},
}
gen := []GalleryEntry{{
Name: "model-apex-ud-q4-k-m",
Files: []EntryFile{{URI: "https://huggingface.co/unsloth/Model-GGUF/resolve/main/UD-Q4_K_M/Model-UD-Q4_K_M-00001-of-00002.gguf"}},
}}
add, reused := Merge(existing, gen)
Expect(add).To(BeEmpty())
Expect(reused).To(HaveKeyWithValue("model-apex-ud-q4-k-m", "model-ud-q4-k-m"))
})
// Several APEX repos share one base model, so the same unsloth rungs are
// generated more than once in a single batch.
It("adds only the first of two generated entries sharing a name", func() {
existing := &ExistingIndex{ByName: map[string]int{}, ByURI: map[string]string{}}
gen := []GalleryEntry{
{
Name: "shared-rung-q8-0",
Files: []EntryFile{{URI: "https://huggingface.co/unsloth/Shared-GGUF/resolve/main/Shared-Q8_0.gguf"}},
},
{
Name: "shared-rung-q8-0",
Files: []EntryFile{{URI: "https://huggingface.co/unsloth/Other-GGUF/resolve/main/Other-Q8_0.gguf"}},
},
}
add, reused := Merge(existing, gen)
Expect(add).To(HaveLen(1))
Expect(add[0].Files[0].URI).To(Equal("https://huggingface.co/unsloth/Shared-GGUF/resolve/main/Shared-Q8_0.gguf"))
Expect(reused).To(HaveKeyWithValue("shared-rung-q8-0", "shared-rung-q8-0"))
})
It("adds only the first of two generated entries sharing a primary URI", func() {
existing := &ExistingIndex{ByName: map[string]int{}, ByURI: map[string]string{}}
gen := []GalleryEntry{
{
Name: "shared-rung-from-apex",
Files: []EntryFile{{URI: "https://huggingface.co/unsloth/Shared-GGUF/resolve/main/Shared-Q8_0.gguf"}},
},
{
Name: "shared-rung-from-apex-mtp",
Files: []EntryFile{{URI: "huggingface://unsloth/Shared-GGUF/Shared-Q8_0.gguf"}},
},
}
add, reused := Merge(existing, gen)
Expect(add).To(HaveLen(1))
Expect(add[0].Name).To(Equal("shared-rung-from-apex"))
Expect(reused).To(HaveKeyWithValue("shared-rung-from-apex-mtp", "shared-rung-from-apex"))
})
// Anything that is not a HuggingFace URI must survive untouched, so an
// unrecognised scheme still dedups against the very same string.
It("leaves a URI in neither recognised form alone and still dedups it exactly", func() {
existing := &ExistingIndex{
ByName: map[string]int{"mirrored-model": 0},
ByURI: map[string]string{
"https://mirror.example.com/weights/Model-Q8_0.gguf": "mirrored-model",
},
}
gen := []GalleryEntry{
{
Name: "mirrored-apex",
Files: []EntryFile{{URI: "https://mirror.example.com/weights/Model-Q8_0.gguf"}},
},
{
Name: "elsewhere-apex",
Files: []EntryFile{{URI: "https://mirror.example.com/weights/Other-Q8_0.gguf"}},
},
}
add, reused := Merge(existing, gen)
Expect(add).To(HaveLen(1))
Expect(add[0].Name).To(Equal("elsewhere-apex"))
Expect(reused).To(HaveKeyWithValue("mirrored-apex", "mirrored-model"))
})
})

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@@ -1,175 +0,0 @@
package main
import (
"fmt"
"path"
"strings"
)
// EntryFile is one downloadable file of a gallery entry.
type EntryFile struct {
Filename string `yaml:"filename"`
SHA256 string `yaml:"sha256"`
URI string `yaml:"uri"`
}
// GalleryEntry is the subset of a gallery entry this generator writes.
//
// Named GalleryEntry rather than Entry because the test files dot-import
// Ginkgo, whose table DSL exports an Entry that a package-level Entry would
// collide with. The yaml tags are what the gallery index sees, so the Go
// identifier is free to differ.
type GalleryEntry struct {
Name string `yaml:"name"`
URL string `yaml:"url"`
Description string `yaml:"description,omitempty"`
Tags []string `yaml:"tags,omitempty"`
Overrides map[string]any `yaml:"overrides,omitempty"`
Files []EntryFile `yaml:"files,omitempty"`
Variants []VariantRef `yaml:"variants,omitempty"`
}
// VariantRef mirrors the gallery's variant reference: a name and nothing else.
type VariantRef struct {
Model string `yaml:"model"`
}
// ChildInput is everything needed to render one non-parent entry.
type ChildInput struct {
Name string
Repo string
// DraftRepo is the repo publishing the drafter, when it is not the repo
// publishing the weights. Speculative pairings routinely cross repos, so
// the drafter cannot be assumed to sit next to the weights. Empty means
// same-repo, which is how the *-APEX-MTP-GGUF repos ship.
DraftRepo string
Template string
Weights []GGUFFile
MMProj *GGUFFile
SpecType string
DraftFile *GGUFFile
BaseTags []string
}
// specTuning is the acceptance-window tuning each spec type ships with, copied
// from the hand-written entries that already run these two mechanisms rather
// than invented here. The two differ because the drafters differ: self-drafted
// MTP heads produce a short, high-confidence proposal (15+ hand-written entries
// use 6 with a 0.75 floor), while a separate DFlash drafter is cheap enough to
// run far ahead unconditionally (the five hand-written dflash entries use 15 and
// set no floor).
var specTuning = map[string][]string{
"draft-mtp": {"spec_n_max:6", "spec_p_min:0.75"},
"draft-dflash": {"spec_n_max:15"},
}
func hfURI(repo, file string) string {
return fmt.Sprintf("https://huggingface.co/%s/resolve/main/%s", repo, file)
}
// localPath is where a downloaded file lands.
//
// The hand-written entries namespace by the repo's BARE name
// (llama-cpp/models/<repo>/<file>), which is not unique. LiquidAI/LFM2.5-8B-A1B-GGUF
// and unsloth/LFM2.5-8B-A1B-GGUF share a basename, so both claim
// llama-cpp/models/LFM2.5-8B-A1B-GGUF/, and installing the second after the first
// either overwrites weights whose recorded sha256 belongs to the other file or is
// skipped as already present. Two owners publishing the same model name is the
// normal case for quantizers, not an edge case, so the owner has to be in the path.
//
// The owner becomes its own path segment rather than being folded into the
// directory name: owner/repo is unique on HuggingFace and "/" cannot occur inside
// either half, so this is the only form that is collision-proof by construction.
// It still reads as the hand-written convention with the owner restored, and the
// extra depth is already present in the index for sharded builds.
func localPath(kind, repo, file string) string {
// path.Dir yields "." for a repo named without an owner, which path.Join
// drops, so such a caller keeps the historical two-segment layout.
return path.Join("llama-cpp", kind, path.Dir(repo), path.Base(repo), file)
}
// RenderChild builds one child entry.
//
// The dflash/mtp tag is added if and only if this entry sets a spec_type,
// because variant ranking reads tags and nothing else, and a tag that does not
// match what the entry configures either promotes a build that is no faster or
// hides one that is.
func RenderChild(in ChildInput) GalleryEntry {
e := GalleryEntry{
Name: in.Name,
URL: fmt.Sprintf("github:mudler/LocalAI/gallery/%s@master", in.Template),
Tags: append([]string{}, in.BaseTags...),
Overrides: map[string]any{},
}
// gallery/virtual.yaml carries no backend, so nothing else would name an
// engine for these entries. Matching the hand-written entries on
// known_usecases too: LocalAI would fall back to the backend defaults, but
// generated entries should not read differently from their neighbours.
e.Overrides["backend"] = "llama-cpp"
e.Overrides["known_usecases"] = []string{"chat"}
options := []string{"use_jinja:true"}
for _, w := range in.Weights {
e.Files = append(e.Files, EntryFile{
Filename: localPath("models", in.Repo, w.Name),
SHA256: w.SHA256,
URI: hfURI(in.Repo, w.Name),
})
}
e.Overrides["parameters"] = map[string]any{
"model": localPath("models", in.Repo, in.Weights[0].Name),
}
if in.MMProj != nil {
// An explicit known_usecases SUPPRESSES the backend-default fallback in
// core/gallery/models_types.go, so a multimodal entry left at chat-only
// never matches FilterGalleryModelsByUsecase(FLAG_VISION) or
// FilterGalleryModelsByMultimodal and vanishes from the UI's vision and
// multimodal filters. 19 of the 45 APEX repos ship an mmproj.
e.Overrides["known_usecases"] = []string{"chat", "vision"}
e.Overrides["mmproj"] = localPath("mmproj", in.Repo, in.MMProj.Name)
e.Files = append(e.Files, EntryFile{
Filename: localPath("mmproj", in.Repo, in.MMProj.Name),
SHA256: in.MMProj.SHA256,
URI: hfURI(in.Repo, in.MMProj.Name),
})
}
// A spec type is configured independently of a drafter FILE. Weights that
// carry their own MTP heads need no second download, and requiring one left
// the *-APEX-MTP-GGUF builds shipping the larger heads-bearing weights with
// the heads switched off: a strictly bigger download at the same speed,
// ranked identically to the plain rung at the same tier.
if in.SpecType != "" {
options = append(options, "spec_type:"+in.SpecType)
options = append(options, specTuning[in.SpecType]...)
// The tag is derived from the spec type this entry sets and from nothing
// else. Variant ranking reads tags only, so a tag taken from a repo or
// entry NAME would promote a build that is no faster whenever the name
// and the configuration disagree.
e.Tags = append(e.Tags, strings.TrimPrefix(in.SpecType, "draft-"))
}
if in.SpecType != "" && in.DraftFile != nil {
// Fall back to the weights repo so pairings that publish the drafter
// alongside the weights keep working without restating the repo.
draftRepo := in.DraftRepo
if draftRepo == "" {
draftRepo = in.Repo
}
draftPath := localPath("models", draftRepo, in.DraftFile.Name)
e.Overrides["draft_model"] = draftPath
e.Overrides["flash_attention"] = "on"
e.Files = append(e.Files, EntryFile{
Filename: draftPath,
SHA256: in.DraftFile.SHA256,
URI: hfURI(draftRepo, in.DraftFile.Name),
})
}
e.Overrides["options"] = options
return e
}

View File

@@ -1,249 +0,0 @@
package main
import (
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("RenderChild", func() {
It("tags an entry that configures draft-dflash", func() {
e := RenderChild(ChildInput{
Name: "qwen3.5-9b-dflash",
Repo: "mudler/Example-APEX-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "Example-APEX-I-Quality.gguf", SHA256: "a"}},
SpecType: "draft-dflash",
DraftFile: &GGUFFile{Name: "Example-DFlash.Q8_0.gguf", SHA256: "b"},
BaseTags: []string{"llm", "gguf"},
})
Expect(e.Tags).To(ContainElement("dflash"))
Expect(e.Tags).ToNot(ContainElement("mtp"))
Expect(e.Overrides["options"]).To(ContainElement("spec_type:draft-dflash"))
Expect(e.Overrides["draft_model"]).ToNot(BeNil())
})
It("does not tag an MTP-named repo that configures no speculation", func() {
// mudler/Qwen3.6-35B-A3B-APEX-MTP-GGUF ships MTP-bearing weights. Weights
// that carry the heads are not an entry that enables them, and tagging it
// would win the feature axis without being any faster.
e := RenderChild(ChildInput{
Name: "qwen3.6-35b-a3b-apex-mtp-i-quality",
Repo: "mudler/Qwen3.6-35B-A3B-APEX-MTP-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "Qwen3.6-35B-A3B-APEX-MTP-I-Quality.gguf", SHA256: "a"}},
BaseTags: []string{"llm", "gguf"},
})
Expect(e.Tags).ToNot(ContainElement("mtp"))
Expect(e.Tags).ToNot(ContainElement("dflash"))
Expect(e.Overrides).ToNot(HaveKey("draft_model"))
})
It("lists every shard of a sharded build and points the model at the first", func() {
e := RenderChild(ChildInput{
Name: "step-3.7-flash-ud-q4-k-m",
Repo: "unsloth/Step-3.7-Flash-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{
{Name: "UD-Q4_K_M/Step-3.7-Flash-UD-Q4_K_M-00001-of-00002.gguf", SHA256: "a"},
{Name: "UD-Q4_K_M/Step-3.7-Flash-UD-Q4_K_M-00002-of-00002.gguf", SHA256: "b"},
},
BaseTags: []string{"llm", "gguf"},
})
Expect(e.Files).To(HaveLen(2))
params, ok := e.Overrides["parameters"].(map[string]any)
Expect(ok).To(BeTrue())
Expect(params["model"]).To(HaveSuffix("00001-of-00002.gguf"))
Expect(e.Files[0].URI).To(Equal(
"https://huggingface.co/unsloth/Step-3.7-Flash-GGUF/resolve/main/UD-Q4_K_M/Step-3.7-Flash-UD-Q4_K_M-00001-of-00002.gguf"))
})
It("wires mmproj when the repo publishes one", func() {
e := RenderChild(ChildInput{
Name: "example-i-mini",
Repo: "mudler/Example-APEX-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "Example-APEX-I-Mini.gguf", SHA256: "a"}},
MMProj: &GGUFFile{Name: "mmproj-F16.gguf", SHA256: "c"},
BaseTags: []string{"llm", "gguf"},
})
Expect(e.Overrides["mmproj"]).ToNot(BeNil())
Expect(e.Files).To(HaveLen(2))
})
It("names the engine and the usecases the hand-written entries name", func() {
// gallery/virtual.yaml supplies no backend, so an entry that omits one
// names no engine at all and cannot load.
e := RenderChild(ChildInput{
Name: "example-i-mini",
Repo: "mudler/Example-APEX-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "Example-APEX-I-Mini.gguf", SHA256: "a"}},
BaseTags: []string{"llm", "gguf"},
})
Expect(e.Overrides["backend"]).To(Equal("llama-cpp"))
Expect(e.Overrides["known_usecases"]).To(ContainElement("chat"))
})
It("draws the drafter from DraftRepo when the pairing spans two repos", func() {
// unsloth/Qwen3-4B-GGUF pairs with a drafter published separately by
// AtomicChat, so a drafter URI built from the weights repo 404s.
e := RenderChild(ChildInput{
Name: "qwen3-4b-dflash",
Repo: "unsloth/Qwen3-4B-GGUF",
DraftRepo: "AtomicChat/Qwen3-4B-DFlash-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "Qwen3-4B-Q4_K_M.gguf", SHA256: "a"}},
SpecType: "draft-dflash",
DraftFile: &GGUFFile{Name: "Qwen3-4B-DFlash.Q8_0.gguf", SHA256: "b"},
BaseTags: []string{"llm", "gguf"},
})
Expect(e.Files[0].URI).To(Equal(
"https://huggingface.co/unsloth/Qwen3-4B-GGUF/resolve/main/Qwen3-4B-Q4_K_M.gguf"))
Expect(e.Files[1].URI).To(Equal(
"https://huggingface.co/AtomicChat/Qwen3-4B-DFlash-GGUF/resolve/main/Qwen3-4B-DFlash.Q8_0.gguf"))
Expect(e.Files[1].Filename).To(Equal(
"llama-cpp/models/AtomicChat/Qwen3-4B-DFlash-GGUF/Qwen3-4B-DFlash.Q8_0.gguf"))
Expect(e.Overrides["draft_model"]).To(Equal(
"llama-cpp/models/AtomicChat/Qwen3-4B-DFlash-GGUF/Qwen3-4B-DFlash.Q8_0.gguf"))
})
It("falls back to the weights repo for the drafter when DraftRepo is empty", func() {
// The *-APEX-MTP-GGUF repos ship the drafter alongside the weights.
e := RenderChild(ChildInput{
Name: "example-apex-dflash",
Repo: "mudler/Example-APEX-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "Example-APEX-I-Quality.gguf", SHA256: "a"}},
SpecType: "draft-dflash",
DraftFile: &GGUFFile{Name: "Example-DFlash.Q8_0.gguf", SHA256: "b"},
BaseTags: []string{"llm", "gguf"},
})
Expect(e.Files[1].URI).To(Equal(
"https://huggingface.co/mudler/Example-APEX-GGUF/resolve/main/Example-DFlash.Q8_0.gguf"))
Expect(e.Files[1].Filename).To(Equal(
"llama-cpp/models/mudler/Example-APEX-GGUF/Example-DFlash.Q8_0.gguf"))
})
})
var _ = Describe("RenderChild known_usecases", func() {
It("declares vision alongside chat when the entry carries an mmproj", func() {
// An explicit known_usecases suppresses the backend-default fallback, so a
// chat-only multimodal entry disappears from the UI's vision filter.
e := RenderChild(ChildInput{
Name: "example-i-quality",
Repo: "mudler/Example-APEX-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "Example-APEX-I-Quality.gguf", SHA256: "a"}},
MMProj: &GGUFFile{Name: "mmproj-F16.gguf", SHA256: "c"},
BaseTags: []string{"llm", "gguf"},
})
Expect(e.Overrides["known_usecases"]).To(ConsistOf("chat", "vision"))
})
It("leaves a text-only entry at chat", func() {
e := RenderChild(ChildInput{
Name: "example-i-quality",
Repo: "mudler/Example-APEX-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "Example-APEX-I-Quality.gguf", SHA256: "a"}},
BaseTags: []string{"llm", "gguf"},
})
Expect(e.Overrides["known_usecases"]).To(ConsistOf("chat"))
})
})
var _ = Describe("localPath", func() {
It("keeps two repos with the same basename but different owners apart", func() {
// LiquidAI and unsloth both publish LFM2.5-8B-A1B-GGUF. A path built from
// the bare repo name gives both the same local file, so installing the
// second overwrites or skips the first and one of them then serves bytes
// that do not match its recorded sha256.
liquid := RenderChild(ChildInput{
Name: "lfm2.5-8b-a1b-i-quality",
Repo: "LiquidAI/LFM2.5-8B-A1B-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "LFM2.5-8B-A1B-Q8_0.gguf", SHA256: "33ab3b8c"}},
BaseTags: []string{"llm", "gguf"},
})
unsloth := RenderChild(ChildInput{
Name: "lfm2.5-8b-a1b-q8-0",
Repo: "unsloth/LFM2.5-8B-A1B-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "LFM2.5-8B-A1B-Q8_0.gguf", SHA256: "ec11666b"}},
BaseTags: []string{"llm", "gguf"},
})
Expect(liquid.Files[0].Filename).ToNot(Equal(unsloth.Files[0].Filename))
Expect(unsloth.Files[0].Filename).To(Equal(
"llama-cpp/models/unsloth/LFM2.5-8B-A1B-GGUF/LFM2.5-8B-A1B-Q8_0.gguf"))
})
It("namespaces the mmproj by owner too", func() {
e := RenderChild(ChildInput{
Name: "example-i-quality",
Repo: "mudler/Example-APEX-GGUF",
Template: "virtual.yaml",
Weights: []GGUFFile{{Name: "Example-APEX-I-Quality.gguf", SHA256: "a"}},
MMProj: &GGUFFile{Name: "mmproj-F16.gguf", SHA256: "c"},
BaseTags: []string{"llm", "gguf"},
})
Expect(e.Overrides["mmproj"]).To(Equal(
"llama-cpp/mmproj/mudler/Example-APEX-GGUF/mmproj-F16.gguf"))
})
})
var _ = Describe("MTP builds", func() {
renderTier := func(repo string) GalleryEntry {
return RenderChild(ChildInput{
Name: "example-i-quality",
Repo: repo,
Template: "virtual.yaml",
SpecType: SpecTypeForRepo(repo),
Weights: []GGUFFile{{Name: "Example-I-Quality.gguf", SHA256: "a"}},
BaseTags: []string{"llm", "gguf"},
})
}
It("turns MTP on for a build off an APEX-MTP repo", func() {
// These weights retain the model's own MTP heads, so shipping them with
// speculation off is a strictly larger download at the same speed,
// ranked identically to the plain rung at the same tier.
e := renderTier("mudler/Qwen3.6-35B-A3B-APEX-MTP-GGUF")
Expect(e.Overrides["options"]).To(ContainElements(
"spec_type:draft-mtp", "spec_n_max:6", "spec_p_min:0.75"))
Expect(e.Tags).To(ContainElement("mtp"))
})
It("needs no drafter file, because the heads travel with the weights", func() {
e := renderTier("mudler/Qwen3.6-35B-A3B-APEX-MTP-GGUF")
Expect(e.Overrides).ToNot(HaveKey("draft_model"))
Expect(e.Files).To(HaveLen(1))
})
It("leaves a build off a plain APEX repo alone", func() {
e := renderTier("mudler/Qwen3.6-35B-A3B-APEX-GGUF")
Expect(e.Tags).ToNot(ContainElement("mtp"))
Expect(e.Overrides["options"]).To(ConsistOf("use_jinja:true"))
})
It("leaves an unsloth counterpart rung alone", func() {
// The counterpart quantizes the plain weights; nothing there carries heads.
e := renderTier("unsloth/Qwen3.6-35B-A3B-GGUF")
Expect(e.Tags).ToNot(ContainElement("mtp"))
Expect(e.Overrides["options"]).To(ConsistOf("use_jinja:true"))
})
})

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@@ -1,71 +0,0 @@
package main
import (
"regexp"
"sort"
"strings"
)
// WantedQuants is the fixed unsloth subset this generator emits. It is a
// deliberate subset: unsloth publishes north of 20 quants per repo, and the
// selector needs useful fitness points rather than every rung.
var WantedQuants = []string{"UD-Q4_K_M", "UD-Q5_K_M", "UD-Q6_K", "Q8_0"}
var shardRE = regexp.MustCompile(`-(\d{5})-of-(\d{5})\.gguf$`)
// QuantBuild is one unsloth quantization, which may be a single file or an
// ordered set of shards.
type QuantBuild struct {
Quant string
Files []GGUFFile
Sharded bool
}
// CounterpartCandidates returns the unsloth repo base names worth probing, most
// likely first. Both derivations are needed: the repo name finds
// unsloth/gemma-4-26B-A4B-it-GGUF, while the file stem is what matches for
// repos whose stem is the canonical model name.
func CounterpartCandidates(repoName, fileStem string) []string {
clean := func(s string) string {
s = strings.TrimSuffix(s, "-GGUF")
s = regexp.MustCompile(`-(MTP|TQ)$`).ReplaceAllString(s, "")
s = strings.TrimSuffix(s, "-APEX")
return regexp.MustCompile(`-(MTP|TQ)$`).ReplaceAllString(s, "")
}
out := []string{clean(repoName)}
if stem := clean(fileStem); stem != out[0] {
out = append(out, stem)
}
return out
}
// DiscoverUnslothQuants returns the wanted quants a repo publishes, handling
// both the flat single-file layout and the sharded layout where a quant lives
// in its own subdirectory.
func DiscoverUnslothQuants(files []GGUFFile) []QuantBuild {
var out []QuantBuild
for _, q := range WantedQuants {
var flat []GGUFFile
var shards []GGUFFile
for _, f := range files {
switch {
case !strings.Contains(f.Name, "/") && strings.HasSuffix(f.Name, "-"+q+".gguf"):
flat = append(flat, f)
case strings.HasPrefix(f.Name, q+"/") && shardRE.MatchString(f.Name):
shards = append(shards, f)
}
}
switch {
case len(flat) > 0:
out = append(out, QuantBuild{Quant: q, Files: flat})
case len(shards) > 0:
sort.Slice(shards, func(i, j int) bool { return shards[i].Name < shards[j].Name })
out = append(out, QuantBuild{Quant: q, Files: shards, Sharded: true})
}
}
return out
}

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@@ -1,75 +0,0 @@
package main
import (
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("CounterpartCandidates", func() {
It("offers both the repo-derived and stem-derived names", func() {
// mudler/gemma-4-26B-A4B-it-APEX-GGUF ships gemma-4-26B-A4B-APEX-*.gguf,
// and only the repo-derived name finds unsloth/gemma-4-26B-A4B-it-GGUF.
got := CounterpartCandidates("gemma-4-26B-A4B-it-APEX-GGUF", "gemma-4-26B-A4B-APEX")
Expect(got).To(Equal([]string{"gemma-4-26B-A4B-it", "gemma-4-26B-A4B"}))
})
It("strips the MTP marker", func() {
got := CounterpartCandidates("Qwopus3.6-35B-A3B-v1-APEX-MTP-GGUF", "Qwopus3.6-35B-A3B-v1-APEX-MTP")
Expect(got[0]).To(Equal("Qwopus3.6-35B-A3B-v1"))
})
It("strips the TQ marker", func() {
// This is the branch that folds mudler/Qwen3.5-35B-A3B-APEX-TQ-GGUF into
// the qwen3.5-35b-a3b hub. Without it the probe is
// unsloth/Qwen3.5-35B-A3B-TQ-GGUF, which does not exist, so the family
// silently loses every unsloth rung.
got := CounterpartCandidates("Qwen3.5-35B-A3B-APEX-TQ-GGUF", "Qwen3.5-35B-A3B-APEX-TQ")
Expect(got).To(Equal([]string{"Qwen3.5-35B-A3B"}))
})
It("does not repeat a candidate when both derivations agree", func() {
got := CounterpartCandidates("Qwen3.6-35B-A3B-APEX-GGUF", "Qwen3.6-35B-A3B-APEX")
Expect(got).To(Equal([]string{"Qwen3.6-35B-A3B"}))
})
})
var _ = Describe("DiscoverUnslothQuants", func() {
It("finds flat single-file quants", func() {
files := []GGUFFile{
{Name: "Qwen3.6-35B-A3B-UD-Q4_K_M.gguf", SHA256: "a"},
{Name: "Qwen3.6-35B-A3B-UD-IQ1_M.gguf", SHA256: "b"},
}
got := DiscoverUnslothQuants(files)
Expect(got).To(HaveLen(1))
Expect(got[0].Quant).To(Equal("UD-Q4_K_M"))
Expect(got[0].Sharded).To(BeFalse())
Expect(got[0].Files).To(HaveLen(1))
})
It("collects a sharded quant from its subdirectory in shard order", func() {
files := []GGUFFile{
{Name: "UD-Q4_K_M/Step-3.7-Flash-UD-Q4_K_M-00002-of-00002.gguf", SHA256: "b"},
{Name: "UD-Q4_K_M/Step-3.7-Flash-UD-Q4_K_M-00001-of-00002.gguf", SHA256: "a"},
}
got := DiscoverUnslothQuants(files)
Expect(got).To(HaveLen(1))
Expect(got[0].Quant).To(Equal("UD-Q4_K_M"))
Expect(got[0].Sharded).To(BeTrue())
Expect(got[0].Files).To(HaveLen(2))
Expect(got[0].Files[0].Name).To(HaveSuffix("00001-of-00002.gguf"))
})
It("ignores quants outside the wanted subset", func() {
files := []GGUFFile{{Name: "Model-UD-IQ2_XXS.gguf", SHA256: "a"}}
Expect(DiscoverUnslothQuants(files)).To(BeEmpty())
})
})

View File

@@ -1,312 +0,0 @@
package main
import (
"fmt"
"os"
"strings"
"gopkg.in/yaml.v3"
)
type verifyEntry struct {
Name string `yaml:"name"`
Tags []string `yaml:"tags"`
Variants []VariantRef `yaml:"variants"`
Overrides struct {
// Backend scopes the checks that only hold for one engine. An entry that
// declares none takes its configuration from the referenced url: template,
// which this verifier never reads, so it cannot be judged either way.
Backend string `yaml:"backend"`
Options []string `yaml:"options"`
// MMProj and DraftModel name the files that are not weights. They are
// the only signal for it: a drafter lands in the same models/ prefix as
// the weights, so the path alone cannot tell them apart.
MMProj string `yaml:"mmproj"`
DraftModel string `yaml:"draft_model"`
} `yaml:"overrides"`
Files []struct {
Filename string `yaml:"filename"`
SHA256 string `yaml:"sha256"`
URI string `yaml:"uri"`
} `yaml:"files"`
}
// Verify checks the invariants the variants schema and the tagging rule
// require. It returns every problem rather than the first, so one run tells the
// author everything that needs fixing.
func Verify(path string) []string {
raw, err := os.ReadFile(path)
if err != nil {
return []string{fmt.Sprintf("reading %s: %v", path, err)}
}
var entries []verifyEntry
if err := yaml.Unmarshal(raw, &entries); err != nil {
return []string{fmt.Sprintf("parsing %s: %v", path, err)}
}
var problems []string
byName := map[string]verifyEntry{}
for _, e := range entries {
if _, seen := byName[e.Name]; seen {
problems = append(problems, fmt.Sprintf("duplicate entry name: %s", e.Name))
continue
}
byName[e.Name] = e
}
for _, e := range entries {
for _, v := range e.Variants {
target, ok := byName[v.Model]
if !ok {
problems = append(problems, fmt.Sprintf("%s: variant %q does not exist", e.Name, v.Model))
continue
}
if len(target.Variants) > 0 {
problems = append(problems, fmt.Sprintf("%s: variant %q declares variants of its own", e.Name, v.Model))
}
}
for _, f := range e.Files {
if requiresSHA256(f.Filename) && f.SHA256 == "" {
problems = append(problems, fmt.Sprintf("%s: file %s has no sha256", e.Name, f.Filename))
}
}
problems = append(problems, checkWeightCount(e)...)
problems = append(problems, checkFeatureTag(e, "dflash")...)
problems = append(problems, checkFeatureTag(e, "mtp")...)
}
problems = append(problems, checkPathCollisions(entries)...)
return problems
}
// checkPathCollisions catches two different upstream files claiming one local
// path. The install layer keys on the local filename, so whichever entry is
// installed second either overwrites weights the first entry recorded a
// different sha256 for or is skipped as already present. Either way some entry
// afterwards serves bytes that do not match its own checksum, and nothing at
// install time says so.
//
// This is an index-wide invariant rather than a per-entry one: neither entry is
// wrong on its own and the collision exists only in their pairing. The usual
// source is a path scheme built from the repo's BARE name, because two owners
// publishing the same model name is routine for quantizers.
//
// Sharing a path is fine when the uri is the same, which is how several entries
// legitimately reuse one projector. Files with no uri are skipped: there is
// nothing to compare.
func checkPathCollisions(entries []verifyEntry) []string {
type source struct{ uri, entry string }
first := map[string]source{}
reported := map[string]bool{}
var problems []string
for _, e := range entries {
for _, f := range e.Files {
if f.Filename == "" || f.URI == "" {
continue
}
prev, seen := first[f.Filename]
if !seen {
first[f.Filename] = source{uri: f.URI, entry: e.Name}
continue
}
if prev.uri == f.URI || reported[f.Filename] {
continue
}
// Reported once per path however many entries pile onto it, so one
// heavily reused filename cannot bury the rest of the report.
reported[f.Filename] = true
problems = append(problems, fmt.Sprintf(
"local path %s is claimed by two different uris: %s (%s) and %s (%s)",
f.Filename, prev.uri, prev.entry, f.URI, e.Name))
}
}
return problems
}
// auxiliaryExtensions are the metadata formats an entry ships beside its
// weights, where an unverified download is a nuisance rather than a hole.
//
// The exclusion is stated as a list of metadata formats on purpose. Requiring
// the checksum only on a blessed list of weight formats would silently exempt
// every format nobody has shipped yet, and it already exempted safetensors
// weights, which are downloaded and loaded exactly like GGUF ones.
var auxiliaryExtensions = []string{".json", ".txt", ".md"}
// requiresSHA256 reports whether an unverified download of this file would be
// a supply-chain hole rather than a cosmetic gap.
func requiresSHA256(filename string) bool {
for _, ext := range auxiliaryExtensions {
if strings.HasSuffix(filename, ext) {
return false
}
}
return true
}
// checkWeightCount catches an entry carrying two whole models. The flat-match
// branch in DiscoverUnslothQuants appends every match, so a quant label that is
// a suffix of another one (Q8_0 of UD-Q8_0) collects both files into one build
// while the rendered model: points at only the first. The result downloads
// twice the bytes and serves whichever file sorted first, silently.
//
// Shards are exempt because a sharded build is legitimately many files.
//
// The collision is a property of llama-cpp quant discovery, so the check is
// scoped to that backend. Multi-component TTS, ASR and diffusion engines ship an
// encoder, a decoder and a vocoder as one model, and there the second GGUF is
// the design rather than a bug.
func checkWeightCount(e verifyEntry) []string {
if e.Overrides.Backend != "llama-cpp" {
return nil
}
var weights []string
for _, f := range e.Files {
switch {
case !strings.HasSuffix(f.Filename, ".gguf"):
case shardRE.MatchString(f.Filename):
case f.Filename == e.Overrides.MMProj:
case f.Filename == e.Overrides.DraftModel:
default:
weights = append(weights, f.Filename)
}
}
if len(weights) > 1 {
return []string{fmt.Sprintf("%s: more than one weight file: %s", e.Name, strings.Join(weights, ", "))}
}
return nil
}
// checkFeatureTag enforces the rule in both directions. A tag without the
// configuration promotes a build that is no faster; configuration without the
// tag leaves a genuinely faster build ranked as plain.
//
// It only speaks about backends whose declaration it can actually read, because
// a rule applied where the evidence is invisible reports noise rather than bugs.
func checkFeatureTag(e verifyEntry, feature string) []string {
decl, configured, judgeable := featureDeclaration(e, feature)
if !judgeable {
return nil
}
tagged := false
for _, t := range e.Tags {
if t == feature {
tagged = true
break
}
}
switch {
case tagged && !configured:
return []string{fmt.Sprintf("%s: tagged %s but sets no %s", e.Name, feature, decl)}
case configured && !tagged:
return []string{fmt.Sprintf("%s: sets %s but is not tagged %s", e.Name, decl, feature)}
}
return nil
}
// featureDeclaration implements the per-backend table in
// .agents/adding-gallery-models.md. It returns the declaration the backend uses
// to configure the feature, whether the entry carries it, and whether this
// verifier is in a position to answer at all.
func featureDeclaration(e verifyEntry, feature string) (decl string, configured, judgeable bool) {
switch e.Overrides.Backend {
case "llama-cpp":
decl = "spec_type:draft-" + feature
for _, o := range e.Overrides.Options {
if strings.TrimSpace(o) == decl {
return decl, true, true
}
}
return decl, false, true
case "ds4":
// ds4 carries the MTP heads in the weights and turns them on with
// mtp_path / mtp_draft. It has no dflash counterpart, so dflash is not a
// question that can be asked of a ds4 entry.
if feature != "mtp" {
return "", false, false
}
decl = "mtp_path:"
for _, o := range e.Overrides.Options {
o = strings.TrimSpace(o)
if strings.HasPrefix(o, "mtp_path:") || strings.HasPrefix(o, "mtp_draft:") {
return decl, true, true
}
}
return decl, false, true
default:
// sglang configures the feature with speculative_algorithm: in the
// referenced gallery/*.yaml, and an entry that declares no backend takes
// its whole configuration from its url: template. Verify reads one index
// file and follows neither, so it must not judge these in either
// direction.
return "", false, false
}
}
// UnaccountedQuants reports a wanted quant the repo demonstrably publishes but
// that discovery produced no build for. The layout that triggers it today is
// root-level shards, which match neither branch of DiscoverUnslothQuants; no
// counterpart ships that way yet, but a batch generator must not drop a build
// with nothing said about it.
func UnaccountedQuants(files []GGUFFile, builds []QuantBuild) []string {
built := map[string]bool{}
for _, b := range builds {
built[b.Quant] = true
}
var problems []string
for _, q := range WantedQuants {
if built[q] {
continue
}
for _, f := range files {
if filePublishesQuant(f.Name, q) {
problems = append(problems, fmt.Sprintf("quant %s is published upstream (%s) but produced no build", q, f.Name))
break
}
}
}
return problems
}
// filePublishesQuant reports whether an upstream file is a publication of
// quant q. It anchors on the quant label the way DiscoverUnslothQuants does,
// as the trailing token of the base name or as the sharding subdirectory, so
// the diagnostic and the discovery it audits cannot disagree about what a file
// is.
//
// An unanchored match would reproduce the very collision this diagnostic warns
// about: Q8_0 is a substring of UD-Q8_0, so a repo publishing only UD-Q8_0
// would be reported as publishing an unbuilt Q8_0, which it does not, and
// UD-Q8_0 is not a wanted quant at all.
func filePublishesQuant(name, q string) bool {
if strings.HasPrefix(name, q+"/") {
return true
}
base := name[strings.LastIndex(name, "/")+1:]
// Shard numbering sits between the quant label and the extension, so it has
// to come off before the label can be read as the trailing token. Root-level
// shards are the layout that matches neither branch of
// DiscoverUnslothQuants, and so the layout this diagnostic mainly catches.
base = shardRE.ReplaceAllString(base, ".gguf")
if !strings.HasSuffix(base, "-"+q+".gguf") {
return false
}
// UD- is unsloth's dynamic-quant modifier, and UD-<q> is a distinct quant
// label rather than a publication of <q>.
return !strings.HasSuffix(base, "-UD-"+q+".gguf")
}

