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feat/buun-llama-cpp-backend
18 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Richard Palethorpe
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49ef40a187 |
feat(classifier/VAD): support voice control on low power devices (#10804)
* feat(llama-cpp): route Score through the slot loop Score previously bypassed the slot loop with a direct llama_decode: a conflict guard aborted the whole process if scoring raced generation, the config validator had to reject score alongside chat/completion/embeddings, and every candidate re-decoded the full shared prompt. Add SERVER_TASK_TYPE_SCORE to the (patched) upstream server so score tasks are scheduled like any other slot work: generation and scoring serialize naturally, the shared prompt is decoded once per call, and the slot's prompt cache carries the conversation prefix across calls. Context checkpoints at the score boundary and at the cache-divergence point keep SWA/hybrid/recurrent models (e.g. LFM2.5) from re-prefilling the whole prompt per candidate: warm-turn scoring on a 6-option set drops from ~8s to ~0.5s on a desktop CPU. The conflict guard and the validation split are removed; declaring score with generation usecases on one config is now supported and shares the slot cache. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier wire types and pipeline config Wire types and YAML config for realtime classifier mode: sessions carry a localai_classifier extension (options with canned replies/tool calls, softmax threshold, normalization, history trimming, fallback modes, and a deterministic wake-word address gate), mirrored by pipeline.classifier in the model YAML and surfaced in the config-meta registry. The localai.classifier.result server event reports the full score distribution per turn. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier response flow Classifier-mode responses: instead of autoregressive generation, each user turn is prefill-scored against the option list (router.ScoreClassifier prompt/candidate shapes over the Score primitive) and the winning option's canned reply and tool call are emitted through the existing response machinery. Below-threshold turns take the configured fallback (none / canned reply / generate); empty transcripts and unaddressed turns (wake word not mentioned) skip scoring entirely. The scoring probe defaults to the latest user message only — small scorers echo canned replies from prior turns back as the top option otherwise. Built for hardware that can afford prompt processing but not decode: with slot-based Score the option list stays KV-cached across turns, so a turn costs roughly one forward pass over the new words. session_update_error events now carry the validation cause instead of a generic message. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): bound the VAD tick's scan window and buffer retention The VAD tick loop re-scanned the entire input buffer every 300ms and only trimmed it on zero-segment ticks or commits. Audio that keeps producing segments without a committing pause (steady noise a mic pipeline lets through, music, continuous speech) grew the buffer toward the 100MB cap with each tick rescanning all of it — O(n^2), measured at ~3.3ms of silero per buffered second: past ~90s retained, ticks run back to back and pin ~4 cores until the stream stops. Silero's recurrent state only carries a few hundred ms of context, so rescanning old audio buys nothing. Clip the slice handed to the VAD to the largest silence the commit test can need to measure (server_vad silence window or the semantic eagerness fallback) plus a warm-up margin, and rebase the returned segment times so every downstream consumer keeps whole-buffer coordinates. An open turn whose clipped window is all silence now commits (the silence outran the window) instead of being discarded as no-speech. Independently, retain at most 90s of raw buffer, rebasing the live-feed and EOU cursors on trim — this also bounds the previously unbounded VAD-error path. Turn boundaries are otherwise unchanged: no forced commits, no new coordinator states. pipeline.turn_detection.vad_window_sec can widen the scan window; values below the automatic floor are ignored. The tick body is extracted into vadTick so specs can drive turn detection synchronously (same shape as classifySoundWindow); the babble reproduction that pinned 4 cores now plateaus under 10% of one core. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(backend): let per-model threads override the global default ModelOptions overrode a set per-model threads value with the app-level --threads whenever the latter was non-zero — and WithThreads defaults it to the physical core count, so it always was. The YAML threads: knob has been dead config: a tiny VAD model could never opt down from the global pool size. SetDefaults already fills an unset per-model value from the app config, which is the intended precedence; resolve threads through a helper that honors it (explicit threads: 0 still means unset). Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(gallery): single-thread the silero VAD Silero is a ~2MB recurrent model with no exploitable graph parallelism: measured per-call latency is identical at 1 and 10 ORT threads, while every extra pool thread just spin-waits between the realtime loop's frequent tiny inferences. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * docs(realtime): classifier mode, VAD scan window, threads precedence Document the realtime classifier mode (options, threshold guidance, wake-word address gate, empty-transcript handling), the VAD scan window and 90s buffer retention (pipeline.turn_detection.vad_window_sec), the per-model threads precedence, and the M3 classifier note in the realtime state-machine design doc. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * perf(llama-cpp): score all candidates in one batched decode One scoring call is now a single SERVER_TASK_TYPE_SCORE task: the slot decodes the shared prefix (prompt + longest common candidate token prefix) once, then forks one sequence per candidate off it (metadata-only for the unified KV cache, copy-on-write for recurrent state) and decodes every candidate's unique tail in one llama_decode. Previously each candidate was its own task that restored the boundary checkpoint and re-decoded its full tail sequentially, paying per-candidate task and decode overhead. The context reserves SERVER_SCORE_FORK_SEQS extra sequence ids (and recurrent-state cells) beyond the parallel slots via the new common_params::n_seq_score_forks. Forking requires the unified KV cache (already this backend's default) since per-sequence streams would shrink n_ctx_seq; an explicit kv_unified:false disables forking and Score calls that need it fail cleanly. Candidates beyond the fork/output budget decode in successive chunks. Wire contract and scores are unchanged: per-token logprobs are stitched from the shared region and the forked tails. Verified bitwise deterministic call-to-call and independent of candidate order (no cross-fork leakage via equal-length candidate swap); ranking matches the per-candidate implementation on the drone battery (winner softmax 0.99996 vs 0.99997), and >16-candidate chunking, prefix-of-another and empty candidates all pass. Measured on a desktop CPU: warm /api/score calls 0.52s -> 0.23s; warm realtime classifier turns 196-303ms. The 9-candidate drone turn decodes ~17 unique tail tokens in one batch instead of nine sequential ~220ms checkpoint-restore tasks. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): gate scoring capacity by model usecase Reserve llama.cpp scoring slots only for models that explicitly declare the score usecase, while allowing score to coexist with chat and completion. Reject incompatible unified-KV settings and classifier activation on models without scoring capacity. Propagate application defaults when resolving realtime and preload pipeline stages so unset thread counts are resolved consistently without overriding explicit model settings. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(ci): honor APT mirrors in the prebuilt llama-cpp compile step The builder-prebuilt path installs gcc-14 with apt directly and ignored the APT_MIRROR/APT_PORTS_MIRROR build args the from-source path already honors, so an ubuntu mirror outage broke every arm64 backend build. Pass the args into the stage and run apt-mirror.sh (already in the build context via COPY . /LocalAI) before the apt step. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier argument slots via constrained completion Hybrid classify-then-complete: a classifier option's canned tool call can declare typed argument slots (number | enum | string, with defaults and prompt hints) referenced as "{{name}}" in the arguments template. When the option wins, the slots are filled by a short grammar-constrained completion that continues the exact scoring prompt — rendered by the same cached ScoreClassifier, so the llama.cpp prompt cache is already warm — with the chosen route JSON re-opened at the first slot field. A GBNF grammar pins the field skeleton and frees only the values; temperature 0, a couple dozen tokens at most (~300ms on a desktop CPU for two slots). Slot declarations and hints ride the option descriptions in the shared system prompt, informing scoring and the fill alike at no per-turn token cost. The localai.classifier.result event carries the final arguments and a fill_latency_ms. On inference failure the slots' defaults apply; a slot without a default fails the response (or falls through with fallback.mode: generate). Slot filling requires completion alongside score in the scoring model's known_usecases. Verified end-to-end on the Pi drone demo: "fly forward three meters" in distance mode classifies forward and infers {"distance": 3, "units": "meters"} in ~310ms, and the drone flies exactly 3 units. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): splice filled slot values into classifier replies A classifier option's spoken reply can now reference its tool's argument slots ("Going forward {{distance}} {{units}}."): the values inferred by the slot-fill completion — or the recovery defaults — are spliced into the reply as plain text before it is emitted, so what the assistant says confirms what it actually inferred. Placeholders without a value stay literal, and options without slots are untouched. FillToolArguments now returns the raw slot values alongside the spliced arguments JSON to make the reply templating possible. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): harden classifier slot completion Reserve context for constrained slot filling, size completions from their encoded output, and encode enum grammar literals as valid JSON. Reject empty enum values and cover the failure modes with regression tests. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): prewarm the classifier scoring prompt on registration Swapping a session's classifier option list (a voice-switched command mode, for instance) made the next turns pay a full re-prefill of the new option-list prompt — measured 2.4s vs 0.3s warm on a desktop CPU, and worse: on hybrid-memory models like LFM2.5, whose state cannot be partially rewound (llama.cpp can only restore checkpoints), *every* probe change re-prefilled from scratch whenever the last checkpoint missed the probe boundary, so even same-list turns intermittently cost full prefills. Registering an option list (pipeline seed or session.update) now fires a best-effort background prewarm: two throwaway scores with distinct probes. The first prefills the new option-list prompt; the second, diverging exactly where per-turn probe text starts, plants the backend's rewind point (KV checkpoint) at the stable-prefix boundary that every real turn reuses. The prewarm hides behind the canned mode-switch reply — by the time it finishes speaking, the cache is warm. Idempotent per option set, detached from the registering request's lifetime. Measured on the drone demo (LFM2.5-1.2B, desktop CPU): first turn after a mode switch 2374ms -> 340ms; intermittent same-list full prefills (1.3-2.1s) all -> under 0.5s. For clients that swap lists frequently, options: [parallel:2] on the scoring model additionally keeps one slot per list via prefix-similarity routing (+26MB RSS, unified KV). Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * perf(llama-cpp): checkpoint scoring at the caller-declared stable prefix Hybrid-memory models (LFM2.5 shortconv, Qwen3.5 deltanet — where new small models are headed) cannot rewind their state, so any prompt-cache reuse that needs a rewind falls back to a full re-prefill. For classifier scoring that meant every probe change re-processed the whole option-list prompt: the server's checkpoints were placed reactively (at wherever the previous task happened to diverge), so a checkpoint past the next divergence was erased rather than restored — measured as intermittent 2-10s turns on prompts with a 95%+ common prefix. The classifier now computes the probe-invariant prompt prefix once (the