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>
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>
* fix(kokoros): implement new Backend RPCs to fix the build
The backend.proto grew six RPCs (SoundDetection, Depth, TokenClassify,
Score and the bidi-streaming Forward) that the kokoros gRPC service never
implemented, so the trait impl no longer satisfies `Backend`:
error[E0046]: not all trait items implemented, missing:
`sound_detection`, `depth`, `token_classify`, `score`,
`ForwardStream`, `forward`
kokoros is a TTS backend with no use for these, so add `unimplemented`
stubs (plus the `ForwardStream` associated type) matching the existing
pattern for every other unsupported RPC in this file.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(fish-speech): add setuptools-rust for the editable source install
install.sh installs the fish-speech source tree editable with
`--no-build-isolation`, which means the build backends of its transitive
dependencies must already be present in the venv. One of them builds a
Rust extension and its metadata step fails with:
ModuleNotFoundError: No module named 'setuptools_rust'
Add setuptools-rust to requirements.txt so installRequirements provisions
it before the editable install runs.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(llama-cpp-quantization): vendor convert_hf_to_gguf.py with conversion/
Upstream llama.cpp split the model-specific logic out of the single
convert_hf_to_gguf.py file into a sibling `conversion/` package, so the
script now starts with `from conversion import ...`. Downloading just the
one file therefore fails at runtime with:
ModuleNotFoundError: No module named 'conversion'
Clone the repo (reusing the clone already needed to build llama-quantize)
and copy both the script and the `conversion/` package into the backend
dir. Python puts the script's own directory on sys.path[0], so the package
resolves when it sits beside the script.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(sglang): pin the CPU source build to sglang v0.5.11
The CPU profile builds sgl-kernel from a `git clone` of sglang with no
ref, so it always tracks master. Recent master added CPU kernels (e.g.
mamba/fla.cpp) that fail to compile in our builder:
constexpr variable 'scale' must be initialized by a constant
static library kineto_LIBRARY-NOTFOUND not found
Pin the clone to v0.5.11, the same release the GPU path already floors on
(requirements-cublas12-after.txt). Overridable via SGLANG_VERSION so the
pin can be bumped deliberately.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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>
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>
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>
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>
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>
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>
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>
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>
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>
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>
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>
golang-darwin.sh's packaging check derived the launch binary by grepping every
$CURDIR/... reference in run.sh and taking the last one. Backends that pick a
runtime CPU variant assign it via unquoted `LIBRARY=$CURDIR/libgo<x>-avx512.so`
lines, so the heuristic returned `libgo<x>-avx512.so` — a variant Darwin never
builds (arm64 builds only fallback) — and the check then failed with
"package/libgo<x>-avx512.so not found ... refusing to package (#10267)",
breaking the darwin builds for whisper, sam3-cpp, vibevoice-cpp and friends.
Scan only the `exec` line(s) (the actual launch contract) and tolerate a
quoted `exec "$CURDIR"/<binary>`. parakeet-cpp's parakeet-cpp-grpc and the
quoted-only backends (sherpa/piper/opus) resolve correctly; no Linux change.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The llama-cpp gRPC backend reconstructs OpenAI messages from proto for the
tokenizer-template path and blindly json::parse'd each message's content
string. LocalAI's Go layer always flattens content to a plain string, so a
user prompt that merely looks like JSON (e.g. mealie's ingredient array
["1/4 cup brown sugar", ...]) was reinterpreted as structured content parts and
rejected by oaicompat_chat_params_parse with "unsupported content[].type".
Normalize content per role instead: user/system/developer content is opaque
text and is never JSON-sniffed; assistant/tool content still collapses a literal
JSON null/object (tool-call bookkeeping) to a string, but a plain string is
never turned into an array/scalar. The array defense is role-independent, so the
role gate only governs the benign null/object case.
While here, extract the duplicated per-message reconstruction and the
pre-template content sanitization into shared, unit-tested helpers
(message_content.h) so the streaming (PredictStream) and non-streaming (Predict)
paths cannot drift. This removes ~490 lines of copy-pasted defensive code, the
dead tool-role parse branches, and the redundant Predict-only tool_calls branch,
while preserving the prior #7324 (null content -> "") and #7528 (tool array
content -> string) fixes.
Tests:
- backend/cpp/llama-cpp/message_content_test.cpp: standalone C++ unit tests for
all three helpers (#10524, #7324, #7528, multimodal), discovered and run by
`make test-backend-cpp` and a new generic tests-backend-cpp CI job. Also wired
as an opt-in CMake/ctest target (-DLLAMA_GRPC_BUILD_TESTS=ON).
- core/schema/message_test.go: Go regression pinning that ToProto flattens a
JSON-array-looking text part to the verbatim string.
- prepare.sh now copies message_content.h into the build tree.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
In distributed mode the admin model endpoints (/models/edit, /models/import,
/models/toggle-state and the PATCH config-json endpoint) wrote the YAML to the
shared models dir but reloaded only the local replica's in-memory
ModelConfigLoader. With multiple frontend replicas behind one service, a save
landed on whichever replica handled the request; peers kept serving their stale
in-memory view, so a load-balanced request was a coin-flip between old and new
config (a created alias visible on one replica and missing on the other, an
edited alias target diverging, etc.).
The NATS cache-invalidation channel (SubjectCacheInvalidateModels +
OnModelsChanged) already existed for the gallery install/delete path; these
admin endpoints simply never published on it. Wire them up via a new
GalleryService.BroadcastModelsChanged helper (no-op in standalone mode).
Also fix delete propagation: LoadModelConfigsFromPath is additive and never
drops an entry whose file is gone, so the subscriber hook (which only reloaded
from disk) could not propagate a removal. ApplyRemoteChange now honors the
event op - pruning the element on "delete" and reloading otherwise - and shuts
down any running instance of the affected model so the new config takes effect.
This closes the same latent gap on the gallery delete path.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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>
fix(gpu-libs): bundle transitive deps of GPU runtime libs
The per-vendor packagers in package-gpu-libs.sh copy an explicit allowlist
of top-level GPU runtime libraries (libamdhip64, libhipblas, librocblas, the
CUDA/Intel equivalents, ...) but never resolved their transitive
dependencies. Backends run through the bundled lib/ld.so with
LD_LIBRARY_PATH=lib, so any transitive dep not in the allowlist is a fatal
"cannot open shared object file" at load time.
On recent ROCm (base image rocm 7.2.1) the runtime libs link against
librocprofiler-register.so.0, which is not in the allowlist, so the rocm
llama-cpp backend (and every other GPU backend sharing this script) failed
to load with:
librocprofiler-register.so.0: cannot open shared object file
The Vulkan path already solved this class of problem with copy_elf_deps
(ldd-based transitive resolution), but that sweep was only wired into the
Vulkan ICD path. This adds a generic sweep_transitive_deps that runs the
same ldd resolution over everything the allowlist already bundled, and wires
it into the ROCm, CUDA and Intel packagers. ldd returns the full recursive
closure, so one pass suffices; core libc-family deps are skipped via
is_core_lib so we never shadow the loader's own libc/libstdc++.
Adds a self-contained regression test (gcc + ldd) that fabricates a primary
lib linking a transitive lib and asserts the sweep bundles the dependency.
Fixes#10537
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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>
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>
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>
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>
fix(nodes): return labels in single-node GET so the detail view shows them
The node detail view (/app/nodes/:id) reads `node.labels` to render a
node's existing labels, but the single-node GET endpoint returned a bare
BackendNode whose Labels live in a separate table - so the list was always
empty and operators could only add labels, never see what was already set
(#10527). The same response also lacked in_flight_count and model_count.
Add NodeRegistry.GetWithExtras, mirroring the existing List vs ListWithExtras
split: bare Get stays cheap for the routing hot paths and existence checks,
while the detail endpoint uses the enriched variant to attach the labels map
and live counts. No frontend change is needed - the UI already renders
existing labels once the data is present.
Closes#10527
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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>
fix(backends): darwin packaging for silero-vad
silero-vad was the last Go backend with Linux-only darwin packaging:
- package.sh fell through to "Could not detect architecture" -> exit 1 on
macOS (no Darwin branch), so its darwin image never packaged.
- run.sh exported LD_LIBRARY_PATH, which macOS dyld ignores, so the bundled
libonnxruntime.dylib couldn't be found at runtime.
Add a Darwin branch to package.sh (skip the glibc/ld.so bundling; add an
@loader_path/lib rpath so @rpath resolves to package/lib/) and a
DYLD_LIBRARY_PATH branch to run.sh — mirroring the piper darwin fix (#10525).
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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>
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>
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>
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>
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>
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>
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]
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>
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>
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>
The metal-darwin-arm64-piper backend crashed at launch on macOS:
DYLD "Library missing"
Library not loaded: @rpath/libucd.dylib
Referenced from: .../piper
Reason: no LC_RPATH's found
The piper binary links libucd, libespeak-ng, libpiper_phonemize and
libonnxruntime via @rpath, but ships with no LC_RPATH, so dyld cannot
expand @rpath and aborts before piper runs. The libraries themselves are
already bundled in package/lib/ by package.sh.
Additionally, package.sh's architecture detection only handled the Linux
glibc loaders (/lib64/ld-linux-x86-64.so.2, /lib/ld-linux-aarch64.so.1)
and otherwise hit `echo "Error: Could not detect architecture"; exit 1`,
so on macOS packaging failed outright.
Add a Darwin branch (before the Linux checks) that skips the glibc/ld.so
bundling macOS has no use for and instead runs
`install_name_tool -add_rpath @loader_path/lib` on the piper binary, so
@rpath resolves to the bundled package/lib/ directory.
Also mirror sherpa-onnx/opus in run.sh: export DYLD_LIBRARY_PATH on
Darwin (LD_LIBRARY_PATH is Linux-only) as a defensive fallback.
Validated by hand on Apple Silicon: with the rpath added, piper
synthesized a real WAV. The darwin build is validated in CI.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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>
Produce a Gatekeeper-clean macOS distribution with no user workaround:
- Launcher DMG + the LocalAI.app inside it are built via fyne, codesigned
with the Developer ID under the hardened runtime, then the DMG is signed,
notarized (notarytool) and stapled. Replaces macos-dmg-creator (which had
no signing hook) with fyne package + hdiutil so we control the .app before
packaging.
- The bare local-ai darwin server binary is signed + notarized via
GoReleaser's native notarize block (quill backend, runs on Linux).
- All signing is gated on secrets being present, so forks/PRs/local builds
stay unsigned and green (contrib/macos/sign-and-notarize.sh no-ops).
- Add hardened-runtime entitlements and FyneApp.toml for deterministic
packaging; update macOS install docs to drop the quarantine workaround.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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>
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>
The opus Go backend (WebRTC audio codec) never built on macOS, so the
published master-metal-darwin-arm64-opus image shipped source only — no
opus binary and no libopusshim — because every step assumed Linux.
- Makefile: hardcoded libopusshim.so with no OS handling. Mirror
sherpa-onnx: SHIM_EXT=so / dylib on Darwin and build
libopusshim.$(SHIM_EXT). On Darwin link the shim with
-undefined dynamic_lookup so it resolves opus_encoder_ctl from the
already globally-loaded libopus (codec.go dlopens it RTLD_GLOBAL
first) instead of baking an absolute Homebrew path into the dylib,
keeping the packaged shim relocatable.
- run.sh: hardcoded LD_LIBRARY_PATH + libopusshim.so even on macOS. Add
a Darwin branch exporting DYLD_LIBRARY_PATH and the .dylib shim, like
sherpa-onnx/run.sh.
- package.sh: bundle libopusshim.$(SHIM_EXT) and libopus*.dylib (not
just .so) into package/lib so the OCI image (which ships package/.)
is self-contained on a runtime with no Homebrew; add a Darwin arch
branch so it doesn't warn/skip.
- backend_build_darwin.yml: install + link opus and pkg-config via brew
so the Makefile's `pkg-config opus` resolves on the macOS runner, and
cache opus' Cellar dir.
Go code is unchanged; darwin build is validated in CI.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