View File

@@ -1,480 +0,0 @@
package main
import (
"os"
"path/filepath"
"strings"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("Verify", func() {
write := func(body string) string {
dir := GinkgoT().TempDir()
p := filepath.Join(dir, "index.yaml")
Expect(os.WriteFile(p, []byte(body), 0o600)).To(Succeed())
return p
}
It("passes a sound index", func() {
Expect(Verify(write(`
- name: parent
variants:
- model: child
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
- name: child
files:
- filename: b.gguf
sha256: bb
uri: https://example.com/b.gguf
`))).To(BeEmpty())
})
It("reports a variant pointing at a missing entry", func() {
Expect(Verify(write(`
- name: parent
variants:
- model: ghost
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
`))).To(ContainElement(ContainSubstring("ghost")))
})
It("reports a variant that itself declares variants", func() {
Expect(Verify(write(`
- name: parent
variants:
- model: child
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
- name: child
variants:
- model: grandchild
files:
- filename: b.gguf
sha256: bb
uri: https://example.com/b.gguf
- name: grandchild
files:
- filename: c.gguf
sha256: cc
uri: https://example.com/c.gguf
`))).To(ContainElement(ContainSubstring("declares variants of its own")))
})
It("reports duplicate entry names", func() {
Expect(Verify(write(`
- name: dup
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
- name: dup
files:
- filename: b.gguf
sha256: bb
uri: https://example.com/b.gguf
`))).To(ContainElement(ContainSubstring("duplicate entry name")))
})
It("reports a file with no sha256", func() {
Expect(Verify(write(`
- name: one
files:
- filename: a.gguf
uri: https://example.com/a.gguf
`))).To(ContainElement(ContainSubstring("no sha256")))
})
It("reports an entry tagged dflash without a matching spec_type", func() {
Expect(Verify(write(`
- name: liar
tags:
- dflash
overrides:
backend: llama-cpp
options:
- use_jinja:true
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
`))).To(ContainElement(ContainSubstring("tagged dflash")))
})
It("reports an entry configuring spec_type without the tag", func() {
Expect(Verify(write(`
- name: shy
overrides:
backend: llama-cpp
options:
- spec_type:draft-mtp
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
`))).To(ContainElement(ContainSubstring("not tagged mtp")))
})
// ds4 carries the MTP heads in the weights and names them with mtp_path, so
// the rule holds there in a different vocabulary rather than not at all.
It("reports a ds4 entry configuring mtp_path without the tag", func() {
Expect(Verify(write(`
- name: ds4-shy
overrides:
backend: ds4
options:
- mtp_path:model-mtp.gguf
- mtp_draft:2
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
`))).To(ContainElement(ContainSubstring("not tagged mtp")))
})
It("reports a ds4 entry tagged mtp that configures no mtp_path", func() {
Expect(Verify(write(`
- name: ds4-liar
tags:
- mtp
overrides:
backend: ds4
options:
- context_size:4096
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
`))).To(ContainElement(ContainSubstring("tagged mtp")))
})
It("accepts a ds4 entry that both configures mtp_path and carries the tag", func() {
Expect(Verify(write(`
- name: ds4-honest
tags:
- mtp
overrides:
backend: ds4
options:
- mtp_path:model-mtp.gguf
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
`))).To(BeEmpty())
})
// sglang declares speculative_algorithm in the referenced gallery/*.yaml,
// which Verify never reads, so it may not judge such an entry either way.
It("says nothing about an sglang entry tagged mtp", func() {
Expect(Verify(write(`
- name: sglang-mtp
tags:
- mtp
overrides:
backend: sglang
files: []
`))).To(BeEmpty())
})
It("says nothing about the tag on an entry with no declared backend", func() {
Expect(Verify(write(`
- name: templated
tags:
- mtp
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
`))).To(BeEmpty())
})
// The flat-match branch in unsloth.go appends every match, so a repo
// publishing both a plain and a UD Q8_0 renders one entry holding two full
// models while model: points at only the first.
It("reports an entry holding more than one non-shard weight file", func() {
Expect(Verify(write(`
- name: greedy
overrides:
backend: llama-cpp
options:
- use_jinja:true
parameters:
model: llama-cpp/models/repo/Model-Q8_0.gguf
files:
- filename: llama-cpp/models/repo/Model-Q8_0.gguf
sha256: aa
uri: https://example.com/a.gguf
- filename: llama-cpp/models/repo/Model-UD-Q8_0.gguf
sha256: bb
uri: https://example.com/b.gguf
`))).To(ContainElement(ContainSubstring("more than one weight file")))
})
It("accepts many shards alongside an mmproj and a drafter", func() {
Expect(Verify(write(`
- name: sharded
tags:
- mtp
overrides:
backend: llama-cpp
options:
- spec_type:draft-mtp
mmproj: llama-cpp/mmproj/repo/mm.gguf
draft_model: llama-cpp/models/repo/Model-draft.gguf
files:
- filename: llama-cpp/models/repo/Model-00001-of-00002.gguf
sha256: aa
uri: https://example.com/a.gguf
- filename: llama-cpp/models/repo/Model-00002-of-00002.gguf
sha256: bb
uri: https://example.com/b.gguf
- filename: llama-cpp/mmproj/repo/mm.gguf
sha256: cc
uri: https://example.com/c.gguf
- filename: llama-cpp/models/repo/Model-draft.gguf
sha256: dd
uri: https://example.com/d.gguf
`))).To(BeEmpty())
})
// Multi-component TTS and ASR engines legitimately ship an encoder, a
// tokenizer, a vocoder and so on as one model, so the collision the weight
// count catches does not exist for them.
It("accepts a multi-component non-llama-cpp entry declaring five weights", func() {
Expect(Verify(write(`
- name: multi
overrides:
backend: qwen3-tts-cpp
files:
- filename: talker.gguf
sha256: aa
uri: https://example.com/a.gguf
- filename: tokenizer.gguf
sha256: bb
uri: https://example.com/b.gguf
- filename: vocoder.gguf
sha256: cc
uri: https://example.com/c.gguf
- filename: encoder.gguf
sha256: dd
uri: https://example.com/d.gguf
- filename: vae.gguf
sha256: ee
uri: https://example.com/e.gguf
`))).To(BeEmpty())
})
It("says nothing about the weight count of an entry with no declared backend", func() {
Expect(Verify(write(`
- name: templated-weights
files:
- filename: model-Q4_K_M.gguf
sha256: aa
uri: https://example.com/a.gguf
- filename: model-mmproj-f16.gguf
sha256: bb
uri: https://example.com/b.gguf
`))).To(BeEmpty())
})
It("says nothing about an auxiliary metadata file carrying no sha256", func() {
Expect(Verify(write(`
- name: aux
files:
- filename: a.gguf
sha256: aa
uri: https://example.com/a.gguf
- filename: params.json
sha256: ""
uri: https://example.com/params.json
`))).To(BeEmpty())
})
// safetensors weights are downloaded and loaded exactly like GGUF weights,
// so an unverified one is the same supply-chain hole.
It("reports a safetensors weight carrying no sha256", func() {
Expect(Verify(write(`
- name: vae
files:
- filename: wan_2.1_vae.safetensors
sha256: ""
uri: https://example.com/vae.safetensors
`))).To(ContainElement(ContainSubstring("no sha256")))
})
It("says nothing about a txt or md file carrying no sha256", func() {
Expect(Verify(write(`
- name: docs
files:
- filename: notes.txt
sha256: ""
uri: https://example.com/notes.txt
- filename: README.md
sha256: ""
uri: https://example.com/README.md
`))).To(BeEmpty())
})
})
var _ = Describe("UnaccountedQuants", func() {
// A quant published only as root-level shards matches neither branch in
// DiscoverUnslothQuants, so without this diagnostic the build would vanish
// from a batch run with nothing said about it.
It("reports a wanted quant upstream publishes but discovery dropped", func() {
files := []GGUFFile{
{Name: "Model-UD-Q4_K_M-00001-of-00003.gguf", SHA256: "aa"},
{Name: "Model-UD-Q4_K_M-00002-of-00003.gguf", SHA256: "bb"},
{Name: "Model-UD-Q4_K_M-00003-of-00003.gguf", SHA256: "cc"},
}
Expect(UnaccountedQuants(files, DiscoverUnslothQuants(files))).
To(ContainElement(ContainSubstring("UD-Q4_K_M")))
})
It("says nothing when every published wanted quant produced a build", func() {
files := []GGUFFile{
{Name: "Model-UD-Q4_K_M.gguf", SHA256: "aa"},
{Name: "UD-Q6_K/Model-UD-Q6_K-00001-of-00002.gguf", SHA256: "bb"},
{Name: "UD-Q6_K/Model-UD-Q6_K-00002-of-00002.gguf", SHA256: "cc"},
}
Expect(UnaccountedQuants(files, DiscoverUnslothQuants(files))).To(BeEmpty())
})
It("says nothing about a wanted quant the repo does not publish at all", func() {
files := []GGUFFile{{Name: "Model-UD-Q4_K_M.gguf", SHA256: "aa"}}
Expect(UnaccountedQuants(files, DiscoverUnslothQuants(files))).To(BeEmpty())
})
// UD-Q8_0 is its own quant label and is not a wanted one. Reading it as a
// publication of Q8_0 is the substring collision this diagnostic exists to
// warn about, and subdirectory-sharded UD quants are the normal unsloth
// layout for large repos, so the false positive would fire on every batch.
It("does not read a subdirectory-sharded UD-Q8_0 as a published Q8_0", func() {
files := []GGUFFile{
{Name: "UD-Q8_0/Model-UD-Q8_0-00001-of-00002.gguf", SHA256: "aa"},
{Name: "UD-Q8_0/Model-UD-Q8_0-00002-of-00002.gguf", SHA256: "bb"},
}
Expect(UnaccountedQuants(files, DiscoverUnslothQuants(files))).To(BeEmpty())
})
// A quant in its own subdirectory but not shard-numbered matches neither
// branch of DiscoverUnslothQuants, so it is genuinely published and
// genuinely undiscovered.
It("reports a wanted quant published in its own subdirectory without shard numbering", func() {
files := []GGUFFile{{Name: "Q8_0/Model-Q8_0.gguf", SHA256: "aa"}}
Expect(UnaccountedQuants(files, DiscoverUnslothQuants(files))).
To(ContainElement(ContainSubstring("quant Q8_0 is published upstream")))
})
// builds is empty on purpose: it isolates the file-to-quant match from
// whatever DiscoverUnslothQuants would have made of the same file.
It("matches the flat single-file layout", func() {
files := []GGUFFile{{Name: "Model-Q8_0.gguf", SHA256: "aa"}}
Expect(UnaccountedQuants(files, nil)).
To(ConsistOf(ContainSubstring("quant Q8_0 is published upstream")))
})
})
var _ = Describe("Verify local path collisions", func() {
write := func(body string) string {
dir := GinkgoT().TempDir()
p := filepath.Join(dir, "index.yaml")
Expect(os.WriteFile(p, []byte(body), 0o600)).To(Succeed())
return p
}
It("reports one local path claimed by two different uris", func() {
// The shape that shipped: LiquidAI and unsloth both publish
// LFM2.5-8B-A1B-GGUF, so a path built from the bare repo name gives both
// entries the same local file under two different checksums.
Expect(Verify(write(`
- name: lfm2.5-8b-a1b
files:
- filename: llama-cpp/models/LFM2.5-8B-A1B-GGUF/LFM2.5-8B-A1B-Q8_0.gguf
sha256: 33ab3b8c
uri: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-GGUF/resolve/main/LFM2.5-8B-A1B-Q8_0.gguf
- name: lfm2.5-8b-a1b-q8-0
files:
- filename: llama-cpp/models/LFM2.5-8B-A1B-GGUF/LFM2.5-8B-A1B-Q8_0.gguf
sha256: ec11666b
uri: https://huggingface.co/unsloth/LFM2.5-8B-A1B-GGUF/resolve/main/LFM2.5-8B-A1B-Q8_0.gguf
`))).To(ContainElement(SatisfyAll(
ContainSubstring("claimed by two different uris"),
ContainSubstring("lfm2.5-8b-a1b-q8-0"),
)))
})
It("accepts two entries reusing one file from the same uri", func() {
// Sibling builds of one repo legitimately share a projector.
Expect(Verify(write(`
- name: a
files:
- filename: llama-cpp/mmproj/mudler/Example-GGUF/mmproj-F16.gguf
sha256: cc
uri: https://huggingface.co/mudler/Example-GGUF/resolve/main/mmproj-F16.gguf
- name: b
files:
- filename: llama-cpp/mmproj/mudler/Example-GGUF/mmproj-F16.gguf
sha256: cc
uri: https://huggingface.co/mudler/Example-GGUF/resolve/main/mmproj-F16.gguf
`))).To(BeEmpty())
})
It("reports a collision once however many entries pile onto the path", func() {
problems := Verify(write(`
- name: a
files:
- filename: shared.gguf
sha256: aa
uri: https://example.com/a.gguf
- name: b
files:
- filename: shared.gguf
sha256: bb
uri: https://example.com/b.gguf
- name: c
files:
- filename: shared.gguf
sha256: cc
uri: https://example.com/c.gguf
`))
var collisions int
for _, p := range problems {
if strings.Contains(p, "claimed by two different uris") {
collisions++
}
}
Expect(collisions).To(Equal(1))
})
It("says nothing about files that carry no uri", func() {
// A hand-written entry may record only a checksum. There is no upstream
// to compare, so the check cannot conclude anything either way.
Expect(Verify(write(`
- name: a
files:
- filename: shared.gguf
sha256: aa
- name: b
files:
- filename: shared.gguf
sha256: bb
`))).To(BeEmpty())
})
})

View File

@@ -1,152 +0,0 @@
// Package galleryedit splices variant references into the LocalAI gallery index
// as TEXT.
//
// Re-serialising the index through a YAML marshaller would reflow 40,000 lines,
// drop the anchors and merge keys the gallery relies on, and produce a diff no
// reviewer could read, which makes a pull request worthless even when the
// content inside it is right. Every generator that adds variants to an entry the
// gallery already ships therefore edits lines, and they share this package so
// that two of them cannot drift apart on where a variants block belongs.
package galleryedit
import (
"fmt"
"regexp"
"sort"
"strings"
)
var (
entryStart = regexp.MustCompile(`^-(?: |$)`)
inlineName = regexp.MustCompile(`^- (?:&\S+ )?name:`)
keyName = regexp.MustCompile(`^ name:`)
keyVariants = regexp.MustCompile(`^ variants:\s*(.*)$`)
variantItem = regexp.MustCompile(`^ - `)
unsafeInName = regexp.MustCompile(`[:#{}\[\],&*?|>'"%@` + "`" + `]|^\s|\s$`)
)
// Entry is the positional view of one gallery entry: what it is called and
// which lines it occupies. Nothing about what the entry MEANS belongs here, so
// each caller keeps its own semantic decode and only hands over the coordinates.
type Entry struct {
Name string
// StartLine and EndLine bound the entry, zero based and half open.
StartLine int
EndLine int
}
// Insert is one entry's pending variants addition. The caller owns the contents
// of Variants: this package neither orders nor deduplicates them, because the
// right order and the right dedup rule differ between generators.
type Insert struct {
Entry Entry
Variants []string
}
// Scan splits index text into lines and reports the line each top level list
// item begins on.
func Scan(text string) (lines []string, starts []int) {
lines = strings.Split(text, "\n")
for i, line := range lines {
if entryStart.MatchString(line) {
starts = append(starts, i)
}
}
return lines, starts
}
// Apply splices every insert into the index lines and returns the new text.
func Apply(lines []string, inserts []Insert) ([]string, error) {
type edit struct {
at int
remove int
insert []string
}
var edits []edit
for _, in := range inserts {
if len(in.Variants) == 0 {
continue
}
items := make([]string, 0, len(in.Variants))
for _, v := range in.Variants {
items = append(items, " - model: "+QuoteName(v))
}
at, remove, err := insertionPoint(lines, in.Entry)
if err != nil {
return nil, err
}
block := items
if remove > 0 || !hasVariantsKey(lines, in.Entry) {
block = append([]string{" variants:"}, items...)
}
edits = append(edits, edit{at: at, remove: remove, insert: block})
}
// Applying from the bottom up keeps every line number computed against the
// original text valid while earlier edits are still pending.
sort.Slice(edits, func(i, j int) bool { return edits[i].at > edits[j].at })
out := append([]string(nil), lines...)
for _, e := range edits {
tail := append([]string(nil), out[e.at+e.remove:]...)
out = append(out[:e.at], append(append([]string(nil), e.insert...), tail...)...)
}
return out, nil
}
func hasVariantsKey(lines []string, e Entry) bool {
for i := e.StartLine; i < e.EndLine; i++ {
if keyVariants.MatchString(lines[i]) {
return true
}
}
return false
}
// insertionPoint reports where new variant items belong, and how many existing
// lines the insertion replaces.
//
// An entry with no variants key gets one right after its name, which is where
// the hand-written families put it. An entry with an empty "variants: []" has
// that line replaced by a block. An entry with a block gets its items appended.
func insertionPoint(lines []string, e Entry) (at int, remove int, err error) {
for i := e.StartLine; i < e.EndLine; i++ {
m := keyVariants.FindStringSubmatch(lines[i])
if m == nil {
continue
}
if strings.TrimSpace(m[1]) == "[]" {
return i, 1, nil
}
if strings.TrimSpace(m[1]) != "" {
return 0, 0, fmt.Errorf("entry %q writes its variants inline (%q); this job only edits block lists", e.Name, strings.TrimSpace(m[1]))
}
last := i
for j := i + 1; j < e.EndLine && variantItem.MatchString(lines[j]); j++ {
last = j
}
return last + 1, 0, nil
}
if inlineName.MatchString(lines[e.StartLine]) {
return e.StartLine + 1, 0, nil
}
for i := e.StartLine; i < e.EndLine; i++ {
if keyName.MatchString(lines[i]) {
return i + 1, 0, nil
}
}
return 0, 0, fmt.Errorf("entry %q has no name line to anchor the insertion to", e.Name)
}
// QuoteName quotes a variant reference when the name would otherwise change
// meaning as bare YAML. Config-suffixed names carry a ":" and always need it.
func QuoteName(name string) string {
if unsafeInName.MatchString(name) {
return `"` + strings.ReplaceAll(name, `"`, `\"`) + `"`
}
return name
}

View File

@@ -1,76 +0,0 @@
#!/usr/bin/env bash
#
# Generate client-side redirects for the documentation URLs that used to live at
# the site root.
#
# Until this site existed, the Hugo docs site WAS localai.io, so pages
# were published at /features/..., /getting-started/..., /faq/ and so on. The
# docs now build under /docs/, and GitHub Pages serves static files only: there
# is no server-side rewrite, no .htaccess, no _redirects. The only way to keep
# every published, bookmarked and search-indexed URL alive is to leave a real
# HTML file at the old address that sends the browser to the new one.
#
# Anything the main site already publishes wins: it owns /, /engines/,
# /blog/ and friends, so an existing file is never replaced.
#
# Usage: gen-redirects.sh <public-dir> [base-url]
# public-dir merged output directory (main site with docs/ inside it)
# base-url absolute or root-relative prefix the deployment is served from,
# trailing slash optional (default "/")
set -euo pipefail
PUBLIC_DIR=${1:?usage: gen-redirects.sh <public-dir> [base-url]}
BASE_URL=${2:-/}
# Normalise to exactly one trailing slash so concatenation below is predictable.
BASE_URL="${BASE_URL%/}/"
DOCS_DIR="${PUBLIC_DIR}/docs"
if [ ! -d "$DOCS_DIR" ]; then
echo "gen-redirects: no docs output at ${DOCS_DIR}" >&2
exit 1
fi
created=0
skipped=0
# Every .html file is a reachable old URL, not just directory indexes: the
# generated model gallery ships as a bare gallery.html and used to sit at the
# root too.
while IFS= read -r src; do
rel=${src#"$DOCS_DIR"/}
dst="${PUBLIC_DIR}/${rel}"
if [ -e "$dst" ]; then
skipped=$((skipped + 1))
continue
fi
# Link to the directory, not to its index.html, so the redirect target is the
# canonical URL the docs site itself advertises.
target="${BASE_URL}docs/${rel%index.html}"
mkdir -p "$(dirname "$dst")"
printf '%s' '<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Moved</title>
<link rel="canonical" href="'"$target"'">
<meta name="robots" content="noindex">
<meta http-equiv="refresh" content="0; url='"$target"'">
</head>
<body>
<p>This page moved to <a href="'"$target"'">'"$target"'</a>.</p>
</body>
</html>
' > "$dst"
created=$((created + 1))
done <<EOF
$(find "$DOCS_DIR" -type f -name '*.html' | sort)
EOF
echo "gen-redirects: ${created} redirect(s) written, ${skipped} path(s) left to the main site"

View File

@@ -1,64 +0,0 @@
#!/usr/bin/env bash
# Refreshes the counters shown on the landing page from the GitHub API.
#
# The numbers used to be typed into the templates by hand, which meant they
# only moved when somebody remembered, and a stale star count on the front
# page is worse than no star count. Everything the API can answer for lives
# in website/data/stats.yaml and is rewritten wholesale by this script.
#
# Anything the API cannot answer for (the Discord member count) is read back
# out of the existing file and carried through untouched.
set -euo pipefail
REPO="${REPO:-mudler/LocalAI}"
OUT="${OUT:-website/data/stats.yaml}"
# The contributors and releases endpoints are paginated and never report a
# total. Asking for one item per page makes the last page number equal to the
# item count, which the Link header hands over.
count_via_link_header() {
local path="$1" link last
link=$(gh api -i "${path}?per_page=1" 2>/dev/null | tr -d '\r' | grep -i '^link:' || true)
if [ -z "$link" ]; then
# No Link header means a single page, so count that page directly.
gh api "${path}?per_page=100" --jq 'length'
return
fi
last=$(sed -n 's/.*[?&]page=\([0-9]*\)>; rel="last".*/\1/p' <<<"$link")
[ -n "$last" ] || { gh api "${path}?per_page=100" --jq 'length'; return; }
printf '%s\n' "$last"
}
read -r stars forks < <(gh api "repos/${REPO}" --jq '"\(.stargazers_count) \(.forks_count)"')
contributors=$(count_via_link_header "repos/${REPO}/contributors")
releases=$(count_via_link_header "repos/${REPO}/releases")
# Not derivable from the GitHub API, so keep whatever is already on disk.
discord=$(sed -n 's/^discord: *\([0-9]*\).*/\1/p' "$OUT" 2>/dev/null | head -1)
discord="${discord:-0}"
for n in stars forks contributors releases; do
v="${!n}"
[[ "$v" =~ ^[0-9]+$ ]] && [ "$v" -gt 0 ] || {
echo "refusing to write: ${n} came back as '${v}'" >&2
exit 1
}
done
cat > "$OUT" <<YAML
# Counters shown on the landing page.
#
# The four GitHub fields are rewritten by .github/ci/refresh-site-counters.sh,
# which runs weekly from .github/workflows/refresh-site-counters.yml. Editing
# them by hand works but will be overwritten on the next run.
stars: ${stars}
forks: ${forks}
contributors: ${contributors}
releases: ${releases}
# The GitHub API cannot answer for this one, so it is maintained by hand and
# the refresh script carries it through untouched.
discord: ${discord}
YAML
echo "stars=${stars} forks=${forks} contributors=${contributors} releases=${releases} discord=${discord}"

View File

@@ -1,133 +0,0 @@
package main
import (
"fmt"
"strings"
)
// RenderBody writes the pull request body.
//
// The body is the product of this job, not the diff. Grouping is a judgement
// call that has gone wrong in both directions before, so a reviewer has to be
// able to accept or reject each family from the body alone, without opening
// HuggingFace to work out whether two entries hold the same weights.
func RenderBody(r *Result, ledgerPath string) string {
var b strings.Builder
b.WriteString("## Proposed gallery variant groupings\n\n")
b.WriteString("This is a proposal, not a decision. The gallery agent adds one build per model and never joins an existing family, so entries that are alternative builds of the same weights drift apart as the gallery grows. This job re-applies the grouping heuristics from the manual sweeps and asks a human to confirm.\n\n")
b.WriteString("Each family below lists the parent, the variants, and the evidence that they are the same weights. **Reject anything whose evidence you do not believe.**\n\n")
b.WriteString(fmt.Sprintf("To decline a family permanently, add one line to `%s` in this pull request and close it:\n\n", ledgerPath))
b.WriteString("```yaml\npairs:\n - {parent: some-model, variant: some-model-thing, reason: \"different finetune\"}\n```\n\n")
b.WriteString(fmt.Sprintf("### Proposed families (%d)\n\n", len(r.Families)))
if len(r.Families) == 0 {
b.WriteString("None.\n\n")
}
for _, f := range r.Families {
b.WriteString(fmt.Sprintf("#### `%s`\n\n", f.Parent))
b.WriteString("| variant | signals | evidence |\n|---|---|---|\n")
for _, p := range f.Proposals {
b.WriteString(fmt.Sprintf("| `%s` | %s | %s |\n", p.Variant, joinSignals(p.Evidence.Signals), describeEvidence(p.Evidence)))
}
b.WriteString("\n")
}
b.WriteString(fmt.Sprintf("### Declined by the ledger (%d)\n\n", len(r.Suppressed)))
if len(r.Suppressed) == 0 {
b.WriteString("Nothing the heuristics found was already on the ledger.\n\n")
} else {
b.WriteString("Candidates the heuristics found and the ledger has already settled. They are listed so the ledger's effect stays visible rather than silently shrinking the job's output.\n\n")
for _, s := range r.Suppressed {
b.WriteString(fmt.Sprintf("- `%s` + `%s`: %s\n", s.A, s.B, s.Reason))
}
b.WriteString("\n")
}
if len(r.AliasSkipped) > 0 {
b.WriteString(fmt.Sprintf("### Aliases, not variants (%d)\n\n", len(r.AliasSkipped)))
b.WriteString("These entries install byte for byte the same payload. An alias exists so clients can send a particular name; folding it under another entry would hide that name.\n\n")
for _, s := range r.AliasSkipped {
b.WriteString(fmt.Sprintf("- `%s` + `%s`: %s\n", s.A, s.B, s.Reason))
}
b.WriteString("\n")
}
if len(r.Refusals) > 0 {
b.WriteString(fmt.Sprintf("### Found but refused (%d)\n\n", len(r.Refusals)))
b.WriteString("Candidates the heuristics found but the authoring rules would not let this job write. They need a human edit or a rule change.\n\n")
for _, ref := range r.Refusals {
b.WriteString(fmt.Sprintf("- %s: %s\n", codeList(ref.Members), ref.Reason))
}
b.WriteString("\n")
}
b.WriteString("---\n\nOpened by `.github/ci/variantproposals`. Heuristics and the rejection ledger live there and in the ledger file; a wrong proposal is a bug in one of the two.\n")
return b.String()
}
func joinSignals(signals []Signal) string {
if len(signals) == 0 {
return "inferred through another member of the family"
}
out := make([]string, 0, len(signals))
for _, s := range signals {
out = append(out, "`"+string(s)+"`")
}
return strings.Join(out, ", ")
}
func describeEvidence(e Evidence) string {
var parts []string
if e.SharedStem != "" {
parts = append(parts, fmt.Sprintf("same name once quantization markers are stripped: `%s`", e.SharedStem))
}
if e.SharedFile != "" {
parts = append(parts, fmt.Sprintf("same primary weight filename once quantization markers are stripped: `%s`", e.SharedFile))
}
if e.SharedRepo != "" {
parts = append(parts, fmt.Sprintf("same upstream repo `%s`", e.SharedRepo))
}
if len(e.QuantTokens) > 0 {
parts = append(parts, "differing quantization tokens: `"+strings.Join(e.QuantTokens, "`, `")+"`")
}
if len(parts) == 0 {
return "reached this family through another member"
}
return strings.Join(parts, "; ")
}
func codeList(names []string) string {
out := make([]string, 0, len(names))
for _, n := range names {
out = append(out, "`"+n+"`")
}
return strings.Join(out, " + ")
}
// RenderSummary is the terminal-facing digest of a run, so the workflow log
// says what happened without anyone opening the pull request.
func RenderSummary(r *Result) string {
var b strings.Builder
fmt.Fprintf(&b, "families proposed: %d\n", len(r.Families))
for _, f := range r.Families {
names := make([]string, 0, len(f.Proposals))
for _, p := range f.Proposals {
names = append(names, p.Variant)
}
fmt.Fprintf(&b, " %s <- %s\n", f.Parent, strings.Join(names, ", "))
}
fmt.Fprintf(&b, "declined by ledger: %d\n", len(r.Suppressed))
for _, s := range r.Suppressed {
fmt.Fprintf(&b, " %s\n", s)
}
fmt.Fprintf(&b, "aliases skipped: %d\n", len(r.AliasSkipped))
for _, s := range r.AliasSkipped {
fmt.Fprintf(&b, " %s\n", s)
}
fmt.Fprintf(&b, "refused: %d\n", len(r.Refusals))
for _, ref := range r.Refusals {
fmt.Fprintf(&b, " %s: %s\n", strings.Join(ref.Members, " + "), ref.Reason)
}
return b.String()
}

View File

@@ -1,42 +0,0 @@
package main
import (
"fmt"
"strings"
"github.com/mudler/LocalAI/.github/ci/galleryedit"
)
// ApplyFamilies writes the proposed variant lists into the index text.
//
// The line editing itself lives in galleryedit, shared with the apexentries
// generator. Both jobs add variants to entries the gallery already ships, and a
// second answer to "where does a variants block go" would drift from this one;
// see that package for why the edit is textual rather than a YAML round trip.
func ApplyFamilies(ix *Index, families []Family) ([]string, error) {
byName, _ := ix.ByName()
var inserts []galleryedit.Insert
for _, f := range families {
entry, ok := byName[strings.ToLower(f.Parent)]
if !ok {
return nil, fmt.Errorf("parent %q is not in the index", f.Parent)
}
variants := make([]string, 0, len(f.Proposals))
for _, p := range f.Proposals {
variants = append(variants, p.Variant)
}
inserts = append(inserts, galleryedit.Insert{
Entry: galleryedit.Entry{
Name: entry.Name,
StartLine: entry.StartLine,
EndLine: entry.EndLine,
},
Variants: variants,
})
}
return galleryedit.Apply(ix.Lines, inserts)
}

View File

@@ -1,153 +0,0 @@
package main
import (
"strings"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("ApplyFamilies", func() {
apply := func(ix *Index, families []Family) []string {
lines, err := ApplyFamilies(ix, families)
ExpectWithOffset(1, err).ToNot(HaveOccurred())
return lines
}
// insertedLines is what a reviewer would see in the diff. A textual editor
// that reflowed the file would show thousands here, which is the failure
// this whole approach exists to avoid.
insertedLines := func(before, after []string) int {
remaining := map[string]int{}
for _, l := range before {
remaining[l]++
}
n := 0
for _, l := range after {
if remaining[l] > 0 {
remaining[l]--
continue
}
n++
}
return n
}
It("adds a variants block right after the entry's name and touches nothing else", func() {
ix := indexOf(
entryYAML("foo-model", "acme/repo", "foo-model-Q4_K_M.gguf", "aa"),
entryYAML("foo-model-q8_0", "acme/repo", "foo-model-Q8_0.gguf", "bb"),
)
out := apply(ix, []Family{{Parent: "foo-model", Proposals: []Proposal{{Variant: "foo-model-q8_0"}}}})
Expect(out[0]).To(Equal("- name: foo-model"))
Expect(out[1]).To(Equal(" variants:"))
Expect(out[2]).To(Equal(" - model: foo-model-q8_0"))
Expect(len(out)).To(Equal(len(ix.Lines) + 2))
Expect(insertedLines(ix.Lines, out)).To(Equal(2))
})
It("appends to a variants block that already exists", func() {
ix := indexOf(`- name: partial
variants:
- model: partial-q8_0
url: u
overrides:
parameters:
model: partial-Q4_K_M.gguf
`, entryYAML("partial-f16", "acme/repo", "partial-f16.gguf", "cc"))
out := apply(ix, []Family{{Parent: "partial", Proposals: []Proposal{{Variant: "partial-f16"}}}})
Expect(out[1]).To(Equal(" variants:"))
Expect(out[2]).To(Equal(" - model: partial-q8_0"))
Expect(out[3]).To(Equal(" - model: partial-f16"))
Expect(out[4]).To(Equal(" url: u"))
})
It("replaces an explicit empty list rather than leaving two variants keys", func() {
ix := indexOf(`- name: emptied
variants: []
url: u
`, entryYAML("emptied-q8_0", "acme/repo", "emptied-Q8_0.gguf", "cc"))
out := apply(ix, []Family{{Parent: "emptied", Proposals: []Proposal{{Variant: "emptied-q8_0"}}}})
Expect(strings.Join(out[:4], "\n")).To(Equal("- name: emptied\n variants:\n - model: emptied-q8_0\n url: u"))
Expect(strings.Count(strings.Join(out, "\n"), "variants:")).To(Equal(1))
})
It("quotes a config-suffixed name so the reference stays a string", func() {
ix := indexOf(
entryYAML("phi-2-chat", "acme/repo", "phi-2-chat-Q4_K_M.gguf", "aa"),
entryYAML("phi-2-chat:Q8_0", "acme/repo", "phi-2-chat-Q8_0.gguf", "bb"),
)
out := apply(ix, []Family{{Parent: "phi-2-chat", Proposals: []Proposal{{Variant: "phi-2-chat:Q8_0"}}}})
Expect(out[2]).To(Equal(` - model: "phi-2-chat:Q8_0"`))
// The result has to still be a gallery, and the reference has to
// resolve to the entry it names.
reparsed, err := ParseIndex(strings.Join(out, "\n"))
Expect(err).ToNot(HaveOccurred())
Expect(reparsed.Entries[0].Variants).To(ConsistOf(VariantRef{Model: "phi-2-chat:Q8_0"}))
})
It("keeps line numbers correct when several entries are edited at once", func() {
ix := indexOf(
entryYAML("alpha", "acme/repo", "alpha-Q4_K_M.gguf", "aa"),
entryYAML("alpha-q8_0", "acme/repo", "alpha-Q8_0.gguf", "bb"),
entryYAML("beta", "acme/repo", "beta-Q4_K_M.gguf", "cc"),
entryYAML("beta-q8_0", "acme/repo", "beta-Q8_0.gguf", "dd"),
)
out := apply(ix, []Family{
{Parent: "alpha", Proposals: []Proposal{{Variant: "alpha-q8_0"}}},
{Parent: "beta", Proposals: []Proposal{{Variant: "beta-q8_0"}}},
})
reparsed, err := ParseIndex(strings.Join(out, "\n"))
Expect(err).ToNot(HaveOccurred())
Expect(reparsed.Entries).To(HaveLen(4))
Expect(reparsed.Entries[0].Variants).To(ConsistOf(VariantRef{Model: "alpha-q8_0"}))
Expect(reparsed.Entries[2].Variants).To(ConsistOf(VariantRef{Model: "beta-q8_0"}))
Expect(reparsed.Entries[1].Variants).To(BeEmpty())
Expect(reparsed.Entries[3].Variants).To(BeEmpty())
})
It("fails loudly rather than editing an entry it cannot find", func() {
ix := indexOf(entryYAML("only", "acme/repo", "only-Q4_K_M.gguf", "aa"))
_, err := ApplyFamilies(ix, []Family{{Parent: "missing", Proposals: []Proposal{{Variant: "x"}}}})
Expect(err).To(MatchError(ContainSubstring("not in the index")))
})
})
var _ = Describe("ParseIndex", func() {
It("records the anchor an entry defines and the anchor an entry merges", func() {
ix := indexOf(`- &anc
name: anchored
url: u
`, `- !!merge <<: *anc
name: child
`)
Expect(ix.Entries[0].AnchorName).To(Equal("anc"))
Expect(ix.Entries[1].MergesFrom).To(Equal("anc"))
Expect(ix.MergeChildren("anc")).To(HaveLen(1))
})
It("carries merged values into the child, so an inherited variants key is visible", func() {
ix := indexOf(`- &anc
name: anchored
url: u
variants:
- model: something
`, `- !!merge <<: *anc
name: child
`)
Expect(ix.Entries[1].HasVariants()).To(BeTrue())
})
It("refuses a list item that decodes to nothing", func() {
// Every line number the editor works from comes from pairing decoded
// entries with top level list items. If those two views can disagree,
// the editor writes into the wrong entry, so the parse refuses instead.
_, err := ParseIndex("- name: one\n url: u\n-\n")
Expect(err).To(MatchError(ContainSubstring("empty")))
})
})

View File

@@ -1,281 +0,0 @@
package main
import (
"fmt"
"os"
"regexp"
"sort"
"strings"
"gopkg.in/yaml.v3"
"github.com/mudler/LocalAI/.github/ci/galleryedit"
)
// File is the subset of a gallery file entry the proposer reads.
type File struct {
Filename string `yaml:"filename"`
URI string `yaml:"uri"`
SHA256 string `yaml:"sha256"`
}
// VariantRef mirrors the gallery's variant reference.
type VariantRef struct {
Model string `yaml:"model"`
}
// GalleryEntry is one gallery entry, carrying both the semantics the heuristics need
// and the text range the editor needs.
//
// The two views are kept together deliberately. The editor must not round-trip
// the index through a YAML marshaller: the gallery is 40,000 lines and a
// reflowed diff cannot be reviewed, which defeats the entire point of a job
// whose output is a human decision.
type GalleryEntry struct {
Name string `yaml:"name"`
URL string `yaml:"url"`
ConfigFile map[string]any `yaml:"config_file"`
Overrides map[string]any `yaml:"overrides"`
Files []File `yaml:"files"`
Variants []VariantRef `yaml:"variants"`
// Index is the entry's position in gallery order.
Index int `yaml:"-"`
// StartLine and EndLine bound the entry's lines, zero based and half open.
StartLine int `yaml:"-"`
EndLine int `yaml:"-"`
// AnchorName is set when the entry defines a YAML anchor. Adding a variants
// key to such an entry is inherited by everything that merges it, which is
// why proposals involving anchors get special treatment.
AnchorName string `yaml:"-"`
// MergesFrom is the anchor this entry pulls in with "!!merge <<:".
MergesFrom string `yaml:"-"`
}
// Index is a parsed gallery index: entries plus the exact lines they came from.
type Index struct {
Lines []string
Entries []*GalleryEntry
}
var (
anchorStart = regexp.MustCompile(`^- &(\S+)`)
mergeStart = regexp.MustCompile(`^- !!merge <<: \*(\S+)`)
)
// LoadIndex reads and parses a gallery index file.
func LoadIndex(path string) (*Index, error) {
data, err := os.ReadFile(path)
if err != nil {
return nil, err
}
return ParseIndex(string(data))
}
// ParseIndex builds an Index from the raw text of a gallery index.
//
// The YAML decode and the textual scan are cross checked against each other: if
// they disagree on how many entries there are, every line number the editor
// would use is suspect, so the run fails rather than editing the wrong entry.
func ParseIndex(text string) (*Index, error) {
var entries []*GalleryEntry
if err := yaml.Unmarshal([]byte(text), &entries); err != nil {
return nil, fmt.Errorf("decoding gallery index: %w", err)
}
lines, starts := galleryedit.Scan(text)
if len(starts) != len(entries) {
return nil, fmt.Errorf("gallery index has %d decoded entries but %d top level list items; refusing to edit by line number", len(entries), len(starts))
}
for i, e := range entries {
if e == nil {
return nil, fmt.Errorf("gallery index list item %d is empty; refusing to edit by line number", i)
}
e.Index = i
e.StartLine = starts[i]
if i+1 < len(starts) {
e.EndLine = starts[i+1]
} else {
e.EndLine = len(lines)
}
if m := anchorStart.FindStringSubmatch(lines[e.StartLine]); m != nil {
e.AnchorName = m[1]
}
if m := mergeStart.FindStringSubmatch(lines[e.StartLine]); m != nil {
e.MergesFrom = m[1]
}
}
return &Index{Lines: lines, Entries: entries}, nil
}
// MergeChildren lists the entries that pull in the given anchor.
func (ix *Index) MergeChildren(anchor string) []*GalleryEntry {
var out []*GalleryEntry
for _, e := range ix.Entries {
if e.MergesFrom == anchor {
out = append(out, e)
}
}
return out
}
// ByName indexes entries by lowercased name. A name appearing twice keeps the
// first occurrence, matching the gallery's own first-match-wins resolution, and
// the duplicates are returned so the caller can refuse to touch them: a
// proposal naming an ambiguous entry cannot be reviewed.
func (ix *Index) ByName() (map[string]*GalleryEntry, map[string]int) {
byName := make(map[string]*GalleryEntry, len(ix.Entries))
counts := make(map[string]int, len(ix.Entries))
for _, e := range ix.Entries {
key := strings.ToLower(e.Name)
counts[key]++
if _, seen := byName[key]; !seen {
byName[key] = e
}
}
dupes := map[string]int{}
for name, n := range counts {
if n > 1 {
dupes[name] = n
}
}
return byName, dupes
}
// Installable reports whether installing this entry would put anything on disk.
// A variant target that installs nothing is a dead end for the selector, so it
// is never proposed as one.
func (e *GalleryEntry) Installable() bool {
return e.URL != "" || len(e.ConfigFile) > 0 || len(e.Overrides) > 0 || len(e.Files) > 0
}
// HasVariants reports whether the entry already offers builds of its own. Such
// an entry cannot be a variant target: nesting is what the gallery's own
// resolution refuses.
func (e *GalleryEntry) HasVariants() bool {
return len(e.Variants) > 0
}
// auxiliaryFile matches the shared side files that several unrelated models
// legitimately hand out the same copy of. Grouping on one of these is how an
// earlier sweep linked four wan-2.1 entries to each other and Z-Image-Turbo to
// qwen3-4b: they shared a text encoder, not weights.
var auxiliaryFile = regexp.MustCompile(`(?i)(mmproj|vae|clip|t5|umt5|text_?encoder|tokenizer|\bae\b|^ae\.|scheduler|config)`)
// IsAuxiliaryFile reports whether a filename is a side file rather than the
// model's own weights.
func IsAuxiliaryFile(filename string) bool {
base := filename
if i := strings.LastIndex(base, "/"); i >= 0 {
base = base[i+1:]
}
return auxiliaryFile.MatchString(base)
}
// PrimaryWeightFile returns the filename of the entry's own weights, and
// whether one could be identified unambiguously.
//
// The declared overrides.parameters.model wins because that is the file the
// backend is actually pointed at. Falling back to the file list only works when
// exactly one non-auxiliary file is present; anything else is ambiguous, and
// guessing is precisely the failure mode this heuristic has already had.
func (e *GalleryEntry) PrimaryWeightFile() (string, bool) {
if params, ok := e.Overrides["parameters"].(map[string]any); ok {
if model, ok := params["model"].(string); ok && model != "" && !IsAuxiliaryFile(model) {
return model, true
}
}
var candidates []string
for _, f := range e.Files {
if f.Filename == "" || IsAuxiliaryFile(f.Filename) {
continue
}
candidates = append(candidates, f.Filename)
}
if len(candidates) == 1 {
return candidates[0], true
}
return "", false
}
// SourceRepo returns the upstream repository the entry's files come from, as a
// coarse "host + owner + repo" key.
func (e *GalleryEntry) SourceRepo() string {
for _, f := range e.Files {
if f.URI == "" {
continue
}
return repoKey(f.URI)
}
return ""
}
func repoKey(uri string) string {
u := strings.ToLower(uri)
u = strings.TrimPrefix(u, "huggingface://")
u = strings.TrimPrefix(u, "https://huggingface.co/")
u = strings.TrimPrefix(u, "http://huggingface.co/")
parts := strings.Split(u, "/")
if len(parts) >= 2 {
return parts[0] + "/" + parts[1]
}
return u
}
// SameInstallPayload reports whether two entries install byte for byte the same
// thing.
//
// Entries like this are aliases, not variants. whisper-1 exists so a client
// speaking the OpenAI API can send that name and get whisper-base; folding it
// under whisper-base as a variant would hide the very name clients send.
func SameInstallPayload(a, b *GalleryEntry) bool {
if a.URL != b.URL {
return false
}
if !sameYAML(a.Overrides, b.Overrides) || !sameYAML(a.ConfigFile, b.ConfigFile) {
return false
}
return sameChecksums(a.Files, b.Files)
}
func sameChecksums(a, b []File) bool {
if len(a) != len(b) || len(a) == 0 {
return false
}
ha := make([]string, 0, len(a))
hb := make([]string, 0, len(b))
for _, f := range a {
if f.SHA256 == "" {
return false
}
ha = append(ha, f.SHA256)
}
for _, f := range b {
if f.SHA256 == "" {
return false
}
hb = append(hb, f.SHA256)
}
sort.Strings(ha)
sort.Strings(hb)
for i := range ha {
if ha[i] != hb[i] {
return false
}
}
return true
}
func sameYAML(a, b any) bool {
ba, err := yaml.Marshal(a)
if err != nil {
return false
}
bb, err := yaml.Marshal(b)
if err != nil {
return false
}
return string(ba) == string(bb)
}