byte-wise common prefix of two synthetic probe renders) and declares its length with every Score request; the server maps it to a token boundary and forces a KV checkpoint exactly there on each score prefill. That checkpoint sits at or before every future divergence under the same option list, so it always survives and always restores — repeat scoring costs probe+candidates regardless of how the probe changes. Also: - prewarm reruns on every option-list registration instead of memoizing per list: with boundary checkpoints a redundant rewarm costs two probe-sized decodes, while skipping one after a slot eviction (three lists sharing fewer slots evict in LRU cascades) silently moves a full re-prefill onto the user's next turn - new llama.cpp backend option rs_seq:N exposes bounded recurrent-state rollback outside speculative decoding; measured impractical for deltanet-scale states (65GB for 64 snapshots on Qwen3.5-4B) but cheap insurance for small-state models - docs: the multi-list recipe (parallel:N + sps:0.5 — the default slot similarity threshold funnels distinct lists onto one slot) Measured on the drone demo (LFM2.5-1.2B scorer, desktop CPU), steady state: every turn 285-421ms including mode switches, vs 2.4s post-switch and intermittent 1.3-2.9s re-prefills before. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): align classifier cache guidance Document the single-score prewarm behavior and clean the vendored score patch formatting. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(llama-cpp): guard score task for fork backends TurboQuant and Bonsai reuse the primary gRPC server against llama.cpp forks that do not carry LocalAI's slot-based Score patches. Compile the Score integration only for the patched primary backend and return UNIMPLEMENTED from fork builds instead of referencing absent task types and common_params fields. Assisted-by: Codex:gpt-5 [gh] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(dev): generate gRPC code before commit lint The coverage phase regenerates ignored protobuf bindings, but lint runs first and can fail against missing or stale output. Generate the pinned bindings before lint so the gate always type-checks the current schema. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
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593f3a8648 |
ci: refactor llama-cpp variant Dockerfiles to consume prebuilt base-grpc images (PR 2/2) (#9738)
* ci(backend_build): plumb builder-base-image and BUILDER_TARGET build-args Adds an optional builder-base-image input. When set, BUILDER_BASE_IMAGE is forwarded as a build-arg AND BUILDER_TARGET=builder-prebuilt is set to select the variant Dockerfile's prebuilt-base stage. When empty, BUILDER_TARGET=builder-fromsource (the default) keeps the existing from-source build path. This makes the prebuilt-base optimization opt-in per matrix entry without breaking local `make backends/<name>` invocations or backends whose Dockerfile doesn't have a prebuilt path. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(llama-cpp,ik-llama-cpp,turboquant): multi-target Dockerfiles for prebuilt + from-source Restructure the three llama.cpp-derived Dockerfiles so each supports two builder paths in a single file, selected via the BUILDER_TARGET build-arg: BUILDER_TARGET=builder-fromsource (default) - Standalone build: gRPC stage + apt installs + (conditionally) CUDA/ROCm/Vulkan + compile. - Used by `make backends/llama-cpp` locally and any caller that doesn't supply a prebuilt base. BUILDER_TARGET=builder-prebuilt - FROM \${BUILDER_BASE_IMAGE} (one of quay.io/go-skynet/ci-cache: base-grpc-* shipped in PR #9737). - Skips ~25-35 min of gRPC compile + ~5-10 min of toolchain installs. - Used by CI when the matrix entry sets builder-base-image. Final FROM scratch resolves BUILDER_TARGET via an aliasing FROM stage (BuildKit doesn't support variable expansion directly in COPY --from), then COPY --from=builder pulls package output from the chosen path. BuildKit prunes the unreferenced builder, so each build only does the work for the chosen path. The compile RUN is identical between both builder stages, so it's factored into .docker/<name>-compile.sh and bind-mounted into both. ccache mount + cache-id stay per-arch / per-build-type. Local DX preserved: `make backends/llama-cpp` (no extra args) defaults to BUILDER_TARGET=builder-fromsource and works exactly as before. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(backend.yml,backend_pr.yml): forward builder-base-image from matrix Plumbs the new optional builder-base-image input from matrix into backend_build.yml. backend_build.yml derives BUILDER_TARGET from whether builder-base-image is set, so matrix entries that map to a prebuilt base get the prebuilt path; entries that don't (python/go/ rust backends) fall through to the default builder-fromsource (which their own Dockerfiles don't reference, so it's a no-op for them). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(backend-matrix): wire builder-base-image to llama-cpp variants For every entry whose Dockerfile is llama-cpp/ik-llama-cpp/turboquant, add a builder-base-image field pointing at the appropriate prebuilt quay.io/go-skynet/ci-cache:base-grpc-* tag. backend_build.yml derives BUILDER_TARGET from this field's presence: non-empty -> builder-prebuilt; empty -> builder-fromsource. So this commit alone activates the prebuilt-base path for these 23 backends in CI, while local `make backends/<name>` (no extra args) keeps the from-source path. Mapping by (build-type, arch): - '' / amd64 -> base-grpc-amd64 - '' / arm64 -> base-grpc-arm64 - cublas-12 / amd64 -> base-grpc-cuda-12-amd64 - cublas-13 / amd64 -> base-grpc-cuda-13-amd64 - cublas-13 / arm64 -> base-grpc-cuda-13-arm64 - hipblas / amd64 -> base-grpc-rocm-amd64 - vulkan / amd64 -> base-grpc-vulkan-amd64 - vulkan / arm64 -> base-grpc-vulkan-arm64 - sycl_* / amd64 -> base-grpc-intel-amd64 - cublas-12 + JetPack r36.4.0 / arm64 -> base-grpc-l4t-cuda-12-arm64 Cold-build savings expected: ~25-35 min per variant (skips the gRPC compile + toolchain install that's now in the base). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: add base-grpc-l4t-cuda-12-arm64 variant for legacy JetPack entries Two matrix entries (-nvidia-l4t-arm64-llama-cpp, -nvidia-l4t-arm64- turboquant) build against nvcr.io/nvidia/l4t-jetpack:r36.4.0 + CUDA 12 ARM64. They're distinct from -nvidia-l4t-cuda-13-arm64-* which use Ubuntu 24.04 + CUDA 13 sbsa. Add the missing JetPack-based variant to base-images.yml so those two entries' builder-base-image mapping in the previous commit resolves. Bootstrap order before merging this PR (re-run base-images.yml on this branch — 9 existing variants hit BuildKit cache, only the new l4t-cuda-12-arm64 builds cold): gh workflow run base-images.yml --ref ci/base-images-consumers Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: extract base-builder install logic into .docker/install-base-deps.sh Pre-extraction, the apt + protoc + cmake + conditional CUDA/ROCm/Vulkan + gRPC install logic was duplicated across four files: - backend/Dockerfile.base-grpc-builder (CI prebuilt-base source of truth) - backend/Dockerfile.llama-cpp (builder-fromsource stage) - backend/Dockerfile.ik-llama-cpp (builder-fromsource stage) - backend/Dockerfile.turboquant (builder-fromsource stage) A bump to e.g. CUDA toolkit packages had to be made in 4 places, and drift between the prebuilt base and the variant-Dockerfile from-source path was a real concern (ik-llama-cpp's hipblas branch was already missing the rocBLAS Kernels echo that llama-cpp / turboquant / base-grpc-builder all had). Factor the install logic into a single .docker/install-base-deps.sh that reads its inputs from env vars and runs conditionally on BUILD_TYPE / CUDA_*_VERSION / TARGETARCH. Each Dockerfile now bind- mounts the script alongside .docker/apt-mirror.sh and invokes it from a single RUN step. The variant Dockerfiles' grpc-source stage is removed entirely — the script handles gRPC compile + install at /opt/grpc, and the builder-fromsource stage mirrors builder-prebuilt by copying /opt/grpc/. to /usr/local/. Result: - install-base-deps.sh: 244 lines (one source of truth) - Dockerfile.base-grpc-builder: 268 -> 98 lines - Dockerfile.llama-cpp: 361 -> 157 lines - Dockerfile.ik-llama-cpp: 348 -> 151 lines - Dockerfile.turboquant: 355 -> 154 lines - Total Dockerfile bytes: 1332 -> 560 lines (58% reduction) Bit-equivalence between prebuilt and from-source paths is now enforced by construction: both invoke the same script with the same inputs. A side-effect is that ik-llama-cpp now also gets the rocBLAS Kernels echo + clblas block parity it was previously missing. Includes the BUILD_TYPE=clblas branch (libclblast-dev) for parity even though no current CI matrix entry uses it. After this commit's force-push, base-images.yml needs to be redispatched on this branch — the Dockerfile.base-grpc-builder content shifts so the existing cache won't apply for the install layer (gRPC layer also rebuilds since it's now in the same RUN step). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(base-images): skip-drivers on JetPack l4t variant cuda-nvcc-12-0 isn't installable via apt on the JetPack r36.4.0 base image — JetPack ships CUDA preinstalled at /usr/local/cuda and its apt feed doesn't carry the cuda-nvcc-* packages from the public repositories. The original matrix entry for -nvidia-l4t-arm64-llama-cpp on master sets skip-drivers: 'true' for exactly this reason; the new base-grpc-l4t-cuda-12-arm64 base needs to match. Also forwards SKIP_DRIVERS as a build-arg from matrix into the build (was missing entirely before this commit). Caught by run 25612030775 — l4t-cuda-12-arm64 failed at: E: Package 'cuda-nvcc-12-0' has no installation candidate Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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9228e5b412 |
ci(llama-cpp): add BuildKit ccache mount to the compile step
The big RUN at line 268 of Dockerfile.llama-cpp re-runs from scratch on every LLAMA_VERSION bump (or any LocalAI source change due to COPY . /LocalAI just before). For CUDA-13 specifically that compile recently hit the GHA 6h hard limit and failed: https://github.com/mudler/LocalAI/actions/runs/25598418931/job/75148244557 Add a BuildKit cache mount on /root/.ccache and thread ccache through CMake (CMAKE_C/CXX/CUDA_COMPILER_LAUNCHER) so most translation units hit cache when their preprocessed source is byte-identical to the previous build. The cache mount is exported to the registry as part of the existing cache-to: type=registry,mode=max in backend_build.yml, so it persists across runs. mount id is keyed on TARGETARCH + BUILD_TYPE so different variants don't thrash the same cache slot; sharing=locked serializes concurrent writes. Cold-build effect (first run after enable, or on LLAMA_VERSION bump that touches every TU): unchanged. Hot-build effect (subsequent runs with the same source, or LLAMA_VERSION bumps that touch a handful of files): ~5-15 min for the llama.cpp compile vs the previous 1-3h cold. For CUDA-13 specifically this should bring rebuilds well under the 6h GHA limit. Does NOT help the *first* post-bump build — that's still cold. For that, follow-up work would be: (a) trim CUDA_DOCKER_ARCH to modern GPUs only, (b) audit which CMake variants the published images actually need, (c) pre-built CUDA+gRPC base image. ccache package is already installed in the builder stage (line 90). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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8edac61e57 |
feat(ci): allow routing apt traffic through an alternate Ubuntu mirror (#9650)
* feat(ci): allow routing apt traffic through an alternate Ubuntu mirror
Adds opt-in APT_MIRROR / APT_PORTS_MIRROR knobs to all Dockerfiles, the
Makefile, and CI workflows so we can fail over to a non-canonical Ubuntu
mirror when archive.ubuntu.com / security.ubuntu.com / ports.ubuntu.com
are degraded (recently observed: multi-day DDoS against the default pool).
Defaults are empty everywhere — behavior is unchanged unless a mirror is
configured. To enable in CI, set the repo-level GitHub Actions variables
APT_MIRROR (and APT_PORTS_MIRROR for arm64 builds). Locally:
make docker APT_MIRROR=http://azure.archive.ubuntu.com
A small POSIX-sh helper in .docker/apt-mirror.sh rewrites both DEB822
(/etc/apt/sources.list.d/ubuntu.sources, Ubuntu 24.04+) and the legacy
/etc/apt/sources.list before the first apt-get update. Dockerfile stages
load it via RUN --mount=type=bind, so there is no extra layer and no
cache invalidation when the script is unchanged. Reusable workflows also
rewrite the runner's own /etc/apt sources before any sudo apt-get call.
Assisted-by: Claude:claude-opus-4-7[1m] [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci(apt-mirror): default to the Azure mirror, visible in the workflow source
Bakes Azure (http://azure.archive.ubuntu.com / http://azure.ports.ubuntu.com)
in as the default for both Docker builds and runner-side apt — rather than
hiding the URL behind a GitHub Actions repo variable that's not visible
from the source tree.