View File

@@ -1,180 +0,0 @@
package main
import (
"fmt"
"os"
"sort"
"strings"
"gopkg.in/yaml.v3"
)
// Ledger records the grouping decisions a human has already made against the
// proposer, so a declined candidate stays declined instead of coming back every
// night until reviewers stop reading the job's pull requests.
//
// It is checked in next to the gallery and is meant to be edited inside the
// proposal pull request itself: declining a family is adding one flow-mapping
// line under pairs or groups and closing the PR.
type Ledger struct {
// Tokens are name segments that mark a distinct model rather than another
// build of the same one: finetune names, language codes, product suffixes.
// A candidate whose two names differ by any of these is never proposed.
Tokens []LedgerToken `yaml:"tokens"`
// Pairs are individual candidates a human considered and declined. Order
// does not matter: the pair is matched both ways round.
Pairs []LedgerPair `yaml:"pairs"`
// Groups decline every pair drawn from a set at once, for families like a
// per-language release where listing each pair would be unreadable.
Groups []LedgerGroup `yaml:"groups"`
}
type LedgerToken struct {
Token string `yaml:"token"`
Reason string `yaml:"reason"`
}
type LedgerPair struct {
Parent string `yaml:"parent"`
Variant string `yaml:"variant"`
Reason string `yaml:"reason"`
}
type LedgerGroup struct {
Members []string `yaml:"members"`
Reason string `yaml:"reason"`
}
// LoadLedger reads a ledger file. A missing file is not an error: a gallery
// that has declined nothing yet is a legitimate state, and failing the job over
// it would only teach people to keep an empty file around.
func LoadLedger(path string) (*Ledger, error) {
data, err := os.ReadFile(path)
if os.IsNotExist(err) {
return &Ledger{}, nil
}
if err != nil {
return nil, err
}
return ParseLedger(data)
}
func ParseLedger(data []byte) (*Ledger, error) {
l := &Ledger{}
if err := yaml.Unmarshal(data, l); err != nil {
return nil, fmt.Errorf("parsing ledger: %w", err)
}
for i, t := range l.Tokens {
if strings.TrimSpace(t.Token) == "" {
return nil, fmt.Errorf("ledger tokens[%d] has an empty token", i)
}
}
for i, p := range l.Pairs {
if strings.TrimSpace(p.Parent) == "" || strings.TrimSpace(p.Variant) == "" {
return nil, fmt.Errorf("ledger pairs[%d] needs both parent and variant", i)
}
}
return l, nil
}
// Suppression is a ledger hit: why a candidate was not proposed, in words a
// reviewer can check against the ledger file.
type Suppression struct {
A string
B string
Reason string
}
func (s Suppression) String() string {
return fmt.Sprintf("%s + %s: %s", s.A, s.B, s.Reason)
}
// Suppresses reports whether the ledger has already declined pairing these two
// entries, and why.
//
// The token rule is applied to the segments the two names do not share. Two
// builds of the same weights differ only in quantization markers, so any
// ledgered token showing up in that difference is by construction a claim that
// the entries are different models.
func (l *Ledger) Suppresses(a, b string) (Suppression, bool) {
la, lb := strings.ToLower(a), strings.ToLower(b)
for _, p := range l.Pairs {
lp, lv := strings.ToLower(p.Parent), strings.ToLower(p.Variant)
if (lp == la && lv == lb) || (lp == lb && lv == la) {
return Suppression{A: a, B: b, Reason: p.Reason}, true
}
}
for _, g := range l.Groups {
var seenA, seenB bool
for _, m := range g.Members {
lm := strings.ToLower(m)
if lm == la {
seenA = true
}
if lm == lb {
seenB = true
}
}
if seenA && seenB {
return Suppression{A: a, B: b, Reason: g.Reason}, true
}
}
diff := differingSegments(la, lb)
for _, t := range l.Tokens {
token := strings.ToLower(strings.TrimSpace(t.Token))
if _, ok := diff[token]; ok {
reason := t.Reason
if reason == "" {
reason = fmt.Sprintf("names differ by %q", token)
}
return Suppression{A: a, B: b, Reason: fmt.Sprintf("%s (token %q)", reason, token)}, true
}
}
return Suppression{}, false
}
// segments splits a name into the atoms the token rules are written against.
func segments(name string) []string {
fields := strings.FieldsFunc(strings.ToLower(name), func(r rune) bool {
return r == '-' || r == '_' || r == '.' || r == ':' || r == '/'
})
return fields
}
// differingSegments returns the set of segments present in exactly one of the
// two names.
func differingSegments(a, b string) map[string]struct{} {
setA := map[string]int{}
for _, s := range segments(a) {
setA[s]++
}
setB := map[string]int{}
for _, s := range segments(b) {
setB[s]++
}
diff := map[string]struct{}{}
for s := range setA {
if setB[s] == 0 {
diff[s] = struct{}{}
}
}
for s := range setB {
if setA[s] == 0 {
diff[s] = struct{}{}
}
}
return diff
}
// SortedSuppressions gives the ledger's effect on one run in a stable order, so
// the pull request body reads the same way for the same gallery.
func SortedSuppressions(in []Suppression) []Suppression {
out := append([]Suppression(nil), in...)
sort.Slice(out, func(i, j int) bool {
if out[i].A != out[j].A {
return out[i].A < out[j].A
}
return out[i].B < out[j].B
})
return out
}

View File

@@ -1,65 +0,0 @@
// Command variant-proposals looks for gallery entries that are alternative
// builds of the same weights but are not grouped under one another, and writes
// a proposal for a human to accept or reject.
//
// It never decides. Grouping has gone wrong repeatedly in both directions, so
// the job's value is catching drift and surfacing candidates with their
// evidence, not automating the call. The scheduled workflow feeds its output to
// a pull request in the same shape as .github/checksum_checker.sh.
package main
import (
"flag"
"fmt"
"os"
"strings"
)
func main() {
index := flag.String("index", "gallery/index.yaml", "path to the gallery index")
ledger := flag.String("ledger", "gallery/variant-exclusions.yaml", "path to the rejection ledger")
bodyOut := flag.String("body-out", "", "write the pull request body here")
apply := flag.Bool("apply", false, "write the proposed groupings back into the index")
flag.Parse()
if err := run(*index, *ledger, *bodyOut, *apply); err != nil {
fmt.Fprintln(os.Stderr, "variant-proposals:", err)
os.Exit(1)
}
}
func run(indexPath, ledgerPath, bodyOut string, apply bool) error {
ix, err := LoadIndex(indexPath)
if err != nil {
return err
}
ledger, err := LoadLedger(ledgerPath)
if err != nil {
return err
}
result := Propose(ix, ledger)
fmt.Print(RenderSummary(result))
if !result.HasProposals() {
// An empty pull request every night is how a proposal job gets muted.
fmt.Println("nothing to propose")
return nil
}
if bodyOut != "" {
if err := os.WriteFile(bodyOut, []byte(RenderBody(result, ledgerPath)), 0o644); err != nil {
return err
}
}
if !apply {
return nil
}
lines, err := ApplyFamilies(ix, result.Families)
if err != nil {
return err
}
return os.WriteFile(indexPath, []byte(strings.Join(lines, "\n")), 0o644)
}

View File

@@ -1,615 +0,0 @@
package main
import (
"fmt"
"regexp"
"sort"
"strings"
)
// Signal names the grouping heuristic that linked two entries.
type Signal string
const (
// SignalName is "same name once quantization markers are stripped".
SignalName Signal = "name-modulo-quant"
// SignalConfigSuffix is the ":" convention, foo:q8_0 as a build of foo.
SignalConfigSuffix Signal = "config-suffix"
// SignalWeightFile is "same primary weight filename once quantization
// markers are stripped", auxiliary files excluded.
SignalWeightFile Signal = "weight-filename"
)
// Evidence is what a reviewer needs in order to agree or disagree without
// opening HuggingFace: what the two entries share, and what differs.
type Evidence struct {
Signals []Signal
SharedStem string
SharedFile string
SharedRepo string
QuantTokens []string
}
// Proposal is one variant target offered to one parent.
type Proposal struct {
Variant string
Evidence Evidence
}
// Family is a complete proposal: one parent gaining one or more variants.
type Family struct {
Parent string
Proposals []Proposal
}
// Refusal is a family the heuristics found but the rules would not let through.
// Refusals are reported rather than dropped: a candidate the job keeps refusing
// is either a rule worth revisiting or a gallery bug worth fixing.
type Refusal struct {
Members []string
Reason string
}
// Result is one run of the proposer.
type Result struct {
Families []Family
Refusals []Refusal
Suppressed []Suppression
AliasSkipped []Suppression
}
// HasProposals reports whether the run found anything to open a pull request
// about. A job that opens an empty pull request every night is a job people
// filter out of their inbox.
func (r *Result) HasProposals() bool {
return len(r.Families) > 0
}
// sizeToken matches a parameter-count marker: 8b, 1.7b, a3b for an active
// expert count, e2b for the Gemma effective sizes, 8x7b for a mixture.
//
// This is a structural rule rather than a ledger entry because it is about the
// shape of the token, not about any one model. Different parameter sizes were
// mis-grouped by an earlier sweep and the failure is systematic.
var sizeToken = regexp.MustCompile(`^(?:[0-9]+(?:\.[0-9]+)?[bm]|[ae][0-9]+(?:\.[0-9]+)?b|[0-9]+x[0-9]+(?:\.[0-9]+)?b)$`)
func differsByParameterSize(a, b string) (string, bool) {
for seg := range differingSegments(a, b) {
if sizeToken.MatchString(seg) {
return seg, true
}
}
return "", false
}
// genericFileStem lists weight filenames too generic to be evidence of
// anything. Two entries both shipping "model.safetensors" share a convention,
// not a set of weights.
var genericFileStem = map[string]struct{}{
"model": {}, "weights": {}, "pytorch_model": {}, "diffusion_pytorch_model": {},
"consolidated": {}, "ggml-model": {}, "model-00001-of-00002": {},
}
// minFileStemLength keeps short, collision-prone filename stems from linking
// unrelated entries.
const minFileStemLength = 6
type pair struct {
a, b int
evidence Evidence
}
// Propose runs the grouping heuristics over a gallery index and returns what it
// would offer a human, what it refused, and what the ledger silenced.
//
// Nothing here touches the network or git, and the index is not modified.
func Propose(ix *Index, ledger *Ledger) *Result {
if ledger == nil {
ledger = &Ledger{}
}
result := &Result{}
byName, dupes := ix.ByName()
// Existing relationships. A target already claimed must not be claimed
// again, and two entries already in one family need no proposal.
claimedBy := map[string]string{}
familyOf := map[string]string{}
for _, e := range ix.Entries {
if !e.HasVariants() {
continue
}
familyOf[strings.ToLower(e.Name)] = strings.ToLower(e.Name)
for _, v := range e.Variants {
target := strings.ToLower(v.Model)
if _, taken := claimedBy[target]; !taken {
claimedBy[target] = strings.ToLower(e.Name)
}
familyOf[target] = strings.ToLower(e.Name)
}
}
candidates := map[[2]int]*Evidence{}
addPair := func(i, j int, sig Signal, apply func(*Evidence)) {
if i == j {
return
}
if i > j {
i, j = j, i
}
key := [2]int{i, j}
ev, ok := candidates[key]
if !ok {
ev = &Evidence{}
candidates[key] = ev
}
for _, s := range ev.Signals {
if s == sig {
apply(ev)
return
}
}
ev.Signals = append(ev.Signals, sig)
apply(ev)
}
// Signal 1 and 2: entries sharing a name stem.
byStem := map[string][]int{}
for _, e := range ix.Entries {
if e.Name == "" {
continue
}
byStem[NameStem(e.Name)] = append(byStem[NameStem(e.Name)], e.Index)
}
for stem, members := range byStem {
if len(members) < 2 {
continue
}
for i := 0; i < len(members); i++ {
for j := i + 1; j < len(members); j++ {
a, b := ix.Entries[members[i]], ix.Entries[members[j]]
sig := SignalName
if HasConfigSuffix(a.Name) || HasConfigSuffix(b.Name) {
sig = SignalConfigSuffix
}
// The bare parent carries no marker in its name, so the
// evidence would read "differs by q8_0" and say nothing about
// what the parent is. The weight filenames fill that in.
fa, _ := a.PrimaryWeightFile()
fb, _ := b.PrimaryWeightFile()
addPair(members[i], members[j], sig, func(ev *Evidence) {
ev.SharedStem = stem
ev.QuantTokens = quantDifference(a.Name, b.Name, fa, fb)
})
}
}
}
// Signal 3: entries whose own weight file is the same file at a different
// quantization. Auxiliary files never take part.
byFile := map[string][]int{}
for _, e := range ix.Entries {
primary, ok := e.PrimaryWeightFile()
if !ok {
continue
}
stem := FileStem(primary)
if len(stem) < minFileStemLength {
continue
}
if _, generic := genericFileStem[stem]; generic {
continue
}
byFile[stem] = append(byFile[stem], e.Index)
}
for stem, members := range byFile {
if len(members) < 2 {
continue
}
for i := 0; i < len(members); i++ {
for j := i + 1; j < len(members); j++ {
a, b := ix.Entries[members[i]], ix.Entries[members[j]]
// The filename alone is not evidence. Publishers reuse the
// upstream filename for finetunes and for models that merely
// embed the base weights: bert-embeddings, an ultravox audio
// model and a roleplay finetune all ship a file called
// llama-3.2-1b-instruct-q4_k_m.gguf. Requiring the same
// upstream repository turns the signal back into what it
// claims to be, one repo publishing one file at two
// quantizations. Two repos holding the same weights is a fact
// no filename proves, so it stays a human call.
repo := a.SourceRepo()
if repo == "" || repo != b.SourceRepo() {
continue
}
fa, _ := a.PrimaryWeightFile()
fb, _ := b.PrimaryWeightFile()
addPair(members[i], members[j], SignalWeightFile, func(ev *Evidence) {
ev.SharedFile = stem
ev.SharedRepo = repo
if len(ev.QuantTokens) == 0 {
ev.QuantTokens = quantDifference(fa, fb)
}
})
}
}
}
// Filter candidates. Everything dropped here is dropped for a reason a
// reviewer can read back off the ledger or the rules.
var kept []pair
for key, ev := range candidates {
a, b := ix.Entries[key[0]], ix.Entries[key[1]]
la, lb := strings.ToLower(a.Name), strings.ToLower(b.Name)
if la == lb {
continue
}
if dupes[la] > 0 || dupes[lb] > 0 {
result.Refusals = append(result.Refusals, Refusal{
Members: []string{a.Name, b.Name},
Reason: "one of these names appears more than once in the gallery, so a variant reference to it is ambiguous",
})
continue
}
if fa, fb := familyOf[la], familyOf[lb]; fa != "" && fa == fb {
continue
}
if seg, differs := differsByParameterSize(la, lb); differs {
result.Suppressed = append(result.Suppressed, Suppression{
A: a.Name, B: b.Name, Reason: fmt.Sprintf("different parameter sizes (segment %q)", seg),
})
continue
}
if s, ok := ledger.Suppresses(a.Name, b.Name); ok {
result.Suppressed = append(result.Suppressed, s)
continue
}
if SameInstallPayload(a, b) {
result.AliasSkipped = append(result.AliasSkipped, Suppression{
A: a.Name, B: b.Name,
Reason: "identical install payload; these are aliases of one build, not alternative builds",
})
continue
}
kept = append(kept, pair{a: key[0], b: key[1], evidence: *ev})
}
sort.Slice(kept, func(i, j int) bool {
if kept[i].a != kept[j].a {
return kept[i].a < kept[j].a
}
return kept[i].b < kept[j].b
})
// Components. A pair from either signal joins the same family, so a chain
// of alternative builds discovered by different signals stays one family
// rather than two overlapping ones that would double claim a target.
parent := map[int]int{}
var find func(int) int
find = func(x int) int {
if p, ok := parent[x]; ok && p != x {
parent[x] = find(p)
return parent[x]
}
if _, ok := parent[x]; !ok {
parent[x] = x
}
return parent[x]
}
union := func(x, y int) {
rx, ry := find(x), find(y)
if rx != ry {
parent[ry] = rx
}
}
evidenceFor := map[[2]int]Evidence{}
for _, p := range kept {
union(p.a, p.b)
evidenceFor[[2]int{p.a, p.b}] = p.evidence
}
components := map[int][]int{}
for _, p := range kept {
for _, m := range []int{p.a, p.b} {
root := find(m)
if !contains(components[root], m) {
components[root] = append(components[root], m)
}
}
}
roots := make([]int, 0, len(components))
for r := range components {
roots = append(roots, r)
}
sort.Ints(roots)
proposedTargets := map[string]string{}
for _, root := range roots {
members := components[root]
sort.Ints(members)
family, refusal := buildFamily(ix, members, evidenceFor, claimedBy, proposedTargets, byName)
if refusal != nil {
result.Refusals = append(result.Refusals, *refusal)
continue
}
if family == nil {
continue
}
for _, p := range family.Proposals {
proposedTargets[strings.ToLower(p.Variant)] = family.Parent
}
result.Families = append(result.Families, *family)
}
sort.Slice(result.Families, func(i, j int) bool { return result.Families[i].Parent < result.Families[j].Parent })
result.Suppressed = SortedSuppressions(result.Suppressed)
result.AliasSkipped = SortedSuppressions(result.AliasSkipped)
result.Refusals = dedupeRefusals(result.Refusals)
return result
}
// dedupeRefusals collapses the same refusal reached from both orderings of a
// pair, and sorts what is left. A reviewer reading the same complaint twice
// learns to skim the section.
func dedupeRefusals(in []Refusal) []Refusal {
seen := map[string]struct{}{}
var out []Refusal
for _, r := range in {
members := append([]string(nil), r.Members...)
sort.Strings(members)
key := strings.Join(members, "\x00") + "\x00" + r.Reason
if _, dup := seen[key]; dup {
continue
}
seen[key] = struct{}{}
out = append(out, r)
}
sort.Slice(out, func(i, j int) bool {
if a, b := strings.Join(out[i].Members, ","), strings.Join(out[j].Members, ","); a != b {
return a < b
}
return out[i].Reason < out[j].Reason
})
return out
}
func contains(xs []int, x int) bool {
for _, v := range xs {
if v == x {
return true
}
}
return false
}
// buildFamily turns a connected component into a proposal, or refuses it.
func buildFamily(ix *Index, members []int, evidenceFor map[[2]int]Evidence, claimedBy map[string]string, proposedTargets map[string]string, byName map[string]*GalleryEntry) (*Family, *Refusal) {
names := make([]string, 0, len(members))
for _, m := range members {
names = append(names, ix.Entries[m].Name)
}
parentIdx, err := selectParent(ix, members)
if err != nil {
return nil, &Refusal{Members: names, Reason: err.Error()}
}
parentEntry := ix.Entries[parentIdx]
parentName := strings.ToLower(parentEntry.Name)
// A parent that is itself somebody's variant would create a chain, which
// the gallery's own resolution refuses to install.
if owner, claimed := claimedBy[parentName]; claimed {
return nil, &Refusal{Members: names, Reason: fmt.Sprintf("the natural parent %q is already a variant of %q; proposing it as a parent would nest variants", parentEntry.Name, owner)}
}
if owner, claimed := proposedTargets[parentName]; claimed {
return nil, &Refusal{Members: names, Reason: fmt.Sprintf("the natural parent %q is already proposed as a variant of %q; proposing it as a parent would nest variants", parentEntry.Name, owner)}
}
// Adding a variants key to an anchor is inherited by every entry that
// merges it, silently grouping models nobody proposed. Handling that means
// editing each merging child too, which is a larger change than this job
// should make unsupervised, so it refuses and hands the reviewer the list.
if parentEntry.AnchorName != "" {
children := ix.MergeChildren(parentEntry.AnchorName)
if len(children) > 0 {
childNames := make([]string, 0, len(children))
for _, c := range children {
childNames = append(childNames, c.Name)
}
return nil, &Refusal{
Members: names,
Reason: fmt.Sprintf("the parent %q defines YAML anchor &%s, and a variants key added there is inherited by the %d entries that merge it (%s). Grouping this family by hand also means adding an explicit `variants: []` to each of those entries",
parentEntry.Name, parentEntry.AnchorName, len(children), strings.Join(childNames, ", ")),
}
}
}
existing := map[string]struct{}{}
for _, v := range parentEntry.Variants {
existing[strings.ToLower(v.Model)] = struct{}{}
}
family := &Family{Parent: parentEntry.Name}
for _, m := range members {
if m == parentIdx {
continue
}
target := ix.Entries[m]
lower := strings.ToLower(target.Name)
if _, already := existing[lower]; already {
continue
}
if target.HasVariants() {
return nil, &Refusal{Members: names, Reason: fmt.Sprintf("%q already offers variants of its own, so it cannot itself be a variant target", target.Name)}
}
if !target.Installable() {
return nil, &Refusal{Members: names, Reason: fmt.Sprintf("%q has no url, config_file, overrides or files, so it is not independently installable", target.Name)}
}
if owner, claimed := claimedBy[lower]; claimed && owner != parentName {
return nil, &Refusal{Members: names, Reason: fmt.Sprintf("%q is already a variant of %q; a target claimed by two parents is not something the gallery resolves predictably", target.Name, owner)}
}
if owner, claimed := proposedTargets[lower]; claimed && owner != parentEntry.Name {
return nil, &Refusal{Members: names, Reason: fmt.Sprintf("%q is already proposed as a variant of %q in this same run", target.Name, owner)}
}
family.Proposals = append(family.Proposals, Proposal{
Variant: target.Name,
Evidence: lookupEvidence(evidenceFor, parentIdx, m),
})
}
if len(family.Proposals) == 0 {
return nil, nil
}
sort.Slice(family.Proposals, func(i, j int) bool { return family.Proposals[i].Variant < family.Proposals[j].Variant })
return family, nil
}
func lookupEvidence(evidenceFor map[[2]int]Evidence, a, b int) Evidence {
if a > b {
a, b = b, a
}
if ev, ok := evidenceFor[[2]int{a, b}]; ok {
return ev
}
// The two entries reached the same family through a third one. Say so
// rather than inventing evidence that was never observed for this pair.
return Evidence{Signals: []Signal{SignalName}}
}
// selectParent picks the entry the others should hang off.
//
// The bare name wins when there is one: it is the name a user types and the one
// documentation links to. Otherwise the smallest build wins, judged by the
// quantization token in the entry's own weight filename, so the default install
// is the one most hosts can actually run.
func selectParent(ix *Index, members []int) (int, error) {
// The family's own stem: the one the most members reduce to, shortest name
// breaking a tie. An entry named exactly that is the bare entry.
stemCount := map[string]int{}
for _, m := range members {
stemCount[NameStem(ix.Entries[m].Name)]++
}
// Only a stem two or more members reduce to is the family's own stem. A
// stem reached by exactly one member is just that member's name, and
// treating it as the family stem would crown whichever name happens to be
// shortest rather than whichever build is the base one.
familyStem := ""
for stem, n := range stemCount {
if n < 2 {
continue
}
if familyStem == "" || n > stemCount[familyStem] ||
(n == stemCount[familyStem] && len(stem) < len(familyStem)) ||
(n == stemCount[familyStem] && len(stem) == len(familyStem) && stem < familyStem) {
familyStem = stem
}
}
var bare []int
for _, m := range members {
e := ix.Entries[m]
if HasConfigSuffix(e.Name) {
continue
}
if strings.ToLower(e.Name) == familyStem {
bare = append(bare, m)
}
}
if len(bare) == 1 {
return bare[0], nil
}
if len(bare) > 1 {
names := make([]string, 0, len(bare))
for _, m := range bare {
names = append(names, ix.Entries[m].Name)
}
return 0, fmt.Errorf("more than one entry is named exactly %q (%s), so which one is the base build is a judgement this job will not make", familyStem, strings.Join(names, ", "))
}
// No shared stem to be named after. An entry whose name every other member
// extends is still recognisably the base one, and this is the only handle
// left for families whose weights carry no readable quantization token at
// all, such as the ONNX builds.
if prefix, ok := uniquePrefixMember(ix, members); ok {
return prefix, nil
}
best := -1
bestWidth := 1 << 20
for _, m := range members {
e := ix.Entries[m]
width := unknownWidth
if primary, ok := e.PrimaryWeightFile(); ok {
width = BuildWidth(primary)
}
// Members are visited in gallery order, so a strict comparison leaves
// the earliest entry holding a tie and the choice is deterministic.
if width < bestWidth {
best, bestWidth = m, width
}
}
if best < 0 {
return 0, fmt.Errorf("no member could be identified as the smallest build")
}
if bestWidth == unknownWidth {
names := make([]string, 0, len(members))
for _, m := range members {
names = append(names, ix.Entries[m].Name)
}
return 0, fmt.Errorf("no member declares a weight file whose quantization can be read (%s), so the smallest build cannot be identified", strings.Join(names, ", "))
}
return best, nil
}
// uniquePrefixMember reports the single member whose name every other member's
// name starts with, if there is exactly one.
func uniquePrefixMember(ix *Index, members []int) (int, bool) {
found := -1
for _, m := range members {
name := strings.ToLower(ix.Entries[m].Name)
isPrefix := true
for _, other := range members {
if other == m {
continue
}
if !strings.HasPrefix(strings.ToLower(ix.Entries[other].Name), name) {
isPrefix = false
break
}
}
if !isPrefix {
continue
}
if found >= 0 {
return 0, false
}
found = m
}
return found, found >= 0
}
// quantDifference lists the quantization tokens that tell two names apart. It
// is the compact form of the evidence: "these differ only by q4_k_m vs q8_0".
func quantDifference(names ...string) []string {
var out []string
seen := map[string]struct{}{}
for _, name := range names {
// Filenames arrive here too, so the extension goes first and "/" counts
// as a separator. "_" deliberately does not: it holds "q4_k_m" together.
trimmed := weightExtension.ReplaceAllString(name, "")
for _, seg := range strings.FieldsFunc(strings.ToLower(trimmed), func(r rune) bool { return r == '-' || r == '/' }) {
if !IsQuantToken(seg) {
continue
}
if _, ok := seen[seg]; ok {
continue
}
seen[seg] = struct{}{}
out = append(out, seg)
}
}
sort.Strings(out)
return out
}

View File

@@ -1,429 +0,0 @@
package main
import (
"fmt"
"strings"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
// entryYAML writes one gallery entry with a single weight file, which is the
// shape almost every real entry has. Specs that need something else write the
// YAML out by hand.
func entryYAML(name, repo, filename, sha string) string {
return fmt.Sprintf(`- name: %s
url: github:mudler/LocalAI/gallery/virtual.yaml@master
overrides:
parameters:
model: %s
files:
- filename: %s
uri: huggingface://%s/%s
sha256: %s
`, name, filename, filename, repo, filename, sha)
}
func indexOf(entries ...string) *Index {
ix, err := ParseIndex(strings.Join(entries, ""))
ExpectWithOffset(1, err).ToNot(HaveOccurred())
return ix
}
// familyNames flattens a result into "parent <- variant, variant" strings, the
// form the specs assert against.
func familyNames(r *Result) []string {
out := make([]string, 0, len(r.Families))
for _, f := range r.Families {
names := make([]string, 0, len(f.Proposals))
for _, p := range f.Proposals {
names = append(names, p.Variant)
}
out = append(out, f.Parent+" <- "+strings.Join(names, ", "))
}
return out
}
func refusalReasons(r *Result) string {
var b strings.Builder
for _, ref := range r.Refusals {
b.WriteString(strings.Join(ref.Members, " + ") + ": " + ref.Reason + "\n")
}
return b.String()
}
func suppressionReasons(r *Result) string {
var b strings.Builder
for _, s := range r.Suppressed {
b.WriteString(s.String() + "\n")
}
return b.String()
}
var _ = Describe("Propose", func() {
Describe("the grouping signals", func() {
It("groups entries whose names differ only by a quantization marker", func() {
ix := indexOf(
entryYAML("foo-model", "acme/foo-GGUF", "foo-model-Q4_K_M.gguf", "aa"),
entryYAML("foo-model-q8_0", "acme/foo-GGUF", "foo-model-Q8_0.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(ConsistOf("foo-model <- foo-model-q8_0"))
Expect(r.Families[0].Proposals[0].Evidence.Signals).To(ContainElement(SignalName))
Expect(r.Families[0].Proposals[0].Evidence.SharedStem).To(Equal("foo-model"))
Expect(r.Families[0].Proposals[0].Evidence.QuantTokens).To(ContainElements("q4_k_m", "q8_0"))
})
It("groups entries that use the colon config-suffix convention", func() {
ix := indexOf(
entryYAML("bar-model", "acme/bar-GGUF", "bar-model-Q4_K_M.gguf", "aa"),
entryYAML("bar-model:grammar-functioncall", "acme/bar-GGUF", "bar-model-Q4_K_M-grammar.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(ConsistOf("bar-model <- bar-model:grammar-functioncall"))
Expect(r.Families[0].Proposals[0].Evidence.Signals).To(ContainElement(SignalConfigSuffix))
})
It("groups entries whose own weight file is the same file at another quantization", func() {
// The names share no stem, so only the filename signal can link
// these two.
ix := indexOf(
entryYAML("omni-cpp", "Serveurperso/Omni-GGUF", "omnivoice-base-Q8_0.gguf", "aa"),
entryYAML("omni-cpp-hq", "Serveurperso/Omni-GGUF", "omnivoice-base-BF16.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(ConsistOf("omni-cpp <- omni-cpp-hq"))
ev := r.Families[0].Proposals[0].Evidence
Expect(ev.Signals).To(ConsistOf(SignalWeightFile))
Expect(ev.SharedFile).To(Equal("omnivoice-base"))
Expect(ev.SharedRepo).To(Equal("serveurperso/omni-gguf"))
})
It("does not let a shared auxiliary file link unrelated models", func() {
// Both entries ship the same text encoder. That is a packaging
// convention, not evidence of shared weights: this is how an
// earlier sweep linked four wan-2.1 entries to each other.
ix := indexOf(`- name: wan-2.1-t2v
url: u
files:
- filename: wan-2.1-t2v-Q4_K_M.gguf
uri: huggingface://acme/wan/wan-2.1-t2v-Q4_K_M.gguf
sha256: aa
- filename: umt5-xxl-encoder-Q8_0.gguf
uri: huggingface://acme/wan/umt5-xxl-encoder-Q8_0.gguf
sha256: cc
`, `- name: z-image-turbo
url: u
files:
- filename: z-image-turbo-Q4_K_M.gguf
uri: huggingface://acme/wan/z-image-turbo-Q4_K_M.gguf
sha256: bb
- filename: umt5-xxl-encoder-Q8_0.gguf
uri: huggingface://acme/wan/umt5-xxl-encoder-Q8_0.gguf
sha256: cc
`)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
})
It("does not treat a shared filename in two different repos as evidence", func() {
// A finetune republished under the base model's filename is the
// most common way this signal misfires.
ix := indexOf(
entryYAML("llama-3.2-3b-instruct", "hugging-quants/Llama-3.2-3B-Instruct-GGUF", "llama-3.2-3b-instruct-q4_k_m.gguf", "aa"),
entryYAML("llama-3.2-3b-shiro-roleplay", "someone/Shiro-GGUF", "Llama-3.2-3B-Instruct.Q8_0.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
})
})
Describe("what must never be proposed", func() {
It("does not group different parameter sizes that share a prefix", func() {
ix := indexOf(
entryYAML("qwen3-tts-cpp-0.6b-base", "Serveurperso/Qwen3-TTS-GGUF", "qwen3-tts-talker-Q4_K_M.gguf", "aa"),
entryYAML("qwen3-tts-cpp-1.7b-base", "Serveurperso/Qwen3-TTS-GGUF", "qwen3-tts-talker-Q8_0.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
Expect(suppressionReasons(r)).To(ContainSubstring("different parameter sizes"))
})
It("does not group the Gemma effective sizes", func() {
ix := indexOf(
entryYAML("gemma-4-e2b-it", "google/gemma-GGUF", "gemma-4-it-Q4_K_M.gguf", "aa"),
entryYAML("gemma-4-e4b-it", "google/gemma-GGUF", "gemma-4-it-Q8_0.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
Expect(suppressionReasons(r)).To(ContainSubstring("different parameter sizes"))
})
It("does not group entries with a byte-identical install payload", func() {
// whisper-1 exists so OpenAI-compatible clients can send that name.
// Folding it under whisper-base would hide the name they send.
payload := ` url: github:mudler/LocalAI/gallery/whisper-base.yaml@master
overrides:
parameters:
model: ggml-whisper-base.bin
files:
- filename: ggml-whisper-base.bin
uri: huggingface://ggerganov/whisper.cpp/ggml-base.bin
sha256: aa
`
ix := indexOf("- name: whisper-base\n"+payload, "- name: whisper-1\n"+payload)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
Expect(r.AliasSkipped).To(HaveLen(1))
Expect(r.AliasSkipped[0].Reason).To(ContainSubstring("aliases"))
})
DescribeTable("declines the categories the ledger records",
func(nameA, nameB string, ledgerYAML string) {
ix := indexOf(
entryYAML(nameA, "acme/repo", "shared-weights-Q4_K_M.gguf", "aa"),
entryYAML(nameB, "acme/repo", "shared-weights-Q8_0.gguf", "bb"),
)
ledger, err := ParseLedger([]byte(ledgerYAML))
Expect(err).ToNot(HaveOccurred())
// Without the ledger these would be proposed, which is what
// makes the ledger load bearing rather than decorative.
Expect(familyNames(Propose(ix, nil))).ToNot(BeEmpty())
r := Propose(ix, ledger)
Expect(familyNames(r)).To(BeEmpty())
Expect(r.Suppressed).To(HaveLen(1))
},
Entry("a finetune", "base-model", "base-model-abliterated",
"tokens:\n - {token: abliterated, reason: finetune}\n"),
Entry("a distill", "base-model", "base-model-distilled",
"tokens:\n - {token: distilled, reason: distilled}\n"),
Entry("English-only versus multilingual ASR", "whisper-small", "whisper-small-en",
"pairs:\n - {parent: whisper-small, variant: whisper-small-en, reason: English-only versus multilingual}\n"),
Entry("two products sharing a prefix", "vibevoice-cpp", "vibevoice-cpp-asr",
"pairs:\n - {parent: vibevoice-cpp, variant: vibevoice-cpp-asr, reason: different products}\n"),
Entry("a per-language release", "kokoros-de", "kokoros-ja",
"groups:\n - {members: [kokoros, kokoros-de, kokoros-ja], reason: different languages}\n"),
)
It("reports the ledger's reason so its effect stays visible", func() {
ix := indexOf(
entryYAML("base-model", "acme/repo", "shared-weights-Q4_K_M.gguf", "aa"),
entryYAML("base-model-heretic", "acme/repo", "shared-weights-Q8_0.gguf", "bb"),
)
ledger, err := ParseLedger([]byte("tokens:\n - {token: heretic, reason: \"finetune, not a re-quantization\"}\n"))
Expect(err).ToNot(HaveOccurred())
r := Propose(ix, ledger)
Expect(suppressionReasons(r)).To(ContainSubstring("finetune, not a re-quantization"))
Expect(suppressionReasons(r)).To(ContainSubstring(`token "heretic"`))
})
})
Describe("parent selection", func() {
It("picks the bare-named entry when one exists", func() {
ix := indexOf(
entryYAML("base-model-q8_0", "acme/repo", "base-model-Q8_0.gguf", "aa"),
entryYAML("base-model", "acme/repo", "base-model-Q4_K_M.gguf", "bb"),
entryYAML("base-model-f16", "acme/repo", "base-model-f16.gguf", "cc"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(ConsistOf("base-model <- base-model-f16, base-model-q8_0"))
})
It("picks the smallest build when no entry is bare-named", func() {
ix := indexOf(
entryYAML("ced-base-f16", "acme/repo", "ced-base-f16.gguf", "aa"),
entryYAML("ced-base-q8", "acme/repo", "ced-base-Q8_0.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(ConsistOf("ced-base-q8 <- ced-base-f16"))
})
It("judges the smallest build by the quantization in the model filename, not the name", func() {
// The names carry no marker at all; only the filenames say which
// build is which.
ix := indexOf(
entryYAML("thing-hq", "acme/repo", "thing-weights-BF16.gguf", "aa"),
entryYAML("thing-lite", "acme/repo", "thing-weights-Q4_K_M.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(ConsistOf("thing-lite <- thing-hq"))
})
})
Describe("the rules a proposal has to respect", func() {
It("refuses to nest: a target that already offers variants of its own", func() {
ix := indexOf(
entryYAML("nest-model", "acme/repo", "nest-model-Q4_K_M.gguf", "aa"),
`- name: nest-model-q8_0
url: u
variants:
- model: nest-model-q8_0-mtp
overrides:
parameters:
model: nest-model-Q8_0.gguf
files:
- filename: nest-model-Q8_0.gguf
uri: huggingface://acme/repo/nest-model-Q8_0.gguf
sha256: bb
`,
entryYAML("nest-model-q8_0-mtp", "other/repo", "nest-model-mtp.gguf", "cc"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
Expect(refusalReasons(r)).To(ContainSubstring("already offers variants of its own"))
})
It("refuses to nest: a parent that is already somebody else's variant", func() {
ix := indexOf(
`- name: outer
url: u
variants:
- model: middle
overrides:
parameters:
model: outer-Q4_K_M.gguf
files:
- filename: outer-Q4_K_M.gguf
uri: huggingface://acme/repo/outer-Q4_K_M.gguf
sha256: aa
`,
entryYAML("middle", "acme/other", "middle-Q4_K_M.gguf", "bb"),
entryYAML("middle-q8_0", "acme/other", "middle-Q8_0.gguf", "cc"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
Expect(refusalReasons(r)).To(ContainSubstring("would nest variants"))
})
It("refuses to let two parents claim one target", func() {
ix := indexOf(
`- name: claimant
url: u
variants:
- model: contested-q8_0
overrides:
parameters:
model: claimant-Q4_K_M.gguf
files:
- filename: claimant-Q4_K_M.gguf
uri: huggingface://acme/repo/claimant-Q4_K_M.gguf
sha256: aa
`,
entryYAML("contested", "acme/other", "contested-Q4_K_M.gguf", "bb"),
entryYAML("contested-q8_0", "acme/other", "contested-Q8_0.gguf", "cc"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
Expect(refusalReasons(r)).To(ContainSubstring("already a variant of"))
})
It("refuses a target that is not independently installable", func() {
ix := indexOf(
entryYAML("stub-model", "acme/repo", "stub-model-Q4_K_M.gguf", "aa"),
"- name: stub-model-q8_0\n description: a stanza nobody finished\n",
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
Expect(refusalReasons(r)).To(ContainSubstring("not independently installable"))
})
It("refuses a family whose parent defines a merge anchor, naming the entries that would inherit", func() {
ix := indexOf(
`- &anchored
name: anchored-model
url: u
overrides:
parameters:
model: anchored-Q4_K_M.gguf
files:
- filename: anchored-Q4_K_M.gguf
uri: huggingface://acme/repo/anchored-Q4_K_M.gguf
sha256: aa
`,
`- !!merge <<: *anchored
name: anchored-child
variants: []
overrides:
parameters:
model: unrelated-child-Q4_K_M.gguf
files:
- filename: unrelated-child-Q4_K_M.gguf
uri: huggingface://other/repo/unrelated-child-Q4_K_M.gguf
sha256: cc
`,
entryYAML("anchored-model-q8_0", "acme/repo", "anchored-Q8_0.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
Expect(refusalReasons(r)).To(ContainSubstring("defines YAML anchor &anchored"))
Expect(refusalReasons(r)).To(ContainSubstring("anchored-child"))
Expect(refusalReasons(r)).To(ContainSubstring("variants: []"))
})
It("refuses an entry whose name is not unique in the gallery", func() {
ix := indexOf(
entryYAML("twin", "acme/repo", "twin-Q4_K_M.gguf", "aa"),
entryYAML("twin", "acme/repo", "twin-Q4_K_M.gguf", "aa"),
entryYAML("twin-q8_0", "acme/repo", "twin-Q8_0.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(BeEmpty())
Expect(refusalReasons(r)).To(ContainSubstring("appears more than once"))
})
It("says nothing about a pair that is already grouped", func() {
ix := indexOf(
`- name: settled
url: u
variants:
- model: settled-q8_0
overrides:
parameters:
model: settled-Q4_K_M.gguf
files:
- filename: settled-Q4_K_M.gguf
uri: huggingface://acme/repo/settled-Q4_K_M.gguf
sha256: aa
`,
entryYAML("settled-q8_0", "acme/repo", "settled-Q8_0.gguf", "bb"),
)
r := Propose(ix, nil)
Expect(r.HasProposals()).To(BeFalse())
Expect(r.Refusals).To(BeEmpty())
Expect(r.Suppressed).To(BeEmpty())
})
It("adds only the missing members to a family that already exists", func() {
ix := indexOf(
`- name: partial
url: u
variants:
- model: partial-q8_0
overrides:
parameters:
model: partial-Q4_K_M.gguf
files:
- filename: partial-Q4_K_M.gguf
uri: huggingface://acme/repo/partial-Q4_K_M.gguf
sha256: aa
`,
entryYAML("partial-q8_0", "acme/repo", "partial-Q8_0.gguf", "bb"),
entryYAML("partial-f16", "acme/repo", "partial-f16.gguf", "cc"),
)
r := Propose(ix, nil)
Expect(familyNames(r)).To(ConsistOf("partial <- partial-f16"))
})
})
It("does not modify the index it was given", func() {
text := entryYAML("foo-model", "acme/foo-GGUF", "foo-model-Q4_K_M.gguf", "aa") +
entryYAML("foo-model-q8_0", "acme/foo-GGUF", "foo-model-Q8_0.gguf", "bb")
ix, err := ParseIndex(text)
Expect(err).ToNot(HaveOccurred())
before := strings.Join(ix.Lines, "\n")
Propose(ix, nil)
Expect(strings.Join(ix.Lines, "\n")).To(Equal(before))
})
})

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@@ -1,151 +0,0 @@
package main
import (
"regexp"
"strconv"
"strings"
)
// Quantization and precision markers that distinguish one build of a set of
// weights from another build of the same weights. Stripping them from a name
// is what lets the proposer notice that two entries are the same model.
//
// qat and apex are in this list on a maintainer ruling: they are quantization
// techniques applied to published weights, not separate weights. Names that use
// "apex" to mean a finetune are handled by the rejection ledger instead, because
// no amount of pattern matching can tell the two uses apart.
const quantAlternation = `q[2-8](?:_[0-9a-z]+)*|pq[2-8](?:_[0-9a-z]+)*|iq[1-9][0-9a-z]*(?:_[0-9a-z]+)*|i1|` +
`f16|f32|bf16|fp16|fp32|fp8|fp4|nvfp4|mxfp4(?:_moe)*|awq|gptq|qat|apex|gguf|ggml|[0-9]+bit|g[0-9]+`
// quantSegment matches a whole hyphen-delimited segment of an entry name.
// Names separate their parts with "-" and keep quantization tokens internally
// joined with "_", so a segment is the right unit here: "q4_k_m" arrives whole.
var quantSegment = regexp.MustCompile(`^(?:` + quantAlternation + `)$`)
// quantFileSuffix matches a trailing quantization token in a weight filename.
// Filenames mix "-", "_" and "." as separators, so unlike entry names they
// cannot be split into segments up front without tearing "Q4_K_M" apart.
var quantFileSuffix = regexp.MustCompile(`(?i)[-_.](?:` + quantAlternation + `)$`)
var weightExtension = regexp.MustCompile(`(?i)\.(gguf|ggml|safetensors|bin|pt|pth|onnx)$`)
// IsQuantToken reports whether a single name segment is a quantization or
// precision marker rather than part of the model's identity.
func IsQuantToken(segment string) bool {
return quantSegment.MatchString(strings.ToLower(segment))
}
// NameStem reduces an entry name to the identity it shares with its alternative
// builds: the config suffix after ":" is dropped, then trailing quantization
// segments are stripped.
//
// It implements the first two grouping signals together because they answer the
// same question. "foo:q8_0" and "foo-q8_0" are both alternative builds of "foo",
// and the caller that needs to report which convention was used can compare the
// name against the stem itself.
//
// At least one segment always survives, so a name made entirely of quantization
// tokens does not collapse to the empty stem and swallow every other such name.
func NameStem(name string) string {
base := strings.ToLower(strings.TrimSpace(name))
if i := strings.Index(base, ":"); i >= 0 {
base = base[:i]
}
segments := strings.Split(base, "-")
for len(segments) > 1 && quantSegment.MatchString(segments[len(segments)-1]) {
segments = segments[:len(segments)-1]
}
return strings.Join(segments, "-")
}
// HasConfigSuffix reports whether a name uses the ":" convention for naming a
// config variant of another entry.
func HasConfigSuffix(name string) bool {
return strings.Contains(name, ":")
}
// FileStem reduces a weight filename to the identity shared by its other
// quantizations: directories, extension and trailing quantization tokens go.
//
// This is the third grouping signal. It is the one that has misfired before, so
// callers must filter auxiliary files out before handing a filename here: a
// shared text encoder is not evidence of shared weights.
func FileStem(filename string) string {
base := filename
if i := strings.LastIndex(base, "/"); i >= 0 {
base = base[i+1:]
}
base = weightExtension.ReplaceAllString(base, "")
for {
stripped := quantFileSuffix.ReplaceAllString(base, "")
if stripped == base {
break
}
base = stripped
}
return strings.ToLower(base)
}
// bitsPerWeight ranks quantization tokens so the smallest build of a family can
// be identified when no bare-named entry exists to be the parent.
//
// The figures are nominal bits per weight, not measured file sizes. Ranking is
// all that is asked of them, and a nominal figure is available from the name
// alone without downloading anything.
func bitsPerWeight(token string) (int, bool) {
t := strings.ToLower(token)
switch {
case t == "i1":
return 1, true
case strings.HasPrefix(t, "nvfp4"), strings.HasPrefix(t, "mxfp4"), t == "fp4":
return 4, true
case t == "fp8":
return 8, true
case t == "f16", t == "bf16", t == "fp16":
return 16, true
case t == "f32", t == "fp32":
return 32, true
case t == "awq", t == "gptq":
return 4, true
}
if m := regexp.MustCompile(`^p?q([1-9])`).FindStringSubmatch(t); m != nil {
n, _ := strconv.Atoi(m[1])
return n, true
}
if m := regexp.MustCompile(`^iq([1-9])`).FindStringSubmatch(t); m != nil {
n, _ := strconv.Atoi(m[1])
return n, true
}
if m := regexp.MustCompile(`^([0-9]+)bit$`).FindStringSubmatch(t); m != nil {
n, _ := strconv.Atoi(m[1])
return n, true
}
return 0, false
}
// unknownWidth sorts after every recognised quantization so an entry whose
// build cannot be read from its filename never wins the "smallest build" tie
// break by accident.
const unknownWidth = 1 << 10
// BuildWidth reports the nominal bits per weight of the build a filename holds.
// An unreadable filename gets unknownWidth.
func BuildWidth(filename string) int {
base := filename
if i := strings.LastIndex(base, "/"); i >= 0 {
base = base[i+1:]
}
base = weightExtension.ReplaceAllString(base, "")
best := unknownWidth
for {
m := quantFileSuffix.FindString(base)
if m == "" {
break
}
if bits, ok := bitsPerWeight(m[1:]); ok && bits < best {
best = bits
}
base = base[:len(base)-len(m)]
}
return best
}

View File

@@ -1,92 +0,0 @@
package main
import (
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("quantization markers", func() {
DescribeTable("NameStem strips the markers that distinguish builds, not models",
func(name, expected string) {
Expect(NameStem(name)).To(Equal(expected))
},
Entry("plain q4", "foo-model-q4_k_m", "foo-model"),
Entry("q8_0", "foo-model-q8_0", "foo-model"),
Entry("q5_1", "foo-model-q5_1", "foo-model"),
Entry("q2 with group size", "ternary-bonsai-8b-q2-g64", "ternary-bonsai-8b"),
Entry("iq variant", "ideogram-4-iq4nl-ggml", "ideogram-4"),
Entry("i1 imatrix", "orca-agent-v0.1-i1", "orca-agent-v0.1"),
Entry("f16", "ced-base-f16", "ced-base"),
Entry("bf16", "some-model-bf16", "some-model"),
Entry("fp8", "some-model-fp8", "some-model"),
Entry("nvfp4", "qwen3.6-27b-nvfp4", "qwen3.6-27b"),
Entry("mxfp4_moe", "huihui-qwen3-vl-30b-a3b-instruct-abliterated-mxfp4_moe", "huihui-qwen3-vl-30b-a3b-instruct-abliterated"),
Entry("pq2", "ternary-bonsai-8b-pq2", "ternary-bonsai-8b"),
Entry("awq", "some-model-awq", "some-model"),
Entry("gptq", "some-model-gptq", "some-model"),
Entry("Nbit", "qwen3-8b-mlx-4bit", "qwen3-8b-mlx"),
Entry("gguf", "some-model-gguf", "some-model"),
Entry("ggml", "flux.1-dev-ggml", "flux.1-dev"),
Entry("qat is a quantization technique", "gemma-3-27b-it-qat", "gemma-3-27b-it"),
Entry("apex is a quantization technique", "qwen3.6-35b-a3b-apex", "qwen3.6-35b-a3b"),
Entry("stacked markers", "gemma-4-e2b-it-qat-q4_0", "gemma-4-e2b-it"),
Entry("the config suffix is dropped", "phi-2-chat:Q8_0", "phi-2-chat"),
Entry("a non-quant config suffix is dropped too", "meta-llama-3.1-8b-instruct:grammar-functioncall", "meta-llama-3.1-8b-instruct"),
)
DescribeTable("NameStem leaves alone what identifies a different model",
func(name, expected string) {
Expect(NameStem(name)).To(Equal(expected))
},
Entry("parameter size", "qwen3-tts-cpp-0.6b-base", "qwen3-tts-cpp-0.6b-base"),
Entry("language suffix", "kokoros-de", "kokoros-de"),
Entry("English-only ASR", "whisper-small-en", "whisper-small-en"),
Entry("finetune", "qwen3-30b-a3b-abliterated", "qwen3-30b-a3b-abliterated"),
Entry("product suffix", "vibevoice-cpp-asr", "vibevoice-cpp-asr"),
)
It("never strips a name down to nothing", func() {
Expect(NameStem("q4_k_m")).To(Equal("q4_k_m"))
Expect(NameStem("f16-q8_0")).To(Equal("f16"))
})
DescribeTable("FileStem reduces a weight filename to the weights it holds",
func(filename, expected string) {
Expect(FileStem(filename)).To(Equal(expected))
},
Entry("directory and extension go", "bonsai/models/Ternary-Bonsai-8B-gguf/Ternary-Bonsai-8B-Q2_0.gguf", "ternary-bonsai-8b"),
Entry("underscored quant token stays whole", "Llama-3.2-1B-Instruct-Q4_K_M.gguf", "llama-3.2-1b-instruct"),
Entry("dot separated quant token", "Llama-3.2-3B-Instruct.Q4_K_M.gguf", "llama-3.2-3b-instruct"),
Entry("group size suffix", "Ternary-Bonsai-8B-Q2_0_g64.gguf", "ternary-bonsai-8b"),
Entry("bf16", "omnivoice-cpp-hq/omnivoice-base-BF16.gguf", "omnivoice-base"),
Entry("safetensors", "some/dir/Model-Name-fp8.safetensors", "model-name"),
)
DescribeTable("BuildWidth reads the nominal width out of a filename",
func(filename string, expected int) {
Expect(BuildWidth(filename)).To(Equal(expected))
},
Entry("q4", "foo-Q4_K_M.gguf", 4),
Entry("q8", "foo-Q8_0.gguf", 8),
Entry("q2", "foo-Q2_0.gguf", 2),
Entry("f16", "foo-f16.gguf", 16),
Entry("bf16", "foo-BF16.gguf", 16),
Entry("iq3", "foo-iq3_xxs.gguf", 3),
Entry("nothing readable sorts last", "foo.gguf", unknownWidth),
)
It("treats an auxiliary file as never being the model's own weights", func() {
for _, f := range []string{
"mmproj-model-f16.gguf",
"dir/vae-BF16.gguf",
"clip_l.safetensors",
"umt5-xxl-encoder-Q8_0.gguf",
"t5xxl_fp16.safetensors",
"ae.safetensors",
"omnivoice-tokenizer-Q8_0.gguf",
} {
Expect(IsAuxiliaryFile(f)).To(BeTrue(), "expected %q to be auxiliary", f)
}
Expect(IsAuxiliaryFile("gemma-3-27b-it-Q4_K_M.gguf")).To(BeFalse())
})
})

View File

@@ -1,13 +0,0 @@
package main
import (
"testing"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
func TestVariantProposals(t *testing.T) {
RegisterFailHandler(Fail)
RunSpecs(t, "gallery variant proposals")
}

View File

@@ -45,16 +45,6 @@ updates:
directory: "/backend/python/diffusers"
schedule:
interval: "weekly"
# torch and transformers are deliberately pinned in this backend (see
# backend/python/diffusers/requirements-*.txt and issue #9979), and the
# l4t12 variant resolves them from the Jetson pip index
# (https://pypi.jetson-ai-lab.io/jp6/cu129/). dependabot cannot authenticate
# against that index and fails the whole weekly update with a
# private_source_authentication_failure. Ignore the two pinned deps we don't
# want bumped anyway so the job stays green.
ignore:
- dependency-name: "torch"
- dependency-name: "transformers"
- package-ecosystem: "pip"
directory: "/backend/python/exllama"
schedule:

39
.github/gh_curl.sh vendored
View File

@@ -1,39 +0,0 @@
#!/bin/bash
# Shared curl wrapper for the nightly dependency-bump scripts.
#
# The bump workflow fans out to ~25 parallel matrix jobs, each querying
# api.github.com. Anonymous API calls are capped at 60/hour per source IP and
# GitHub-hosted runners egress through shared NAT addresses, so a random handful
# of jobs were getting rate-limited (HTTP 403 -> curl exit 22, empty response)
# every single night. Authenticating with GITHUB_TOKEN lifts the ceiling to
# 1000/hour; the retries absorb whatever transient blips remain.
# Wraps curl with GitHub auth (when a token is present) plus retry/timeout
# hardening. Callers pass their own headers and the URL.
gh_curl() {
# The bump scripts run under `set -x`; without this the Authorization header
# would be echoed into the job log on every call.
local had_xtrace=0
case "$-" in
*x*) had_xtrace=1; set +x ;;
esac
local args=(
--silent --show-error --location --fail
# --retry-all-errors so 403 rate-limit responses are retried too; plain
# --retry only covers 408/429/5xx. curl honours Retry-After when sent.
--retry 5 --retry-delay 3 --retry-all-errors
--connect-timeout 15 --max-time 60
)
if [ -n "${GITHUB_TOKEN:-}" ]; then
args+=(--header "Authorization: Bearer ${GITHUB_TOKEN}")
fi
curl "${args[@]}" "$@"
local rc=$?
if [ "$had_xtrace" -eq 1 ]; then
set -x
fi
return $rc
}

77
.github/scripts/paged-canary-apply.sh vendored Executable file
View File

@@ -0,0 +1,77 @@
#!/usr/bin/env bash
#
# paged-canary-apply.sh - apply the vendored paged-attention patch series
# (backend/cpp/llama-cpp-localai-paged/patches/paged/0001-0030) to a llama.cpp checkout, the
# same way the build does, but tolerating the ONE known-benign pre-existing
# quirk in the series. Used by the early-warning canary
# (.github/workflows/llama-cpp-paged-canary.yml) so it only goes red on a REAL
# upstream break, never on that quirk.
#
# Usage: paged-canary-apply.sh <llama.cpp-checkout-dir> <patches-dir>
# <patches-dir> is normally backend/cpp/llama-cpp-localai-paged/patches (it holds the
# top-level base series 0*.patch, currently empty, and the paged/ subseries).
#
# Exit 0 = the whole series applied -> patches still fit upstream.
# Exit !=0 = a patch failed to apply = the red signal: an upstream change moved
# the tree out from under the patches, so it is time to run a PIN_SYNC.
#
# Apply method MIRRORS backend/cpp/llama-cpp/Makefile's `llama.cpp` target:
# plain `git apply --verbose`, which natively tolerates @@ line-number offsets
# but NOT context-line changes. Matching the build's method is the point - the
# canary's apply result is exactly what the real build's apply would do.
#
# The ONLY tolerance, and it is path-scoped (not a blanket `|| true`): patch
# 0019 carries a stray *modify* hunk against the dev-only doc
# SSM_DECODE_FIX_RESULTS.md, a file that exists only on the DGX dev tree and is
# absent from any clean upstream checkout. `git apply` is atomic, so that single
# missing-file hunk rejects the whole patch - and because 0021/0022/0026/0028
# build on 0019's code, the rejection cascades to them too. This is a
# PRE-EXISTING shipped-series defect, present identically on every pin, NOT an
# upstream break (see backend/cpp/llama-cpp-localai-paged/README.md section 7,
# "Pin + maintenance policy"). We exclude ONLY that dev-doc path and still
# apply 0019's real code hunks atomically, so a genuine code-hunk break in 0019
# still fails the canary. prepare.sh tolerates the same hunk via
# `patch ... || true`; this mirrors that tolerance precisely.
set -euo pipefail
CHECKOUT="${1:?usage: paged-canary-apply.sh <llama.cpp-checkout> <patches-dir>}"
PATCHES="${2:?usage: paged-canary-apply.sh <llama.cpp-checkout> <patches-dir>}"
# The lone tolerated dev-doc, and the only patch allowed to carry it.
DEVDOC_GLOB='*SSM_DECODE_FIX_RESULTS.md'
DEVDOC_PATCH='0019-qwen35-ssm-decode-fused-gather.patch'
# Resolve to absolute paths so the apply works after we cd into the checkout.
PATCHES="$(cd "$PATCHES" && pwd)"
cd "$CHECKOUT"
shopt -s nullglob
apply_one() {
local p="$1"; shift
echo "paged-canary: applying $(basename "$p")"
if ! git apply --verbose "$@" "$p"; then
echo "::error::paged patch no longer applies to the upstream llama.cpp tip: $(basename "$p")"
echo "::error::upstream drifted past the vendored paged series - run a PIN_SYNC (see backend/cpp/llama-cpp-localai-paged/README.md section 7, Pin + maintenance policy), do NOT bump the pin blindly"
exit 1
fi
}
# Base series first (parity with the build: patches/0*.patch before
# patches/paged/0*.patch). Currently empty; nullglob makes this a no-op.
for p in "$PATCHES"/0*.patch; do
apply_one "$p"
done
# Paged series, in order.
for p in "$PATCHES"/paged/0*.patch; do
if [ "$(basename "$p")" = "$DEVDOC_PATCH" ]; then
# Apply 0019's real code hunks; exclude ONLY the benign dev-doc hunk.
apply_one "$p" --exclude="$DEVDOC_GLOB"
else
apply_one "$p"
fi
done
echo "paged-canary: the full paged patch series applied cleanly to the upstream tip"

View File

@@ -32,30 +32,16 @@ jobs:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
outputs:
matrix-singlearch: ${{ steps.set-matrix.outputs['matrix-singlearch'] }}
matrix-multiarch: ${{ steps.set-matrix.outputs['matrix-multiarch'] }}
matrix-darwin: ${{ steps.set-matrix.outputs['matrix-darwin'] }}
merge-matrix-multiarch: ${{ steps.set-matrix.outputs['merge-matrix-multiarch'] }}
merge-matrix-singlearch: ${{ steps.set-matrix.outputs['merge-matrix-singlearch'] }}
has-backends-singlearch: ${{ steps.set-matrix.outputs['has-backends-singlearch'] }}
has-backends-multiarch: ${{ steps.set-matrix.outputs['has-backends-multiarch'] }}
has-backends-darwin: ${{ steps.set-matrix.outputs['has-backends-darwin'] }}
has-merges-multiarch: ${{ steps.set-matrix.outputs['has-merges-multiarch'] }}
# Single-arch backends are sharded across SINGLEARCH_SHARDS matrix jobs to
# stay under GitHub's 256-jobs-per-matrix limit (see changed-backends.js).
matrix-singlearch-1: ${{ steps.set-matrix.outputs['matrix-singlearch-1'] }}
merge-matrix-singlearch-1: ${{ steps.set-matrix.outputs['merge-matrix-singlearch-1'] }}
has-backends-singlearch-1: ${{ steps.set-matrix.outputs['has-backends-singlearch-1'] }}
has-merges-singlearch-1: ${{ steps.set-matrix.outputs['has-merges-singlearch-1'] }}
matrix-singlearch-2: ${{ steps.set-matrix.outputs['matrix-singlearch-2'] }}
merge-matrix-singlearch-2: ${{ steps.set-matrix.outputs['merge-matrix-singlearch-2'] }}
has-backends-singlearch-2: ${{ steps.set-matrix.outputs['has-backends-singlearch-2'] }}
has-merges-singlearch-2: ${{ steps.set-matrix.outputs['has-merges-singlearch-2'] }}
matrix-singlearch-3: ${{ steps.set-matrix.outputs['matrix-singlearch-3'] }}
merge-matrix-singlearch-3: ${{ steps.set-matrix.outputs['merge-matrix-singlearch-3'] }}
has-backends-singlearch-3: ${{ steps.set-matrix.outputs['has-backends-singlearch-3'] }}
has-merges-singlearch-3: ${{ steps.set-matrix.outputs['has-merges-singlearch-3'] }}
matrix-singlearch-4: ${{ steps.set-matrix.outputs['matrix-singlearch-4'] }}
merge-matrix-singlearch-4: ${{ steps.set-matrix.outputs['merge-matrix-singlearch-4'] }}
has-backends-singlearch-4: ${{ steps.set-matrix.outputs['has-backends-singlearch-4'] }}
has-merges-singlearch-4: ${{ steps.set-matrix.outputs['has-merges-singlearch-4'] }}
has-merges-singlearch: ${{ steps.set-matrix.outputs['has-merges-singlearch'] }}
steps:
- name: Checkout repository
uses: actions/checkout@v7
@@ -123,9 +109,9 @@ jobs:
# take their full ~6h cold without blocking manifest assembly for the
# multi-arch backends whose per-arch digests would otherwise sit untagged
# on quay long enough to be GC'd.
backend-jobs-singlearch-1:
backend-jobs-singlearch:
needs: generate-matrix
if: needs.generate-matrix.outputs['has-backends-singlearch-1'] == 'true'
if: needs.generate-matrix.outputs['has-backends-singlearch'] == 'true'
uses: ./.github/workflows/backend_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
@@ -152,100 +138,7 @@ jobs:
strategy:
fail-fast: false
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch-1']) }}
backend-jobs-singlearch-2:
needs: generate-matrix
if: needs.generate-matrix.outputs['has-backends-singlearch-2'] == 'true'
uses: ./.github/workflows/backend_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
platform-tag: ${{ matrix.platform-tag || '' }}
runs-on: ${{ matrix.runs-on }}
builder-base-image: ${{ matrix.builder-base-image || '' }}
base-image: ${{ matrix.base-image }}
backend: ${{ matrix.backend }}
dockerfile: ${{ matrix.dockerfile }}
skip-drivers: ${{ matrix.skip-drivers }}
context: ${{ matrix.context }}
ubuntu-version: ${{ matrix.ubuntu-version }}
amdgpu-targets: ${{ matrix.amdgpu-targets || 'gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201' }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch-2']) }}
backend-jobs-singlearch-3:
needs: generate-matrix
if: needs.generate-matrix.outputs['has-backends-singlearch-3'] == 'true'
uses: ./.github/workflows/backend_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
platform-tag: ${{ matrix.platform-tag || '' }}
runs-on: ${{ matrix.runs-on }}
builder-base-image: ${{ matrix.builder-base-image || '' }}
base-image: ${{ matrix.base-image }}
backend: ${{ matrix.backend }}
dockerfile: ${{ matrix.dockerfile }}
skip-drivers: ${{ matrix.skip-drivers }}
context: ${{ matrix.context }}
ubuntu-version: ${{ matrix.ubuntu-version }}
amdgpu-targets: ${{ matrix.amdgpu-targets || 'gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201' }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch-3']) }}
backend-jobs-singlearch-4:
needs: generate-matrix
if: needs.generate-matrix.outputs['has-backends-singlearch-4'] == 'true'
uses: ./.github/workflows/backend_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
platform-tag: ${{ matrix.platform-tag || '' }}
runs-on: ${{ matrix.runs-on }}
builder-base-image: ${{ matrix.builder-base-image || '' }}
base-image: ${{ matrix.base-image }}
backend: ${{ matrix.backend }}
dockerfile: ${{ matrix.dockerfile }}
skip-drivers: ${{ matrix.skip-drivers }}
context: ${{ matrix.context }}
ubuntu-version: ${{ matrix.ubuntu-version }}
amdgpu-targets: ${{ matrix.amdgpu-targets || 'gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201' }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch-4']) }}
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch']) }}
# Apply tags to per-arch digests via `imagetools create`. Split into two
# jobs that mirror the build split so each merge waits ONLY on its
@@ -281,12 +174,10 @@ jobs:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-multiarch']) }}
# One merge shard per build shard: backend-merge-jobs-singlearch-<n> needs only
# backend-jobs-singlearch-<n>, preserving the "merge waits only on its own
# build" property while staying under the 256-jobs-per-matrix limit.
backend-merge-jobs-singlearch-1:
needs: [generate-matrix, backend-jobs-singlearch-1]
if: ${{ !cancelled() && needs.generate-matrix.outputs['has-merges-singlearch-1'] == 'true' }}
backend-merge-jobs-singlearch:
needs: [generate-matrix, backend-jobs-singlearch]
# See note on backend-merge-jobs-multiarch above for !cancelled().
if: ${{ !cancelled() && needs.generate-matrix.outputs['has-merges-singlearch'] == 'true' }}
uses: ./.github/workflows/backend_merge.yml
with:
tag-latest: ${{ matrix.tag-latest }}
@@ -298,55 +189,7 @@ jobs:
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch-1']) }}
backend-merge-jobs-singlearch-2:
needs: [generate-matrix, backend-jobs-singlearch-2]
if: ${{ !cancelled() && needs.generate-matrix.outputs['has-merges-singlearch-2'] == 'true' }}
uses: ./.github/workflows/backend_merge.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch-2']) }}
backend-merge-jobs-singlearch-3:
needs: [generate-matrix, backend-jobs-singlearch-3]
if: ${{ !cancelled() && needs.generate-matrix.outputs['has-merges-singlearch-3'] == 'true' }}
uses: ./.github/workflows/backend_merge.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch-3']) }}
backend-merge-jobs-singlearch-4:
needs: [generate-matrix, backend-jobs-singlearch-4]
if: ${{ !cancelled() && needs.generate-matrix.outputs['has-merges-singlearch-4'] == 'true' }}
uses: ./.github/workflows/backend_merge.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch-4']) }}
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch']) }}
backend-jobs-darwin:
needs: generate-matrix

View File

@@ -82,7 +82,7 @@ jobs:
# as the Linux registry cache.
- name: Restore Homebrew cache
id: brew-cache
uses: actions/cache/restore@v6
uses: actions/cache/restore@v4
with:
path: |
~/Library/Caches/Homebrew/downloads
@@ -142,7 +142,7 @@ jobs:
- name: Save Homebrew cache
if: github.event_name != 'pull_request' && steps.brew-cache.outputs.cache-hit != 'true'
uses: actions/cache/save@v6
uses: actions/cache/save@v4
with:
path: |
~/Library/Caches/Homebrew/downloads
@@ -169,16 +169,16 @@ jobs:
# invalidates cleanly; restore-keys fall back to the latest entry for the
# same pin so unchanged TUs stay warm even when the cache is fresh.
- name: Compute llama.cpp version
if: inputs.backend == 'llama-cpp'
if: inputs.backend == 'llama-cpp' || inputs.backend == 'llama-cpp-localai-paged'
id: llama-version
run: |
version=$(grep '^LLAMA_VERSION' backend/cpp/llama-cpp/Makefile | head -1 | cut -d= -f2 | cut -d'?' -f1 | tr -d ' ')
echo "version=${version}" >> "$GITHUB_OUTPUT"
- name: Restore ccache
if: inputs.backend == 'llama-cpp'
if: inputs.backend == 'llama-cpp' || inputs.backend == 'llama-cpp-localai-paged'
id: ccache-cache
uses: actions/cache/restore@v6
uses: actions/cache/restore@v4
with:
path: ~/Library/Caches/ccache
key: ccache-llama-${{ runner.arch }}-${{ steps.llama-version.outputs.version }}-${{ github.run_id }}
@@ -186,7 +186,7 @@ jobs:
ccache-llama-${{ runner.arch }}-${{ steps.llama-version.outputs.version }}-
- name: Configure ccache
if: inputs.backend == 'llama-cpp'
if: inputs.backend == 'llama-cpp' || inputs.backend == 'llama-cpp-localai-paged'
run: |
mkdir -p "$HOME/Library/Caches/ccache"
ccache -M 2G
@@ -211,7 +211,7 @@ jobs:
- name: Restore Python wheel cache
if: inputs.lang == 'python'
id: pyenv-cache
uses: actions/cache/restore@v6
uses: actions/cache/restore@v4
with:
path: |
~/Library/Caches/pip
@@ -244,37 +244,31 @@ jobs:
make protogen-go
make backends/privacy-filter-darwin
# audio-cpp is a C++/ggml backend like ds4 and privacy-filter - a single
# grpc-server with otool dylib bundling, plus bundled VAD assets - so it
# gets its own bespoke darwin script rather than the generic
# build-darwin-go-backend path, which would look for a backend/go/audio-cpp
# that does not exist. Keep this set in sync with DARWIN_BESPOKE_BUILDERS
# in scripts/lib/backend-filter.mjs.
- name: Build audio-cpp backend (Darwin Metal)
if: inputs.backend == 'audio-cpp'
run: |
make backends/audio-cpp-darwin
- name: Build ${{ inputs.backend }}-darwin
if: inputs.backend != 'llama-cpp' && inputs.backend != 'ds4' && inputs.backend != 'privacy-filter' && inputs.backend != 'audio-cpp'
if: inputs.backend != 'llama-cpp' && inputs.backend != 'ds4' && inputs.backend != 'privacy-filter'
run: |
make protogen-go
BACKEND=${{ inputs.backend }} BUILD_TYPE=${{ inputs.build-type }} USE_PIP=${{ inputs.use-pip }} make build-darwin-${{ inputs.lang }}-backend
- name: ccache stats
if: inputs.backend == 'llama-cpp'
if: inputs.backend == 'llama-cpp' || inputs.backend == 'llama-cpp-localai-paged'
run: ccache -s
# Only stock llama-cpp persists the ccache: both backends share the same
# ccache-llama-<arch>-<version>-<run_id> key, so the paged job restores from
# the shared prefix (warm) but must NOT also save under the identical key in
# the same run (it would collide). The shared upstream TUs stay warm via the
# stock save; the paged-only patched TUs are a small recompile.
- name: Save ccache
if: inputs.backend == 'llama-cpp' && github.event_name != 'pull_request'
uses: actions/cache/save@v6
uses: actions/cache/save@v4
with:
path: ~/Library/Caches/ccache
key: ccache-llama-${{ runner.arch }}-${{ steps.llama-version.outputs.version }}-${{ github.run_id }}
- name: Save Python wheel cache
if: inputs.lang == 'python' && github.event_name != 'pull_request' && steps.pyenv-cache.outputs.cache-hit != 'true'
uses: actions/cache/save@v6
uses: actions/cache/save@v4
with:
path: |
~/Library/Caches/pip

View File

@@ -11,30 +11,16 @@ jobs:
generate-matrix:
runs-on: ubuntu-latest
outputs:
matrix-singlearch: ${{ steps.set-matrix.outputs['matrix-singlearch'] }}
matrix-multiarch: ${{ steps.set-matrix.outputs['matrix-multiarch'] }}
matrix-darwin: ${{ steps.set-matrix.outputs['matrix-darwin'] }}
merge-matrix-multiarch: ${{ steps.set-matrix.outputs['merge-matrix-multiarch'] }}
merge-matrix-singlearch: ${{ steps.set-matrix.outputs['merge-matrix-singlearch'] }}
has-backends-singlearch: ${{ steps.set-matrix.outputs['has-backends-singlearch'] }}
has-backends-multiarch: ${{ steps.set-matrix.outputs['has-backends-multiarch'] }}
has-backends-darwin: ${{ steps.set-matrix.outputs['has-backends-darwin'] }}
has-merges-multiarch: ${{ steps.set-matrix.outputs['has-merges-multiarch'] }}
# Single-arch backends are sharded across SINGLEARCH_SHARDS matrix jobs to
# stay under GitHub's 256-jobs-per-matrix limit (see changed-backends.js).
matrix-singlearch-1: ${{ steps.set-matrix.outputs['matrix-singlearch-1'] }}
merge-matrix-singlearch-1: ${{ steps.set-matrix.outputs['merge-matrix-singlearch-1'] }}
has-backends-singlearch-1: ${{ steps.set-matrix.outputs['has-backends-singlearch-1'] }}
has-merges-singlearch-1: ${{ steps.set-matrix.outputs['has-merges-singlearch-1'] }}
matrix-singlearch-2: ${{ steps.set-matrix.outputs['matrix-singlearch-2'] }}
merge-matrix-singlearch-2: ${{ steps.set-matrix.outputs['merge-matrix-singlearch-2'] }}
has-backends-singlearch-2: ${{ steps.set-matrix.outputs['has-backends-singlearch-2'] }}
has-merges-singlearch-2: ${{ steps.set-matrix.outputs['has-merges-singlearch-2'] }}
matrix-singlearch-3: ${{ steps.set-matrix.outputs['matrix-singlearch-3'] }}
merge-matrix-singlearch-3: ${{ steps.set-matrix.outputs['merge-matrix-singlearch-3'] }}
has-backends-singlearch-3: ${{ steps.set-matrix.outputs['has-backends-singlearch-3'] }}
has-merges-singlearch-3: ${{ steps.set-matrix.outputs['has-merges-singlearch-3'] }}
matrix-singlearch-4: ${{ steps.set-matrix.outputs['matrix-singlearch-4'] }}
merge-matrix-singlearch-4: ${{ steps.set-matrix.outputs['merge-matrix-singlearch-4'] }}
has-backends-singlearch-4: ${{ steps.set-matrix.outputs['has-backends-singlearch-4'] }}
has-merges-singlearch-4: ${{ steps.set-matrix.outputs['has-merges-singlearch-4'] }}
has-merges-singlearch: ${{ steps.set-matrix.outputs['has-merges-singlearch'] }}
steps:
- name: Checkout repository
uses: actions/checkout@v7
@@ -85,10 +71,10 @@ jobs:
fail-fast: true
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-multiarch']) }}
backend-jobs-singlearch-1:
backend-jobs-singlearch:
needs: generate-matrix
if: needs.generate-matrix.outputs['has-backends-singlearch-1'] == 'true'
uses: ./.github/workflows/backend_build.yml
if: needs.generate-matrix.outputs['has-backends-singlearch'] == 'true'
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
@@ -112,94 +98,7 @@ jobs:
strategy:
fail-fast: true
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch-1']) }}
backend-jobs-singlearch-2:
needs: generate-matrix
if: needs.generate-matrix.outputs['has-backends-singlearch-2'] == 'true'
uses: ./.github/workflows/backend_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
platform-tag: ${{ matrix.platform-tag || '' }}
runs-on: ${{ matrix.runs-on }}
builder-base-image: ${{ matrix.builder-base-image || '' }}
base-image: ${{ matrix.base-image }}
backend: ${{ matrix.backend }}
dockerfile: ${{ matrix.dockerfile }}
skip-drivers: ${{ matrix.skip-drivers }}
context: ${{ matrix.context }}
ubuntu-version: ${{ matrix.ubuntu-version }}
amdgpu-targets: ${{ matrix.amdgpu-targets || 'gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201' }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: true
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch-2']) }}
backend-jobs-singlearch-3:
needs: generate-matrix
if: needs.generate-matrix.outputs['has-backends-singlearch-3'] == 'true'
uses: ./.github/workflows/backend_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
platform-tag: ${{ matrix.platform-tag || '' }}
runs-on: ${{ matrix.runs-on }}
builder-base-image: ${{ matrix.builder-base-image || '' }}
base-image: ${{ matrix.base-image }}
backend: ${{ matrix.backend }}
dockerfile: ${{ matrix.dockerfile }}
skip-drivers: ${{ matrix.skip-drivers }}
context: ${{ matrix.context }}
ubuntu-version: ${{ matrix.ubuntu-version }}
amdgpu-targets: ${{ matrix.amdgpu-targets || 'gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201' }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: true
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch-3']) }}
backend-jobs-singlearch-4:
needs: generate-matrix
if: needs.generate-matrix.outputs['has-backends-singlearch-4'] == 'true'
uses: ./.github/workflows/backend_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
platform-tag: ${{ matrix.platform-tag || '' }}
runs-on: ${{ matrix.runs-on }}
builder-base-image: ${{ matrix.builder-base-image || '' }}
base-image: ${{ matrix.base-image }}
backend: ${{ matrix.backend }}
dockerfile: ${{ matrix.dockerfile }}
skip-drivers: ${{ matrix.skip-drivers }}
context: ${{ matrix.context }}
ubuntu-version: ${{ matrix.ubuntu-version }}
amdgpu-targets: ${{ matrix.amdgpu-targets || 'gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201' }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: true
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch-4']) }}
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch']) }}
backend-merge-jobs-multiarch:
needs: [generate-matrix, backend-jobs-multiarch]
# backend_merge.yml's push-side steps are all gated on
@@ -219,9 +118,9 @@ jobs:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-multiarch']) }}
backend-merge-jobs-singlearch-1:
needs: [generate-matrix, backend-jobs-singlearch-1]
if: ${{ !cancelled() && github.event_name != 'pull_request' && needs.generate-matrix.outputs['has-merges-singlearch-1'] == 'true' }}
backend-merge-jobs-singlearch:
needs: [generate-matrix, backend-jobs-singlearch]
if: ${{ !cancelled() && github.event_name != 'pull_request' && needs.generate-matrix.outputs['has-merges-singlearch'] == 'true' }}
uses: ./.github/workflows/backend_merge.yml
with:
tag-latest: ${{ matrix.tag-latest }}
@@ -231,49 +130,7 @@ jobs:
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch-1']) }}
backend-merge-jobs-singlearch-2:
needs: [generate-matrix, backend-jobs-singlearch-2]
if: ${{ !cancelled() && github.event_name != 'pull_request' && needs.generate-matrix.outputs['has-merges-singlearch-2'] == 'true' }}
uses: ./.github/workflows/backend_merge.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch-2']) }}
backend-merge-jobs-singlearch-3:
needs: [generate-matrix, backend-jobs-singlearch-3]
if: ${{ !cancelled() && github.event_name != 'pull_request' && needs.generate-matrix.outputs['has-merges-singlearch-3'] == 'true' }}
uses: ./.github/workflows/backend_merge.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch-3']) }}
backend-merge-jobs-singlearch-4:
needs: [generate-matrix, backend-jobs-singlearch-4]
if: ${{ !cancelled() && github.event_name != 'pull_request' && needs.generate-matrix.outputs['has-merges-singlearch-4'] == 'true' }}
uses: ./.github/workflows/backend_merge.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch-4']) }}
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch']) }}
backend-jobs-darwin:
needs: generate-matrix
uses: ./.github/workflows/backend_build_darwin.yml

View File

@@ -5,32 +5,6 @@ on:
branches:
- master
pull_request:
# GoReleaser and the darwin launcher take no gallery, docs or markdown
# input, so a diff confined to these paths cannot change either binary.
# The darwin job matters here: macOS is the scarcest runner class.
#
# backend/{cpp,go,python}/**: GoReleaser builds ./cmd/local-ai and the
# launcher builds ./cmd/launcher; neither compiles a backend. The
# before-hooks still matter, but their input is backend/backend.proto
# (protogen-go) and go.mod/go.sum (go mod tidy), none of which live under
# these prefixes, so a change to any of those still triggers a full run.
# See .agents/ci-caching.md.
paths-ignore:
- 'gallery/**'
- 'docs/**'
- 'examples/**'
- '**/*.md'
- 'backend/cpp/**'
- 'backend/go/**'
- 'backend/python/**'
# Supersede an in-flight run when a PR gets a new push. Keyed on the PR number
# so every push to the same PR shares a group; on a master push the key falls
# back to github.sha (unique per commit) and cancel-in-progress is false, so
# master runs never cancel each other -- each commit is built on its own.
concurrency:
group: ci-build-test-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
build-test:
@@ -44,15 +18,9 @@ jobs:
uses: actions/setup-go@v5
with:
go-version: 1.25
# A PR builds only the host target. The three-platform cross-compile
# (linux/amd64, linux/arm64, darwin/arm64) is the bulk of this job's
# ~6.6min median and no PR consumes the resulting binaries. The
# before-hooks (protogen-go, react-ui, go mod tidy) run either way, so the
# "is the release build broken" signal is unchanged. master and tags still
# build everything.
- name: Run GoReleaser
run: |
make ${{ github.event_name == 'pull_request' && 'dev-dist-single' || 'dev-dist' }}
make dev-dist
launcher-build-darwin:
runs-on: macos-latest
steps:

View File

@@ -9,6 +9,23 @@ jobs:
strategy:
fail-fast: false
matrix:
# NOTE: there is intentionally NO entry for the llama-cpp-localai-paged
# backend. It carries a vendored paged-attention patch series
# (backend/cpp/llama-cpp-localai-paged/patches/paged/) hand-verified bit-exact against
# ONE specific llama.cpp tip; a naive nightly bump would move the tip out
# from under the patches and break `git apply` at build time. Its pin is
# therefore decoupled (its own LLAMA_VERSION in
# backend/cpp/llama-cpp-localai-paged/Makefile) and advanced ONLY by the
# manual PIN_SYNC process. Do not add it here. (turboquant CAN be
# auto-bumped below because its fork branch carries the patches.)
#
# Excluding it from the auto-bumper removed the early warning of upstream
# drift; that signal is restored separately by the dedicated canary
# .github/workflows/llama-cpp-paged-canary.yml, which weekly applies +
# compiles the paged series against the latest llama.cpp tip and goes red
# when upstream breaks it (prompting a PIN_SYNC). The canary is
# signal-only - it never opens a bump PR and never moves the pin - so
# this dep-bump workflow and its PRs stay green regardless.
include:
- repository: "ggml-org/llama.cpp"
variable: "LLAMA_VERSION"
@@ -22,22 +39,10 @@ jobs:
variable: "TURBOQUANT_VERSION"
branch: "feature/turboquant-kv-cache"
file: "backend/cpp/turboquant/Makefile"
- repository: "PrismML-Eng/llama.cpp"
variable: "BONSAI_VERSION"
branch: "prism"
file: "backend/cpp/bonsai/Makefile"
- repository: "antirez/ds4"
variable: "DS4_VERSION"
branch: "main"
file: "backend/cpp/ds4/Makefile"
- repository: "0xShug0/audio.cpp"
variable: "AUDIO_CPP_VERSION"
branch: "main"
file: "backend/cpp/audio-cpp/Makefile"
- repository: "meituan-longcat/LongCat-Video"
variable: "LONGCAT_VIDEO_VERSION"
branch: "main"
file: "backend/python/longcat-video/Makefile"
- repository: "localai-org/privacy-filter.cpp"
variable: "PRIVACY_FILTER_VERSION"
branch: "master"
@@ -54,19 +59,11 @@ jobs:
variable: "PARAKEET_VERSION"
branch: "master"
file: "backend/go/parakeet-cpp/Makefile"
- repository: "mudler/vllm.cpp"
variable: "VLLM_CPP_VERSION"
branch: "main"
file: "backend/go/vllm-cpp/Makefile"
- repository: "localai-org/moss-transcribe.cpp"
variable: "MOSS_VERSION"
branch: "master"
file: "backend/go/moss-transcribe-cpp/Makefile"
- repository: "localai-org/ced.cpp"
- repository: "mudler/ced.cpp"
variable: "CED_VERSION"
branch: "main"
branch: "master"
file: "backend/go/ced/Makefile"
- repository: "localai-org/voice-detect.cpp"
- repository: "mudler/voice-detect.cpp"
variable: "VOICEDETECT_VERSION"
branch: "master"
file: "backend/go/voice-detect/Makefile"
@@ -82,10 +79,6 @@ jobs:
variable: "STABLEDIFFUSION_GGML_VERSION"
branch: "master"
file: "backend/go/stablediffusion-ggml/Makefile"
- repository: "localai-org/trellis2cpp"
variable: "TRELLIS2CPP_VERSION"
branch: "pbr-textures"
file: "backend/go/trellis2cpp/Makefile"
- repository: "mudler/go-piper"
variable: "PIPER_VERSION"
branch: "master"
@@ -102,7 +95,7 @@ jobs:
variable: "SAM3_VERSION"
branch: "main"
file: "backend/go/sam3-cpp/Makefile"
- repository: "localai-org/rf-detr.cpp"
- repository: "mudler/rf-detr.cpp"
variable: "RFDETR_VERSION"
branch: "main"
file: "backend/go/rfdetr-cpp/Makefile"
@@ -110,14 +103,10 @@ jobs:
variable: "LOCATEANYTHING_VERSION"
branch: "master"
file: "backend/go/locate-anything-cpp/Makefile"
# qwentts.cpp is held, not tracked: upstream master hangs in synthesis
# (see the comment on QWEN3TTS_CPP_VERSION in the backend Makefile).
# Leaving it here would re-bump the pin back onto the hang every night.
# Restore this entry once the upstream fix lands.
# - repository: "ServeurpersoCom/qwentts.cpp"
# variable: "QWEN3TTS_CPP_VERSION"
# branch: "master"
# file: "backend/go/qwen3-tts-cpp/Makefile"
- repository: "ServeurpersoCom/qwentts.cpp"
variable: "QWEN3TTS_CPP_VERSION"
branch: "master"
file: "backend/go/qwen3-tts-cpp/Makefile"
- repository: "ServeurpersoCom/omnivoice.cpp"
variable: "OMNIVOICE_VERSION"
branch: "master"
@@ -126,21 +115,11 @@ jobs:
variable: "VIBEVOICE_CPP_VERSION"
branch: "master"
file: "backend/go/vibevoice-cpp/Makefile"
- repository: "mudler/magpie-tts.cpp"
variable: "MAGPIETTS_CPP_VERSION"
branch: "main"
file: "backend/go/magpie-tts-cpp/Makefile"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- name: Bump dependencies 🔧
id: bump
env:
# This job fans out to ~25 parallel matrix entries, all querying
# api.github.com from runner IPs that share the 60/hour anonymous
# rate limit. Authenticating raises it to 1000/hour, which is what
# kept a random handful of these red every night.
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
bash .github/bump_deps.sh ${{ matrix.repository }} ${{ matrix.branch }} ${{ matrix.variable }} ${{ matrix.file }}
{
@@ -177,8 +156,6 @@ jobs:
- uses: actions/checkout@v7
- name: Bump vLLM cu130 wheel pin 🔧
id: bump
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
bash .github/bump_vllm_wheel.sh vllm-project/vllm backend/python/vllm/requirements-cublas13-after.txt VLLM_VERSION
{
@@ -215,8 +192,6 @@ jobs:
- uses: actions/checkout@v7
- name: Bump vllm-metal pin 🔧
id: bump
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
bash .github/bump_vllm_metal.sh vllm-project/vllm-metal backend/python/vllm/install.sh VLLM_METAL_VERSION
{