A new composite action at .github/actions/configure-apt-mirror is the
single source of truth for runner-side rewrites. Five standalone
workflows (build-test, release, tests-e2e, tests-ui-e2e, update_swagger)
just `uses: ./.github/actions/configure-apt-mirror`.
Three workflows (image_build, backend_build, checksum_checker) keep an
inline bash rewrite, because they install/upgrade git via apt *before*
the checkout step (so the local composite action isn't loadable yet).
The Azure URL is visible in those files too.
The `apt-mirror` / `apt-ports-mirror` inputs of the reusable workflows
keep their now-Azure defaults — they still feed the Docker build-args
block in addition to the inline runner-side rewrite. Callers (image.yml,
image-pr.yml, backend.yml, backend_pr.yml) drop the previous
`vars.APT_MIRROR` plumbing and rely on those defaults.
Assisted-by: Claude:claude-opus-4-7[1m] [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci(apt-mirror): drop Force Install GIT, consolidate on the composite action
The PPA git upgrade ran add-apt-repository ppa:git-core/ppa, which talks
to api.launchpad.net — also part of Canonical's infrastructure and
currently returning HTTP 504. The Azure mirror only covers
archive.ubuntu.com / security.ubuntu.com / ports.ubuntu.com, not PPAs.
The system git that ubuntu-latest already ships is sufficient for
actions/checkout and the build pipeline, so just drop the upgrade. With
that gone, the apt-before-checkout constraint disappears too — all three
holdouts (image_build, backend_build, checksum_checker) can now switch
to ./.github/actions/configure-apt-mirror like the other five.
Net: 0 inline apt-mirror blocks, all 8 workflows route through the
composite action.
Assisted-by: Claude:claude-opus-4-7[1m] [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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Andreas Egli
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1d0de757c3 |
fix: add hipblaslt library (#9541)
Signed-off-by: Andreas Egli <github@kharan.ch> |
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Keith Mattix II
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8839a71c87 |
fix(rocm): add gfx1151 support and expose AMDGPU_TARGETS build-arg (#9410)
Add gfx1151 (AMD Strix Halo / Ryzen AI MAX) to the default AMDGPU_TARGETS list in the llama-cpp backend Makefile. ROCm 7.2.1 ships with gfx1151 Tensile libraries, so this architecture should be included in default builds. Also expose AMDGPU_TARGETS as an ARG/ENV in Dockerfile.llama-cpp so that users building for non-default GPU architectures can override the target list via --build-arg AMDGPU_TARGETS=<arch>. Previously, passing -DAMDGPU_TARGETS=<arch> through CMAKE_ARGS was silently overridden by the Makefile's own append of the default target list. Fixes #9374 Signed-off-by: Keith Mattix <keithmattix2@gmail.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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Ettore Di Giacinto
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151ad271f2 |
feat(rocm): bump to 7.x (#9323)
feat(rocm): bump to 7.2.1 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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7891c33cb1 |
chore(vulkan): bump vulkan-sdk to 1.4.335.0 (#7981)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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917c7aa9f3 |
chore(ci): roll back l4t-cuda12 configurations (#7935)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Copilot
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b2ff1cea2a |
feat: enable Vulkan arm64 image builds (#7912)
* Initial plan * Add arm64 support for Vulkan builds in Dockerfiles and workflows Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Richard Palethorpe
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e6ba26c3e7 |
chore: Update to Ubuntu24.04 (cont #7423) (#7769)
* ci(workflows): bump GitHub Actions images to Ubuntu 24.04 Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * ci(workflows): remove CUDA 11.x support from GitHub Actions (incompatible with ubuntu:24.04) Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * ci(workflows): bump GitHub Actions CUDA support to 12.9 Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * build(docker): bump base image to ubuntu:24.04 and adjust Vulkan SDK/packages Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * fix(backend): correct context paths for Python backends in workflows, Makefile and Dockerfile Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * chore(make): disable parallel backend builds to avoid race conditions Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * chore(make): export CUDA_MAJOR_VERSION and CUDA_MINOR_VERSION for override Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * build(backend): update backend Dockerfiles to Ubuntu 24.04 Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * chore(backend): add ROCm env vars and default AMDGPU_TARGETS for hipBLAS builds Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * chore(chatterbox): bump ROCm PyTorch to 2.9.1+rocm6.4 and update index URL; align hipblas requirements Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * chore: add local-ai-launcher to .gitignore Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * ci(workflows): fix backends GitHub Actions workflows after rebase Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * build(docker): use build-time UBUNTU_VERSION variable Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * chore(docker): remove libquadmath0 from requirements-stage base image Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * chore(make): add backends/vllm to .NOTPARALLEL to prevent parallel builds Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * fix(docker): correct CUDA installation steps in backend