View File

@@ -15,10 +15,6 @@ jobs:
steps:
- uses: actions/checkout@v7
- name: Bump dependencies 🔧
env:
# Authenticated API calls get 1000 req/hour instead of the 60/hour
# anonymous cap that is shared across every job on the runner's IP.
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
bash .github/bump_docs.sh ${{ matrix.repository }}
- name: Create Pull Request

View File

@@ -1,39 +0,0 @@
---
# The packages under .github/ci/ are invisible to `go list ./...`, so neither
# `make lint` nor the repository test run ever touches them. Their specs are
# dead weight until a workflow names each package explicitly.
name: 'CI tool tests'
on:
pull_request:
paths:
- '.github/ci/**'
- '.github/workflows/ci-tools-tests.yaml'
push:
branches:
- master
paths:
- '.github/ci/**'
jobs:
ci-tools:
name: 'Test the .github/ci generators'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
cache: false
# The discovery heuristics are the risky part of these tools. A regression
# produces confident, wrong gallery entries, which is worse than no tool.
- name: 'Test the APEX entry generator'
run: go test ./.github/ci/apexentries/
- name: 'Test the variant proposer'
run: go test ./.github/ci/variantproposals/
# Shared by both generators above. Its behaviour is exercised through their
# specs; this step exists so a break in the shared package fails under its
# own name rather than as a puzzling failure in whichever caller ran first.
- name: 'Test the shared gallery editor'
run: go test ./.github/ci/galleryedit/

View File

@@ -1,54 +0,0 @@
name: Propose gallery variant groupings
on:
schedule:
- cron: 0 4 * * 1
workflow_dispatch:
jobs:
variant_proposals:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
cache: false
# The heuristics are the risky part of this job. A regression in them
# produces confident, wrong proposals, which is worse than no job at all.
- name: Test the proposer
run: go test ./.github/ci/variantproposals/
- name: Propose groupings 🔧
id: propose
run: |
rm -f /tmp/variant-proposals-body.md
go run ./.github/ci/variantproposals \
-index gallery/index.yaml \
-ledger gallery/variant-exclusions.yaml \
-body-out /tmp/variant-proposals-body.md \
-apply
if [ -s /tmp/variant-proposals-body.md ]; then
echo "have_proposals=true" >> "$GITHUB_OUTPUT"
{
echo 'body<<VARIANT_PROPOSAL_BODY_EOF'
cat /tmp/variant-proposals-body.md
echo VARIANT_PROPOSAL_BODY_EOF
} >> "$GITHUB_OUTPUT"
else
echo "have_proposals=false" >> "$GITHUB_OUTPUT"
fi
# No body file means the proposer found nothing. Opening an empty pull
# request every run is how a proposal job gets muted by its reviewers.
- name: Create Pull Request
if: steps.propose.outputs.have_proposals == 'true'
uses: peter-evans/create-pull-request@v8
with:
token: ${{ secrets.UPDATE_BOT_TOKEN }}
push-to-fork: ci-forks/LocalAI
commit-message: 'chore(model-gallery): propose variant groupings'
title: 'chore(model-gallery): propose variant groupings for review'
branch: "propose/variant-groupings"
body: ${{ steps.propose.outputs.body }}
signoff: true

View File

@@ -1,4 +1,4 @@
name: Deploy site to GitHub Pages
name: Deploy docs to GitHub Pages
on:
push:
@@ -6,11 +6,9 @@ on:
- master
paths:
- 'docs/**'
- 'website/**'
- 'gallery/**'
- 'images/**'
- '.github/ci/modelslist.go'
- '.github/ci/gen-redirects.sh'
- '.github/workflows/gh-pages.yml'
workflow_dispatch:
@@ -25,20 +23,7 @@ concurrency:
jobs:
build:
# Self-hosted. This workflow is push-to-master + workflow_dispatch only, so
# it never executes pull-request code and a fork cannot reach the runner
# with untrusted changes. The repository guard keeps forks (whose own master
# pushes would otherwise queue forever against a label they do not have) on
# the hosted pool.
#
# Why: the GitHub-hosted pool is shared account-wide and has repeatedly
# starved (2026-07-31: 35 consecutive minutes at zero scheduled jobs, while
# arc-runner-set kept completing work throughout). Publishing the site is
# small, frequent, and must not sit behind a saturated hosted queue.
#
# Needs only git, tar and curl on the runner: setup-go and actions-hugo
# fetch their own toolchains, and no step uses sudo, apt, make or unzip.
runs-on: ${{ github.repository == 'mudler/LocalAI' && 'arc-runner-set' || 'ubuntu-latest' }}
runs-on: ubuntu-latest
env:
HUGO_VERSION: "0.146.3"
steps:
@@ -51,16 +36,7 @@ jobs:
- name: Setup Go
uses: actions/setup-go@v5
with:
# Track go.mod rather than a literal. Pinned at 1.22 this installed a
# toolchain older than the module's `go 1.26.0`, so the `go run` below
# downloaded the real one from proxy.golang.org on every run. That
# fetch is not always reachable from the runner and the deploy failed
# on five of eight consecutive master pushes with:
# go: download go1.26.0: ... connect: network is unreachable
# ##[error]Command failed: go env GOPATH
# Installing the version the module asks for removes the download
# instead of depending on it succeeding.
go-version-file: go.mod
go-version: '1.22'
cache: false
- name: Setup Hugo
@@ -73,46 +49,25 @@ jobs:
id: pages
uses: actions/configure-pages@v6
# The gallery page is generated from the model index and shipped as a
# static asset of the docs site, so it has to exist before Hugo runs.
- name: Generate gallery
run: go run ./.github/ci/modelslist.go ./gallery/index.yaml > docs/static/gallery.html
# Two Hugo sites, one Pages artifact: the main site owns the root,
# the docs site is nested under /docs/.
- name: Build the main site
working-directory: website
run: hugo --minify --baseURL "${{ steps.pages.outputs.base_url }}/"
- name: Build documentation site
- name: Build site
working-directory: docs
run: |
mkdir -p layouts/_default
hugo --minify --baseURL "${{ steps.pages.outputs.base_url }}/docs/"
- name: Merge documentation into the main site
run: |
mkdir -p website/public/docs
cp -R docs/public/. website/public/docs/
# Keeps the pre-split URLs alive; see the script header.
- name: Generate legacy URL redirects
run: .github/ci/gen-redirects.sh website/public "${{ steps.pages.outputs.base_url }}/"
hugo --minify --baseURL "${{ steps.pages.outputs.base_url }}/"
- name: Upload artifact
uses: actions/upload-pages-artifact@v5
with:
path: website/public
path: docs/public
deploy:
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
# Same routing as build: a hosted slot for a ~10s deploy is exactly the kind
# of job that should not block on a starved pool. deploy-pages authenticates
# with the job's OIDC token (id-token: write above), which self-hosted
# runners issue the same way hosted ones do.
runs-on: ${{ github.repository == 'mudler/LocalAI' && 'arc-runner-set' || 'ubuntu-latest' }}
runs-on: ubuntu-latest
needs: build
steps:
- name: Deploy to GitHub Pages

View File

@@ -3,28 +3,7 @@
on:
pull_request:
# None of these seven image builds can observe a diff confined to these
# paths. Gallery metadata is parsed at runtime and never copied into an
# image; docs and markdown never enter one at all. Gallery content is
# still checked by yaml-check.yml and by
# core/gallery/variants_lint_test.go under 'tests'.
#
# backend/{cpp,go,python}/**: this workflow builds the core image, whose
# only compiled output is `make build` -> `go build ./cmd/local-ai`. The
# per-backend trees are copied into the builder but nothing in them reaches
# the binary or the final stage. backend/backend.proto is deliberately not
# listed: it feeds protogen-go and so does change the binary, and it does
# not live under any of these prefixes, so it still triggers a full run.
# See .agents/ci-caching.md.
paths-ignore:
- 'gallery/**'
- 'docs/**'
- 'examples/**'
- '**/*.md'
- 'backend/cpp/**'
- 'backend/go/**'
- 'backend/python/**'
concurrency:
group: ci-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
@@ -73,7 +52,7 @@
tag-latest: 'false'
tag-suffix: '-gpu-nvidia-cuda-13'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
base-image: "ubuntu:22.04"
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'hipblas'

View File

@@ -13,53 +13,8 @@
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
# Decide once whether this push can change any image. Gallery metadata is
# fetched at runtime and never baked into an image, and docs/markdown never
# enter one, so a push confined to those paths produces byte-identical
# images. On 2026-07-30, 12 of the 23 queued runs of this workflow were
# commits like "add 1 new model to gallery" or a docs fix, each rebuilding
# all 18 images.
#
# A job-level gate rather than `paths-ignore` on the trigger: paths-ignore
# would also apply to tag pushes, and a tag created on an existing commit
# carries an empty commits list, which would silently skip the release image
# build. Tags short-circuit to "build" below, as does a push whose base
# commit cannot be resolved -- the same run-everything posture the backend
# matrix filter takes for a truncated diff.
changes:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
outputs:
build: ${{ steps.decide.outputs.build }}
steps:
- uses: actions/checkout@v7
with:
fetch-depth: 0
- id: decide
env:
BEFORE: ${{ github.event.before }}
AFTER: ${{ github.sha }}
run: |
set -euo pipefail
emit() { echo "$2"; echo "build=$1" >> "$GITHUB_OUTPUT"; exit 0; }
case "${GITHUB_REF}" in
refs/tags/*) emit true "tag push: building every image" ;;
esac
if [ -z "${BEFORE:-}" ] || [ "${BEFORE}" = "0000000000000000000000000000000000000000" ] \
|| ! git cat-file -e "${BEFORE}^{commit}" 2>/dev/null; then
emit true "no resolvable base commit: building every image"
fi
files="$(git diff --name-only "${BEFORE}" "${AFTER}")"
echo "changed files:"; echo "${files:-<none>}"
[ -z "${files}" ] && emit true "empty diff: building every image"
if echo "${files}" | grep -qvE '^(gallery/|docs/|examples/)|\.md$'; then
emit true "push touches image-visible content: building"
fi
emit false "only gallery/docs/markdown changed: images identical, skipping"
hipblas-jobs:
needs: changes
if: github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true'
if: github.repository == 'mudler/LocalAI'
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
@@ -92,8 +47,7 @@
ubuntu-codename: 'noble'
core-image-build:
needs: changes
if: github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true'
if: github.repository == 'mudler/LocalAI'
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
@@ -159,7 +113,7 @@
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
base-image: "ubuntu:22.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
ubuntu-version: '2404'
@@ -201,8 +155,8 @@
# merge whenever any matrix cell of the parent build fails or is
# cancelled. Same fix as backend.yml's merge jobs — we still want to
# publish the manifest list for tag-suffixes whose legs all succeeded.
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true' }}
needs: [changes, core-image-build]
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: core-image-build
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
@@ -214,8 +168,8 @@
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
gpu-vulkan-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true' }}
needs: [changes, core-image-build]
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: core-image-build
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
@@ -233,8 +187,8 @@
# Each merge job needs only its parent build matrix and is filtered by
# tag-suffix in image_merge.yml's artifact-download pattern.
gpu-nvidia-cuda-12-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true' }}
needs: [changes, core-image-build]
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: core-image-build
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
@@ -246,8 +200,8 @@
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
gpu-nvidia-cuda-13-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true' }}
needs: [changes, core-image-build]
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: core-image-build
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
@@ -259,8 +213,8 @@
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
gpu-intel-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true' }}
needs: [changes, core-image-build]
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: core-image-build
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
@@ -272,8 +226,8 @@
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
gpu-hipblas-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true' }}
needs: [changes, hipblas-jobs]
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: hipblas-jobs
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
@@ -285,8 +239,8 @@
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
nvidia-l4t-arm64-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true' }}
needs: [changes, gh-runner]
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: gh-runner
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
@@ -298,8 +252,8 @@
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
nvidia-l4t-arm64-cuda-13-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true' }}
needs: [changes, gh-runner]
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: gh-runner
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
@@ -311,8 +265,7 @@
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
gh-runner:
needs: changes
if: github.repository == 'mudler/LocalAI' && needs.changes.outputs.build == 'true'
if: github.repository == 'mudler/LocalAI'
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}

View File

@@ -8,9 +8,6 @@ on:
- 'examples/**'
- 'README.md'
- '**/*.md'
# golangci-lint runs new-from-merge-base, so a diff with no touched Go
# lines can only ever be a no-op. See .agents/ci-caching.md.
- 'gallery/**'
push:
branches:
- master
@@ -21,41 +18,8 @@ concurrency:
jobs:
golangci-lint:
# Self-hosted for PUSH only, and only in the canonical repo.
#
# This workflow also runs on pull_request, which for a fork PR means
# executing untrusted contributor code. That must never land on a
# self-hosted runner, so anything that is not a push to mudler/LocalAI stays
# on the ephemeral hosted pool. Pushes to master are trusted code that has
# already been reviewed and merged.
#
# Why at all: the hosted pool is shared account-wide and starved for 35
# straight minutes on 2026-07-31 while arc-runner-set kept completing jobs.
# Lint is small and runs on every commit, so it is a good candidate to move
# off the contended pool.
# REVERTED to hosted: the arc-runner-set image has git, curl, unzip, tar,
# ldd and python3, but NOT make (nor gcc). Measured on run 30637392862,
# where the preflight below named both. Re-route here once the runner image
# ships a C toolchain and make; the preflight stays so the next attempt
# fails by name in one second instead of opaquely mid-build.
runs-on: ubuntu-latest
steps:
- name: Preflight - required host tools
# The hosted images ship these; a self-hosted container image may not.
# Check up front so a missing tool reports itself by name instead of
# surfacing as an opaque failure inside `make protogen-go` (which needs
# curl + unzip for protoc) or `make lint`.
run: |
missing=""
for t in git curl unzip make tar; do
command -v "$t" >/dev/null 2>&1 || missing="$missing $t"
done
echo "runner: ${RUNNER_NAME:-unknown} os: $(uname -sm)"
if [ -n "$missing" ]; then
echo "::error::missing required tools on this runner:$missing"
exit 1
fi
echo "all required tools present"
- uses: actions/checkout@v7
with:
# Full history so golangci-lint's new-from-merge-base can reach
@@ -82,51 +46,3 @@ jobs:
touch core/http/react-ui/dist/index.html
- name: lint
run: make lint
build-scripts:
# The image packaging scripts encode invariants that only surface inside a
# container build (a missing transitive dep, a partial cuDNN family). Their
# shell tests need nothing but bash + gcc + ldd, so run them on every PR
# rather than waiting on a multi-GB cross-arch backend image build.
#
# Push-only self-hosted routing, same fork-safety reasoning as
# golangci-lint above.
# REVERTED to hosted: the arc-runner-set image has git, curl, unzip, tar,
# ldd and python3, but NOT make (nor gcc). Measured on run 30637392862,
# where the preflight below named both. Re-route here once the runner image
# ships a C toolchain and make; the preflight stays so the next attempt
# fails by name in one second instead of opaquely mid-build.
runs-on: ubuntu-latest
steps:
- name: Preflight - required host tools
# This job additionally needs a C toolchain: the packaging-script tests
# compile a throwaway binary and inspect it with ldd.
run: |
missing=""
for t in git make gcc ldd python3; do
command -v "$t" >/dev/null 2>&1 || missing="$missing $t"
done
echo "runner: ${RUNNER_NAME:-unknown} os: $(uname -sm)"
if [ -n "$missing" ]; then
echo "::error::missing required tools on this runner:$missing"
exit 1
fi
echo "all required tools present"
- uses: actions/checkout@v7
- name: run packaging script tests
run: make test-build-scripts
# The backend matrix path filter fails silently: a miss emits an empty
# matrix, every job goes green, and the change reaches no image (#10946).
# Its tests need only node, so they ride along with this job.
- uses: actions/setup-node@v7
with:
node-version: '20'
- name: run CI script tests
run: make test-ci-scripts
# The shared python backend helpers (Options[] parsing, engine-arg
# mapping, model reference resolution) are stdlib-only, so their tests
# ride along here instead of waiting on a multi-GB backend image build.
- name: run shared python backend helper tests
run: make test-python-helpers

View File

@@ -0,0 +1,179 @@
name: 'llama.cpp paged patches: upstream canary'
# EARLY-WARNING CANARY for the vendored paged-attention patch series
# (backend/cpp/llama-cpp-localai-paged/patches/paged/0001-0030).
#
# WHY THIS EXISTS
# The paged backend (backend/cpp/llama-cpp-localai-paged) pins its OWN verified
# llama.cpp tip (LLAMA_VERSION in backend/cpp/llama-cpp-localai-paged/Makefile)
# and is intentionally EXCLUDED from the nightly auto-bumper
# (.github/workflows/bump_deps.yaml), so a naive upstream bump can never silently
# break the shipped build. The cost of that safety: nobody finds out when
# upstream DRIFTS past the patches. This canary restores that signal WITHOUT
# touching the shipped pin - weekly it tries the patch series + a real compile
# against the LATEST llama.cpp master tip and goes red the moment upstream breaks
# the patches.
#
# RED HERE means: time to run a PIN_SYNC (rebase the patches onto the new tip,
# pass the bit-exact gate on the GPU, re-export the .patch files, THEN advance
# the pin in backend/cpp/llama-cpp-localai-paged/Makefile). See the backend README
# section 7 (Pin + maintenance policy):
# backend/cpp/llama-cpp-localai-paged/README.md.
#
# SIGNAL-ONLY: this workflow moves no pinned version, ships nothing, and is fully
# decoupled from bump_deps - so the main dep-bump PR stays green regardless. A
# green run means "the paged series still applies and compiles on upstream HEAD";
# a red run means "upstream moved - schedule a pin-sync".
on:
schedule:
# Weekly (Mondays 06:00 UTC), mirroring the weekly DEPS_REFRESH / bump_deps
# cadence. Offset from bump_deps' nightly 20:00 so the two never pile up.
- cron: '0 6 * * 1'
workflow_dispatch:
permissions:
contents: read
concurrency:
group: llama-cpp-paged-canary
cancel-in-progress: false
env:
# Upstream source of truth - the same repo/branch bump_deps tracks for the
# stock llama-cpp pin.
LLAMA_UPSTREAM: 'https://github.com/ggml-org/llama.cpp'
jobs:
apply-check:
# Cheap, fast, toolchain-free early warning: does the series still APPLY to
# the latest upstream tip? A patch no longer applying is by far the most
# common way upstream breaks a vendored series, so this runs first, is
# reliable on a free runner, and feeds the resolved tip to the compile job.
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
timeout-minutes: 20
outputs:
tip: ${{ steps.resolve.outputs.tip }}
steps:
- name: Checkout LocalAI
uses: actions/checkout@v7
- name: Resolve latest llama.cpp master tip
id: resolve
run: |
tip="$(git ls-remote "$LLAMA_UPSTREAM" refs/heads/master | cut -f1)"
if [ -z "$tip" ]; then
echo "::error::could not resolve llama.cpp master tip from $LLAMA_UPSTREAM"
exit 1
fi
pin="$(grep -m1 'LLAMA_VERSION?=' backend/cpp/llama-cpp-localai-paged/Makefile | cut -d= -f2)"
echo "latest llama.cpp master tip: $tip"
echo "shipped paged pin: $pin"
echo "tip=$tip" >> "$GITHUB_OUTPUT"
{
echo "## llama.cpp paged canary"
echo ""
echo "- upstream master tip: \`$tip\`"
echo "- shipped paged pin: \`$pin\`"
} >> "$GITHUB_STEP_SUMMARY"
- name: Checkout llama.cpp at latest tip (shallow)
run: |
mkdir -p /tmp/llama.cpp
cd /tmp/llama.cpp
git init -q
git remote add origin "$LLAMA_UPSTREAM"
git fetch -q --depth 1 origin "${{ steps.resolve.outputs.tip }}"
git checkout -q FETCH_HEAD
git log --oneline -1
- name: Apply paged patch series (build's git-apply method)
run: |
bash .github/scripts/paged-canary-apply.sh \
/tmp/llama.cpp \
"$PWD/backend/cpp/llama-cpp-localai-paged/patches"
echo "- apply: full paged series applies to the upstream tip :white_check_mark:" >> "$GITHUB_STEP_SUMMARY"
compile:
# Proves the patches still COMPILE against the latest tip, using the SAME
# toolchain + build target the shipped paged backend uses (the
# base-grpc-cuda-12 builder base + the Makefile `grpc-server` cublas target),
# so a failure means upstream drift, not toolchain noise. CUDA is compiled
# (nvcc; no GPU required) because most of the paged series is CUDA kernels.
# Runs only if the apply check passed, on the exact tip it validated.
#
# If a full CUDA compile on the hosted runner ever proves too heavy/flaky,
# switch `runs-on` to 'bigger-runner' (the runner class the real paged CUDA
# build uses), or drop to a CPU build (BUILD_TYPE='') which still compiles
# all host + CPU paged code, leaving CUDA-kernel coverage to the apply check
# plus the manual PIN_SYNC GPU gate.
needs: apply-check
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
timeout-minutes: 180
steps:
- name: Checkout LocalAI
uses: actions/checkout@v7
- name: Free disk space
uses: ./.github/actions/free-disk-space
with:
mode: hosted
- name: Login to Quay.io
uses: docker/login-action@v4
with:
registry: quay.io
username: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
password: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
- name: Compile paged backend against latest tip (cublas)
env:
TIP: ${{ needs.apply-check.outputs.tip }}
BUILDER_BASE_IMAGE: 'quay.io/go-skynet/ci-cache:base-grpc-cuda-12-amd64'
run: |
docker run --rm \
-v "$PWD":/LocalAI -w /LocalAI \
-e TIP -e LLAMA_UPSTREAM \
"$BUILDER_BASE_IMAGE" bash -euxo pipefail -c '
# Mirror the Dockerfile: gRPC lives at /opt/grpc in the base image;
# copy it to the prefix CMake find_package expects.
cp -a /opt/grpc/. /usr/local/
# Pre-populate the llama.cpp checkout at the latest tip with the
# paged series applied via the tolerant canary apply. Because
# backend/cpp/llama-cpp/llama.cpp now exists, the stock Makefile's
# llama.cpp target (clone + base-patch apply) is skipped and the
# now patch-free prepare.sh only copies the grpc-server sources -
# so we drive the REAL grpc-server build path on top of our paged
# apply. The stock llama-cpp backend no longer carries the paged
# series (it lives in backend/cpp/llama-cpp-localai-paged/patches/
# paged); we build it here in the stock dir only because that is
# where the shared build infra (Makefile / grpc-server.cpp /
# CMakeLists.txt / prepare.sh) lives.
cd backend/cpp/llama-cpp/
mkdir -p llama.cpp
cd llama.cpp
git init -q
git remote add origin "$LLAMA_UPSTREAM"
git fetch -q --depth 1 origin "$TIP"
git checkout -q FETCH_HEAD
cd /LocalAI
bash .github/scripts/paged-canary-apply.sh \
backend/cpp/llama-cpp/llama.cpp \
"$PWD/backend/cpp/llama-cpp-localai-paged/patches"
# Cheapest real CUDA build that proves the patches compile: one
# CUDA arch, cublas. CMAKE_ARGS is passed via the environment (not
# as a make arg) so the Makefile += flags are still appended,
# exactly like .docker/llama-cpp-localai-paged-compile.sh. The paged
# series is already applied to the checkout above, so the stock
# build just compiles the patched tree.
cd backend/cpp/llama-cpp/
BUILD_TYPE=cublas \
CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=80" \
make grpc-server
test -x grpc-server
'
echo "- compile: paged series builds (cublas) against the upstream tip :white_check_mark:" >> "$GITHUB_STEP_SUMMARY"

View File

@@ -1,69 +0,0 @@
---
name: 'realtime-conformance'
# Verifies the realtime state-machine implementations conform to their formal
# designs (docs/design/realtime-state-machines.md, formal-verification/). BOTH
# layers are enforced and the gate is fail-closed: the Go conformance layer
# (respcoord + turncoord transition/rapid tests under -race) AND the FizzBee model check of
# the authoritative specs. FizzBee is pinned + checksum-verified
# (formal-verification/fizzbee.sha256), so a failed install fails the job rather
# than silently skipping verification.
on:
pull_request:
paths:
- 'core/http/endpoints/openai/coordinator/**'
- 'core/http/endpoints/openai/respcoord/**'
- 'core/http/endpoints/openai/turncoord/**'
- 'core/http/endpoints/openai/conncoord/**'
- 'core/http/endpoints/openai/compactcoord/**'
- 'core/http/endpoints/openai/ttscoord/**'
- 'formal-verification/**'
- 'scripts/realtime-conformance.sh'
- 'scripts/install-fizzbee.sh'
- '.github/workflows/realtime-conformance.yml'
push:
branches:
- master
paths:
- 'core/http/endpoints/openai/coordinator/**'
- 'core/http/endpoints/openai/respcoord/**'
- 'core/http/endpoints/openai/turncoord/**'
- 'core/http/endpoints/openai/conncoord/**'
- 'core/http/endpoints/openai/compactcoord/**'
- 'core/http/endpoints/openai/ttscoord/**'
- 'formal-verification/**'
- 'scripts/realtime-conformance.sh'
concurrency:
group: realtime-conformance-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
conformance:
runs-on: ubuntu-latest
strategy:
matrix:
go-version: ['1.26.x']
steps:
- name: Clone
uses: actions/checkout@v7
- name: Setup Go ${{ matrix.go-version }}
uses: actions/setup-go@v5
with:
go-version: ${{ matrix.go-version }}
cache: false
- name: Cache FizzBee
uses: actions/cache@v6
with:
path: .tools/fizzbee
key: fizzbee-v0.5.2-${{ runner.os }}-${{ hashFiles('formal-verification/fizzbee.sha256') }}
- name: Install FizzBee (pinned, checksum-verified)
# No `|| true`: a failed/forged download must fail the job, not silently
# drop the design verification. install-fizzbee.sh is a no-op if the
# cached binary is already present and valid.
run: ./scripts/install-fizzbee.sh
- name: Run conformance gate (fail-closed)
# No skip env: both the Go conformance and the FizzBee model check are
# required. The gate auto-detects .tools/fizzbee/fizz.
run: make test-realtime-conformance

View File

@@ -1,44 +0,0 @@
name: Refresh site counters
# The landing page shows a star count, a contributor count and a release
# count. They were typed in by hand, so they drifted the moment somebody
# forgot. This pulls the real numbers once a week and commits them only when
# they have actually moved, which in turn triggers the usual Pages deploy.
on:
schedule:
# Mondays, 06:17 UTC. Off the hour on purpose, since the scheduler queues
# everything that asks for :00 and drops what it cannot run.
- cron: '17 6 * * 1'
workflow_dispatch:
permissions:
contents: write
concurrency:
group: refresh-site-counters
cancel-in-progress: false
jobs:
refresh:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Read the counts off the GitHub API
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: ./.github/ci/refresh-site-counters.sh
- name: Commit only if something moved
run: |
if git diff --quiet -- website/data/stats.yaml; then
echo "counters unchanged, nothing to commit"
exit 0
fi
git diff --unified=0 -- website/data/stats.yaml
git config user.name "github-actions[bot]"
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
git add website/data/stats.yaml
git commit -m "chore(website): refresh the counters"
git push

View File

@@ -7,19 +7,6 @@ on:
schedule:
- cron: '0 0 * * 0'
# `push:` is deliberately unfiltered, so this fires on every push to every
# branch and there is no pull_request event to key on -- the usual
# `github.event.pull_request.number || github.sha` idiom used elsewhere would
# key on the unique-per-commit sha and dedup nothing. Group on the ref instead
# so successive pushes to the same feature branch supersede one another.
#
# Cancelling is safe here: the only output is a SARIF upload, and code scanning
# tracks the latest result per ref, so a superseded scan has nothing to lose.
# master is excluded anyway -- every commit on master gets its own scan.
concurrency:
group: ci-secscan-${{ github.ref }}-${{ github.repository }}
cancel-in-progress: ${{ github.ref != 'refs/heads/master' }}
jobs:
tests:
runs-on: ubuntu-latest
@@ -28,9 +15,9 @@ jobs:
steps:
- name: Checkout Source
uses: actions/checkout@v7
if: ${{ !github.repository.fork && github.actor != 'dependabot[bot]' }}
if: ${{ github.actor != 'dependabot[bot]' }}
- name: Run Gosec Security Scanner
if: ${{ !github.repository.fork && github.actor != 'dependabot[bot]' }}
if: ${{ github.actor != 'dependabot[bot]' }}
uses: securego/gosec@v2.27.1
with:
# we let the report trigger content trigger a failure using the GitHub Security features.
@@ -39,7 +26,7 @@ jobs:
# noise, G104 unhandled errors) are inherent to that upstream code, not ours to rewrite.
args: '-no-fail -exclude-dir=backend/go/supertonic -fmt sarif -out results.sarif ./...'
- name: Upload SARIF file
if: ${{ !github.repository.fork && github.actor != 'dependabot[bot]' }}
if: ${{ github.actor != 'dependabot[bot]' }}
uses: github/codeql-action/upload-sarif@v4
with:
# Path to SARIF file relative to the root of the repository

View File

@@ -11,7 +11,7 @@ jobs:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- uses: actions/stale@1e223db275d687790206a7acac4d1a11bd6fe629 # v9
- uses: actions/stale@eb5cf3af3ac0a1aa4c9c45633dd1ae542a27a899 # v9
with:
stale-issue-message: 'This issue is stale because it has been open 90 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
stale-pr-message: 'This PR is stale because it has been open 90 days with no activity. Remove stale label or comment or this will be closed in 10 days.'

View File

@@ -37,8 +37,6 @@ jobs:
sglang: ${{ steps.detect.outputs.sglang }}
acestep-cpp: ${{ steps.detect.outputs.acestep-cpp }}
qwen3-tts-cpp: ${{ steps.detect.outputs.qwen3-tts-cpp }}
magpie-tts-cpp: ${{ steps.detect.outputs.magpie-tts-cpp }}
trellis2cpp: ${{ steps.detect.outputs.trellis2cpp }}
rfdetr-cpp: ${{ steps.detect.outputs.rfdetr-cpp }}
locate-anything-cpp: ${{ steps.detect.outputs.locate-anything-cpp }}
vibevoice-cpp: ${{ steps.detect.outputs.vibevoice-cpp }}
@@ -589,7 +587,7 @@ jobs:
with:
go-version: '1.25.4'
- name: Setup Node.js
uses: actions/setup-node@v7
uses: actions/setup-node@v6
with:
node-version: '22'
- name: Build sherpa-onnx backend image and run realtime e2e tests
@@ -868,38 +866,6 @@ jobs:
- name: Test qwen3-tts-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/qwen3-tts-cpp test
tests-magpie-tts-cpp:
needs: detect-changes
if: needs.detect-changes.outputs.magpie-tts-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential cmake curl libopenblas-dev ffmpeg
- name: Setup Go
uses: actions/setup-go@v5
- name: Display Go version
run: go version
- name: Proto Dependencies
run: |
# Install protoc
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Build magpie-tts-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/magpie-tts-cpp
- name: Test magpie-tts-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/magpie-tts-cpp test
# Per-backend smoke for rfdetr-cpp: builds the .so + Go binary and runs
# `make -C backend/go/rfdetr-cpp test`. test.sh fetches the small (~20 MB)
# rfdetr-nano-q8_0 GGUF from the published mudler/rfdetr-cpp-nano HF repo
@@ -936,41 +902,6 @@ jobs:
- name: Test rfdetr-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/rfdetr-cpp test
# Weight-free packaged-backend smoke for trellis2cpp. Starting run.sh loads
# libtrellis2 + ggml, resolves the complete C ABI (including remeshing), and
# answers gRPC Health without downloading or loading the multi-GB model set.
tests-trellis2cpp:
needs: detect-changes
if: needs.detect-changes.outputs.trellis2cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential cmake curl unzip
- name: Setup Go
uses: actions/setup-go@v5
- name: Display Go version
run: go version
- name: Proto Dependencies
run: |
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Build trellis2cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/trellis2cpp
- name: Test trellis2cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/trellis2cpp test
# Per-backend e2e for locate-anything-cpp: builds the .so + Go binary and
# runs `make -C backend/go/locate-anything-cpp test`. test.sh fetches the
# locate-anything-q8_0 GGUF (~6.3 GB, NVIDIA LocateAnything-3B) from the

View File

@@ -24,13 +24,8 @@ jobs:
uses: actions/checkout@v7
with:
submodules: true
# No free-disk-space step here on purpose. That action exists to make room
# for docker buildx layers, and this job runs no buildx step. It was also
# sized for a `make test` that downloaded multi-GB GGUF/whisper fixtures
# and built llama-cpp/whisper/stablediffusion-ggml; after the test-suite
# reorg it does neither (see the Makefile test target). It cost ~3min of
# every run, and its tool-cache:true wipe also forced setup-go and
# setup-node to re-download toolchains that ship preinstalled.
- name: Free disk space
uses: ./.github/actions/free-disk-space
- name: Setup Go ${{ matrix.go-version }}
uses: actions/setup-go@v5
with:
@@ -53,7 +48,7 @@ jobs:
sudo apt-get update
sudo apt-get install curl ffmpeg libopus-dev
- name: Setup Node.js
uses: actions/setup-node@v7
uses: actions/setup-node@v6
with:
node-version: '22'
- name: Build React UI
@@ -105,7 +100,7 @@ jobs:
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm opus ffmpeg
pip install --user --no-cache-dir grpcio-tools grpcio
- name: Setup Node.js
uses: actions/setup-node@v7
uses: actions/setup-node@v6
with:
node-version: '22'
- name: Build React UI

View File

@@ -3,14 +3,6 @@ name: 'E2E Backend Tests'
on:
pull_request:
# The e2e suite drives backends over gRPC directly and reads none of these
# paths, so a diff confined to them cannot move it.
# See .agents/ci-caching.md.
paths-ignore:
- 'gallery/**'
- 'docs/**'
- 'examples/**'
- '**/*.md'
push:
branches:
- master
@@ -55,7 +47,7 @@ jobs:
sudo apt-get update
sudo apt-get install -y build-essential libopus-dev
- name: Setup Node.js
uses: actions/setup-node@v7
uses: actions/setup-node@v6
with:
node-version: '22'
- name: Build React UI

View File

@@ -34,7 +34,7 @@ jobs:
go-version: ${{ matrix.go-version }}
cache: false
- name: Setup Node.js
uses: actions/setup-node@v7
uses: actions/setup-node@v6
with:
node-version: '22'
- name: Setup Bun

View File

@@ -1,11 +1,6 @@
name: 'Yamllint GitHub Actions'
on:
- pull_request
concurrency:
group: ci-yamllint-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
yamllint:
name: 'Yamllint'

43
.gitignore vendored
View File

@@ -9,6 +9,15 @@ prepare-sources
/backend/cpp/llama-cpp/llama.cpp
/backend/cpp/llama-*
!backend/cpp/llama-cpp
# llama-cpp-localai-paged is a tracked source dir (a thin wrapper Makefile over
# backend/cpp/llama-cpp). Re-include it like llama-cpp above; its sibling
# *-build dirs are still ignored by the /backend/cpp/llama-* rule, and its
# in-dir build artifacts (binaries, package output, collected ggml .so set) are
# re-ignored just below.
!backend/cpp/llama-cpp-localai-paged
/backend/cpp/llama-cpp-localai-paged/llama-cpp-localai-paged-*
/backend/cpp/llama-cpp-localai-paged/package
/backend/cpp/llama-cpp-localai-paged/ggml-shared-libs
/backends
/backend-images
/result.yaml
@@ -30,7 +39,6 @@ LocalAI
# Go backend packages whose main lives under backend/go/.
/cloud-proxy
/local-store
/valkey-store
# prevent above rules from omitting the helm chart
!charts/*
# prevent above rules from omitting the api/localai folder
@@ -42,12 +50,7 @@ models/*
test-models/
test-dir/
tests/e2e-aio/backends
# The mock backend binary built by `make build-mock-backend`. Anchored to its
# full path: a bare `mock-backend` also matched the *directory* holding the
# source, so git would not descend into it and adding a file there needed -f.
# tests/e2e/mock-backend/.gitignore covers the same binary; kept here too so
# the artifact stays ignored if that scoped file is ever removed.
/tests/e2e/mock-backend/mock-backend
mock-backend
release/
@@ -62,11 +65,6 @@ prepare
/ggml-metal.metal
docs/static/gallery.html
# Hugo build output and lock files (docs/ and website/)
docs/public/
website/public/
.hugo_build.lock
# Protobuf generated files
*.pb.go
*pb2.py
@@ -108,24 +106,3 @@ core/http/react-ui/test-results/
# Local Apple signing material (never commit)
.certs/
# Pinned dev tools (e.g. FizzBee for the realtime-conformance gate)
.tools/
# FizzBee model-check artifacts: the parser emits <spec>.json next to each
# .fizz and the checker writes run dirs under out/. Both are regenerated by
# the realtime-conformance gate; only the .fizz sources are authoritative.
formal-verification/*.json
formal-verification/out/
# `go build ./.github/ci/apexentries` drops a binary of the package name into
# whatever directory it runs in, one `git add -A` away from being committed.
# Both paths are anchored: an unanchored `apexentries` would also match the
# package directory itself and untrack the source.
/apexentries
/.github/ci/apexentries/apexentries
# Runtime state written by `local-ai run` when it is started from the repo
# root, which is what a contributor testing a build does. Nothing under here is
# source: it is the instance's own models, outputs, traces and identity.
/data/

View File

@@ -1,6 +0,0 @@
{
"files": ["core/http/react-ui/index.html"],
"insertBefore": "</body>",
"commentSyntax": "html",
"cspChecked": true
}

View File

@@ -1,48 +0,0 @@
# Adopters
Organisations running LocalAI, listed by the people who run it.
If your organisation uses LocalAI and you are happy to say so publicly, open a
pull request adding a row to the table below. That pull request is how we know
we have permission to list you, which is why we do not add anybody ourselves.
You do not need to be a large company, and you do not need to disclose anything
sensitive. A sentence on what you use it for is more useful to other readers
than a logo.
## How to add yourself
1. Add a row to the table, in alphabetical order.
2. Use your organisation's usual name and a link to your site.
3. Say briefly what you use LocalAI for, and whether it is in production.
4. Open the pull request from an account that makes it plausible you speak for
the organisation, or say in the description who you are. We may ask.
To be removed, open a pull request deleting your row, or email
[info@localai.io](mailto:info@localai.io). We will not ask why.
## Who is using LocalAI
<!-- Keep alphabetical. Columns: Organisation | What for | Status -->
| Organisation | What they use it for | Status |
|---|---|---|
| _Your organisation here_ | | |
## What this list is not
This is not a list of everyone who has ever starred the repository, and it is
not a list of the employers of people who have contributed a patch. Both of
those are easy to scrape and neither means what a logo wall implies.
The website shows two separate things, both of which are checkable without
anybody's permission:
- **Engineers from these companies have contributed code.** Evidence is the
commit history plus the employer on that person's public GitHub profile. It
is a claim about a person, not about their employer.
- **These projects integrate LocalAI.** Evidence is a reference to LocalAI in
that project's own repository or documentation.
Those two lists live in [`website/data/ecosystem.yaml`](website/data/ecosystem.yaml).
This file is the third, stronger thing: organisations that chose to say so.

View File

@@ -23,6 +23,8 @@ LocalAI follows the Linux kernel project's [guidelines for AI coding assistants]
| [.agents/adding-backends.md](.agents/adding-backends.md) | Adding a new backend (Python, Go, or C++) — full step-by-step checklist, including importer integration (the `/import-model` dropdown is server-driven from `GET /backends/known`) |
| [.agents/coding-style.md](.agents/coding-style.md) | Code style, editorconfig, logging, documentation conventions |
| [.agents/llama-cpp-backend.md](.agents/llama-cpp-backend.md) | Working on the llama.cpp backend — architecture, updating, tool call parsing |
| [.agents/llama-cpp-localai-paged-backend.md](.agents/llama-cpp-localai-paged-backend.md) | Working on the CUDA-only paged-attention llama.cpp variant (Qwen3.6 hybrid-SSM / Blackwell NVFP4 decode) - patchset scope, the bit-exact gate, the manual pin-sync + weekly canary, CUDA-only invariants, stock-stays-pure, Metal/SYCL/Vulkan follow-up scope |
| [.agents/vllm-parity-methodology.md](.agents/vllm-parity-methodology.md) | The methodology for closing the vLLM decode-throughput gap in llama.cpp - bit-exact gating, profile-don't-assume, both-engine ground-truth, per-lever A/B discipline, recording rejected levers, multi-agent GPU orchestration |
| [.agents/vllm-backend.md](.agents/vllm-backend.md) | Working on the vLLM / vLLM-omni backends — native parsers, ChatDelta, CPU build, libnuma packaging, backend hooks |
| [.agents/sglang-backend.md](.agents/sglang-backend.md) | Working on the SGLang backend — `engine_args` validation against ServerArgs, speculative-decoding (EAGLE/EAGLE3/DFLASH/MTP) recipes, parser handling |
| [.agents/ds4-backend.md](.agents/ds4-backend.md) | Working on the ds4 backend - DSML state machine, thinking modes, KV cache, Metal+CUDA matrix |
@@ -32,19 +34,17 @@ LocalAI follows the Linux kernel project's [guidelines for AI coding assistants]
| [.agents/adding-gallery-models.md](.agents/adding-gallery-models.md) | Adding GGUF models from HuggingFace to the model gallery |
| [.agents/localai-assistant-mcp.md](.agents/localai-assistant-mcp.md) | LocalAI Assistant chat modality — adding admin tools to the in-process MCP server, editing skill prompts, keeping REST + MCP + skills in sync |
| [.agents/backend-signing.md](.agents/backend-signing.md) | Backend OCI image signing (keyless cosign + sigstore-go) — producer-side CI setup, consumer-side gallery `verification:` block, strict mode (`LOCALAI_REQUIRE_BACKEND_INTEGRITY`), revocation via `not_before` |
| [.agents/preparing-a-release.md](.agents/preparing-a-release.md) | Cutting a release: PR labels, `RELEASE_NOTES_vX.Y.Z.md`, the blog post under `website/content/blog/`, and the demo clips under `website/static/media/` |
## Quick Reference
- **Coverage gates**: Never lower a coverage baseline or widen a gate's tolerance to turn a red gate green — the coverage ratchet only moves up. If a change drops coverage, add tests to raise it (e.g. render-smoke specs). See [.agents/building-and-testing.md](.agents/building-and-testing.md).
- **Git hooks & coverage gates**: Run `make install-hooks` once per clone so the pre-commit lint + coverage gates run. **Never bypass them with `git commit --no-verify`, and never lower a coverage baseline or widen a gate's tolerance to turn a red gate green** — the coverage ratchet only moves up. If a change drops coverage, add tests to raise it (e.g. render-smoke specs). See [.agents/building-and-testing.md](.agents/building-and-testing.md).
- **Logging**: Use `github.com/mudler/xlog` (same API as slog)
- **Paged llama.cpp backend**: `llama-cpp-localai-paged` is a CUDA-only variant that owns its own patch series + its own pinned llama.cpp (manual pin-sync, weekly canary); the stock `llama-cpp` backend stays patch-free. Read [.agents/llama-cpp-localai-paged-backend.md](.agents/llama-cpp-localai-paged-backend.md) before touching either, and [.agents/vllm-parity-methodology.md](.agents/vllm-parity-methodology.md) for the decode-parity methodology behind it.
- **Go style**: Prefer `any` over `interface{}`
- **Comments**: Explain *why*, not *what*
- **Docs (docs-with-code rule)**: When you change user-facing behavior (API endpoints, CLI flags, config keys, or features), update the corresponding page under `docs/content/` in the SAME change, not as a follow-up. A user-facing change without a matching docs update is incomplete. See also the documentation conventions in [.agents/coding-style.md](.agents/coding-style.md).
- **Docs**: Update `docs/content/` when adding features or changing config
- **New API endpoints**: LocalAI advertises its capability surface in several independent places — swagger `@Tags`, `/api/instructions` registry, auth `RouteFeatureRegistry`, React UI `capabilities.js`, docs. Read [.agents/api-endpoints-and-auth.md](.agents/api-endpoints-and-auth.md) and follow its checklist — missing any surface means clients, admins, and the UI won't know the endpoint exists.
- **Admin endpoints → MCP tool**: every admin endpoint that an admin would manage conversationally (install/list/edit/toggle/upgrade) MUST also be exposed as an MCP tool in `pkg/mcp/localaitools/`. The LocalAI Assistant chat modality and the standalone `local-ai mcp-server` consume that package; drift between REST and MCP is a real risk. Read [.agents/localai-assistant-mcp.md](.agents/localai-assistant-mcp.md) — the `TestToolHTTPRouteMappingComplete` test fails until you wire the new tool and update the route map.
- **Releases ship with a post and clips**: a release is not done at the tag. It needs labelled PRs, `RELEASE_NOTES_vX.Y.Z.md`, a blog post under `website/content/blog/`, and a short demo clip in `website/static/media/` for each notable feature. See [.agents/preparing-a-release.md](.agents/preparing-a-release.md).
- **Build**: Inspect `Makefile` and `.github/workflows/` — ask the user before running long builds
- **Backend OS coverage**: a new backend must target every OS it can build for, not just Linux. `.github/backend-matrix.yml` has two matrices — `include:` (Linux) and `includeDarwin:` (macOS / Apple Silicon). Most C/C++/GGML and many Python backends build on Darwin too — wire the `includeDarwin` entry + `backend/index.yaml` `metal:` entries, or say in the PR why an OS is unsupported. See the darwin checklist in [.agents/adding-backends.md](.agents/adding-backends.md).
- **Gallery variant ranking**: a gallery entry can declare `variants` (alternative builds of the same weights), and LocalAI ranks the ones a host can run by engine preference first, size second. A new backend that should be preferred on some hardware must be listed in `engineNamePreferenceRules` in `pkg/system/capabilities.go`; the sibling `backendBuildTagPreferenceRules` speaks build tags rather than engine names, and using the wrong table matches nothing without erroring. See [.agents/adding-backends.md](.agents/adding-backends.md).
- **UI**: The active UI is the React app in `core/http/react-ui/`. The older Alpine.js/HTML UI in `core/http/static/` is pending deprecation — all new UI work goes in the React UI

View File

@@ -198,6 +198,7 @@ For AI-assisted development, see [`AGENTS.md`](AGENTS.md) (or the equivalent [`C
- Prefer modern Go idioms — for example, use `any` instead of `interface{}`.
- Use [`golangci-lint`](https://golangci-lint.run) to catch common issues before submitting a PR.
- Run `make install-hooks` once per clone to enable the pre-commit hook: Go changes run `make lint` + the coverage gate (`make test-coverage-check`); `core/http/react-ui/` changes run the Playwright e2e suite (`make test-ui`). Bypass a single commit with `git commit --no-verify`.
- Use [`github.com/mudler/xlog`](https://github.com/mudler/xlog) for logging (same API as `slog`). Do not use `fmt.Println` or the standard `log` package for operational logging.
- Use tab indentation for Go files (as defined in `.editorconfig`).
@@ -267,7 +268,7 @@ make test-e2e
### React UI tests and coverage
The React UI (`core/http/react-ui/`) is covered by Playwright e2e specs, gated by a **monotonic line-coverage ratchet** (`make test-ui-coverage-check`, run in CI). The metric is non-deterministic — a fast local box reads higher than a slow CI runner for the same code — so a small tolerance is unavoidable.
The React UI (`core/http/react-ui/`) is covered by Playwright e2e specs, gated by a **monotonic line-coverage ratchet** (`make test-ui-coverage-check`, run in CI and pre-commit). The metric is non-deterministic — a fast local box reads higher than a slow CI runner for the same code — so a small tolerance is unavoidable.
**If your change lowers UI coverage, raise it back by adding specs — do not widen the tolerance or hand-lower the baseline.** A *render-smoke* spec (navigate to a page, assert its header is visible) cheaply covers an entire lazy page. See `core/http/react-ui/e2e/page-render-smoke.spec.js` and the full policy in [.agents/building-and-testing.md](.agents/building-and-testing.md#react-ui-coverage).

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@@ -12,16 +12,12 @@ ARG APT_MIRROR
ARG APT_PORTS_MIRROR
ENV DEBIAN_FRONTEND=noninteractive
# hwdata ships /usr/share/hwdata/pci.ids. Without it, the ghw library we use
# for hardware detection cannot resolve PCI vendor IDs and fails to enumerate
# GPUs at all, so the image reports "No GPU detected" (see issue #10941).
RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
APT_MIRROR="${APT_MIRROR}" APT_PORTS_MIRROR="${APT_PORTS_MIRROR}" sh /usr/local/sbin/apt-mirror && \
apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates curl wget espeak-ng libgomp1 \
ffmpeg libopenblas0 libopenblas-dev libopus0 sox \
hwdata && \
ffmpeg libopenblas0 libopenblas-dev libopus0 sox && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
@@ -175,17 +171,6 @@ RUN if [ "${BUILD_TYPE}" = "hipblas" ]; then \
ln -s /opt/rocm-**/lib/llvm/lib/libomp.so /usr/lib/libomp.so \
; fi
# ROCm's bundled libdrm_amdgpu is built with a hardcoded fallback lookup path
# for the ASIC ID table (/opt/amdgpu/share/libdrm/amdgpu.ids), which only exists
# if AMD's full amdgpu graphics/DKMS stack is installed. This compute-only image
# doesn't have it, so hipblas/rocBLAS log "No such file or directory" on every
# model load and can fail to identify the GPU. Point it at the equivalent file
# Ubuntu's libdrm-common package already ships.
RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ -f /usr/share/libdrm/amdgpu.ids ] && [ ! -e /opt/amdgpu/share/libdrm/amdgpu.ids ]; then \
mkdir -p /opt/amdgpu/share/libdrm && \
ln -s /usr/share/libdrm/amdgpu.ids /opt/amdgpu/share/libdrm/amdgpu.ids \
; fi
RUN expr "${BUILD_TYPE}" = intel && echo "intel" > /run/localai/capability || echo "not intel"
# Cuda
@@ -393,12 +378,7 @@ RUN go install github.com/mikefarah/yq/v4@latest
# If you cannot find a more suitable place for an addition, this layer is a suitable place for it.
FROM requirements-drivers
# Optional override for the HEALTHCHECK target. Left empty so healthcheck.sh
# derives the endpoint from the mode the container is actually running — the
# same image runs `local-ai run` (HTTP on 8080) and `local-ai worker` (HTTP on
# the gRPC base port minus one), and a hardcoded default marked every worker
# permanently unhealthy (#10987). Set it to pin an explicit URL.
ENV HEALTHCHECK_ENDPOINT=""
ENV HEALTHCHECK_ENDPOINT=http://localhost:8080/readyz
ARG CUDA_MAJOR_VERSION=12
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
@@ -408,7 +388,6 @@ ENV NVIDIA_VISIBLE_DEVICES=all
WORKDIR /
COPY ./entrypoint.sh .
COPY ./scripts/build/healthcheck.sh .
# Copy the binary
COPY --from=builder /build/local-ai ./
@@ -419,22 +398,9 @@ RUN --mount=from=builder,src=/build/,dst=/mnt/build \
# Make sure the models directory exists
RUN mkdir -p /models /backends /data
# Define the health check command.
#
# --start-period is the knob for slow starts, not --timeout/--retries. Since
# #10949 a frontend's startup preload materializes HuggingFace artifacts before
# the HTTP server binds (31 GB observed on a live cluster), so a healthy replica
# can legitimately fail probes for a long time. Failures inside the start period
# leave the container `starting` instead of burning retries, and the period ends
# early on the first success — so a generous value costs a fast-starting
# container nothing. A process that actually died is handled by the restart
# policy, not by health.
#
# --timeout is a per-probe deadline: 10m meant a wedged probe could hang for ten
# minutes and stretch detection without bound. A localhost curl that has not
# answered in 10s is itself the fault being detected.
HEALTHCHECK --start-period=60m --interval=1m --timeout=10s --retries=3 \
CMD /healthcheck.sh
# Define the health check command
HEALTHCHECK --interval=1m --timeout=10m --retries=10 \
CMD curl -f ${HEALTHCHECK_ENDPOINT} || exit 1
VOLUME /models /backends /configuration /data
EXPOSE 8080

199
Makefile
View File

@@ -1,5 +1,5 @@
# Disable parallel execution for backend builds
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/bonsai backends/outetts backends/piper backends/stablediffusion-ggml backends/trellis2cpp backends/trellis2cpp-darwin backends/whisper backends/crispasr backends/parakeet-cpp backends/moss-transcribe-cpp backends/faster-whisper backends/silero-vad backends/local-store backends/valkey-store backends/cloud-proxy backends/huggingface backends/rfdetr backends/rfdetr-cpp backends/insightface backends/speaker-recognition backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/mlx-distributed backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/longcat-video backends/sglang backends/moonshine backends/pocket-tts backends/qwen-tts backends/faster-qwen3-tts backends/qwen-asr backends/nemo backends/voxcpm backends/whisperx backends/ace-step backends/acestep-cpp backends/fish-speech backends/voxtral backends/opus backends/trl backends/llama-cpp-quantization backends/kokoros backends/sam3-cpp backends/qwen3-tts-cpp backends/moss-tts-cpp backends/magpie-tts-cpp backends/vllm-cpp backends/omnivoice-cpp backends/vibevoice-cpp backends/localvqe backends/tinygrad backends/sherpa-onnx backends/ds4 backends/ds4-darwin backends/liquid-audio backends/supertonic backends/depth-anything-cpp backends/privacy-filter backends/privacy-filter-darwin backends/audio-cpp backends/audio-cpp-darwin
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/outetts backends/piper backends/stablediffusion-ggml backends/whisper backends/crispasr backends/parakeet-cpp backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/rfdetr-cpp backends/insightface backends/speaker-recognition backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/mlx-distributed backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/sglang backends/moonshine backends/pocket-tts backends/qwen-tts backends/faster-qwen3-tts backends/qwen-asr backends/nemo backends/voxcpm backends/whisperx backends/ace-step backends/acestep-cpp backends/fish-speech backends/voxtral backends/opus backends/trl backends/llama-cpp-quantization backends/kokoros backends/sam3-cpp backends/qwen3-tts-cpp backends/omnivoice-cpp backends/vibevoice-cpp backends/localvqe backends/tinygrad backends/sherpa-onnx backends/ds4 backends/ds4-darwin backends/liquid-audio backends/supertonic backends/depth-anything-cpp backends/privacy-filter backends/privacy-filter-darwin backends/llama-cpp-localai-paged
GOCMD=go
GOTEST=$(GOCMD) test
@@ -69,7 +69,7 @@ else
GORELEASER=$(shell which goreleaser)
endif
TEST_PATHS?=./api/... ./pkg/... ./core/... ./backend/go/cloud-proxy/... ./backend/go/local-store/... ./backend/go/valkey-store/...
TEST_PATHS?=./api/... ./pkg/... ./core/... ./backend/go/cloud-proxy/... ./backend/go/local-store/...
## Coverage output and the committed baseline that CI compares against.
## The gate is strict: total coverage must never decrease (no tolerance).
@@ -103,7 +103,7 @@ COVERAGE_E2E_LABELS?=!real-models
COVERAGE_EXCLUDE_RE?=grpc/proto/.*[.]pb[.]go
.PHONY: all test test-coverage test-coverage-baseline test-coverage-check test-backend-cpp test-build-scripts test-ui test-ui-coverage-baseline test-ui-coverage-check build vendor lint lint-all
.PHONY: all test test-coverage test-coverage-baseline test-coverage-check test-backend-cpp test-ui test-ui-coverage-baseline test-ui-coverage-check install-hooks build vendor lint lint-all
all: help
@@ -172,15 +172,6 @@ build-dev: ## Run LocalAI in dev mode with live reload
dev-dist:
$(GORELEASER) build --snapshot --clean
## PR-time variant of dev-dist: builds only the host platform instead of all
## three release targets (linux/amd64, linux/arm64, darwin/arm64). The point of
## running goreleaser on a PR is to catch a broken config or a broken
## before-hook (protogen-go, react-ui, go mod tidy), and --single-target still
## exercises every one of those. Nothing consumes a PR's cross-compiled
## binaries. master pushes and tags still run the full dev-dist/dist.
dev-dist-single:
$(GORELEASER) build --snapshot --clean --single-target
dist:
$(GORELEASER) build --clean
@@ -217,28 +208,6 @@ test: prepare-test
test-backend-cpp:
bash backend/cpp/run-unit-tests.sh
## Runs the shell-level regression tests for the image packaging scripts
## (scripts/build/*_test.sh). These guard invariants that only ever break
## inside a container build - a missing transitive dep, a partial cuDNN
## family - and that no Go test can observe. Needs only bash + gcc + ldd.
test-build-scripts:
@set -e; for t in scripts/build/*_test.sh; do echo "== $$t"; bash "$$t"; done
## Runs the unit tests for the CI helper scripts under scripts/lib/. Currently
## the backend matrix path filter, whose failure mode is invisible in CI: it
## emits an empty matrix, every job goes green, and the change ships to no
## image at all (see PR #10946). Plain `node --test`, no dependencies.
test-ci-scripts:
@set -e; for t in scripts/lib/*_test.mjs; do echo "== $$t"; node --test "$$t"; done
## Runs the unit tests for the shared python backend helpers. These modules are
## pure stdlib on purpose so they run without any backend venv; the list is
## explicit because their siblings (model_identity_test) import grpc and the
## generated protobufs, which only exist inside a built backend.
PYTHON_HELPER_TESTS?=python_utils_test vllm_utils_test model_utils_test mlx_utils_test parent_watch_test
test-python-helpers:
cd backend/python/common && python3 -m unittest $(PYTHON_HELPER_TESTS)
## Runs the core suite ($(TEST_PATHS)) with statement-coverage instrumentation
## and writes a merged profile to $(COVERAGE_PROFILE). Deliberately omits
## --fail-fast so a single failure doesn't truncate the coverage number, and
@@ -286,7 +255,8 @@ LINT_EXCLUDE_DIRS_RE=/(backend/go/(piper|silero-vad|llm)|cmd/launcher)(/|$$)
## Set LINT_NEW_FROM to a git ref to override .golangci.yml's
## new-from-merge-base (origin/master). Useful from a fork clone where
## origin/master is stale relative to the canonical repo.
## origin/master is stale relative to the canonical repo — the pre-commit
## hook passes the resolved upstream ref here so local lint matches CI.
LINT_NEW_FROM?=
lint:
@command -v golangci-lint >/dev/null 2>&1 || { \
@@ -305,6 +275,17 @@ lint-all:
}
golangci-lint run --new=false --new-from-merge-base= --new-from-rev= $$(go list -e -f '{{.Dir}}' ./... | grep -vE '$(LINT_EXCLUDE_DIRS_RE)')
########################################################
## Git hooks
########################################################
## Points git at the versioned .githooks/ directory so the pre-commit hook
## (lint + coverage gate) runs locally. Run once per clone. Undo with:
## `git config --unset core.hooksPath`. Skip a single commit with
## `git commit --no-verify`.
install-hooks:
git config core.hooksPath .githooks
@echo 'Installed git hooks: core.hooksPath -> .githooks (pre-commit runs lint + test-coverage-check on Go changes)'
########################################################
## E2E AIO tests (uses standard image with pre-configured models)
########################################################
@@ -403,15 +384,6 @@ test-stores: backends/local-store
BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r tests/integration
## Valkey-backed vector-store integration. Requires a running Valkey Search
## server (valkey/valkey-bundle:9.1.0) reachable at $$VALKEY_ADDR — the suite
## skips itself when VALKEY_ADDR is unset. Builds the backend on demand and
## points the model loader at it via BACKENDS_PATH. Label-filtered to the
## valkey specs so it does not also run the in-memory local-store suite.
test-valkey-store: backends/valkey-store
BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) --label-filter='valkey' -v -r tests/integration
test-opus:
@echo 'Running opus backend tests'
$(MAKE) -C backend/go/opus libopusshim.so
@@ -433,23 +405,6 @@ test-realtime: build-mock-backend
@echo 'Running realtime e2e tests (mock backend)'
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime && !real-models" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
# Verify the realtime state-machine implementations conform to their formal
# designs (Go transition/rapid tests under -race + FizzBee model check of the
# authoritative specs). See docs/design/realtime-state-machines.md (Part 6) and
# docs/design/specs/README.md.
test-realtime-conformance:
GOCMD=$(GOCMD) ./scripts/realtime-conformance.sh
# Verify the shared model-loader shutdown behavior independently of any API
# modality (focused loader/gRPC/distributed/worker tests under -race + FizzBee).
test-model-lifecycle-conformance:
GOCMD=$(GOCMD) ./scripts/model-lifecycle-conformance.sh
# Install the pinned, checksum-verified FizzBee model checker (into .tools/,
# gitignored) used by the conformance targets. Idempotent; no-op if present.
install-fizzbee:
./scripts/install-fizzbee.sh
# Container-based real-model realtime testing. Build env vars / pipeline
# definition kept here so test-realtime-models-docker can drive a fully wired
# pipeline (VAD + STT + LLM + TTS) from inside a containerised runner.
@@ -598,7 +553,6 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/python/chatterbox
$(MAKE) -C backend/python/vllm
$(MAKE) -C backend/python/vllm-omni
$(MAKE) -C backend/python/longcat-video
$(MAKE) -C backend/python/sglang
$(MAKE) -C backend/python/vibevoice
$(MAKE) -C backend/python/liquid-audio
@@ -620,8 +574,6 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/rust/kokoros kokoros-grpc
$(MAKE) -C backend/go/rfdetr-cpp
$(MAKE) -C backend/go/locate-anything-cpp
$(MAKE) -C backend/go/trellis2cpp
$(MAKE) -C backend/go/valkey-store
test-extra: prepare-test-extra
$(MAKE) -C backend/python/transformers test
@@ -630,7 +582,6 @@ test-extra: prepare-test-extra
$(MAKE) -C backend/python/chatterbox test
$(MAKE) -C backend/python/vllm test
$(MAKE) -C backend/python/vllm-omni test
$(MAKE) -C backend/python/longcat-video test
$(MAKE) -C backend/python/vibevoice test
$(MAKE) -C backend/python/liquid-audio test
$(MAKE) -C backend/python/moonshine test
@@ -653,9 +604,6 @@ test-extra: prepare-test-extra
$(MAKE) -C backend/go/locate-anything-cpp test
$(MAKE) -C backend/go/depth-anything-cpp test
$(MAKE) -C backend/go/supertonic test
$(MAKE) -C backend/go/vllm-cpp test
$(MAKE) -C backend/go/trellis2cpp test
$(MAKE) -C backend/go/valkey-store test
##
## End-to-end gRPC tests that exercise a built backend container image.
@@ -685,9 +633,6 @@ test-extra: prepare-test-extra
## suite against it.
##
BACKEND_TEST_MODEL_URL?=https://huggingface.co/Qwen/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B-Q8_0.gguf
## Suite timeout for `go test`. Wrappers whose model download alone can eat
## most of the default (multi-GB models on a slow HF CDN day) override this.
BACKEND_TEST_TIMEOUT?=30m
## Generic target — runs the suite against whatever BACKEND_IMAGE points at.
## Depends on protogen-go so pkg/grpc/proto is generated before `go test`.
@@ -715,7 +660,7 @@ test-extra-backend: protogen-go
BACKEND_TEST_FACE_IMAGE_3_URL="$$BACKEND_TEST_FACE_IMAGE_3_URL" \
BACKEND_TEST_FACE_IMAGE_3_FILE="$$BACKEND_TEST_FACE_IMAGE_3_FILE" \
BACKEND_TEST_VERIFY_DISTANCE_CEILING="$$BACKEND_TEST_VERIFY_DISTANCE_CEILING" \
go test -v -timeout $(BACKEND_TEST_TIMEOUT) ./tests/e2e-backends/...
go test -v -timeout 30m ./tests/e2e-backends/...
## Convenience wrappers: build the image, then exercise it.
test-extra-backend-llama-cpp: docker-build-llama-cpp
@@ -726,6 +671,15 @@ test-extra-backend-llama-cpp: docker-build-llama-cpp
test-extra-backend-ik-llama-cpp: docker-build-ik-llama-cpp
BACKEND_IMAGE=local-ai-backend:ik-llama-cpp $(MAKE) test-extra-backend
## llama-cpp-localai-paged: the LocalAI paged-attention llama.cpp variant. Same
## GGUF surface as stock llama-cpp (the paged engine is runtime-gated by the
## LLAMA_KV_PAGED env the grpc-server option hooks set), so the standard
## llama-cpp capability set is what we exercise here.
test-extra-backend-llama-cpp-localai-paged: docker-build-llama-cpp-localai-paged
BACKEND_IMAGE=local-ai-backend:llama-cpp-localai-paged \
BACKEND_TEST_CAPS=health,load,predict,stream,logprobs,logit_bias \
$(MAKE) test-extra-backend
## turboquant: exercises the llama.cpp-fork backend with the fork's
## *TurboQuant-specific* KV-cache types (turbo3 for both K and V). turbo3
## is what makes this backend distinct from stock llama-cpp — picking q8_0
@@ -738,16 +692,6 @@ test-extra-backend-turboquant: docker-build-turboquant
BACKEND_TEST_CACHE_TYPE_V=turbo3 \
$(MAKE) test-extra-backend
## bonsai: exercises the llama.cpp-fork backend with a real Q1_0 (1-bit) model —
## the PrismML Bonsai-8B GGUF, whose weight quant is *only* decodable by the fork's
## Q1_0 kernels. Loading it is what makes this backend distinct from stock llama-cpp;
## a standard-quant model would only test the upstream code path the llama-cpp backend
## already covers.
test-extra-backend-bonsai: docker-build-bonsai
BACKEND_IMAGE=local-ai-backend:bonsai \
BACKEND_TEST_MODEL_URL=https://huggingface.co/prism-ml/Bonsai-8B-gguf/resolve/main/Bonsai-8B-Q1_0.gguf \
$(MAKE) test-extra-backend
## Audio transcription wrapper for the llama-cpp backend.
## Drives the new AudioTranscription / AudioTranscriptionStream RPCs against
## ggml-org/Qwen3-ASR-0.6B-GGUF (a small ASR model that requires its mmproj
@@ -1065,7 +1009,6 @@ test-extra-backend-vibevoice-cpp-tts: docker-build-vibevoice-cpp
## post-image disk budget.
test-extra-backend-vibevoice-cpp-transcription: docker-build-vibevoice-cpp
BACKEND_IMAGE=local-ai-backend:vibevoice-cpp \
BACKEND_TEST_TIMEOUT=120m \
BACKEND_TEST_MODEL_URL='https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/vibevoice-asr-q4_k.gguf#vibevoice-asr-q4_k.gguf' \
BACKEND_TEST_EXTRA_FILES='https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/tokenizer.gguf#tokenizer.gguf' \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
@@ -1093,19 +1036,7 @@ test-extra-backend-whisper-transcription: docker-build-whisper
## is reachable.
test-extra-backend-parakeet-cpp-transcription: docker-build-parakeet-cpp
BACKEND_IMAGE=local-ai-backend:parakeet-cpp \
BACKEND_TEST_MODEL_URL=https://huggingface.co/mudler/parakeet-cpp-gguf/resolve/main/realtime_eou_120m-v1-f16.gguf \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
BACKEND_TEST_CAPS=health,load,transcription \
$(MAKE) test-extra-backend
## Audio transcription wrapper for the moss-transcribe-cpp (moss-transcribe.cpp
## ggml port) backend. Mirrors test-extra-backend-parakeet-cpp-transcription:
## drives the AudioTranscription RPC against a published MOSS GGUF using the JFK
## 11s clip from whisper.cpp's CI samples. Not part of the default test suite -
## run explicitly once the pinned model URL is reachable.
test-extra-backend-moss-transcribe-cpp-transcription: docker-build-moss-transcribe-cpp
BACKEND_IMAGE=local-ai-backend:moss-transcribe-cpp \
BACKEND_TEST_MODEL_URL=https://huggingface.co/mudler/moss-transcribe.cpp-gguf/resolve/main/moss-transcribe-q5_k.gguf \
BACKEND_TEST_MODEL_URL=https://huggingface.co/mudler/parakeet-cpp-gguf/resolve/main/tdt_ctc-110m-f16.gguf \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
BACKEND_TEST_CAPS=health,load,transcription \
$(MAKE) test-extra-backend
@@ -1218,10 +1149,6 @@ backends/privacy-filter-darwin: build
bash ./scripts/build/privacy-filter-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/privacy-filter.tar)"
backends/audio-cpp-darwin: build
bash ./scripts/build/audio-cpp-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/audio-cpp.tar)"
build-darwin-python-backend: build
bash ./scripts/build/python-darwin.sh
@@ -1252,10 +1179,6 @@ backends/stablediffusion-ggml-darwin:
BACKEND=stablediffusion-ggml BUILD_TYPE=metal $(MAKE) build-darwin-go-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/stablediffusion-ggml.tar)"
backends/trellis2cpp-darwin:
BACKEND=trellis2cpp BUILD_TYPE=metal $(MAKE) build-darwin-go-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/trellis2cpp.tar)"
backend-images:
mkdir -p backend-images
@@ -1267,10 +1190,10 @@ BACKEND_IK_LLAMA_CPP = ik-llama-cpp|ik-llama-cpp|.|false|false
# turboquant is a llama.cpp fork with TurboQuant KV-cache quantization.
# Reuses backend/cpp/llama-cpp grpc-server sources via a thin wrapper Makefile.
BACKEND_TURBOQUANT = turboquant|turboquant|.|false|false
# bonsai is a llama.cpp fork (PrismML) adding the Q1_0 (1-bit) and Q2_0 (ternary)
# weight-quant kernels the Bonsai / Ternary-Bonsai models ship in. Reuses
# backend/cpp/llama-cpp grpc-server sources via a thin wrapper Makefile.
BACKEND_BONSAI = bonsai|bonsai|.|false|false
# llama-cpp-localai-paged = stock llama.cpp grpc-server + the LocalAI paged-attention
# patch series (vendored in this wrapper backend). Reuses backend/cpp/llama-cpp sources via a thin
# wrapper Makefile (same upstream pin as stock llama-cpp; no fork, no patch-grpc-server).
BACKEND_LLAMA_CPP_LOCALAI_PAGED = llama-cpp-localai-paged|llama-cpp-localai-paged|.|false|false
# ds4 is antirez/ds4, a DeepSeek V4 Flash-specific inference engine.
# Single-model; hardware-only validation lives at tests/e2e-backends/
# (BACKEND_BINARY mode); see docs/superpowers/plans/2026-05-11-ds4-backend.md.
@@ -1279,32 +1202,21 @@ BACKEND_DS4 = ds4|ds4|.|false|false
# openai-privacy-filter PII/NER token classifier) — the TokenClassify RPC for
# the PII redactor tier, on stock ggml with no llama.cpp carry-patches.
BACKEND_PRIVACY_FILTER = privacy-filter|privacy-filter|.|false|false
# audio-cpp wraps 0xShug0/audio.cpp, a multi-family ggml audio inference engine
# (TTS, ASR, VAD, diarization, source separation, music generation). Builds
# against apt gRPC/protobuf rather than a prebuilt base-grpc image; the reason
# is on the audio-cpp block in .github/backend-matrix.yml.
BACKEND_AUDIO_CPP = audio-cpp|audio-cpp|.|false|false
# Golang backends
BACKEND_PIPER = piper|golang|.|false|true
BACKEND_LOCAL_STORE = local-store|golang|.|false|true
BACKEND_VALKEY_STORE = valkey-store|golang|.|false|true
BACKEND_CLOUD_PROXY = cloud-proxy|golang|.|false|true
BACKEND_HUGGINGFACE = huggingface|golang|.|false|true
BACKEND_SILERO_VAD = silero-vad|golang|.|false|true
BACKEND_STABLEDIFFUSION_GGML = stablediffusion-ggml|golang|.|--progress=plain|true
BACKEND_TRELLIS2CPP = trellis2cpp|golang|.|--progress=plain|true
BACKEND_WHISPER = whisper|golang|.|false|true
BACKEND_CRISPASR = crispasr|golang|.|false|true
BACKEND_PARAKEET_CPP = parakeet-cpp|golang|.|false|true
BACKEND_MOSS_TRANSCRIBE_CPP = moss-transcribe-cpp|golang|.|false|true
BACKEND_DEPTH_ANYTHING_CPP = depth-anything-cpp|golang|.|false|true
BACKEND_VOXTRAL = voxtral|golang|.|false|true
BACKEND_ACESTEP_CPP = acestep-cpp|golang|.|false|true
BACKEND_QWEN3_TTS_CPP = qwen3-tts-cpp|golang|.|false|true
BACKEND_MOSS_TTS_CPP = moss-tts-cpp|golang|.|false|true
BACKEND_MAGPIE_TTS_CPP = magpie-tts-cpp|golang|.|false|true
BACKEND_VLLM_CPP = vllm-cpp|golang|.|false|true
BACKEND_OMNIVOICE_CPP = omnivoice-cpp|golang|.|false|true
BACKEND_VIBEVOICE_CPP = vibevoice-cpp|golang|.|false|true
BACKEND_LOCALVQE = localvqe|golang|.|false|true
@@ -1326,7 +1238,6 @@ BACKEND_NEUTTS = neutts|python|.|false|true
BACKEND_KOKORO = kokoro|python|.|false|true
BACKEND_VLLM = vllm|python|.|false|true
BACKEND_VLLM_OMNI = vllm-omni|python|.|false|true
BACKEND_LONGCAT_VIDEO = longcat-video|python|.|--progress=plain|true
BACKEND_SGLANG = sglang|python|.|false|true
BACKEND_DIFFUSERS = diffusers|python|.|--progress=plain|true
BACKEND_CHATTERBOX = chatterbox|python|.|false|true
@@ -1384,22 +1295,18 @@ endef
$(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_IK_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_TURBOQUANT)))
$(eval $(call generate-docker-build-target,$(BACKEND_BONSAI)))
$(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP_LOCALAI_PAGED)))
$(eval $(call generate-docker-build-target,$(BACKEND_DS4)))
$(eval $(call generate-docker-build-target,$(BACKEND_PRIVACY_FILTER)))
$(eval $(call generate-docker-build-target,$(BACKEND_AUDIO_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_PIPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_LOCAL_STORE)))
$(eval $(call generate-docker-build-target,$(BACKEND_VALKEY_STORE)))
$(eval $(call generate-docker-build-target,$(BACKEND_CLOUD_PROXY)))
$(eval $(call generate-docker-build-target,$(BACKEND_HUGGINGFACE)))
$(eval $(call generate-docker-build-target,$(BACKEND_SILERO_VAD)))
$(eval $(call generate-docker-build-target,$(BACKEND_STABLEDIFFUSION_GGML)))
$(eval $(call generate-docker-build-target,$(BACKEND_TRELLIS2CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_WHISPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_CRISPASR)))
$(eval $(call generate-docker-build-target,$(BACKEND_PARAKEET_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_MOSS_TRANSCRIBE_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_DEPTH_ANYTHING_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_VOXTRAL)))
$(eval $(call generate-docker-build-target,$(BACKEND_OPUS)))
@@ -1416,7 +1323,6 @@ $(eval $(call generate-docker-build-target,$(BACKEND_NEUTTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_KOKORO)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM_OMNI)))
$(eval $(call generate-docker-build-target,$(BACKEND_LONGCAT_VIDEO)))
$(eval $(call generate-docker-build-target,$(BACKEND_SGLANG)))
$(eval $(call generate-docker-build-target,$(BACKEND_DIFFUSERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_CHATTERBOX)))
@@ -1434,9 +1340,6 @@ $(eval $(call generate-docker-build-target,$(BACKEND_WHISPERX)))
$(eval $(call generate-docker-build-target,$(BACKEND_ACE_STEP)))
$(eval $(call generate-docker-build-target,$(BACKEND_ACESTEP_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN3_TTS_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_MOSS_TTS_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_MAGPIE_TTS_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_OMNIVOICE_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_VIBEVOICE_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_LOCALVQE)))
@@ -1456,7 +1359,7 @@ $(eval $(call generate-docker-build-target,$(BACKEND_SUPERTONIC)))
docker-save-%: backend-images
docker save local-ai-backend:$* -o backend-images/$*.tar
docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-bonsai docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-longcat-video docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-crispasr docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-liquid-audio docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed docker-build-trl docker-build-llama-cpp-quantization docker-build-tinygrad docker-build-kokoros docker-build-sam3-cpp docker-build-rfdetr-cpp docker-build-qwen3-tts-cpp docker-build-moss-tts-cpp docker-build-magpie-tts-cpp docker-build-vllm-cpp docker-build-omnivoice-cpp docker-build-vibevoice-cpp docker-build-localvqe docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx docker-build-cloud-proxy docker-build-supertonic docker-build-depth-anything-cpp docker-build-moss-transcribe-cpp docker-build-privacy-filter docker-build-trellis2cpp docker-build-valkey-store docker-build-audio-cpp
docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-llama-cpp-localai-paged docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-crispasr docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-liquid-audio docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed docker-build-trl docker-build-llama-cpp-quantization docker-build-tinygrad docker-build-kokoros docker-build-sam3-cpp docker-build-rfdetr-cpp docker-build-qwen3-tts-cpp docker-build-omnivoice-cpp docker-build-vibevoice-cpp docker-build-localvqe docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx docker-build-cloud-proxy docker-build-supertonic docker-build-depth-anything-cpp docker-build-privacy-filter
########################################################
### Mock Backend for E2E Tests
@@ -1491,7 +1394,7 @@ test-ui-e2e: build-ui-test-server
UI_TEST_WORKERS ?=
PLAYWRIGHT_WORKERS_FLAG = $(if $(UI_TEST_WORKERS),--workers=$(UI_TEST_WORKERS),)
## Fast Playwright e2e run for local React UI validation.
## Fast Playwright e2e run used by the pre-commit hook on React UI changes.
## Force-rebuilds the (non-instrumented) dist so the suite tests the working
## tree — not a stale dist the `react-ui` skip-guard would leave — re-embeds
## it into ui-test-server, and runs the specs. Uses the nix-provided browser
@@ -1548,12 +1451,7 @@ swagger:
gen-assets:
$(GOCMD) run core/dependencies_manager/manager.go webui_static.yaml core/http/static/assets
## Documentation and website
# The published site is two Hugo sites: website/ owns the root, docs/ is nested
# under /docs/. Serve them separately while editing; use `make site` to get the
# merged tree (including the legacy URL redirects) that GitHub Pages deploys.
SITE_BASE_URL?=http://localhost:8000
## Documentation
docs/layouts/_default:
mkdir -p docs/layouts/_default
@@ -1565,30 +1463,12 @@ docs/public: docs/layouts/_default docs/static/gallery.html
docs-clean:
rm -rf docs/public
rm -rf website/public
rm -rf docs/static/gallery.html
.PHONY: docs
docs: docs/static/gallery.html
cd docs && hugo serve
.PHONY: website
website:
cd website && hugo serve
.PHONY: site
site: docs/static/gallery.html
rm -rf website/public docs/public
cd website && hugo --minify --baseURL "$(SITE_BASE_URL)/"
cd docs && hugo --minify --baseURL "$(SITE_BASE_URL)/docs/"
mkdir -p website/public/docs
cp -R docs/public/. website/public/docs/
./.github/ci/gen-redirects.sh website/public "$(SITE_BASE_URL)/"
.PHONY: site-serve
site-serve: site
cd website/public && python3 -m http.server 8000
########################################################
## Platform-specific builds
########################################################
@@ -1604,13 +1484,8 @@ build-launcher-darwin:
mv cmd/launcher/LocalAI.app dist/LocalAI.app
bash contrib/macos/sign-and-notarize.sh sign dist/LocalAI.app
# Notarize + staple the .app itself, then wrap it into a drag-to-Applications
# DMG via hdiutil and sign the DMG. The app is stapled BEFORE packaging so the
# bundle carries its own ticket and verifies offline (a dmg-only staple leaves
# the app relying on an online Gatekeeper check, which fails offline / once the
# app is copied out of the dmg). No-op without notary secrets.
# Wrap the (signed) app into a drag-to-Applications DMG via hdiutil, then sign the DMG.
dmg-launcher-darwin: build-launcher-darwin
bash contrib/macos/sign-and-notarize.sh notarize-app dist/LocalAI.app
rm -rf dist/dmg dist/LocalAI.dmg
mkdir -p dist/dmg
cp -R dist/LocalAI.app dist/dmg/LocalAI.app
@@ -1622,7 +1497,7 @@ dmg-launcher-darwin: build-launcher-darwin
notarize-launcher-darwin: dmg-launcher-darwin
bash contrib/macos/sign-and-notarize.sh notarize dist/LocalAI.dmg
# Single entrypoint for CI: build -> sign app -> notarize+staple app -> dmg -> sign dmg -> notarize+staple dmg.
# Single entrypoint for CI: build -> sign app -> dmg -> sign dmg -> notarize -> staple.
release-launcher-darwin: notarize-launcher-darwin
@echo "dist/LocalAI.dmg is ready"

View File

@@ -161,7 +161,7 @@ local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
local-ai run oci://localai/phi-2:latest
```
To work with a running LocalAI server from the terminal, start the built-in agent from another shell. It answers questions, reads your files and runs commands on your machine, asking you to approve anything that changes state. Inside a session, `/models` lists installed models and `/model <name>` switches between them. See the [Terminal agent](https://localai.io/docs/features/terminal-agent/) docs.
To test a running LocalAI server from the terminal, open an interactive chat session from another shell. Inside the prompt, `/models` lists installed models and `/model <name>` switches between them.
```bash
# Terminal 1
@@ -177,7 +177,7 @@ For more details, see the [Getting Started guide](https://localai.io/basics/gett
## Latest News
- **June 2026**: New native biometric backends from the LocalAI team: [voice-detect.cpp](https://github.com/localai-org/voice-detect.cpp) for speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion) and [face-detect.cpp](https://github.com/mudler/face-detect.cpp) for face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace). Both are from-scratch C++/ggml engines with no Python or onnxruntime at inference, self-contained GGUF weights, bit-exact parity with the reference, and GPU cuDNN parity, replacing the heavier Python `insightface` and `speaker-recognition` backends ([PR #10441](https://github.com/mudler/LocalAI/pull/10441)).
- **June 2026**: New native biometric backends from the LocalAI team: [voice-detect.cpp](https://github.com/mudler/voice-detect.cpp) for speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion) and [face-detect.cpp](https://github.com/mudler/face-detect.cpp) for face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace). Both are from-scratch C++/ggml engines with no Python or onnxruntime at inference, self-contained GGUF weights, bit-exact parity with the reference, and GPU cuDNN parity, replacing the heavier Python `insightface` and `speaker-recognition` backends ([PR #10441](https://github.com/mudler/LocalAI/pull/10441)).
- **June 2026**: New [realtime voice assistant demo](https://github.com/localai-org/localai-realtime-demo) (a tiny Go client for the Realtime API with a full talk-back voice loop and tool calling), plus [streaming of the realtime LLM / TTS / transcription pipeline stages](https://github.com/mudler/LocalAI/pull/10176) and [configurable WebRTC ICE candidates](https://github.com/mudler/LocalAI/pull/10231).
- **June 2026**: Big speech push: the [parakeet.cpp](https://github.com/mudler/parakeet.cpp) ASR engine gains [NeMo-faithful segment timestamps](https://github.com/mudler/LocalAI/pull/10207), a [multilingual streaming Nemotron-3.5 model](https://github.com/mudler/LocalAI/pull/10199), [dynamic batching for concurrent transcription](https://github.com/mudler/LocalAI/pull/10112) and [CUDA graphs](https://github.com/mudler/LocalAI/pull/10273); the new [CrispASR backend](https://github.com/mudler/LocalAI/pull/10099) adds multi-architecture ASR + TTS, and [60 Piper TTS voices across 42 languages](https://github.com/mudler/LocalAI/pull/10296) land in the gallery (plus [per-request TTS instructions and params](https://github.com/mudler/LocalAI/pull/10172)).
- **June 2026**: New backends and models: [locate-anything.cpp](https://github.com/mudler/LocalAI/pull/10264) for open-vocabulary object detection via ggml, [Ideogram4 image generation](https://github.com/mudler/LocalAI/pull/10201) in stablediffusion-ggml, [llama.cpp video input](https://github.com/mudler/LocalAI/pull/10216), and the [Gemma 4 QAT family with MTP speculative-decoding pairs](https://github.com/mudler/LocalAI/pull/10215). Plus an [interactive CLI chat mode](https://github.com/mudler/LocalAI/pull/10226) and [RAG source citations in agent responses](https://github.com/mudler/LocalAI/pull/10228).
@@ -195,7 +195,7 @@ For more details, see the [Getting Started guide](https://localai.io/basics/gett
- **August 2025**: MLX, MLX-VLM, Diffusers, llama.cpp now supported on Apple Silicon
- **July 2025**: All backends migrated outside the main binary — [lightweight, modular architecture](https://github.com/mudler/LocalAI/releases/tag/v3.2.0)
For older news and full release notes, see [GitHub Releases](https://github.com/mudler/LocalAI/releases) and the [blog](https://localai.io/blog/).
For older news and full release notes, see [GitHub Releases](https://github.com/mudler/LocalAI/releases) and the [News page](https://localai.io/basics/news/).
## Features
@@ -231,21 +231,13 @@ Most backends wrap a best-in-class upstream engine. A handful of them are native
| Backend | What it does |
|---------|-------------|
| [vllm.cpp](https://github.com/mudler/vllm.cpp) | From-scratch C++20 port of vLLM for text generation: paged KV cache, continuous batching, prefix caching, safetensors + GGUF loading, engine-enforced structured output, on CPU, CUDA, Metal and Vulkan |
| [parakeet.cpp](https://github.com/mudler/parakeet.cpp) | C++/GGML port of NVIDIA NeMo Parakeet ASR (tdt/ctc/rnnt/hybrid), with cache-aware streaming transcription |
| [moss-transcribe.cpp](https://github.com/localai-org/moss-transcribe.cpp) | C++/GGML port of OpenMOSS MOSS-Transcribe-Diarize: joint long-form transcription, speaker diarization and timestamping in a single pass |
| [moss-tts.cpp](https://github.com/mudler/moss-tts.cpp) | C++/GGML port of the OpenMOSS MOSS-TTS family: text-to-speech (MOSS-TTS-Local v1.5, 48 kHz stereo) with reference-audio voice cloning, through the MOSS-Audio-Tokenizer neural codec |
| [magpie-tts.cpp](https://github.com/mudler/magpie-tts.cpp) | C++/GGML port of NVIDIA's Magpie TTS Multilingual 357M: 22.05 kHz mono text-to-speech in 5 voices and 9+ languages, with the NanoCodec neural codec and tokenizer/G2P embedded in a single GGUF |
| [ced.cpp](https://github.com/localai-org/ced.cpp) | C++/GGML port of the CED audio-tagging models: sound-event classification (527-class AudioSet) over REST and the realtime API for live recognition |
| [voice-detect.cpp](https://github.com/localai-org/voice-detect.cpp) | Speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion), replacing the Python speaker-recognition backend |
| [voxtral-tts.c](https://github.com/mudler/voxtral-tts.c) | Mistral Voxtral-4B-TTS text-to-speech in pure C: 20 preset voices across 9 languages, 24 kHz WAV output, no dependencies beyond libc |
| [ced.cpp](https://github.com/mudler/ced.cpp) | C++/GGML port of the CED audio-tagging models: sound-event classification (527-class AudioSet) over REST and the realtime API for live recognition |
| [voxtral.c](https://github.com/mudler/voxtral.c) | Voxtral Realtime 4B speech-to-text in pure C |
| [vibevoice.cpp](https://github.com/mudler/vibevoice.cpp) | Native port of Microsoft VibeVoice for TTS (voice cloning) and long-form ASR with speaker diarization |
| [rf-detr.cpp](https://github.com/localai-org/rf-detr.cpp) | Native RF-DETR object detection and instance segmentation |
| [rf-detr.cpp](https://github.com/mudler/rf-detr.cpp) | Native RF-DETR object detection and instance segmentation |
| [locate-anything.cpp](https://github.com/mudler/locate-anything.cpp) | Open-vocabulary object detection and visual grounding (LocateAnything-3B) |
| [depth-anything.cpp](https://github.com/mudler/depth-anything.cpp) | Depth Anything 3 monocular metric depth + camera pose estimation |
| [face-detect.cpp](https://github.com/mudler/face-detect.cpp) | Face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace), replacing the Python insightface backend |
| [free-splatter.cpp](https://github.com/localai-org/free-splatter.cpp) | Pose-free 3D reconstruction (FreeSplatter): turns a handful of plain photos into 3D Gaussians, no camera poses or GPU required |
| [trellis2.cpp](https://github.com/localai-org/trellis2cpp) | C++/GGML port of Microsoft TRELLIS.2: single-image to textured 3D mesh (GLB with PBR materials) |
| [privacy-filter.cpp](https://github.com/localai-org/privacy-filter.cpp) | Standalone GGML PII/NER token-classification engine powering LocalAI's PII redaction tier |
| [LocalVQE](https://github.com/localai-org/LocalVQE) | Joint acoustic echo cancellation, noise suppression, and dereverberation |
| [local-store](https://github.com/mudler/LocalAI) | Local-first vector database for embeddings (shipped in-tree) |
@@ -260,7 +252,7 @@ We also maintain [apex-quant](https://github.com/localai-org/apex-quant), a per-
- [Kubernetes installation](https://localai.io/basics/getting_started/#run-localai-in-kubernetes)
- [Integrations & community projects](https://localai.io/docs/integrations/)
- [Installation video walkthrough](https://www.youtube.com/watch?v=cMVNnlqwfw4)
- [Blog: release write-ups, benchmarks and engineering notes](https://localai.io/blog/)
- [Media & blog posts](https://localai.io/basics/news/#media-blogs-social)
- [Examples](https://github.com/mudler/LocalAI-examples) — including the [realtime voice assistant demo](https://github.com/localai-org/localai-realtime-demo) (Go client for the Realtime API with tool calling)
## Team

View File

@@ -1,120 +0,0 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
# audio-cpp: 0xShug0/audio.cpp, a ggml audio inference framework covering TTS,
# ASR, VAD, diarization, source separation and music generation, wrapped as a
# LocalAI gRPC backend.
#
# BASE_IMAGE is ubuntu:24.04 for cpu and vulkan builds, or
# nvidia/cuda:<ver>-devel-ubuntu24.04 for cublas builds; both ship apt and
# Ubuntu Noble packages, and the CUDA base additionally provides
# /usr/local/cuda. BUILD_TYPE selects the engine backend in the Makefile:
# "" = portable CPU with all ggml CPU variants, "cublas" ->
# -DENGINE_ENABLE_CUDA=ON, "vulkan" -> -DENGINE_ENABLE_VULKAN=ON. Darwin
# (Metal) builds bypass this Dockerfile entirely.
#
# Upstream needs GCC 13 or newer, which ubuntu:24.04 and the CUDA 12/13
# devel-ubuntu24.04 images all provide.
#
# THIS BACKEND CANNOT USE .docker/install-base-deps.sh OR THE PREBUILT
# quay.io/go-skynet/ci-cache:base-grpc-* IMAGES, AND THAT IS NOT A STYLE CHOICE.
#
# Both supply gRPC v1.65 built from source at /opt/grpc, which downstream
# Dockerfiles copy to /usr/local. That gRPC vendors protobuf v26, and protobuf
# has depended on abseil since v22: google/protobuf/message_lite.h includes
# absl/strings/cord.h. audio.cpp links sentencepiece, and our CMakeLists sets
# SPM_PROTOBUF_PROVIDER=package so sentencepiece uses the same protobuf the
# generated backend.pb.cc was built against (the alternative broke every
# nested-message parse; the full account is in backend/cpp/audio-cpp/CMakeLists.txt).
# That makes sentencepiece's init.h include the external message_lite.h while it
# still includes its own vendored mini-abseil from third_party/absl. The vendored
# copy declares `namespace absl { namespace internal { ... } }` and real abseil
# declares `namespace absl { inline namespace lts_20240116 { namespace internal
# { ... } } }`, so every `absl::internal::` reference becomes ambiguous and the
# compile dies in absl/base/casts.h. Verified, not theorised: building this image
# against the base-grpc-amd64 prebuilt fails at
# sentencepiece-static/error.cc.o with "reference to 'internal' is ambiguous".
#
# Ubuntu Noble's apt protobuf is 3.21.12, which predates the abseil dependency,
# so message_lite.h pulls in no abseil and the vendored copy is the only one in
# scope. That is also the exact protobuf/gRPC pair every unit and end-to-end run
# of this backend has been verified against. Keep it: a from-source gRPC here
# does not buy a faster build, it buys a broken one.
#
# The install-base-deps path is additionally unsafe because it drops protoc 27.1
# into /usr/local/bin, which shadows apt's protoc on PATH and would generate
# protobuf-27 sources to be compiled against 3.21 headers.
FROM ${BASE_IMAGE} AS builder
ARG BUILD_TYPE
ARG TARGETARCH
ARG TARGETVARIANT
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
# Selects the CUDA architecture list in backend/cpp/audio-cpp/Makefile. It has
# to be forwarded: upstream compiles engine_runtime for `native` when
# CMAKE_CUDA_ARCHITECTURES is unset, and no CI runner has a GPU to enumerate.
# The value is the same cuda-major-version the matrix entry declares.
ARG CUDA_MAJOR_VERSION
ENV BUILD_TYPE=${BUILD_TYPE} \
CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
APT_MIRROR=${APT_MIRROR} \
APT_PORTS_MIRROR=${APT_PORTS_MIRROR} \
DEBIAN_FRONTEND=noninteractive \
PATH=/usr/local/cuda/bin:${PATH}
WORKDIR /build
# gRPC/protobuf from apt, deliberately; see the block above. libgrpc++-dev ships
# a CMake config so find_package(gRPC CONFIG) resolves, and libprotobuf-dev
# lands in the layout CMake's FindProtobuf module expects, which matters because
# sentencepiece runs a bare find_package(Protobuf REQUIRED) with no CONFIG
# fallback of its own.
#
# BUILD_TYPE=vulkan additionally needs the loader headers and glslc; both are in
# Noble. The CUDA toolkit for BUILD_TYPE=cublas comes from BASE_IMAGE.
RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
sh /usr/local/sbin/apt-mirror && \
apt-get update && \
apt-get install -y --no-install-recommends \
git cmake build-essential pkg-config ca-certificates \
libgrpc++-dev libprotobuf-dev protobuf-compiler protobuf-compiler-grpc && \
if [ "${BUILD_TYPE}" = "vulkan" ]; then \
apt-get install -y --no-install-recommends libvulkan-dev glslc; \
fi && \
if [ "${TARGETARCH}" = "arm64" ]; then \
apt-get install -y --no-install-recommends gcc-14 g++-14; \
fi && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
COPY . /LocalAI
# gcc-14 on arm64, for the same reason llama-cpp does it in
# .docker/llama-cpp-compile.sh: ggml's CPU_ALL_VARIANTS table includes armv9.2
# variants built with -march=...+sme, and Noble's default gcc-13 rejects that
# feature modifier outright ("invalid feature modifier 'sme'"). Every variant in
# the table has to COMPILE even though a host only ever dlopens the one its own
# CPU supports, so one unbuildable variant fails the whole image.
#
# ON EVERY arm64 BUILD_TYPE, which is where this differs from llama-cpp's script
# and why that difference is spelled out rather than assumed. llama-cpp only
# needs gcc-14 for its pure-CPU image because its GPU builds run
# llama-cpp-fallback, which has no variant table at all. This backend's Makefile
# sets ENGINE_ENABLE_CPU_ALL_VARIANTS for every non-Darwin build, GPU included,
# so an arm64 GPU image would hit the identical compile error. Gating this on an
# empty BUILD_TYPE would leave that trap armed for the first arm64 GPU entry
# added to the matrix, which today has none.
RUN --mount=type=cache,target=/root/.ccache,id=audio-cpp-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
if [ "${TARGETARCH}" = "arm64" ]; then \
export CC=gcc-14 CXX=g++-14; \
fi && \
make -C /LocalAI/backend/cpp/audio-cpp BUILD_TYPE=${BUILD_TYPE} \
CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} NATIVE=false grpc-server package
# The package directory is the whole image: run.sh, grpc-server, the dlopened
# ggml CPU variants, the bundled loader and its library closure, and the
# bundled silero_vad / marblenet_vad assets. Nothing else exists at run time.
FROM scratch
COPY --from=builder /LocalAI/backend/cpp/audio-cpp/package/. ./

View File

@@ -221,75 +221,10 @@ RUN if [ "${BACKEND}" = "crispasr" ]; then \
apt-get clean && rm -rf /var/lib/apt/lists/*; \
fi
# sherpa-onnx links onnxruntime's CUDA execution provider, and
# libonnxruntime_providers_cuda.so has cuDNN as a hard DT_NEEDED. The
# onnxruntime GPU tarball does not ship cuDNN itself, so without this the
# builder has none (the arm64 + CUDA 13 branch above is the only other place
# that installs it) and package-gpu-libs.sh correctly refuses to produce a
# package that references cuDNN with no cuDNN available to it.
#
# Installed per-backend rather than for every cublas build: the auto-detection
# in package-gpu-libs.sh bundles only what a package actually references, so
# the ggml backends would not grow either way, but they would all pay ~1.1 GB
# of builder layer and registry cache for a library they never call.
#
# Runtime package only, no -dev: sherpa-onnx consumes onnxruntime's prebuilt
# CUDA provider and never compiles against cuDNN headers. libcudnn9-cuda-N
# carries the dispatcher plus all seven dlopen()ed sublibraries, which is what
# complete_cudnn_family needs to assemble a whole bundle.
RUN <<EOT bash
if [ "${BACKEND}" = "sherpa-onnx" ] && [ "${BUILD_TYPE}" = "cublas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
libcudnn9-cuda-${CUDA_MAJOR_VERSION} && \
ldconfig && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
COPY . /LocalAI
RUN git config --global --add safe.directory /LocalAI
# Prebuild the native engine from a layer that depends on this backend's own
# directory and nothing else.
#
# The expensive part of a C++ backend build is the engine: each of these
# Makefiles clones an upstream repo at a pinned SHA and compiles it once per
# SIMD variant (depth-anything-cpp builds four: avx, avx2, avx512, fallback),
# and those variant targets depend only on the clone. They cannot observe a
# change anywhere else in the LocalAI tree. Building them below `COPY . /LocalAI`
# threw that away: any Go-side edit invalidated the layer and recompiled C++ that
# had not changed. Measured on 2026-07-30, that is a 100+ minute rebuild for the
# larger engines.
#
# Copying only this backend's directory first keeps the compile in a layer that
# survives any change elsewhere in the tree, so `cache-from: type=registry`
# restores it. That covers the expensive cases directly: a shared-build-input or
# backend.proto change, the weekly full-matrix cron and a tag push all rebuild
# every backend while touching none of their directories. This is the mechanism
# behind base-grpc-* applied one level down; unlike a --mount=type=cache it is a
# real layer, which is what actually survives to the registry.
#
# The whole directory rather than just the Makefile: the CMake targets also need
# CMakeLists.txt, and the file list differs per backend. The cost is that editing
# this backend's Go sources also invalidates the engine layer.
#
# Backends whose Makefile has no `engine` target are unaffected: the guard skips
# the prebuild and their engine still compiles in the `build` step below.
COPY backend/go/${BACKEND}/ /LocalAI/backend/go/${BACKEND}/
RUN cd /LocalAI/backend/go/${BACKEND} && \
if make -n engine >/dev/null 2>&1; then \
echo "==> prebuilding engine for ${BACKEND} (cacheable layer)" && \
make engine; \
else \
echo "==> ${BACKEND} has no engine target; it builds with the backend"; \
fi
COPY . /LocalAI
# The engine variants built above survive this COPY (they are build outputs, not
# tracked files) and are newer than the pinned clone, so make treats them as up
# to date and goes straight to the Go binary.
RUN cd /LocalAI && make protogen-go && make -C /LocalAI/backend/go/${BACKEND} build
FROM scratch

View File

@@ -111,10 +111,6 @@ RUN make -BC /LocalAI/backend/cpp/llama-cpp package
# ============================================================================
FROM ${BUILDER_BASE_IMAGE} AS builder-prebuilt
ARG APT_MIRROR
ENV APT_MIRROR=${APT_MIRROR}
ARG APT_PORTS_MIRROR
ENV APT_PORTS_MIRROR=${APT_PORTS_MIRROR}
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
ARG CUDA_DOCKER_ARCH

View File

@@ -7,7 +7,7 @@ ARG BUILDER_BASE_IMAGE=${BASE_IMAGE}
# BUILDER_TARGET selects which builder stage the final scratch image copies
# package output from. Declared at global scope (before any FROM) so it's
# usable in `FROM ${BUILDER_TARGET}` below. Default keeps local
# `make backends/bonsai` on the from-source path.
# `make backends/llama-cpp-localai-paged` on the from-source path.
ARG BUILDER_TARGET=builder-fromsource
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
@@ -18,7 +18,7 @@ ARG APT_PORTS_MIRROR=""
# Runs .docker/install-base-deps.sh (apt deps + cmake + protoc + gRPC +
# conditional CUDA/ROCm/Vulkan), copies /opt/grpc to /usr/local, then
# compiles the variant. Used when BUILDER_TARGET=builder-fromsource (the
# default; local `make backends/bonsai`).
# default; local `make backends/llama-cpp-localai-paged`).
#
# The install script is the same one that backend/Dockerfile.base-grpc-builder
# runs, so the result is bit-equivalent to the prebuilt-base path
@@ -84,21 +84,22 @@ RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
# BuildKit cache mount for ccache. See Dockerfile.llama-cpp (commit 9228e5b4)
# for rationale. bonsai is a llama.cpp fork that reuses
# backend/cpp/llama-cpp source via a thin wrapper Makefile, so MOST TUs
# are content-identical to the upstream llama-cpp build. Sharing a cache
# id with llama-cpp could give cross-fork hits — but for now keep them
# separate so a regression in one doesn't poison the other. Revisit
# sharing after measuring the actual hit rate.
# for rationale. llama-cpp-localai-paged is the SAME upstream llama.cpp with
# the LocalAI paged patch series applied; it reuses backend/cpp/llama-cpp
# source via a thin wrapper Makefile, so MOST TUs are content-identical to the
# stock llama-cpp build. Sharing a cache id with llama-cpp could give
# cross-variant hits — but for now keep them separate (mirroring turboquant) so
# a regression in one doesn't poison the other. Revisit sharing after measuring
# the actual hit rate.
#
# The compile body is shared with builder-prebuilt via .docker/bonsai-compile.sh.
RUN --mount=type=bind,source=.docker/bonsai-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=bonsai-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
# The compile body is shared with builder-prebuilt via .docker/llama-cpp-localai-paged-compile.sh.
RUN --mount=type=bind,source=.docker/llama-cpp-localai-paged-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=llama-cpp-localai-paged-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
# Copy libraries using a script to handle architecture differences
RUN make -BC /LocalAI/backend/cpp/bonsai package
RUN make -BC /LocalAI/backend/cpp/llama-cpp-localai-paged package
# ============================================================================
@@ -107,7 +108,9 @@ RUN make -BC /LocalAI/backend/cpp/bonsai package
# That image already has gRPC at /opt/grpc + apt deps + CUDA/ROCm/Vulkan
# pre-installed, so we just copy gRPC to /usr/local and compile. Used when
# BUILDER_TARGET=builder-prebuilt (CI when the matrix entry sets
# builder-base-image).
# builder-base-image). llama-cpp-localai-paged reuses the SAME base-grpc-* tags
# as the stock llama-cpp backend (same gRPC + same toolchain), so no new
# base-images.yml variant is required.
# ============================================================================
FROM ${BUILDER_BASE_IMAGE} AS builder-prebuilt
@@ -118,9 +121,9 @@ ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
# AMDGPU_TARGETS must be forwarded into the env here too — backend/cpp/llama-cpp/Makefile
# (which the bonsai Makefile reuses via a sibling build dir) errors out when the var
# is empty on a hipblas build, and the prebuilt path is what CI exercises most of the
# time. The builder-fromsource stage above already does this; mirror it here.
# (which the llama-cpp-localai-paged Makefile reuses via a sibling build dir) errors out
# when the var is empty on a hipblas build, and the prebuilt path is what CI exercises most
# of the time. The builder-fromsource stage above already does this; mirror it here.
ARG AMDGPU_TARGETS
ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}
ARG TARGETARCH
@@ -133,11 +136,11 @@ RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
RUN --mount=type=bind,source=.docker/bonsai-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=bonsai-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
RUN --mount=type=bind,source=.docker/llama-cpp-localai-paged-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=llama-cpp-localai-paged-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
RUN make -BC /LocalAI/backend/cpp/bonsai package
RUN make -BC /LocalAI/backend/cpp/llama-cpp-localai-paged package
# ============================================================================
@@ -157,4 +160,4 @@ FROM scratch
# Copy all available binaries (the build process only creates the appropriate ones for the target architecture)
COPY --from=builder /LocalAI/backend/cpp/bonsai/package/. ./
COPY --from=builder /LocalAI/backend/cpp/llama-cpp-localai-paged/package/. ./

View File

@@ -224,11 +224,7 @@ ARG DEPS_REFRESH=initial
RUN cd /${BACKEND} && PORTABLE_PYTHON=true make
# Package GPU libraries into the backend's lib directory.
#
# Must stay after the venv is built above: package-gpu-libs.sh inspects
# /${BACKEND}/venv to decide whether this backend already carries a complete
# cuDNN from pip, and bundles one only when it does not (issue #10905).
# Package GPU libraries into the backend's lib directory
RUN mkdir -p /${BACKEND}/lib && \
TARGET_LIB_DIR="/${BACKEND}/lib" BUILD_TYPE="${BUILD_TYPE}" CUDA_MAJOR_VERSION="${CUDA_MAJOR_VERSION}" \
bash /package-gpu-libs.sh "/${BACKEND}/lib"

View File

@@ -46,7 +46,6 @@ The backend system provides language-specific Dockerfiles that handle the build
- **vllm**: High-performance LLM inference
- **mlx**: Apple Silicon optimization
- **diffusers**: Stable Diffusion models
- **longcat-video**: CUDA text/image-to-video and speech-driven avatar generation
- **Audio**: coqui, faster-whisper, kitten-tts
- **Vision**: mlx-vlm, rfdetr
- **Specialized**: rerankers, chatterbox, kokoro
@@ -56,7 +55,6 @@ The backend system provides language-specific Dockerfiles that handle the build
- **stablediffusion-ggml**: Stable Diffusion in Go with GGML Cpp backend
- **piper**: Text-to-speech synthesis Golang with C bindings using rhaspy/piper
- **local-store**: Vector storage backend
- **valkey-store**: Durable vector storage backend backed by Valkey Search (FT.*)
#### C++ Backends (`cpp/`)
- **llama-cpp**: Llama.cpp integration

View File

@@ -15,28 +15,13 @@ service Backend {
rpc PredictStream(PredictOptions) returns (stream Reply) {}
rpc Embedding(PredictOptions) returns (EmbeddingResult) {}
rpc GenerateImage(GenerateImageRequest) returns (Result) {}
rpc UpscaleImage(UpscaleImageRequest) returns (Result) {}
rpc GenerateVideo(GenerateVideoRequest) returns (Result) {}
rpc Generate3D(Generate3DRequest) returns (Result) {}
rpc AudioTranscription(TranscriptRequest) returns (TranscriptResult) {}
rpc AudioTranscriptionStream(TranscriptRequest) returns (stream TranscriptStreamResponse) {}
// AudioTranscriptionLive is the bidirectional live-microphone ASR RPC. The
// first message MUST carry a Config; subsequent messages carry Audio frames
// (mono float PCM at config.sample_rate, 16 kHz default). After a
// successful open the backend replies with a single ready ack
// (TranscriptLiveResponse{ready:true}); backends or models without
// cache-aware streaming support return UNIMPLEMENTED instead. Newly
// finalized text streams back as deltas; eou=true marks the model's
// end-of-utterance token. One stream spans many utterances (the decoder
// resets itself after each EOU). Closing the send side finalizes: the
// backend flushes the decoder tail and emits a terminal message carrying
// final_result. A second Config mid-stream resets the decode session.
rpc AudioTranscriptionLive(stream TranscriptLiveRequest) returns (stream TranscriptLiveResponse) {}
rpc TTS(TTSRequest) returns (Result) {}
rpc TTSStream(TTSRequest) returns (stream Reply) {}
rpc SoundGeneration(SoundGenerationRequest) returns (Result) {}
rpc TokenizeString(PredictOptions) returns (TokenizationResponse) {}
rpc Detokenize(DetokenizeRequest) returns (DetokenizeResponse) {}
rpc Status(HealthMessage) returns (StatusResponse) {}
rpc Detect(DetectOptions) returns (DetectResponse) {}
// SoundDetection runs an audio-tagging / sound-event-classification model
@@ -139,10 +124,6 @@ message MetricsResponse {
message TokenClassifyRequest {
string text = 1;
float threshold = 2;
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 3;
}
// TokenClassifyEntity is one detected entity span. Byte offsets are
@@ -180,17 +161,6 @@ message ScoreRequest {
// candidates differ in length and the consumer wants a per-token
// measure comparable across them (PMI-style scoring).
bool length_normalize = 4;
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 5;
// Byte length of the prompt prefix that stays identical across
// repeated scoring calls (e.g. a classifier's option-list system
// prompt — everything before the per-turn probe text). Backends that
// snapshot state (hybrid/recurrent models cannot rewind otherwise)
// use it to place a reuse point exactly at the boundary, so the next
// call re-processes only the tokens after it. 0 means unknown.
int32 stable_prefix_len = 6;
}
// CandidateScore is one row in the ScoreResponse, matching by index
@@ -222,10 +192,6 @@ message RerankRequest {
string query = 1;
repeated string documents = 2;
int32 top_n = 3;
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 4;
}
message RerankResult {
@@ -337,39 +303,6 @@ message PredictOptions {
int32 TopLogprobs = 51; // Number of top logprobs to return per token (maps to OpenAI top_logprobs parameter)
map<string, string> Metadata = 52; // Generic per-request metadata (e.g., enable_thinking)
float MinP = 53; // Minimum probability sampling threshold (0.0 = disabled)
// ModelIdentity names the model this request is for, so a backend can reject
// a request that reached it by mistake instead of answering from whatever
// model it happens to hold. In distributed mode a worker can recycle a
// stopped backend's gRPC port for a different model's backend, and a
// liveness-only health probe cannot tell that apart from a valid cached
// route (#10952).
//
// The value is the controller's ModelConfig.Model, the SAME expression that
// produces ModelOptions.Model at LoadModel time, so the two are equal by
// construction rather than by convention.
//
// Empty means "no identity supplied": backends MUST skip the check. That
// keeps an old controller talking to a new backend working, and covers
// callers that legitimately synthesize a PredictOptions internally.
//
// Do NOT reuse TTSRequest.model or SoundGenerationRequest.model for this
// purpose. FileStagingClient already rewrites those to worker-local absolute
// paths (core/services/nodes/file_staging_client.go), so in distributed mode
// they already differ from the load-time value and comparing them would
// reject valid requests. Extending identity to those RPCs needs a separate
// field carrying the untranslated value - which is exactly what
// TTSRequest.ModelIdentity and SoundGenerationRequest.ModelIdentity are.
//
// Every other request message that reaches a backend through the distributed
// router now carries the same ModelIdentity field, populated from the same
// ModelConfig.Model. FileStagingClient rewrites Src/Dst/Voice/Model/
// StartImage/EndImage/Audio and never ModelIdentity, so what the backend
// compares is always what the controller sent.
string ModelIdentity = 54;
// 24 was never assigned; reserve it so it is not silently reused.
reserved 24;
}
// ToolCallDelta represents an incremental tool call update from the C++ parser.
@@ -503,11 +436,6 @@ message ModelOptions {
// Proxy carries the cloud-proxy backend's per-model configuration.
// Empty for non-proxy backends.
ProxyOptions Proxy = 74;
// EnableScore reserves backend resources for the Score RPC. It is derived
// from the model's explicit `known_usecases: [score]` declaration so models
// that never score retain their ordinary serving footprint.
bool EnableScore = 75;
}
// ProxyOptions configures the cloud-proxy backend. UpstreamURL and
@@ -523,12 +451,6 @@ message ProxyOptions {
string api_key_file = 5;
string upstream_model = 6;
int32 request_timeout_seconds = 7;
// cache_prompt enables automatic Anthropic prompt-cache breakpoints
// (cache_control: ephemeral) on the stable prefix — system, tools, and
// the last message block — when translating to the Anthropic provider.
// Cuts input cost on repeated/agentic calls (cache read = 0.1x). Only
// meaningful for mode=translate + provider=anthropic; ignored otherwise.
bool cache_prompt = 8;
}
message Result {
@@ -550,10 +472,6 @@ message TranscriptRequest {
float temperature = 8;
repeated string timestamp_granularities = 9;
bool stream = 10;
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 11;
}
message TranscriptResult {
@@ -561,10 +479,6 @@ message TranscriptResult {
string text = 2;
string language = 3;
float duration = 4;
// True when the decode ended on the model's end-of-utterance special token
// (<EOU>/<EOB>, emitted by cache-aware streaming models such as
// parakeet_realtime_eou_120m-v1). The marker itself is stripped from text.
bool eou = 5;
}
message TranscriptStreamResponse {
@@ -572,34 +486,6 @@ message TranscriptStreamResponse {
TranscriptResult final_result = 2;
}
// === AudioTranscriptionLive messages =====================================
message TranscriptLiveRequest {
oneof payload {
TranscriptLiveConfig config = 1;
TranscriptLiveAudio audio = 2;
}
}
message TranscriptLiveConfig {
string language = 1; // "" => model default
int32 sample_rate = 2; // 0 => 16000; backends may reject others
map<string, string> params = 3; // backend-specific tuning
}
message TranscriptLiveAudio {
repeated float pcm = 1; // mono PCM in [-1,1] at config.sample_rate
}
message TranscriptLiveResponse {
bool ready = 1; // open ack: sent once, before any delta
string delta = 2; // newly-finalized text since previous response
bool eou = 3; // <EOU> fired during this feed (the user yielded the turn)
repeated TranscriptWord words = 4; // words finalized by this feed (stream-relative ns)
TranscriptResult final_result = 5; // terminal message only, after the send side closes
bool eob = 6; // <EOB> fired: a backchannel ("uh-huh") ended — NOT a turn boundary
}
message TranscriptWord {
int64 start = 1;
int64 end = 2;
@@ -632,16 +518,6 @@ message GenerateImageRequest {
// Reference images for models that support them (e.g., Flux Kontext)
repeated string ref_images = 12;
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 13;
}
message UpscaleImageRequest {
string src = 1; // input image path
string dst = 2; // output image path
int32 scale = 3; // upscale factor (e.g. 2 or 4)
}
message GenerateVideoRequest {
@@ -657,28 +533,6 @@ message GenerateVideoRequest {
float cfg_scale = 10; // Classifier-free guidance scale
int32 step = 11; // Number of inference steps
string dst = 12; // Output path for the generated video
string audio = 13; // Path to staged audio for audio-conditioned video
// Backend-specific per-request generation parameters. Values are strings
// and are validated/coerced by the selected backend.
map<string, string> params = 14;
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 15;
}
message Generate3DRequest {
string src = 1; // Path to the staged conditioning image (3D generation is image-conditioned)
string dst = 2; // Output path for the generated binary glTF (.glb) asset
int32 seed = 3; // <=0 lets the backend pick a random seed
int32 step = 4; // Flow sampling steps; <=0 uses the backend default
float cfg_scale = 5; // Classifier-free guidance scale; <=0 uses the backend default
int32 texture_steps = 6; // Texture flow sampling steps; <=0 uses the backend default
string quality = 7; // Mesh pipeline: ""|"auto"|"coarse"|"512"|"1024"
string background = 8; // Conditioning-image background handling: ""|"auto"|"keep"|"black"|"white"
// Backend-specific per-request generation parameters. Values are strings
// and are validated/coerced by the selected backend.
map<string, string> params = 9;
}
message TTSRequest {
@@ -696,26 +550,10 @@ message TTSRequest {
// (e.g. Chatterbox exaggeration/cfg_weight/temperature). Values are strings and
// coerced by the backend; unset leaves the backend's configured defaults.
map<string, string> params = 7;
// ModelIdentity is a SEPARATE field from `model` above and carries the
// UNTRANSLATED controller-side ModelConfig.Model, so a backend can reject a
// request that reached it through a stale distributed route (#10952).
//
// `model` cannot be reused for this: FileStagingClient.TTS/.TTSStream and the
// SoundGeneration path rewrite it into a worker-local absolute path
// (core/services/nodes/file_staging_client.go), while the load-time value is
// untranslated. In distributed mode - exactly the configuration this guards -
// the two already differ, so comparing them would reject valid requests.
//
// Empty means "no identity supplied" and backends MUST skip the check.
string ModelIdentity = 8;
}
message VADRequest {
repeated float audio = 1;
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 2;
}
message VADSegment {
@@ -747,10 +585,6 @@ message DiarizeRequest {
float min_duration_on = 8; // discard segments shorter than this (seconds); 0 = backend default
float min_duration_off = 9; // merge gaps shorter than this (seconds); 0 = backend default
bool include_text = 10; // when the backend can emit per-segment transcript for free, ask it to populate `text`
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 11;
}
message DiarizeSegment {
@@ -785,18 +619,6 @@ message SoundGenerationRequest {
optional string language = 14;
optional string timesignature = 15;
optional bool instrumental = 17;
// ModelIdentity is a SEPARATE field from `model` above and carries the
// UNTRANSLATED controller-side ModelConfig.Model, so a backend can reject a
// request that reached it through a stale distributed route (#10952).
//
// `model` cannot be reused for this: FileStagingClient.TTS/.TTSStream and the
// SoundGeneration path rewrite it into a worker-local absolute path
// (core/services/nodes/file_staging_client.go), while the load-time value is
// untranslated. In distributed mode - exactly the configuration this guards -
// the two already differ, so comparing them would reject valid requests.
//
// Empty means "no identity supplied" and backends MUST skip the check.
string ModelIdentity = 18;
}
message TokenizationResponse {
@@ -804,14 +626,6 @@ message TokenizationResponse {
repeated int32 tokens = 2;
}
message DetokenizeRequest {
repeated int32 tokens = 1;
}
message DetokenizeResponse {
string content = 1;
}
message MemoryUsageData {
uint64 total = 1;
map<string, uint64> breakdown = 2;
@@ -844,10 +658,6 @@ message DetectOptions {
repeated float points = 3; // Point coordinates as [x1, y1, label1, x2, y2, label2, ...] (label: 1=pos, 0=neg)
repeated float boxes = 4; // Box coordinates as [x1, y1, x2, y2, ...]
float threshold = 5; // Detection confidence threshold
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 6;
}
message Detection {
@@ -870,10 +680,6 @@ message SoundDetectionRequest {
string src = 1; // audio file path (LocalAI writes the upload to disk)
int32 top_k = 2; // number of top tags to return (0 = all classes)
float threshold = 3; // optional: drop tags scoring below this
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 4;
}
message SoundClass {
@@ -898,10 +704,6 @@ message DepthRequest {
bool include_points = 7; // back-project to a 3D point cloud (DualDPT)
float points_conf_thresh = 8; // keep points with confidence >= this threshold
repeated string exports = 9; // requested exports: "glb", "colmap"
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 10;
}
message DepthResponse {
@@ -933,10 +735,6 @@ message FaceVerifyRequest {
string img2 = 2; // base64-encoded image
float threshold = 3; // cosine-distance threshold; 0 = use backend default
bool anti_spoofing = 4; // run MiniFASNet liveness on each image; failed liveness forces verified=false
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 5;
}
message FaceVerifyResponse {
@@ -958,10 +756,6 @@ message FaceAnalyzeRequest {
string img = 1; // base64-encoded image
repeated string actions = 2; // subset of ["age","gender","emotion","race"]; empty = all-supported
bool anti_spoofing = 3;
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 4;
}
message FaceAnalysis {
@@ -994,10 +788,6 @@ message VoiceVerifyRequest {
string audio2 = 2; // path to second audio clip
float threshold = 3; // cosine-distance threshold; 0 = use backend default
bool anti_spoofing = 4; // reserved for future AASIST bolt-on
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 5;
}
message VoiceVerifyResponse {
@@ -1012,10 +802,6 @@ message VoiceVerifyResponse {
message VoiceAnalyzeRequest {
string audio = 1; // path to audio clip
repeated string actions = 2; // subset of ["age","gender","emotion"]; empty = all-supported
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 3;
}
message VoiceAnalysis {
@@ -1034,10 +820,6 @@ message VoiceAnalyzeResponse {
message VoiceEmbedRequest {
string audio = 1; // path to audio clip
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 2;
}
message VoiceEmbedResponse {
@@ -1132,18 +914,6 @@ message AudioTransformRequest {
string reference_path = 2; // optional auxiliary; empty => zero-fill
string dst = 3; // required, output file path
map<string, string> params = 4; // backend-specific tuning
// ModelIdentity names the model this request is for; see
// PredictOptions.ModelIdentity for the full rationale. Empty means "no
// identity supplied" and backends MUST skip the check.
string ModelIdentity = 5;
}
// One named output of a transform that produces several from a single run.
// Source separation is the case that needs it: htdemucs yields drums, bass,
// other and vocals from one pass over the input.
message AudioTransformStem {
string name = 1; // the model's own stem id, e.g. "vocals"
string dst = 2; // path of the file written for that stem
}
message AudioTransformResult {
@@ -1151,15 +921,6 @@ message AudioTransformResult {
int32 sample_rate = 2;
int32 samples = 3;
bool reference_provided = 4;
// Every named output the run produced, in the model's own order, including
// the one copied into dst. Empty for a transform with a single output.
//
// It exists because dst carries one file while separation produces several,
// and running the model once per stem would cost four full separations of
// the same audio. The backend runs once, writes each stem beside dst, and
// names them here; without this field the other stems are on disk but no
// caller can find them, which is the same as not having produced them.
repeated AudioTransformStem stems = 5;
}
// Bidirectional streaming audio transform. The first message MUST carry a
@@ -1451,3 +1212,4 @@ message ForwardReply {
repeated ForwardHeader headers = 2;
bytes body_chunk = 3;
}

View File

@@ -1,8 +0,0 @@
audio.cpp/
build/
package/
grpc-server
backend.pb.cc
backend.pb.h
backend.grpc.pb.cc
backend.grpc.pb.h

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@@ -1,331 +0,0 @@
cmake_minimum_required(VERSION 3.20)
project(audio-cpp-grpc-server LANGUAGES C CXX)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(TARGET grpc-server)
set(AUDIO_CPP_DIR "${CMAKE_CURRENT_SOURCE_DIR}/audio.cpp"
CACHE PATH "Path to the pinned audio.cpp checkout")
option(AUDIO_CPP_GRPC_BUILD_TESTS "Build engine-linked ctest binaries" OFF)
if(NOT EXISTS "${AUDIO_CPP_DIR}/CMakeLists.txt")
message(FATAL_ERROR
"AUDIO_CPP_DIR does not contain an audio.cpp checkout: ${AUDIO_CPP_DIR}. "
"Run 'make audio.cpp' first.")
endif()
if(APPLE)
# Homebrew installs protobuf/grpc under a non-default prefix.
if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "arm64")
set(HOMEBREW_DEFAULT_PREFIX "/opt/homebrew")
else()
set(HOMEBREW_DEFAULT_PREFIX "/usr/local")
endif()
link_directories("${HOMEBREW_DEFAULT_PREFIX}/lib")
include_directories("${HOMEBREW_DEFAULT_PREFIX}/include")
endif()
find_package(Threads REQUIRED)
find_package(Protobuf CONFIG QUIET)
if(NOT Protobuf_FOUND)
find_package(Protobuf REQUIRED)
endif()
find_package(gRPC CONFIG QUIET)
if(NOT gRPC_FOUND)
# Reached only on distros whose grpc++ packaging ships no CMake config.
# Ubuntu's libgrpc-dev does ship one, so this is dead code on LocalAI's own
# build distro. Kept for the distros that do not.
find_library(GRPCPP_LIB grpc++ REQUIRED)
find_library(GRPCPP_REFLECTION_LIB grpc++_reflection REQUIRED)
add_library(gRPC::grpc++ INTERFACE IMPORTED)
set_target_properties(gRPC::grpc++ PROPERTIES
INTERFACE_LINK_LIBRARIES "${GRPCPP_LIB}")
add_library(gRPC::grpc++_reflection INTERFACE IMPORTED)
set_target_properties(gRPC::grpc++_reflection PROPERTIES
INTERFACE_LINK_LIBRARIES "${GRPCPP_REFLECTION_LIB}")
endif()
find_program(_PROTOC NAMES protoc REQUIRED)
find_program(_GRPC_CPP_PLUGIN NAMES grpc_cpp_plugin REQUIRED)
get_filename_component(HW_PROTO "${CMAKE_CURRENT_SOURCE_DIR}/../../backend.proto" ABSOLUTE)
get_filename_component(HW_PROTO_PATH "${HW_PROTO}" PATH)
set(HW_PROTO_SRCS "${CMAKE_CURRENT_BINARY_DIR}/backend.pb.cc")
set(HW_PROTO_HDRS "${CMAKE_CURRENT_BINARY_DIR}/backend.pb.h")
set(HW_GRPC_SRCS "${CMAKE_CURRENT_BINARY_DIR}/backend.grpc.pb.cc")
set(HW_GRPC_HDRS "${CMAKE_CURRENT_BINARY_DIR}/backend.grpc.pb.h")
add_custom_command(
OUTPUT "${HW_PROTO_SRCS}" "${HW_PROTO_HDRS}" "${HW_GRPC_SRCS}" "${HW_GRPC_HDRS}"
COMMAND ${_PROTOC}
ARGS --grpc_out "${CMAKE_CURRENT_BINARY_DIR}"
--cpp_out "${CMAKE_CURRENT_BINARY_DIR}"
-I "${HW_PROTO_PATH}"
--plugin=protoc-gen-grpc="${_GRPC_CPP_PLUGIN}"
"${HW_PROTO}"
DEPENDS "${HW_PROTO}")
add_library(hw_grpc_proto STATIC
${HW_GRPC_SRCS} ${HW_GRPC_HDRS}
${HW_PROTO_SRCS} ${HW_PROTO_HDRS})
target_include_directories(hw_grpc_proto PUBLIC ${CMAKE_CURRENT_BINARY_DIR})
# Required on macOS: without these the Homebrew protobuf/grpc include dirs never
# reach this target and google/protobuf/runtime_version.h is not found.
target_link_libraries(hw_grpc_proto PUBLIC protobuf::libprotobuf gRPC::grpc++)
# TWO PROTOBUF RUNTIMES IN ONE BINARY, AND THE ONE THAT WON WAS THE WRONG ONE.
#
# engine_runtime links sentencepiece, whose default SPM_PROTOBUF_PROVIDER
# ("internal") builds the protobuf-lite 3.14.0 sources vendored under
# external/sentencepiece/third_party/protobuf-lite. Our generated backend.pb.cc
# is compiled against the toolchain's protobuf 3.21.12 headers and links
# libprotobuf.so 3.21.12. Both used to end up in the executable: 476
# google::protobuf:: symbols from that archive, 278 of them also defined by
# libprotobuf.so.
#
# The binding is decided at STATIC LINK time. Once ld pulls a sentencepiece
# member in for sentencepiece's own code, that member's protobuf definitions are
# in the executable and references from libhw_grpc_proto.a bind to them. Do NOT
# reach for -Wl,--exclude-libs: it flips those symbols to LOCAL in .dynsym and
# the breakage is unchanged, because no visibility flag revisits a static
# binding already made.
#
# What broke, measured rather than assumed:
# google::protobuf::internal::ParseContext::ParseMessage(MessageLite*, const char*)
# is what every generated _InternalParse calls for a SUBMESSAGE field and for
# nothing else. Bound to the 3.14 definition it fails, so a flat message parsed
# and every nested one did not: a TranscriptResult carrying segments serialized
# to correct bytes that the same process could not read back, and
# TranscriptLiveRequest, a oneof of submessages, could not have been parsed at
# all. 3.21 generated code was also running 3.14 arena, ArenaStringPtr and
# ExtensionSet code, which is an ABI mismatch rather than a missing feature, so
# "not observed to bite yet" was never a reason to leave it.
#
# "package" makes sentencepiece use the protobuf found above, which is the one
# the generated code was built against. It must be set before add_subdirectory,
# since that is when sentencepiece's own cache entry is created.
#
# WHAT THAT BUYS IS ONE PROTOBUF RUNTIME, not an executable free of protobuf
# symbols, and the difference matters to whoever checks this next. Measured with
# nm -C --defined-only on the linked grpc-server, 2515 google::protobuf::
# symbols are still DEFINED in it, and that is what should be there: they are
# generated code, sentencepiece::ModelProto's own _InternalParse and
# CheckTypeAndMergeFrom among them, which name protobuf types in their
# signatures and are compiled into every user of a .proto. Expecting zero would
# send a reader looking for a regression that is not one.
#
# The claim that decides whether the ABI mismatch above is gone is the RUNTIME
# one, and it holds: google::protobuf::internal::ParseContext::ParseMessage is
# UNDEFINED in the executable, so every generated _InternalParse resolves it to
# libprotobuf.so at load instead of to a vendored 3.14 copy. No vendored
# protobuf-lite archive is pulled in at all, and citrinet_asr, which parses a
# SentencePiece ModelProto at load, tokenizes correctly as a result.
set(SPM_PROTOBUF_PROVIDER "package" CACHE STRING
"Make sentencepiece use the found protobuf, not its vendored 3.14 copy" FORCE)
# Upstream's global add_compile_options(-Wall -Wextra -Wpedantic -pedantic-errors)
# is a directory property of the subdirectory and does not reach our targets.
#
# EXCLUDE_FROM_ALL is load-bearing, do not drop it: upstream's default target set
# includes its CLI, server, converter and test binaries, none of which we ship.
# Without it every build would compile all of them. The targets we do name in
# target_link_libraries below are still built on demand, so nothing is lost.
add_subdirectory("${AUDIO_CPP_DIR}" "${CMAKE_CURRENT_BINARY_DIR}/audio-cpp" EXCLUDE_FROM_ALL)
add_executable(${TARGET}
grpc-server.cpp
model_options.cpp
capability_routing.cpp
family_gate.cpp
loaded_model.cpp
audio_io.cpp
audio_units.cpp
transcript_assembly.cpp
result_map.cpp
stem_selection.cpp
generation_request.cpp
stream_delta.cpp
wav_header.cpp
inference_lane.cpp
live_watchdog.cpp
)
# Two files carry a switch over an enum with no `default:` label, deliberately,
# so that -Wswitch reports an enumerator nobody handled. -Wswitch is only a
# warning by default, and a warning in a 600-file build log is a warning nobody
# reads, so it is promoted to an error on exactly these two translation units.
# Not project-wide: upstream's own sources are not held to this, and they are
# where the churn is.
#
# loaded_model.cpp mirrors engine::runtime::VoiceTaskKind onto its own Task enum.
# Its static_asserts catch an insertion or a reorder, but an enumerator APPENDED
# after the last one shifts no value, so no assertion can see it. What does see
# it is from_engine_task's switch over the engine enum. This is the difference
# between a build failure and a backend that silently runs the wrong task.
#
# capability_routing.cpp's unsupported_surface() switches UnsupportedRpc onto the
# row of unsupported_surfaces() that explains it. Left as a warning, a sixth
# enumerator added without a row BUILDS AND SHIPS, and its trailing
# `return surfaces[0];` then answers the new RPC with AudioEncode's codec reason:
# a confident, specific and false statement about audio.cpp, on the wire, on the
# one code path whose entire job is to be truthful about what this backend
# cannot do. Verified rather than assumed: adding a sixth enumerator and building
# the shipping target produced exit 0, a binary, and one warning. A compile-time
# check is the better trade than the runtime fallback it replaced only if it is
# fatal, so here it is fatal.
if(NOT MSVC)
set_source_files_properties(loaded_model.cpp capability_routing.cpp
PROPERTIES COMPILE_OPTIONS "-Werror=switch")
endif()
target_include_directories(${TARGET} PRIVATE
"${AUDIO_CPP_DIR}/include"
"${CMAKE_CURRENT_SOURCE_DIR}")
# The shipping binary is held to the same bar as the tests below. Upstream's own
# add_compile_options is a property of its directory and never reached this
# target, so until now "the build was clean" meant only that nothing was being
# checked.
if(NOT MSVC)
target_compile_options(${TARGET} PRIVATE -Wall -Wextra -Wpedantic)
endif()
target_link_libraries(${TARGET} PRIVATE
hw_grpc_proto
engine_runtime
ggml
gRPC::grpc++
gRPC::grpc++_reflection
protobuf::libprotobuf
Threads::Threads)
# ENGINE_ENABLE_CPU_ALL_VARIANTS builds ggml backends as shared objects that sit
# next to the binary in the package, so the binary must search its own directory.
# BUILD_WITH_INSTALL_RPATH keeps the build-tree binary at exactly "$ORIGIN".
# Upstream sets CMAKE_BUILD_WITH_INSTALL_RPATH in its own directory scope, which
# does not reach ours, so without this CMake also appends its build-tree library
# directory. That absolute build-host path would survive into the copied binary
# and let a package.sh that forgot to bundle libggml*.so still pass on the build
# machine while failing everywhere else.
set_target_properties(${TARGET} PROPERTIES
BUILD_RPATH "$ORIGIN"
INSTALL_RPATH "$ORIGIN"
BUILD_WITH_INSTALL_RPATH TRUE)
if(AUDIO_CPP_GRPC_BUILD_TESTS)
enable_testing()
# These are the units whose tests CANNOT run under
# backend/cpp/run-unit-tests.sh, because that script compiles each
# *_test.cpp standalone with no protobuf and no audio.cpp include path.
# They are named *_ctest.cpp so the script's glob does not pick them up and
# fail every backend's suite; everything that can be stdlib-only still is,
# and still lives in a *_test.cpp beside its unit.
add_executable(result_map_ctest
result_map_ctest.cpp
result_map.cpp
transcript_assembly.cpp
audio_units.cpp)
target_include_directories(result_map_ctest PRIVATE
"${AUDIO_CPP_DIR}/include"
"${CMAKE_CURRENT_SOURCE_DIR}"
# session.h reaches ggml.h through core/backend.h. Every other target
# here inherits that directory from the ggml target it links; this one
# links no ggml, so it has to name it.
"${AUDIO_CPP_DIR}/external/ggml/include")
# No engine_runtime: result_map touches only the plain structs in
# engine/framework/runtime/session.h, so the header is all it needs.
target_link_libraries(result_map_ctest PRIVATE
hw_grpc_proto
protobuf::libprotobuf
Threads::Threads)
target_compile_options(result_map_ctest PRIVATE -Wall -Wextra -Wpedantic)
add_test(NAME result_map COMMAND result_map_ctest)
# Same shape as result_map_ctest: generation_request touches only the plain
# structs in engine/framework/runtime/session.h plus the generated protobuf
# messages, so the headers are all it needs and no engine_runtime is linked.
add_executable(generation_request_ctest
generation_request_ctest.cpp
generation_request.cpp)
target_include_directories(generation_request_ctest PRIVATE
"${AUDIO_CPP_DIR}/include"
"${CMAKE_CURRENT_SOURCE_DIR}"
# session.h reaches ggml.h through core/backend.h, and this target links
# no ggml, so it has to name the include directory itself.
"${AUDIO_CPP_DIR}/external/ggml/include")
target_link_libraries(generation_request_ctest PRIVATE
hw_grpc_proto
protobuf::libprotobuf
Threads::Threads)
target_compile_options(generation_request_ctest PRIVATE -Wall -Wextra -Wpedantic)
add_test(NAME generation_request COMMAND generation_request_ctest)
add_executable(audio_io_ctest
audio_io_ctest.cpp
audio_io.cpp)
target_include_directories(audio_io_ctest PRIVATE
"${AUDIO_CPP_DIR}/include"
"${CMAKE_CURRENT_SOURCE_DIR}")
target_link_libraries(audio_io_ctest PRIVATE
engine_runtime
ggml
Threads::Threads)
target_compile_options(audio_io_ctest PRIVATE -Wall -Wextra -Wpedantic)
set_target_properties(audio_io_ctest PROPERTIES
BUILD_RPATH "$ORIGIN"
INSTALL_RPATH "$ORIGIN"
BUILD_WITH_INSTALL_RPATH TRUE)
add_test(NAME audio_io COMMAND audio_io_ctest)
# The streaming drivers live in loaded_model.cpp, which links the engine, so
# this cannot be a standalone *_test.cpp. It builds no model and reads no
# file: LoadedModel::Session is a plain struct holding a pointer to an
# engine interface, so the drivers are exercised against fake sessions.
add_executable(streaming_driver_ctest
streaming_driver_ctest.cpp
loaded_model.cpp
capability_routing.cpp
family_gate.cpp
model_options.cpp
inference_lane.cpp)
target_include_directories(streaming_driver_ctest PRIVATE
"${AUDIO_CPP_DIR}/include"
"${CMAKE_CURRENT_SOURCE_DIR}")
target_link_libraries(streaming_driver_ctest PRIVATE
engine_runtime
ggml
Threads::Threads)
target_compile_options(streaming_driver_ctest PRIVATE -Wall -Wextra -Wpedantic)
# No "$ORIGIN" rpath override here, unlike the shipping target and unlike
# audio_io_ctest. loaded_model.cpp reaches make_default_registry, so this
# binary genuinely links libggml, and CMake's own build-tree rpath is what
# finds it: the ggml shared objects land in ${CMAKE_CURRENT_BINARY_DIR}/bin
# while the test binary sits one directory up. A build-host absolute path in
# a test binary is harmless, since package.sh ships only grpc-server, and
# forcing "$ORIGIN" here means ctest cannot start the binary at all.
add_test(NAME streaming_driver COMMAND streaming_driver_ctest)
# Asserts that the upstream ABSENCES capability_routing.cpp's refusal
# messages rest on are still absences, by querying make_default_registry()
# rather than by re-reading upstream. This is what makes an AUDIO_CPP_VERSION
# bump that adds a codec task kind, an spk family or a streaming converter
# fail the build instead of leaving a false statement on the wire.
#
# It links engine_runtime purely to run that query, which is why it lives
# here rather than with the standalone *_test.cpp files, and it needs no
# "$ORIGIN" rpath override for the same reason streaming_driver_ctest does
# not: see the note above.
add_executable(upstream_absence_ctest upstream_absence_ctest.cpp)
target_include_directories(upstream_absence_ctest PRIVATE
"${AUDIO_CPP_DIR}/include"
"${CMAKE_CURRENT_SOURCE_DIR}")
target_link_libraries(upstream_absence_ctest PRIVATE
engine_runtime
ggml
Threads::Threads)
target_compile_options(upstream_absence_ctest PRIVATE -Wall -Wextra -Wpedantic)
add_test(NAME upstream_absence COMMAND upstream_absence_ctest)
endif()

View File

@@ -1,172 +0,0 @@
# audio.cpp backend Makefile.
#
# Upstream pin lives below in the AUDIO_CPP_VERSION variable, so
# .github/bump_deps.sh can find and update it, matching the llama-cpp / ds4
# convention. That script seds every line matching the variable name followed by
# an assignment, so this comment deliberately spells the name on its own: a
# comment repeating the full assignment token gets rewritten and mangled by the
# first auto-bump (backend/cpp/ds4/Makefile shows the damage). The clone
# recipe is a make target (not a prepare.sh) so 'make purge && make' is a clean
# rebuild and so the bump bot can see the pin.
AUDIO_CPP_VERSION?=4e3aea2fd99aeaa5924e71c51eb2793846045332
AUDIO_CPP_REPO?=https://github.com/0xShug0/audio.cpp
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
BUILD_DIR := build
BUILD_TYPE ?=
NATIVE ?= false
JOBS ?= $(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4)
UNAME_S := $(shell uname -s)
# AUDIOCPP_DEPLOYMENT_BUILD compiles the model_specs/*.json catalog into
# engine_runtime, so the shipped package needs no model_specs directory and a
# safetensors model tree still resolves its family spec.
CMAKE_ARGS ?= -DCMAKE_BUILD_TYPE=Release -DAUDIOCPP_DEPLOYMENT_BUILD=ON
# CMAKE_CUDA_ARCHITECTURES must be set explicitly for a cublas build, and this
# is not a tuning knob: upstream's CMakeLists sets CUDA_ARCHITECTURES to
# `native` on the engine_runtime target whenever the root-scope variable is
# unset (audio.cpp/CMakeLists.txt, the `if (CMAKE_CUDA_ARCHITECTURES)` branch
# next to the istft/torch_random .cu sources), and docs/build/linux.md says so
# outright: "Leave CMAKE_CUDA_ARCHITECTURES unset to build for the GPUs present
# at build time (native)". No CI runner has a GPU, so `native` has nothing to
# enumerate. ggml's own default (external/ggml/src/ggml-cuda/CMakeLists.txt)
# does not rescue this: it list(APPEND)s in the ggml subdirectory scope, which
# never reaches the root scope where the engine_runtime property is decided.
#
# The values below are ggml's list for the matching toolkit, copied rather than
# invented, so the two targets compile for exactly the same set:
# - CUDA 13 drops the Maxwell/Pascal/Volta virtual archs (50/61/70).
# - 121a-real needs CUDA >= 12.9, so the CUDA 12 list (built against 12.8)
# stops at 120a-real.
# - `a`-suffixed archs are used rather than ggml's rejected 120f-virtual: the
# `f` suffix needs CMake >= 3.31.8, and Ubuntu Noble ships 3.28.3. The
# 3.28 validator (Modules/Internal/CMakeCUDAArchitecturesValidate.cmake)
# accepts `[0-9]+a?(-real|-virtual)?`.
#
# Setting it here also pins ggml's copy, since its default is guarded by
# `if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)`. CUDA_MAJOR_VERSION is the CI
# build-arg, forwarded by Dockerfile.audio-cpp.
#
# An EMPTY major maps to `native`, NOT to the CUDA 12 list. Only CI declares a
# major; a developer running `BUILD_TYPE=cublas make` locally declares none, and
# the CUDA 12 list contains 120a-real, which needs nvcc >= 12.8. Falling through
# to it turned every local build on a CUDA 12.0-12.7 host into a compile error,
# where upstream's documented behaviour ("Leave CMAKE_CUDA_ARCHITECTURES unset
# to build for the GPUs present at build time") worked. `native` restores that.
# It does require a GPU to enumerate, so the escape hatch for a GPU-less local
# cross-build is to set CUDA_ARCHITECTURES on the command line, which the ?=
# assignments below leave untouched.
CUDA_MAJOR_VERSION ?=
ifeq ($(CUDA_MAJOR_VERSION),13)
CUDA_ARCHITECTURES ?= 75-virtual;80-virtual;86-real;89-real;120a-real;121a-real
else ifeq ($(CUDA_MAJOR_VERSION),12)
CUDA_ARCHITECTURES ?= 50-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real;89-real;120a-real
else ifeq ($(CUDA_MAJOR_VERSION),)
CUDA_ARCHITECTURES ?= native
else ifeq ($(BUILD_TYPE),cublas)
# Gated on cublas because the variable means nothing to any other build, so a
# stray CUDA_MAJOR_VERSION in the environment must not break `make clean` or
# a CPU build. It does still error for `BUILD_TYPE=cublas make clean`, which
# is the right trade: that invocation is asking about a CUDA build tree.
$(error CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) has no architecture list here (12 and 13 do). Leave it empty for a native build, or pass CUDA_ARCHITECTURES explicitly.)
endif
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS += -DENGINE_ENABLE_CUDA=ON "-DCMAKE_CUDA_ARCHITECTURES=$(CUDA_ARCHITECTURES)"
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS += -DENGINE_ENABLE_VULKAN=ON
else ifeq ($(UNAME_S),Darwin)
# Metal. ggml embeds the shader library by default (GGML_METAL_EMBED_LIBRARY
# defaults to GGML_METAL), so the package needs no .metallib beside the
# binary. Darwin builds go through scripts/build/audio-cpp-darwin.sh.
CMAKE_ARGS += -DENGINE_ENABLE_METAL=ON
# AppleClang ships no OpenMP runtime and Homebrew's libomp is keg-only, so
# neither libomp.dylib nor omp.h is symlinked into /opt/homebrew and CMake's
# FindOpenMP cannot find them on its own (the workflow's `brew link libomp`
# is a no-op for a keg-only formula, and its failure is swallowed).
# audio.cpp calls find_package(OpenMP REQUIRED COMPONENTS CXX) whenever
# ENGINE_ENABLE_OPENMP is ON, so with no hint the macOS build dies at
# configure time before compiling anything. OpenMP_ROOT is honoured by the
# find_library/find_path calls inside FindOpenMP under CMP0074, which is NEW
# here because audio.cpp requires CMake 3.20.
#
# If the keg is absent, turn OpenMP off rather than fail: the tree's only
# <omp.h> include is guarded by #ifdef _OPENMP and a #pragma omp without
# -fopenmp is simply ignored, so an OpenMP-less build is CORRECT. It is not
# cheap, though: 108 `#pragma omp` directives across ~30 files (roformer,
# demucs, chatterbox, moss, supertonic, seed_vc, framework/audio/dsp) are
# compiled out, and clang says nothing about an ignored omp pragma unless
# -Wsource-uses-openmp is on. A green package that is quietly single-threaded
# in every host DSP loop gets blamed on Metal, not on packaging, so the
# fallback announces itself.
ifeq ($(origin LIBOMP_PREFIX),undefined)
LIBOMP_PREFIX := $(shell brew --prefix libomp 2>/dev/null)
endif
# Nested ifneq rather than $(and ...): $(and) needs GNU make 3.81, and while
# that is what Apple ships, an older make expands it to empty and would take
# the OpenMP-OFF branch with no way to tell that from a genuinely missing
# keg. Two plain conditionals cannot fail that way.
LIBOMP_USABLE :=
ifneq ($(wildcard $(LIBOMP_PREFIX)/lib/libomp.dylib),)
ifneq ($(wildcard $(LIBOMP_PREFIX)/include/omp.h),)
LIBOMP_USABLE := yes
endif
endif
ifeq ($(LIBOMP_USABLE),yes)
CMAKE_ARGS += "-DOpenMP_ROOT=$(LIBOMP_PREFIX)"
else
$(warning audio-cpp: libomp not found at '$(LIBOMP_PREFIX)'; building without OpenMP (single-threaded host DSP). Install it with `brew install libomp`, or set LIBOMP_PREFIX.)
CMAKE_ARGS += -DENGINE_ENABLE_OPENMP=OFF
endif
else
# Portable Linux CPU. Upstream wires this to GGML_BACKEND_DL +
# GGML_CPU_ALL_VARIANTS + $ORIGIN rpath, so one build serves every CPU
# tier instead of an AVX-tier image fan-out.
CMAKE_ARGS += -DENGINE_ENABLE_CPU_ALL_VARIANTS=ON
endif
ifneq ($(NATIVE),true)
CMAKE_ARGS += -DENGINE_ENABLE_NATIVE_CPU=OFF
endif
.PHONY: all grpc-server package test test-engine clean purge
all: grpc-server
# Clone the upstream source at the pinned commit. The directory is the target
# so make only re-clones when it is missing. After bumping AUDIO_CPP_VERSION,
# run 'make purge && make' to refetch.
audio.cpp:
mkdir -p audio.cpp
cd audio.cpp && \
git init -q && \
git remote add origin $(AUDIO_CPP_REPO) && \
git fetch --depth 1 origin $(AUDIO_CPP_VERSION) && \
git checkout FETCH_HEAD
grpc-server: audio.cpp
mkdir -p $(BUILD_DIR)
cd $(BUILD_DIR) && cmake $(CMAKE_ARGS) $(CURRENT_MAKEFILE_DIR) && \
cmake --build . --config Release -j $(JOBS)
cp $(BUILD_DIR)/grpc-server grpc-server
package: grpc-server
bash package.sh
test:
@echo "audio-cpp: standalone unit tests run from the repo root via 'make test-backend-cpp'"
# Engine-linked tests. Needs the upstream checkout and a full engine build.
test-engine: audio.cpp
mkdir -p $(BUILD_DIR)
cd $(BUILD_DIR) && cmake $(CMAKE_ARGS) -DAUDIO_CPP_GRPC_BUILD_TESTS=ON $(CURRENT_MAKEFILE_DIR) && \
cmake --build . --config Release -j $(JOBS) && ctest --output-on-failure --no-tests=error
clean:
rm -rf $(BUILD_DIR) grpc-server package
purge: clean
rm -rf audio.cpp

View File

@@ -1,124 +0,0 @@
#include "audio_io.h"
#include "loaded_model.h"
#include "engine/framework/audio/conversion.h"
#include "engine/framework/audio/wav_reader.h"
#include "engine/framework/audio/wav_writer.h"
#include <filesystem>
#include <string>
#include <utility>
namespace audiocpp_backend {
engine::runtime::AudioBuffer read_audio_file(const std::string &path,
int target_sample_rate) {
if (path.empty()) {
throw ConfigError("audio-cpp: no input audio path was supplied");
}
std::error_code ec;
const bool present = std::filesystem::exists(std::filesystem::path(path), ec);
if (ec) {
// exists() returning false with ec set does NOT mean the file is
// absent, it means the question could not be answered: most often a
// parent directory is not searchable. Reporting that as "does not
// exist" sends the operator after the file when the fault is the
// permissions on the directory above it.
throw ConfigError("audio-cpp: cannot stat input audio " + path + ": " +
ec.message());
}
if (!present) {
throw ConfigError("audio-cpp: input audio does not exist: " + path);
}
engine::audio::WavData wav;
try {
wav = engine::audio::read_wav_f32(std::filesystem::path(path));
} catch (const std::exception &err) {
throw ConfigError("audio-cpp: cannot read " + path +
" as WAV: " + err.what());
}
if (wav.sample_rate <= 0) {
throw ConfigError("audio-cpp: " + path +
" declares a non-positive sample rate; every "
"timestamp derived from it would be zero");
}
// AudioBuffer's own default is 1, and a reader that reports 0 channels
// still gave us an interleaving of one. Normalised before the conversion
// below rather than after, because mixdown_interleaved_to_mono_average
// throws on a non-positive channel count.
if (wav.channels <= 0) {
wav.channels = 1;
}
engine::runtime::AudioBuffer buffer;
if (target_sample_rate <= 0) {
buffer.sample_rate = wav.sample_rate;
buffer.channels = wav.channels;
buffer.samples = std::move(wav.samples);
return buffer;
}
buffer.sample_rate = target_sample_rate;
buffer.channels = 1;
try {
// A no-op copy when the rates already match, so the common 16 kHz
// upload pays only the mono mixdown it would have paid inside the
// family anyway.
buffer.samples =
engine::audio::convert_wav_to_mono_linear_resampled(wav, target_sample_rate);
} catch (const std::exception &err) {
// ConfigError, so this is INVALID_ARGUMENT rather than INTERNAL. What
// reaches here is a malformed input: a sample count that is not a whole
// number of frames is the realistic one, and it is the uploader's file
// that is truncated, not this backend that is broken.
throw ConfigError("audio-cpp: cannot resample " + path + " from " +
std::to_string(wav.sample_rate) + " Hz to " +
std::to_string(target_sample_rate) +
" Hz: " + err.what());
}
return buffer;
}
void write_audio_file(const std::string &path,
const engine::runtime::AudioBuffer &audio) {
if (path.empty()) {
throw ConfigError("audio-cpp: no output path was supplied");
}
const std::filesystem::path destination(path);
if (destination.has_parent_path()) {
// Best effort: a failure here shows up as a write failure below, with a
// message naming the file the caller actually asked for.
std::error_code ec;
std::filesystem::create_directories(destination.parent_path(), ec);
}
try {
engine::audio::write_pcm16_wav(destination, audio.sample_rate,
audio.channels > 0 ? audio.channels : 1,
audio.samples);
} catch (const std::exception &err) {
// NOT a ConfigError, and the distinction is not cosmetic. The
// destination is chosen by LocalAI rather than by the caller: it is a
// unique name inside GeneratedContentDir. A failure to write it is a
// full disk, a permission fault on the server's own directory, or a bad
// mount, none of which the caller can fix or is to blame for. As a
// ConfigError this surfaced as INVALID_ARGUMENT, which tells a client
// its request was wrong and not to retry; a plain runtime_error maps to
// INTERNAL, which is both true and retryable. The empty path above
// stays INVALID_ARGUMENT, because that one really is a malformed
// request.
throw std::runtime_error("audio-cpp: cannot write " + path + ": " +
err.what());
}
}
engine::runtime::AudioBuffer buffer_from_mono(std::vector<float> samples,
int sample_rate) {
engine::runtime::AudioBuffer buffer;
buffer.sample_rate = sample_rate;
buffer.channels = 1;
buffer.samples = std::move(samples);
return buffer;
}
} // namespace audiocpp_backend

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@@ -1,61 +0,0 @@
#pragma once
// Thin wrappers over the framework's public audio IO. Engine-linked, so this
// unit is built and tested through the CMake target rather than by
// backend/cpp/run-unit-tests.sh. The pure part of the arithmetic these
// wrappers feed lives in audio_units, which is stdlib-only and does have a
// standalone test.
#include "engine/framework/runtime/session.h"
#include <string>
#include <vector>
namespace audiocpp_backend {
// Reads a WAV file. Throws ConfigError when the file is missing, is not
// readable as WAV, or declares a non-positive sample rate: all three are
// user-fixable input problems rather than backend faults.
//
// A declared sample rate of zero is refused rather than passed on, because
// every downstream conversion in audio_units answers 0 for a non-positive rate.
// Accepting it would turn a corrupt header into a response full of zero
// timestamps, which reads as a real answer.
//
// `target_sample_rate` is the rate the CALLER needs, in Hz:
//
// 0 (or negative) keep the file's own rate and channel count.
// positive downmix to mono and resample to that rate. Resampling is
// skipped when the file already declares it, so passing the
// rate a route needs costs nothing on the common input.
//
// It is a parameter, and not a constant inside this function, because the
// routes that read audio do not agree on an answer. Speech routes want 16 kHz
// mono; source separation does not, and folding a 44.1 kHz stereo input to
// 16 kHz mono for demucs or roformer would destroy the very thing they separate
// (both refuse a rate other than their own outright). Making the caller name
// the rate keeps that decision where the route is known.
//
// Downmixing along with the resample is not an extra liberty: every family a
// positive rate is used for (silero_vad, sortformer_diar and every ASR family)
// begins by calling the same mixdown_interleaved_to_mono_average on whatever it
// is given. Doing it once here produces the identical samples and halves the
// buffer that is then moved through the request.
engine::runtime::AudioBuffer read_audio_file(const std::string &path,
int target_sample_rate);
// Writes 16-bit PCM WAV, creating parent directories.
//
// Throws ConfigError, i.e. INVALID_ARGUMENT, ONLY for an empty path, which is a
// malformed request. Every other failure throws a plain runtime_error, i.e.
// INTERNAL: the destination is LocalAI's own generated-content directory and
// not anything the caller named, so a full disk or a permission fault there is
// a server fault and is worth retrying, which is the opposite of what
// INVALID_ARGUMENT tells a client.
void write_audio_file(const std::string &path,
const engine::runtime::AudioBuffer &audio);
engine::runtime::AudioBuffer buffer_from_mono(std::vector<float> samples,
int sample_rate);
} // namespace audiocpp_backend

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@@ -1,217 +0,0 @@
// Tests for audio_io's reading contract, and in particular for the resampling
// that keeps a 44.1 or 48 kHz upload from reaching a family that only accepts
// 16 kHz.
//
// NAMED _ctest AND NOT _test ON PURPOSE: see the note at the top of
// result_map_ctest.cpp. This file links the audio.cpp engine, so it is built
// and run by ctest, not by backend/cpp/run-unit-tests.sh.
//
// make -C backend/cpp/audio-cpp test-engine
#include "audio_io.h"
#include "loaded_model.h"
#include <algorithm>
#include <cmath>
#include <cstdio>
#include <filesystem>
#include <string>
#include <vector>
static int failures = 0;
static void check(bool ok, const std::string &name) {
if (!ok) {
failures++;
fprintf(stderr, "FAIL: %s\n", name.c_str());
} else {
fprintf(stderr, "ok: %s\n", name.c_str());
}
}
using namespace audiocpp_backend;
// A one-second tone, interleaved across `channels`. Real audio rather than
// silence so a resample that dropped its input would be visible as a flat
// buffer, not just as a different length.
static engine::runtime::AudioBuffer tone(int sample_rate, int channels,
float seconds) {
engine::runtime::AudioBuffer buffer;
buffer.sample_rate = sample_rate;
buffer.channels = channels;
const auto frames =
static_cast<size_t>(static_cast<double>(sample_rate) * seconds);
buffer.samples.reserve(frames * static_cast<size_t>(channels));
for (size_t frame = 0; frame < frames; ++frame) {
const float value = 0.5f * std::sin(2.0f * 3.14159265f * 220.0f *
static_cast<float>(frame) /
static_cast<float>(sample_rate));
for (int channel = 0; channel < channels; ++channel) {
buffer.samples.push_back(value);
}
}
return buffer;
}
static float peak(const std::vector<float> &samples) {
float highest = 0.0f;
for (const float sample : samples) {
highest = std::max(highest, std::abs(sample));
}
return highest;
}
static std::filesystem::path scratch_dir() {
const auto dir = std::filesystem::temp_directory_path() / "audiocpp-io-ctest";
std::filesystem::create_directories(dir);
return dir;
}
// The I2 fixture. Before the resample this returned a 44.1 kHz buffer, which
// silero_vad and sortformer_diar both reject with a plain runtime_error, which
// the server maps to INTERNAL. A 44.1 kHz WAV is an ordinary upload.
static void test_441k_stereo_is_read_as_16k_mono() {
const auto path = scratch_dir() / "input-44100-stereo.wav";
write_audio_file(path.string(), tone(44100, 2, 1.0f));
const auto audio = read_audio_file(path.string(), 16000);
check(audio.sample_rate == 16000, "44.1 kHz input is resampled to 16 kHz");
check(audio.channels == 1, "stereo input is downmixed to mono");
// Linear resampling lands within a sample or two of the exact ratio.
const auto frames = static_cast<long long>(audio.samples.size());
check(frames > 15990 && frames < 16010,
"one second in stays one second out");
check(peak(audio.samples) > 0.2f,
"the resampled buffer still carries the signal");
}
static void test_48k_is_read_as_16k() {
const auto path = scratch_dir() / "input-48000-mono.wav";
write_audio_file(path.string(), tone(48000, 1, 0.5f));
const auto audio = read_audio_file(path.string(), 16000);
check(audio.sample_rate == 16000, "48 kHz input is resampled to 16 kHz");
const auto frames = static_cast<long long>(audio.samples.size());
check(frames > 7990 && frames < 8010, "half a second in, half a second out");
}
// The common case: the upload is already 16 kHz mono, and nothing is resampled.
static void test_16k_mono_passes_through_unchanged() {
const auto path = scratch_dir() / "input-16000-mono.wav";
const auto source = tone(16000, 1, 1.0f);
write_audio_file(path.string(), source);
const auto audio = read_audio_file(path.string(), 16000);
check(audio.sample_rate == 16000, "16 kHz stays 16 kHz");
check(audio.channels == 1, "mono stays mono");
check(audio.samples.size() == source.samples.size(),
"a matching rate resamples nothing");
}
// Rate 0 means "give me the file as it is", which is what a source separation
// route needs: demucs and roformer refuse anything but their own 44.1 kHz and
// work on stereo, so the reader must not force them to 16 kHz mono.
static void test_zero_target_keeps_the_native_format() {
const auto path = scratch_dir() / "input-native.wav";
write_audio_file(path.string(), tone(44100, 2, 0.25f));
const auto audio = read_audio_file(path.string(), 0);
check(audio.sample_rate == 44100, "a zero target keeps the file's rate");
check(audio.channels == 2, "a zero target keeps the file's channels");
}
static void test_missing_file_is_a_config_error() {
bool threw_config_error = false;
try {
read_audio_file((scratch_dir() / "does-not-exist.wav").string(), 16000);
} catch (const ConfigError &) {
threw_config_error = true;
} catch (const std::exception &) {
// Any other type maps to INTERNAL, which is what this asserts against.
}
check(threw_config_error, "a missing input file is INVALID_ARGUMENT, not INTERNAL");
}
static void test_unreadable_file_is_a_config_error() {
const auto path = scratch_dir() / "not-a-wav.wav";
{
FILE *file = fopen(path.string().c_str(), "wb");
if (file != nullptr) {
fputs("this is not a RIFF header", file);
fclose(file);
}
}
bool threw_config_error = false;
try {
read_audio_file(path.string(), 16000);
} catch (const ConfigError &) {
threw_config_error = true;
} catch (const std::exception &) {
}
check(threw_config_error, "a non-WAV input is INVALID_ARGUMENT, not INTERNAL");
}
// The write side of the same distinction. The destination is LocalAI's own
// generated-content directory, not a caller-supplied path, so a failure to
// write it is a server fault: INTERNAL, which a client may retry, and not
// INVALID_ARGUMENT, which tells it the request itself was wrong.
static void test_write_failure_is_not_a_config_error() {
// A regular file where a directory has to be. ENOTDIR defeats root as well
// as an ordinary user, unlike a chmod, which CI running as root would walk
// straight through.
const auto blocker = scratch_dir() / "blocking-file";
{
FILE *file = fopen(blocker.string().c_str(), "wb");
if (file != nullptr) {
fputs("not a directory", file);
fclose(file);
}
}
const auto path = blocker / "nested" / "out.wav";
bool threw_config_error = false;
bool threw_something = false;
try {
write_audio_file(path.string(), tone(16000, 1, 0.05f));
} catch (const ConfigError &) {
threw_config_error = true;
threw_something = true;
} catch (const std::exception &) {
threw_something = true;
}
check(threw_something, "an unwritable destination is reported at all");
check(!threw_config_error,
"a failed write is INTERNAL, not INVALID_ARGUMENT: the caller did not "
"choose the destination and cannot fix it");
check(!std::filesystem::exists(path), "and nothing was written");
}
static void test_empty_output_path_is_a_config_error() {
// The one write failure that IS the caller's: no path at all.
bool threw_config_error = false;
try {
write_audio_file("", tone(16000, 1, 0.05f));
} catch (const ConfigError &) {
threw_config_error = true;
} catch (const std::exception &) {
}
check(threw_config_error, "an empty output path stays INVALID_ARGUMENT");
}
int main() {
test_441k_stereo_is_read_as_16k_mono();
test_48k_is_read_as_16k();
test_16k_mono_passes_through_unchanged();
test_zero_target_keeps_the_native_format();
test_missing_file_is_a_config_error();
test_unreadable_file_is_a_config_error();
test_write_failure_is_not_a_config_error();
test_empty_output_path_is_a_config_error();
if (failures) {
fprintf(stderr, "%d check(s) failed\n", failures);
return 1;
}
fprintf(stderr, "all audio_io checks passed\n");
return 0;
}

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