Dockerfiles Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * chore(backend): update ROCm to 6.4 and align Python hipblas requirements Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * ci(workflows): switch GitHub Actions runners to Ubuntu-24.04 for CUDA on arm64 builds Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * build(docker): update base image and backend Dockerfiles for Ubuntu 24.04 compatibility on arm64 Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * build(backend): increase timeout for uv installs behind slow networks on backend/Dockerfile.python Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * ci(workflows): switch GitHub Actions runners to Ubuntu-24.04 for vibevoice backend Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * ci(workflows): fix failing GitHub Actions runners Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> * fix: Allow FROM_SOURCE to be unset, use upstream Intel images etc. Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(build): rm all traces of CUDA 11 Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(build): Add Ubuntu codename as an argument Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> Signed-off-by: Richard Palethorpe <io@richiejp.com> Co-authored-by: Alessandro Sturniolo <alessandro.sturniolo@gmail.com> |
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coffeerunhobby
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5add7b47f5 |
fix: BMI2 crash on AVX-only CPUs (Intel Ivy Bridge/Sandy Bridge) (#7864)
* Fix BMI2 crash on AVX-only CPUs (Intel Ivy Bridge/Sandy Bridge) Signed-off-by: coffeerunhobby <coffeerunhobby@users.noreply.github.com> * Address feedback from review Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: coffeerunhobby <coffeerunhobby@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: coffeerunhobby <coffeerunhobby@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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Ettore Di Giacinto
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dc6182bbb1 |
chore(ci): add wget to llama-cpp docker image builder
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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1d1d52da59 |
chore(ci): small fixups to build arm64 images
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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ab4f2742a6 |
chore(ci): minor fixup
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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03f3bf2d94 |
chore(ci): only install runtime libs needed on arm64
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Chakib Benziane
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c28e5b39d6 |
fix: llama dockerfile make package (#6694)
the make package rule does not currently always run resulting in an empty scratch image. - added `make -B` flag to force the `make package` rule Signed-off-by: blob42 <contact@blob42.xyz> |
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Ettore Di Giacinto
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294f7022f3 |
feat: do not bundle llama-cpp anymore (#5790)
* Build llama.cpp separately Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * WIP Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * WIP Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * WIP Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Start to try to attach some tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add git and small fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: correctly autoload external backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Try to run AIO tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Slightly update the Makefile helps Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Adapt auto-bumper Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Try to run linux test Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add llama-cpp into build pipelines Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add default capability (for cpu) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop llama-cpp specific logic from the backend loader Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * drop grpc install in ci for tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Pass by backends path for tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Build protogen at start Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(tests): set backends path consistently Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Correctly configure the backends path Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Try to build for darwin Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * WIP Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Compile for metal on arm64/darwin Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Try to run build off from cross-arch Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add to the backend index nvidia-l4t and cpu's llama-cpp backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Build also darwin-x86 for llama-cpp Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Disable arm64 builds temporary Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Test backend build on PR Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fixup build backend reusable workflow Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * pass by skip drivers Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Use crane Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Skip drivers Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * x86 darwin Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add packaging step for llama.cpp Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fix leftover from bark-cpp extraction Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Try to fix hipblas build Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |