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Commits
| Author | SHA1 | Message | Date | |
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 mudler's LocalAI [bot]andlocalai-org-maint-bot
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4c4911fe2c |
chore: ⬆️ Update ggml-org/llama.cpp to 0021a77de0a8966059dc94548fb3b96654e0bb12 (#11508)
* chore(llama-cpp): update upstream revision Assisted-by: Codex:gpt-5.6 * fix(llama-cpp): refresh server patch contexts The new llama.cpp pin changed the slot reset and prompt batch code. GNU patch accepted stale hunks with fuzz, which left the L4T build with invalid source. Refresh both server patches against the pinned source so each hunk applies at its intended location. Assisted-by: Codex:gpt-5 * fix(llama-cpp): adapt metrics result fields The updated llama.cpp groups cumulative counters under server_metrics. Probe the result layout so the shared adapter also compiles against older forks. Assisted-by: Codex:gpt-5 * fix(llama-cpp): refresh TTS patch offsets GNU patch rejects the stale pre-decode hunk after the score patch changes the same file. Anchor the TTS hunks to the pinned llama.cpp source so the full series applies without fuzz. Assisted-by: Codex:gpt-5.4 * fix(llama-cpp): normalize batch threads The updated llama.cpp creates its batch threadpool during model initialization, before the context-level fallback can replace the -1 sentinel. Resolve that sentinel from the inference thread count so model loading does not overflow the threadpool allocation.\n\nAssisted-by: Codex:gpt-5.4 --------- Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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Richard Palethorpe
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d10374f849 |
feat(router): make KNN a first-class classifier with a persisted, curated corpus (#10652)
* feat(router): make KNN a first-class classifier with a persisted, curated corpus
Add `classifier: knn` — similarity-weighted voting over labelled
example prompts. Unlike score/colbert it needs no classifier model:
label knowledge lives in a corpus seeded and curated through the
admin API, so routing decisions are deterministic, auditable, and
grounded in graded experience rather than a model's opinion.
Epistemic gate: corpus entries below knn.similarity_threshold cannot
vote; when none clears it the classifier activates no labels and the
router uses the fallback — a prompt unlike all labelled experience is
treated as undecidable, not guessed. Decisions record
nearest_similarity (also on fallback rows) so admins can see how far
the nearest labelled experience was; the Routing tab explains
out-of-corpus fallbacks and shows per-label corpus counts.
Persistence: one JSONL file per router under
<data path>/router-corpus (text, labels, vector, embedder
fingerprint). The file is the source of truth; the local-store index
is rebuilt from it at classifier build time and stays a pure
in-memory index. Entries recorded under a different embedding model
re-embed on load. Also corrects the docs' false claim that
local-store collections persist — the embedding cache never survived
restarts (and still doesn't); the corpus does.
Corpus input is API-only by design (entries may contain example user
content): POST /api/router/{name}/corpus seeds (labels validated
against declared policies, embedded server-side, indexed
immediately), GET .../corpus/stats inspects — label counts only,
entry texts are never returned by any surface — DELETE .../corpus
wipes. Admin-gated like the sibling router endpoints, and exposed as
MCP tools (seed_router_corpus / get_router_corpus_stats /
clear_router_corpus) in both the httpapi and inproc clients with
coverage-test route mappings.
Plumbing: VectorStore gains SearchK (top-K was hardcoded to 1);
local-store gets InsertBatch/Delete as optional fast paths;
RouterConfig gains a knn block (embedding_model, k,
similarity_threshold, vote_threshold, store_name) with meta-registry
fields; the classifier dropdown now offers knn and the
previously-missing colbert; embedding_cache is ignored (with a
warning) for knn — it IS an embedding-KNN lookup; the stale
/api/instructions intelligent-routing entry is rewritten (it
described a classifier that no longer exists); swagger regenerated.
Tests: KNN vote/gate specs with hand-computed vote shares, corpus
manager suite (restart reload without re-embedding, fingerprint
re-embed, dedupe, hostile store names), middleware specs (corpus
routing, gate fallback, config validation, cache-wrap refusal),
corpus endpoint specs pinning the texts-never-returned contract, MCP
catalog + route-mapping gates, and a Playwright spec for corpus
stats and the out-of-corpus decision detail.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(router): name consulted corpus neighbours in knn decisions
Every knn decision (decision log rows and the /api/router/decide
response) now carries neighbors: the K retrieved corpus entries by
descending similarity - including ones below the epistemic gate, which
is what makes fallback decisions diagnosable - each as {id, similarity,
labels}. The id is the entry's content hash (first 8 bytes of the
SHA-256 of its text, hex): stable across reseeds and re-embeds, and
text-free, so an external platform that seeded the corpus can recompute
text->id on its own copy and bucket decisions by corpus region (per-
region reliability accounting) without corpus text ever leaving the
server. A corrupt index payload surfaces as an id-less neighbour at a
real similarity instead of disappearing.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* refactor(router): deduplicate knn plumbing and cut corpus hot-path waste
Post-review cleanup of the knn-first-class-router branch; no behaviour
changes on the API surface.
Reuse/altitude:
- RouterKNNConfig.ResolvedStoreName is now the single source of the
router-corpus-<name> default (was hand-derived in four files).
- corpus.ResolveKNNRouter + corpus.Seed carry the shared model
resolution and seed validation; the REST endpoints and the assistant
MCP client are thin transport adapters over them, with sentinel
errors mapped to HTTP statuses at the echo boundary.
- middleware.NewClassifierDeps assembles the classifier dependency set
once for all five entry points (OpenAI, Anthropic, realtime, decide,
corpus) instead of five hand-copied literals.
- router.AllClassifiers feeds both the status endpoint and the
unknown-classifier error, ending the classifier-list drift.
- Per-classifier requirements moved out of validateRouterPolicies into
their buildClassifier arms; the knn arm owns its embedding_cache
opt-out instead of a name-check in the shared wrap tail.
- adminOnly replaces four inline copies of the admin gate in the
middleware routes.
- localVectorStore.Search delegates to SearchK (identical traces).
Efficiency:
- Manager.Add embeds outside the manager mutex and appends to the
JSONL file (O(new) instead of O(corpus) rewrite); a torn tail from a
crash mid-append is tolerated on read and repaired on next write.
- Stats memoises per store keyed on the file's stat fingerprint and no
longer takes the manager mutex, so the 5s status poll stops parsing
vector-laden JSONL and stops blocking behind seeds.
- KNN Classify decodes each neighbour payload once (was twice) and
builds refs and votes in a single pass with one fallback return.
- Corpus file writes fsync before rename/close.
- The corpus manager is built eagerly in newApplication (sync.Once
dropped); test helper dead branch removed.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(router): bind knn corpus vectors to an embedder fingerprint and fail closed on mismatch
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(mcp): align corpus tool prompts and the mutating-tool safety list
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(proto,backend): report embedding shape from the llama-cpp backend
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(embeddings): Go-side pooling — mean/last/decayed_mean with half-life
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(embeddings): accept chat messages[] and per-request pooling on /v1/embeddings
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(middleware): name the failing fields when post-merge validation 400s
An intermittent post-merge validation failure surfaced as an opaque 400
during integration (pooling scheme mismatch that no client had sent).
Log the model, the request's pooling override, and the merged config's
pooling fields at the failure point so the next occurrence identifies
whether the request or the stored config carried the bad value.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(embeddings): scheme override must not inherit the config's half-life
A model config defaulting to decayed_mean pooling carries
pooling_half_life_tokens; a request overriding the scheme to mean/last
without its own half-life inherited that value, and post-merge
validation rejected the pair the server itself had assembled. Zero the
inherited half-life when the overridden scheme is not decayed_mean; a
request that explicitly pairs a half-life with a non-decayed scheme
still 400s.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix embedding pooling validation and router bounds
Declare backend embedding layouts and reject incompatible pooling modes. Reset local-store dimensions after a full clear, validate KNN thresholds, and add real backend and store integration coverage.
Assisted-by: Codex:gpt-5
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* ci: run local-store integration tests
Build and install the local-store backend in the Linux test job, then run the existing store integration suite so new specs are discovered automatically.
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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mudler's LocalAI [bot]andEttore Di Giacinto
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7b9167eaad |
feat(llama-cpp): serve Qwen3-TTS through the llama.cpp backend (#11392)
* fix(config): do not read a TTS speaker-encoder mmproj as vision support Qwen3-TTS on llama-cpp ships an mmproj holding the speaker encoder and code predictor. VisionSupported() treated any non-empty MMProj as proof of image input, so every such model would be advertised as vision-capable. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): add TTS request option parsing helper Validates text and speaker reference presence and strictly parses the top_k / top_p per-request params, in a header with no llama.cpp or gRPC dependencies so the standalone C++ unit test gate picks it up. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): range-check the TTS top_k and top_p request params Format validation alone let NaN, infinity and out-of-range values through. The consumer copies both values into the audio generation input unconditionally and only guards its separate sampler assignment with "> 0", a test NaN also fails, so a NaN reached llama.cpp with the guard never firing. top_k must now be >= 0 and top_p must fall within 0.0 to 1.0 inclusive, with the bound written as a negated in-range test so NaN is rejected rather than silently accepted. Also cover the two checks the suite could not previously kill: the whole-string check in the float parser and the int32 range check. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): bump pin to f9e832c10 and carry the TTS server task Picks up ggml-org/llama.cpp#26254 (Qwen3-TTS via mtmd) and #26536 (the short-input audio chunk fix). Adds 0002-add-server-task-type-tts.patch, the server-side half of the still-draft #26603, so TTS runs through the slot scheduler instead of racing it. Remove that patch when #26603 merges. The patch is rebased on top of the score patch: its tokenize-switch hunk collided with the SERVER_TASK_TYPE_SCORE case, and its lone SRV_WRN call passes no variadic argument, which the macro cannot expand. The score patch itself needed no refresh. Also fixes fallout from the bump in grpc-server.cpp: upstream dropped the per-slot n_ctx argument from server_schema::eval_llama_cmpl_schema. Only the schema branch loses it, since forks predating the server-schema split still expect the old argument list. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): implement the TTS and TTSStream RPCs Both were declared in backend.proto but unimplemented. They now submit a SERVER_TASK_TYPE_TTS task and drain the response reader, the same shape PredictStream uses. The streaming path emits a leading sample_rate message and then raw PCM, because ModelTTSStream builds the WAV header itself; the non-streaming path emits a complete WAV to the requested dst. The streamed samples are converted from the pipeline's float32 to signed 16-bit first. MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM hands back floats, while the header ModelTTSStream writes announces 16-bit samples, so shipping the floats verbatim would decode as noise. prepare.sh and CMakeLists.txt now stage tts_request_options.h alongside the other grpc-server helpers, and register its standalone test with ctest the way passthrough_options_test is registered. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): mask non-codec tokens for Qwen3-TTS generation The Qwen3-TTS gen-audio pipeline maps a sampled backbone token to a codebook row with an unchecked subtraction, in mtmd-helper-gen.cpp: inp.code0 = sampled - codec_0; For ggml-org/Qwen3-TTS-12Hz-1.7B-Base-GGUF the vocab is 155008 tokens, <|codec_0|> is 151936 and the codec codes end at 153983. The model's own tokenizer.ggml.suppress_tokens holds 1023 ids covering 153984..155007, every special above the codec range except <|codec_eos_token|> (154086) which stays reachable as the stop token. Nothing masks the text range 0..151935, so the backbone can sample a text token at any step, the subtraction goes negative, and ggml_compute_forward_get_rows aborts the whole backend process on GGML_ASSERT(i01 >= 0 && i01 < ne01). Complete the mask upstream started: bias every token below <|codec_0|> to -INFINITY for TTS tasks so only codec codes and the codec EOS remain reachable. The biases are appended to task.params.sampling.logit_bias, which common_sampler_init already merges with the model's suppress tokens into one llama_sampler_init_logit_bias, so no sampler is added to the chain. Measured cost is 0.082 ms per sampled token and 1.16 MB, set against a forward pass in the multi-millisecond range. It lands in launch_slot_with_task rather than in a route handler so that llama.cpp's own POST /tts and LocalAI's TTS/TTSStream RPCs are both covered, and <|codec_0|> is resolved from the vocab rather than hardcoded so a model without it is left alone. This is reproducible with upstream's own llama-tts and no LocalAI code loaded, aborting at frame 55 on Q4_K_M and frame 71 on Q8_0, so it is neither a quantization artifact nor an artifact of the gRPC adapter. Two further defects in the same draft pipeline still prevent end-to-end audio; they are independent of this one and are recorded in the task report for an upstream bug report. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): bump pin to 9de0fcf2b and drop the TTS codec mask Upstream fixed the Qwen3-TTS abort in ggml-org/llama.cpp c8e03ce81 ("mtmd/ggml: add ggml_build_forward_order", #26649), landed one hour after the previous pin. ggml_build_forward_expand marks a tensor and all its ancestors for compute, so using it as a pure ordering hint defeated ggml_build_forward_select and made GEN_WAV calls execute the GEN_CODE branch against a stale inp_code0, hitting the get_rows bound assert in ggml_compute_forward_get_rows. That single defect accounts for every abort seen on this model, so 0003-mask-non-codec-tokens-for-tts.patch is removed rather than rebased. The mask changed the observed behavior, but it was perturbing a graph ordering bug rather than fixing a sampling one: at the new pin the whole path works without it. Keeping it would have meant carrying a 152k-entry logit bias, and rebasing it on every pin bump, for no benefit. Verified at 9de0fcf2b with only 0001 and 0002 applied, which both apply clean with no fuzz and needed no rebase: non-streaming HTTP 200, 410924 bytes, 8.56 s RIFF (little-endian) data, WAVE audio, Microsoft PCM, 16 bit, mono 24000 Hz streaming HTTP 200, 560684 bytes, 11.68 s, exactly one RIFF at byte 0, same format, which also exercises the float32-to-s16 conversion at runtime for the first time Pristine unpatched llama-tts at the same pin now also completes, 130 frames to a valid WAV, where it aborted at frame 55 before. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): clear the TTS slot sequence between requests Only the first TTS request in a backend process succeeded. Every later one failed instantly, in about 0.13 s, with "TTS prompt processing failed" from step_prompt, regardless of streaming or non-streaming and regardless of the text. With LOCALAI_SINGLE_ACTIVE_BACKEND=true the process is kept alive between requests, so a deployment would have served exactly one utterance per backend start. The cause is missing KV hygiene, not anything in the gRPC adapter. TTS slots never enter the shared batch: pre_decode() returns early for them and process_tts_slots() drives them instead, so they skip the prompt-cache bookkeeping that clears a slot's sequence between requests. Nothing in the gen-audio path makes up for it: mtmd_helper_gen_audio_reset only clears host-side buffers, and the pipeline always decodes from position 0 into the sequence identified by slot.id. So the second task on a slot writes positions 0..N over the first task's tokens and llama_decode fails. Fix is one call to slot.prompt_clear(), the same helper the normal path uses, in the SERVER_TASK_TYPE_TTS branch of launch_slot_with_task before set_input. It goes into 0002 rather than a new patch file because it is a defect in the code that patch introduces, and the header now records it as ours so we know whether it still needs carrying if #26603 merges without it. Verified in one backend process, different text on every request: three consecutive non-streaming requests, three consecutive streaming requests, and an interleaved non-streaming, streaming, non-streaming, streaming run. All ten returned HTTP 200 with RIFF ... WAVE audio, Microsoft PCM, 16 bit, mono 24000 Hz, the streamed ones carrying exactly one RIFF header at byte 0, and every output measured as real speech rather than silence or a truncated fragment. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): expose max_frames for TTS requests The Qwen3-TTS backbone does not always emit <|codec_eos_token|>, and when it does not, generation runs to upstream's 512-frame n_predict default. At the model's 12.5 Hz frame rate that is 40.96 s of audio, which a short input can trigger: one request in this session produced 40.96 s for a ten-word sentence. prepareTTSTask hardcoded n_predict to -1, so callers had no way to bound it. Add a max_frames key alongside top_k and top_p, parsed with the same strict whole-string parsing so a typo is an error rather than a silently truncated value, and rejected with a field-naming message when negative. 0 keeps the existing sentinel convention and means unset, so a request that omits it behaves exactly as before. Named max_frames rather than n_predict because frames are what the parameter means at a TTS endpoint: one frame is 0.08 s of audio. The 512-frame default is deliberately unchanged. Lowering it would truncate legitimately long inputs, which is a worse failure than an occasionally overlong one. Verified end to end on one text of thirty words: max_frames=25 HTTP 200, 96044 bytes, 2.00 s, exactly 25 frames max_frames=50 HTTP 200, 192044 bytes, 4.00 s, exactly 50 frames no max_frames HTTP 200, 572204 bytes, 11.92 s, stopped at its own codec EOS after 149 frames, unchanged behavior max_frames=-1 InvalidArgument "max_frames must be >= 0, got \"-1\"" max_frames=many InvalidArgument "max_frames must be an integer, got \"many\"" Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): send the TTS sample rate up front, and tidy three review items Four items from the Task 4 review. Streaming first-byte latency. TTSStream sent the sample-rate reply only once the first audio result arrived, and a chunk needs a whole 72-frame window, roughly 5.8 s of audio and far longer in wall time on CPU. The Go side blocks on that reply before it can emit the WAV header, so a streaming client sat at zero bytes for the whole stretch. The rate is a property of the loaded model and is available synchronously from mtmd_gen_audio_get_info, so it now goes out immediately after post_task and the rate_sent bookkeeping is gone. Measured on a warm model, first byte drops from 30.48 s to 0.014 s, and the output is still a valid WAV with exactly one RIFF header at byte 0. Unchecked close. The non-streaming path ignored ofstream::close(), so a failure that only surfaces on flush was reported as success while leaving a truncated file at dst. It now returns INTERNAL like the other write failures. Wrong comment on set_lang. gen_audio::inp::get() already maps a stored blank to nullptr, so our guard is behavior-preserving, not behavior-fixing. The comment claimed otherwise; the code was right. Repetition penalty. penalty_last_n = -1 is inert at this pin, because llama_sampler_init_penalties clamps it with std::max(penalty_last_n, 0) and then builds a disabled sampler, so the 1.05 penalty never applies. Upstream's README attributes looping to a missing repeat_penalty, so it was worth testing as a root-cause fix for the model running to the frame cap. Dropping the line lets the sampling default of 64 apply, which was confirmed in the sampler chain trace as penalty_last_n = 64 with repeat_penalty = 1.050. Over 15 uncapped short requests each way it did not help: 0 of 15 ran to the cap with the penalty inert, 1 of 15 with it active. Both lines are therefore kept for parity with upstream's draft, and a comment now records that the pair is inert and why, so the next reader does not believe a penalty is applied. max_frames remains the way to bound output. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * build(llama-cpp): let unpatched forks opt out of the TTS task turboquant and bonsai copy grpc-server.cpp into llama.cpp forks that do not carry our patches. disable-tts-task.sh injects the same kind of preprocessor switch disable-score-task.sh already uses, so those builds answer UNIMPLEMENTED rather than failing to compile. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(config): keep a TTS speaker-encoder projector out of vision detection Task 1 exempted a declared-TTS model's mmproj from VisionSupported, but the first real gallery entry with an mmproj still came back vision-capable through two paths the earlier fix did not close. GuessUsecases has no FLAG_VISION branch, so it falls through to true for any chat-ish model. That is not just a wrong answer at the call site: syncKnownUsecasesFromString rewrites KnownUsecaseStrings from HasUsecases, and the loader calls it more than once per config file, so the guessed FLAG_VISION is written out and parsed back into KnownUsecases as if the operator had declared it. Give GuessUsecases a FLAG_VISION branch that defers to the same explicit signals VisionSupported uses. Second, llama.cpp builds an mtmd context for the speaker-encoder projector and reports its media marker on the first chat probe, which resurrected vision after the model had been used once. Apply the same declared-TTS exemption to MediaMarker that the mmproj check already had. Verified against the qwen3-tts-llamacpp-q4 gallery entry: no vision capability and no image input modality, before load, after a TTS request, and after a chat probe. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add Qwen3-TTS entries for the llama-cpp backend Two entries over upstream's own GGUF conversion, Q8_0 and Q4_K_M, each pairing a backbone with the Q8_0 projector. Named to sit alongside the existing qwen3-tts-cpp entries rather than replace them. Also tags the llama-cpp backend text-to-speech / TTS so the backend browser surfaces the capability. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: cover Qwen3-TTS on the llama-cpp backend Adds the gallery variants, the two-file mmproj configuration, the required voice reference, and the language and sampling knobs. Also corrects the streaming-support list, which named only voxcpm. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(config): register llama-cpp as a TTS and voice-cloning backend The branch taught the llama-cpp backend to serve Qwen3-TTS and shipped two gallery entries for it, but never told the capability table. llama-cpp still declared only the text RPCs and usecases, so: - VoiceCloningForModel returned nil at the capability check, before it ever reached the model's own tts.voice_cloning override, and /tts answered 400 "selected model does not support reference-audio voice cloning" for any localai://voice-profiles/... voice. No model YAML could opt back in. - GET /api/backends/usecases did not list tts for llama-cpp, so the gallery greyed out the TTS filter for the entries this branch adds. - The React TTS page saw voice_cloning: null and kept both models out of the Voice Library. Add the TTS RPCs and usecase, and the reference-audio contract. The contract needs narrowing, because the per-backend switch in VoiceCloningForModel ends in a permissive default: an unnarrowed entry would have advertised reference-audio cloning on every GGUF chat model in the gallery. Narrow on the declared TTS usecase rather than the model name. The TTS checkpoints are the only llama-cpp models carrying known_usecases: [tts]; name matching would have to guess at third-party repacks, and "base", the substring the neighbouring Qwen and vLLM cases key on, is a routine word in text-model names. The check reads the declared bit directly instead of going through HasUsecases, which falls through to GuessUsecases and would hand the decision to a heuristic that never had a llama.cpp TTS model in mind. DefaultUsecases stays [chat]: a bare GGUF served by llama.cpp is a chat model, and both the gallery filter and the importer read that field. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(gallery): declare what nemotron-3-nano-omni actually accepts The entry is backend: vllm-omni with known_usecases: [chat, completion], no mmproj and no media marker, so it used to report vision only through the blanket GuessUsecases fallthrough that the vision branch in this branch removed. Nemotron 3 Nano Omni is a multimodal understanding model: image, video and audio in, text out. Declaring that is what the sibling vllm-omni-qwen3-omni-30b already does. known_usecases gains vision only. FLAG_VIDEO is video GENERATION, an output modality, and this model generates none; video and audio input belong in known_input_modalities, which is where AudioInputSupported and VideoInputSupported read them from. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(importers): import a Qwen3-TTS GGUF repo as TTS, not chat The llama-cpp importer hardcodes known_usecases: [chat] and assigns any mmproj-matching file as a vision projector, so ggml-org/Qwen3-TTS-12Hz-1.7B- Base-GGUF imported as a chat model with vision. Both fields were wrong, and the model was unreachable from /tts and from the Voice Library. Filenames cannot fix this. A Qwen3-TTS repo has the exact shape of a vision repo, one backbone GGUF plus one mmproj-*.gguf, so the projector's own header is the only honest signal: mtmd writes clip.has_gen_audio_encoder for the projectors it can drive as a speech pipeline and refuses to build one without it. Probe the selected mmproj for that flag, reusing the range-fetch the MTP detection already does, and declare tts when it is set. The mmproj assignment then stops reading as vision on its own, since a declared-TTS model already exempts its projector from vision detection. The probe is best-effort like the MTP one: a network blip leaves the chat default in place rather than failing the import. Verified against the real artifacts on disk: the Qwen3-TTS projector reports gen-audio, its backbone does not. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): stop non-TTS models crashing on the new pin Two regressions, both hit every ordinary llama-cpp model and neither was caught locally because every test on this branch loaded a TTS model. The first is a null dereference. server_slot::tts_ctx::reset() called mtmd_helper_gen_audio_reset() unconditionally, but the gen-audio pipeline is only allocated for models carrying a gen-audio mmproj, and upstream's implementation reads ctx->pipeline before null-checking anything. Since server_slot::reset() runs during slot initialization for every model, any non-TTS model segfaulted the backend the moment it loaded. Guard the call on the is_supported() predicate already defined beside it, and keep the plain field resets unconditional. The second is unrelated to TTS and came in with the pin bump. PredictOptions.Penalty is a bare proto float, so a caller that names no repetition penalty sends 0 rather than omitting the field. Since 9de0fcf2b, common_sampler_init() rejects a non-positive penalty_repeat outright because it would divide logits by zero, turning every such request into "Failed to initialize samplers". Treat 0 as unset and leave llama.cpp's own neutral default in place. Verified with the same suite CI runs, which is what caught both: tests/e2e-backends passes 6 of 6 including the load and predict specs that were red. Qwen3-TTS still synthesises on both paths, 24 kHz mono 16-bit WAV with exactly one RIFF header on the streamed output. Assisted-by: Claude:claude-fable-5 [Claude Code] 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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Adiraandlocalai-org-maint-bot
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ef724a3c9d |
feat(api): add /v1/detokenize endpoint (#9620)
* feat(api): add /v1/detokenize endpoint Closes #1649. Mirror of the existing /v1/tokenize path, requested by @benniekiss in the issue thread for "complete API workflow" use cases that need to turn token IDs back into text without local processing. - Add Detokenize gRPC RPC with DetokenizeRequest{tokens} / DetokenizeResponse{content} messages. - Implement in the llama.cpp backend using common_token_to_piece, the same primitive TokenizeString already uses internally. - Other backends inherit the default Unimplemented from base.Base, in line with how Detect, Rerank, etc. are gated per-backend. - Wire up the Go gRPC interface, server, client, and in-process embed wrapper alongside their TokenizeString counterparts. - Add the schema types, ModelDetokenize wrapper, HTTP handler, route registration, RouteFeatureRegistry entry (gated by FeatureTokenize so no new feature flag is needed), and the discovery map entry under ai_functions. - Regenerated swagger reflects the new endpoint and types. - Update authentication.md to list /v1/detokenize alongside /v1/tokenize. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> * test(e2e): add mock backend tests for /v1/detokenize Add Detokenize to the mock gRPC backend and wire up two e2e tests in the MockBackend suite: one that posts known token IDs and asserts a non-empty content response, and a round-trip that tokenizes first then detokenizes the returned IDs. Addresses reviewer feedback on #9620. Assisted-by: Claude:claude-sonnet-4-6 Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> * fix(kokoros): implement detokenize in the Rust backend service The Detokenize RPC added in this PR grows the tonic-generated Backend trait. Unlike the other languages there is nothing to inherit a default from — Rust trait impls must list every method — so backend/rust/kokoros failed to compile: error[E0046]: not all trait items implemented, missing: `detokenize` --> src/service.rs:72:1 72 | impl Backend for KokorosService { Go backends pick up the Unimplemented default from base.Base, and the generated C++/Python servicer bases default to UNIMPLEMENTED, which is why the Rust backend was the only one that broke. kokoros is the sole Rust crate in the tree, so this is the full extent of the fallout. Return Status::unimplemented("Not supported"), matching how this same file already gates tokenize_string and ~20 other unsupported RPCs. Fixes the tests-kokoros and backend-jobs-singlearch-4 (-cpu-kokoros) failures on the previous head. Assisted-by: Claude:claude-opus-5 cargo Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> --------- Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> Co-authored-by: localai-org-maint-bot <bot-opensource@localaisrl.com> |
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localai-org-maint-botandlocalai-org-maint-bot
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5e541894df |
fix(llama-cpp): preserve GPU layers during option passthrough (#11193)
Stage the negative GPU-layer sentinels expected by the upstream argument parser, then restore LocalAI resolved values unless a passthrough flag explicitly overrides them. This avoids the parser assertion that terminated the backend for any generic option. Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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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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mudler's LocalAI [bot]andEttore Di Giacinto
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0a8a7fbbb4 |
chore(llama-cpp): bump llama.cpp and adapt to the load-mode refactor (#11140)
Bump LLAMA_VERSION to 0d47ea7427463093e69128bf2c2f9cd06b3ee5b3 (73 commits touching common/, src/ and tools/server/). Two upstream changes break the backend: * ggml-org/llama.cpp#20834 folded common_params::use_mmap / use_mlock / use_direct_io into a single `load_mode` enum. LocalAI still exposes the three as independent settings (`mmap`, `mmlock`, and the `direct_io` option), so params_parse folds them once all three have been read, keeping the precedence the separate booleans had: direct I/O bypasses the page cache, mlock implies mmap, everything off is a plain buffered read. turboquant and bonsai compile this same grpc-server.cpp against forks that predate the refactor, so prepare.sh probes the checkout for LLAMA_LOAD_MODE_MMAP and generates llama_compat.h with LOCALAI_LEGACY_LOAD_MODE set accordingly. Probing beats a per-fork build flag here because the fork flavor targets disagree on whether they forward CMAKE_ARGS or EXTRA_CMAKE_ARGS, and it heals itself once a fork rebases past the refactor. * The MiniMax M3 patch no longer applies. Upstream merged the model half of llama.cpp#24523 (LLM_ARCH_MINIMAX_M3, src/models/minimax-m3.cpp, the gguf-py constants and conversion/minimax.py) but not the chat half, so the patch is re-cut to carry only the common/chat.cpp template detection and PEG parser, rebased onto the new pin and onto the thinking_end_tag -> thinking_end_tags rename. Dropping it wholesale (as #11008 did, reverted in #11136) would have silently regressed MiniMax M3 tool calling and thinking. Verified with a CPU docker build of the backend plus LoadModel and Predict against a real GGUF over gRPC in all four load modes. Assisted-by: Claude:claude-opus-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]andEttore Di Giacinto
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1cd7d63c7b |
fix(distributed): reject wrong-model requests on the remaining modalities (#10990)
#10970 gave the four PredictOptions RPCs a model-identity check so a backend reached through a stale distributed route rejects the request instead of answering from whatever model it holds (#10952). Every other modality shares that exposure: the route is cached by host:port, a worker can recycle a stopped backend's port for another model's backend, and a liveness-only probe cannot tell a stale row from a valid one. Extends the same mechanism to the 21 remaining request messages that reach a backend through the router, using the pattern #10970 established rather than a parallel one: - proto: ModelIdentity on each modality request message. - controller: populated from ModelConfig.Model at the call site that also builds ModelOptions, so load-time and request-time values are equal by construction. - backends: one generic guard in pkg/grpc/server.go (27 Go backends), the method set in backend/python/common (36 Python backends), llama-cpp (AudioTranscription/Stream, Rerank, Score) and privacy-filter (TokenClassify). - reconcile already drops the stale row on IsModelMismatch; no change. TTSRequest and SoundGenerationRequest get a SEPARATE ModelIdentity field rather than reusing their existing `model`: FileStagingClient rewrites `model` to a worker-local path, so comparing it would reject valid requests in exactly the configuration this guards. AudioEncode/AudioDecode are deliberately left unguarded: the opus codec backend is loaded from a literal rather than a ModelConfig, so no value carries the equality guarantee the comparison depends on. The four bidirectional stream RPCs are out of scope; they bypass reconcile. Empty means skip on both sides, so an old controller, an old backend, and the bare request structs in tests/e2e-backends all keep working. Assisted-by: Claude Code:claude-opus-4-8 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]andEttore Di Giacinto
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465d488c90 |
fix(distributed): reject wrong-model requests at the backend (#10970)
fix(distributed): reject wrong-model requests at the backend (#10952) In distributed mode the controller caches a NodeModel row naming a backend's host:port. A worker can recycle a stopped backend's gRPC port for a different model's backend, and probeHealth verifies liveness rather than identity, so the probe succeeds against whatever now occupies the port and the request is dispatched to the wrong backend. The caller gets a silent wrong-model answer. Nothing in the request could catch this: PredictOptions had no model field, so model identity crossed the wire only in ModelOptions.Model at LoadModel time, and the cached-hit path issues no LoadModel. Every backend's "model not loaded" guard checks a nil handle, which a process holding a different model passes, so the stale row was never dropped either. Add PredictOptions.ModelIdentity and enforce it at the point of use: - The controller populates it in gRPCPredictOpts from ModelConfig.Model, the same expression ModelOptions feeds to model.WithModel and therefore the same value the backend received as ModelOptions.Model. Both are read from one config value in one function, so they are equal by construction and the comparison cannot false-reject. - Backends compare it against what they loaded and return NOT_FOUND with a fixed sentinel. Enforced in pkg/grpc/server.go (27 Go backends), an interceptor in backend/python/common (all 36 Python backends, no per-backend change), and the llama-cpp / ik-llama-cpp / ds4 C++ servers. That is every backend with real exposure: kokoros answers all four RPCs with unimplemented and privacy-filter implements none of them. - The router's reconcile drops the stale replica row on a mismatch, so the next request reloads somewhere correct. Empty means "skip the check" on both sides: a controller that predates the field sends nothing, a backend loaded by such a controller has nothing to compare, and the C++ server synthesizes PredictOptions internally for ASR. That keeps upgrades working in both directions. Scoped to the four PredictOptions RPCs. TTSRequest.model and SoundGenerationRequest.model are deliberately NOT validated: FileStagingClient already rewrites them to worker-local absolute paths, so in distributed mode they already differ from the load-time value and comparing them would reject valid requests. IsModelMismatch requires both the NOT_FOUND code and the sentinel, unlike the neighbouring helpers which accept either. insightface's Embedding returns NOT_FOUND "no face detected" on a PredictOptions RPC, and a code-only check would drop a healthy replica row on every faceless image. Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Roman Mazurenkoandrvmzes
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a3fdfbc0d1 |
feat(llama-cpp): add device selection option (#10724)
Allow llama.cpp model configs to select the backend devices used for offload, matching upstream --device behavior so users can exclude a display or debug GPU. Signed-off-by: rvmzes <rvmzes@rvmzess-MacBook-Pro.local> Co-authored-by: rvmzes <rvmzes@rvmzess-MacBook-Pro.local> |
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a4e6e01e4d |
fix(process): give backend workers a parent-death safety net (#10639)
* fix(grpc): self-terminate backend workers when LocalAI dies non-gracefully Symptom: a backend model-worker subprocess (the per-model gRPC server LocalAI spawns) can be orphaned and linger — holding VRAM and its listen port — if the LocalAI process is killed non-gracefully (e.g. a supervisor's graceful-shutdown grace period elapses and LocalAI is SIGKILLed) before its own teardown runs. Root cause: LocalAI's graceful teardown (pkg/signals/handler.go installs the SIGINT/SIGTERM handler; core/cli/run.go registers app.Shutdown -> ModelLoader.StopAllGRPC -> process.Stop in pkg/model/process.go) only runs when LocalAI receives a catchable signal and survives long enough to run its handlers. Backends are spawned via github.com/mudler/go-processmanager v0.1.1, whose getSysProcAttr() sets Setpgid:true (own process group, so the group can be signalled) but never PR_SET_PDEATHSIG/Pdeathsig, and exposes no Config field or option for a caller to inject/extend SysProcAttr. LocalAI fully delegates spawning to that library (it never builds the exec.Cmd itself), so it cannot set a kernel parent-death signal at the spawn site. If LocalAI is SIGKILLed, nothing tells the backend to exit and it is reparented to init. Fix: add a best-effort, backend-side safety net at the one shared choke point every out-of-process Go backend routes through — grpc.StartServer / RunServer in pkg/grpc. On startup it captures getppid() and polls; when the process is reparented (getppid changes / becomes 1 — the standard POSIX signal the original parent died) it logs and self-terminates. getppid() reparent detection is portable (Linux + macOS), unlike Linux-only PR_SET_PDEATHSIG. Toggle via LOCALAI_BACKEND_PARENT_WATCH (default on; off on Windows) and LOCALAI_BACKEND_PARENT_WATCH_INTERVAL. This is strictly a backstop alongside the existing graceful SIGTERM->grace->SIGKILL teardown, which is unchanged. Scope/limitations: covers Go-based backends (everything using pkg/grpc). The C++ backends (e.g. llama-cpp) and Python backends do not route through pkg/grpc and are not covered by this mechanism — they would each need an equivalent parent-death check (follow-up). The fully general fix is for go-processmanager to expose SysProcAttr injection so LocalAI can set Pdeathsig at spawn for every backend regardless of language (suggested upstream follow-up; out of scope for this LocalAI-only PR). Test: pkg/grpc/parentwatch_test.go builds a real test -> middle -> grandchild process tree, lets the middle process exit to orphan the grandchild running the real watchParentDeath, and asserts it detects the reparent and self-terminates. Unix-only (build-tagged), runs in CI (Linux). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(process): extend parent-death backstop to C++ and Python backends The Go parent-death watcher (pkg/grpc/parentwatch.go, commit 772b435d5) only protects backends that route through pkg/grpc. C++ and Python backends don't, so the originally-reported case — the llama.cpp gRPC worker surviving a non-graceful LocalAI death — was still uncovered. Extend the same best-effort backstop to both languages, reusing the exact mechanism and semantics: - capture getppid() at startup, skip if already orphaned (<=1) - a background thread polls getppid() and self-exits on reparenting (getppid() != orig || == 1), portable across Linux/macOS, no-op on Windows - same env vars: LOCALAI_BACKEND_PARENT_WATCH (default on; falsy false/0/no/off disable) and LOCALAI_BACKEND_PARENT_WATCH_INTERVAL (default 2s; accepts Go-style durations like 500ms/2s/1m) C++: implemented in backend/cpp/llama-cpp (the reported, most-used C++ backend) as a dependency-free header parent_watch.h, wired into grpc-server.cpp's main() and copied at build time via prepare.sh. C++ backends have no shared server scaffolding, so other C++ backends (ds4, ik-llama-cpp, privacy-filter, ...) are not yet covered and would each need the same one-line include+call as follow-ups. Python: implemented once in the shared common/parent_watch.py and armed from common/grpc_auth.py's get_auth_interceptors() — the single helper every one of the 35 Python backends invokes while building its gRPC server — so all Python backends (and future ones) are covered with no per-backend edits and no duplicated implementation. Tests (real process-tree reparent detection, mirroring the Go test): - backend/cpp/llama-cpp/parent_watch_test.cpp (via run-unit-tests.sh) - backend/python/common/parent_watch_test.py (python -m unittest) Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> 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> Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com> |
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13b1ae53bc |
chore: ⬆️ Update ggml-org/llama.cpp to 0ed235ea2c17a19fc8238668653946721ed136fd (#10536)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): link server-stream.cpp TU into grpc-server for upstream 0ed235ea (#10536) Upstream llama.cpp 0ed235ea added an SSE stream-resumption layer in a new translation unit tools/server/server-stream.cpp, which defines stream_session, stream_pipe_producer and the g_stream_sessions manager. server-context.cpp (already #included into grpc-server.cpp) now calls into it via spipe->cleanup(), stream_aware_should_stop() and stream_session_attach_pipe(), so without the new TU the grpc-server link fails on every arch with: undefined reference to `stream_pipe_producer::cleanup()' prepare.sh already copies every tools/server/* file into tools/grpc-server/, so the source is present; the only missing piece was including its definitions. Add an __has_include-guarded #include "server-stream.cpp" before server-context.cpp, mirroring the existing server-chat.cpp and server-schema.cpp guards, keeping the source compatible with older pins/forks that predate the split. The file is self-contained (its only external symbols come from server-common, already in the TU) so it adds no new undefined references; the http route-handler factories it also defines are unused in the grpc path but harmless. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(llama-cpp): build renamed ggml-rpc-server target for upstream 0ed235ea (#10536) Upstream renamed the RPC server CMake target and binary from `rpc-server` to `ggml-rpc-server` (tools/rpc/CMakeLists.txt: `set(TARGET ggml-rpc-server)`), so the RPC-enabled grpc build failed with "No rule to make target 'rpc-server'". The grpc-server itself links fine after the server-stream.cpp fix; this only updates the RPC target name and the binary path copied to llama-cpp-rpc-server. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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f0d0bff232 |
fix(llama-cpp): stop reinterpreting plain-string message content as JSON (#10524) (#10538)
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> |
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LocalAI [bot]andEttore Di Giacinto
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066abf82c0 |
feat(llama-cpp): cpu_moe/n_cpu_moe options + generic upstream-flag passthrough (#10490)
* feat(llama-cpp): add main-model cpu_moe/n_cpu_moe options Mirror the existing draft_cpu_moe/draft_n_cpu_moe siblings for the main model, matching upstream --cpu-moe / --n-cpu-moe (common/arg.cpp). Lets users keep MoE expert weights on CPU to manage VRAM on large MoE models. Closes part of #10483 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): forward unknown '-' options to upstream arg parser Any options: entry starting with '-' is collected and passed verbatim to llama.cpp's own common_params_parse (LLAMA_EXAMPLE_SERVER) at the end of params_parse, so every upstream llama-server flag works without a new hand-wired branch. Passthrough runs last and wins on overlap; n_parallel is snapshotted to survive parser_init's SERVER reset, and help/usage/completion flags are skipped to avoid exiting the backend. Closes #10483 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(llama-cpp): document cpu_moe/n_cpu_moe and option passthrough Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): terminate tensor/kv override vectors after passthrough The tensor_buft_overrides padding and the kv/draft override terminators ran before the generic option passthrough, so a passthrough flag (--cpu-moe, --override-tensor, --override-kv, ...) appended a real entry after the null sentinel - tripping the model loader's back().pattern == nullptr assertion (crash) or being silently dropped. Move all three termination/padding blocks to the end of params_parse, after both the named-option loop and common_params_parse have pushed their real entries. Also widen the exit()-flag skip list so --version, --license, --list-devices and --cache-list cannot terminate the backend. 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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518381278e |
chore: ⬆️ Update ggml-org/llama.cpp to e475fa2b5f9fb50c3d6fc3e7c6fdf1e004465b62 (#10392)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): adapt grpc-server to upstream server-schema split Upstream llama.cpp (e475fa2) extracted the JSON request-schema evaluation out of the static server_task::params_from_json_cmpl into the new server_schema::eval_llama_cmpl_schema (tools/server/server-schema.cpp). The grpc-server unity build still called the old static member, breaking every llama-cpp backend build with "no member named 'params_from_json_cmpl' in 'server_task'". Pull server-schema.cpp into the translation unit and call the new function, keeping both guarded by __has_include so forks that predate the split (e.g. llama-cpp-turboquant, which still exposes params_from_json_cmpl) keep compiling against the old static member. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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1ab61a0875 |
feat: generic chat_template_kwargs (model config + per-request metadata) (#10359)
* feat(config): add chat_template_kwargs model field + resolver Adds the ChatTemplateKwargs model-config map and RequestMetadata carrier, plus ResolveChatTemplateKwargs which layers the config map under coerced request metadata. Foundation for generic jinja chat-template kwargs (issue #10329). Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(backend): forward resolved chat_template_kwargs blob to backends gRPCPredictOpts now merges per-request client metadata over the server-derived enable_thinking/reasoning_effort (reaching all backends via the standalone keys) and serialises the resolved chat_template_kwargs map into a JSON blob for llama.cpp, written last so a client cannot clobber it. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(http): wire request metadata to config.RequestMetadata The OpenAI request metadata field was parsed but unused; stamp it onto the per-request ModelConfig so gRPCPredictOpts forwards it as chat_template_kwargs overrides. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): generic chat_template_kwargs merge (drop per-key blocks) Replace the per-key enable_thinking/reasoning_effort handling in both the streaming and non-streaming chat paths with a single block that parses the chat_template_kwargs JSON blob resolved by the Go layer and merges every key into body_json. New jinja template levers (e.g. preserve_thinking) now need no C++ change. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: document custom chat_template_kwargs (model + per-request) Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(backend): pin reasoning_effort as a string in the chat_template_kwargs blob Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(http): e2e guard pinning chat_template_kwargs forwarded to gRPC Adds an ECHO_PREDICT_METADATA marker to the mock-backend that echoes the received PredictOptions.Metadata, and an app_test.go spec that drives a real /v1/chat/completions request (model chat_template_kwargs + per-request metadata override) and asserts the exact metadata + chat_template_kwargs blob the REST layer forwards to gRPC. Locks the REST->gRPC contract against regressions. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(config): grandfather chat_template_kwargs in registry coverage chat_template_kwargs is a free-form map[string]any (like engine_args, already on the list), not a scalar the config UI registry can surface, so it is exempt from the registry-entry requirement. Fixes the TestAllFieldsHaveRegistryEntries failure introduced by the new field. Issue #10329. Assisted-by: Claude:claude-opus-4-8 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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Richard Palethorpe
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085fc53bbc |
fix(router): production-ready request router + auto-size batch for embedding/rerank (#10104)
* fix(router): score classifier production-readiness Conversation trimming runs through the classifier model's chat template and trims by exact token count, sized to the model's n_batch which is now scaled to context so long probes can't crash the backend. Missing chat_message templates are a hard error at router build time. Router- facing factories (Embedder/Scorer/Reranker/TokenCounter) re-resolve ModelConfig per call so a model installed post-startup doesn't bind a stub Backend="" config and silently fall into the loader's auto- iterate path. New 'vector_store' backend trace recorded inside localVectorStore on every Search/Insert — including the backend-load-failure path that previously vanished into an xlog.Warn — with outcome tagging (hit/miss/empty_store/backend_load_error/find_error/insert_error/ok). Companion cleanup drops misleading similarity:0 and input_tokens_count:0 from non-hit and text-mode traces. Gallery local-store-development aliases to 'local-store' so the master image satisfies pkg/model.LocalStoreBackend lookups from the embedding cache. Misc: llama-cpp TokenizeString reads the correct 'prompt' JSON key (the original bug); ModelTokenize nil-guard; non-fatal mitm proxy startup; PII 'route_local' renamed to 'allow' with docs/UI in sync; model-editor footer no longer eats the edit area on small screens; several config-editor template/dropdown/section fixes. Tests: e2e router specs (casual/code-hint + long-conversation trim), vector_store trace specs, lazy-factory specs, gallery dev-alias resolution, Playwright trace badge + scroll regression. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(backend): auto-size batch to context for embedding and rerank models Embedding and rerank models pool over the whole input in a single physical batch (n_ubatch). With batch left at the 512 default, the backend rejects longer inputs with "input is too large to process", silently capping a large-context embedder (e.g. 8k/32k) at 512 tokens. Size n_batch to the context for these single-pass usecases, mirroring the existing FLAG_SCORE behaviour; an explicit batch: still wins. Extracts EffectiveContextSize/EffectiveBatchSize from grpcModelOpts so the effective decode window has one home for other callers to reuse. Adds an e2e-aio regression test that embeds a >512-token input. The AIO embedding model is switched to nomic-embed-text-v1.5 (2048 context) because the previous granite model was capped at 512 tokens and could not exercise the larger batch. Assisted-by: claude-code:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(gallery): raise arch-router scoring output cap via parallel:64 Scoring decodes the whole prompt+candidate in a single llama_decode and reads one logit row per candidate token. The vendored llama.cpp server caps causal output rows at n_parallel, so the default of 1 aborts with GGML_ASSERT(n_outputs_max <= cparams.n_outputs_max) on multi-token route labels. Set options: [parallel:64] on both arch-router quant entries to lift the cap; kv_unified (the grpc-server default) keeps the full context per sequence, so this does not split the KV cache. Assisted-by: claude-code:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]andEttore Di Giacinto
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9323f4b5ca |
feat(llama-cpp): video input support (mtmd #24269) (#10216)
* chore(llama-cpp): bump to 8f83d6c for mtmd video input support Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): forward video input to mtmd (template + non-template paths) Wire request->videos() into grpc-server.cpp mirroring the existing image and audio handling: a video_data build + non-template files extraction, and input_video chat chunks on the tokenizer-template path. allow_video is auto-set at model load by the vendored upstream chat_params. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): add video attachment support to the chat UI Mirror the image/audio attachment path for video: emit video_url content parts, accept video/* in the picker, keep video files as base64, show a film icon badge, and render attached video inline with a <video> player. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): patch mtmd video stdin double-close (heap crash) Upstream mtmd video input (ggml-org/llama.cpp#24269) double-fcloses the ffmpeg/ffprobe stdin FILE: feed_stdin() fclose()s the FILE returned by subprocess_stdin() (which is sp->stdin_file), then subprocess_destroy() fclose()s the same pointer again -> heap corruption that aborts the backend on any base64 input_video request (the CLI --video file path is unaffected). Vendor a one-line fix (null sp->stdin_file after fclose) via prepare.sh's patches/ until upstream merges it. Verified e2e with gemma-4-e2b-it-qat-q4_0: video frames decode via ffmpeg and the model answers correctly (red clip -> 'Red', blue -> 'Blue'). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): re-pin to upstream #24316, drop vendored stdin patch Upstream replaced the ad-hoc video stdin handling with a proper RAII refactor (ggml-org/llama.cpp#24316, "mtmd: refactor video subproc handling"), which includes the same `sp->stdin_file = nullptr` guard our patch added (plus join-before-destroy ordering). Re-pin LLAMA_VERSION to that branch head and drop patches/0001 - it's now redundant. Verified e2e with gemma-4-e2b-it-qat-q4_0: no crash, video frames decode and the model answers correctly (red clip -> "Red", blue -> "Blue"). NOTE: #24316 is not yet merged, so this pins to its branch-head commit (28ca1e60). Re-pin to the squash-merge commit on master once it lands, otherwise `git fetch` may lose the commit after the branch is deleted. 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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LocalAI [bot]andEttore Di Giacinto
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7402d1fd20 |
chore(turboquant): bump to 7d9715f1 + fix compilation against rebased fork (#10205)
* chore(turboquant): bump TheTom/llama-cpp-turboquant to 7d9715f1 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(turboquant): drop obsolete legacy-spec shim after fork rebased The TheTom/llama-cpp-turboquant fork (pin c9aa86a) rebased past the upstream common_params_speculative refactor (ggml-org/llama.cpp #22397/#22838/#22964), the model_tgt rename (#22838) and get_media_marker (#21962). The old fork-compat shim forced now-wrong legacy code paths, breaking the build with errors like 'struct common_params_speculative has no member named mparams_dft / type' and 'server_context_impl has no member named model'. Remove the obsolete LOCALAI_LEGACY_LLAMA_CPP_SPEC branches from the shared grpc-server.cpp (stock llama-cpp and the modern fork both take the modern path now), and narrow the one remaining gap (the fork still lacks common_params::checkpoint_min_step) to a dedicated LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP guard injected by patch-grpc-server.sh. The patch script now only adds the turbo2/3/4 KV-cache types and injects that one macro. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(turboquant): HIP-port the fork's CUDA additions (copy2d 3D-peer + cudaEventCreate) The turboquant fork adds/modifies a few ggml-cuda.cu spots with CUDA APIs that ggml's HIP/MUSA shim does not provide, breaking the -gpu-rocm-hipblas-turboquant build. patches/0001-hip-guard-copy2d-peer-fastpath.patch (applied by apply-patches.sh) ports them: - Guard ggml_cuda_copy2d_across_devices's 3D-peer copy fast path with #if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) so HIP/MUSA fall through to the existing cudaMemcpyAsync staging fallback (HIP genuinely lacks cudaMemcpy3DPeerAsync, per the fork's own comment). - Create the device event in ggml_backend_cuda_device_event_new with the HIP-aliased cudaEventCreateWithFlags(.., cudaEventDisableTiming) instead of the un-aliased plain cudaEventCreate, matching this file's own usage elsewhere. CUDA builds are unaffected. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * ci(turboquant): drop the ROCm/hipblas build flavor The TheTom/llama-cpp-turboquant fork is not ROCm-clean at the current pin: beyond the CUDA-API gaps already patched (3D-peer copy, cudaEventCreate), its llama.cpp base fails to compile the flash-attention MMA f16 kernels for head-dim 640 under HIP (cols_per_warp evaluates to 0 -> division-by-zero / non-constant static asserts in fattn-mma-f16.cuh). That is a deep ggml-on-ROCm kernel issue, not something a small fork patch can paper over. Drop -gpu-rocm-hipblas-turboquant from the build matrix so turboquant still ships for cpu / cublas / vulkan / sycl. Re-add it once the fork's HIP path compiles (or upstream ggml fixes the large-head-dim MMA kernels for ROCm). 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> |
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LocalAI [bot]andEttore Di Giacinto
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e837921c2c |
feat: forward reasoning_effort to the backend so jinja models honor it (#10184)
* feat: forward reasoning_effort to the backend so jinja models honor it reasoning_effort was only mapped to the binary enable_thinking toggle and otherwise reached Go-side templates — it was never sent to the backend. So jinja-templated models whose chat template keys on reasoning_effort (gpt-oss Harmony, LFM2.5) could not be driven by it: LFM2.5 ignores enable_thinking and kept emitting <think>. Forward the effective reasoning_effort to the backend as a chat_template_kwarg (mirroring enable_thinking) in grpc-server.cpp, and put it in PredictOptions metadata (gRPCPredictOpts). Add a config-level default: ModelConfig.reasoning_effort and Pipeline.reasoning_effort, resolved by ModelConfig.ApplyReasoningEffort (request value overrides config default, none->disable / level->enable, an operator's reasoning.disable wins). request.go now uses that helper. Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): set the pipeline LLM's reasoning_effort Apply Pipeline.ReasoningEffort to the pipeline's LLM config when the realtime model is built (per-session copy, overrides the LLM's own reasoning_effort), and surface the resolved effort on the template input so Go-templated models get it too. jinja models receive it via the backend metadata. This lets a realtime pipeline disable thinking on models that only honor reasoning_effort (e.g. LFM2.5), which enable_thinking can't. Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint 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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aa80d4681b |
chore: ⬆️ Update ggml-org/llama.cpp to d6588daa800058dfa54f1d7ea695b1a810c8ae18 (#10093)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): skip begin-of-stream null partial in PredictStream Upstream llama.cpp (ggml-org/llama.cpp#23884), pulled in by this bump, now emits an initial "begin" partial whose to_json() returns null. It exists only to signal the HTTP layer to flush 200 status headers before any token is produced. gRPC has no such concept, and PredictStream had no guard: the null result was fed straight into build_reply_from_json, which threw an uncaught exception. That surfaced as a generic "Unexpected error in RPC handling" and the task was cancelled the instant it launched, breaking the PredictStream e2e spec. Skip null results in both the first-result handling and the streaming loop, mirroring upstream's own `if (first_result_json == nullptr)` guard. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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1c92b00918 |
fix(turboquant): guard upstream-only grpc-server fields for fork (#10043)
fix(turboquant): guard upstream-only grpc-server fields for fork build
backend/cpp/llama-cpp/grpc-server.cpp is reused by the turboquant build,
which compiles against an older llama.cpp fork (TheTom/llama-cpp-turboquant).
Two recent changes added references to upstream-only struct fields outside the
existing LOCALAI_LEGACY_LLAMA_CPP_SPEC guards:
- common_params::checkpoint_min_step (default + option handler), added with
the ggml-org/llama.cpp 35c9b1f3 bump (#9998)
- the common_params_speculative::draft tensor_buft_overrides sentinel
termination (#9919), which sat after the guard's #endif
The fork has neither field, so grpc-server.cpp failed to compile for every
turboquant flavor. Wrap the three references in #ifndef
LOCALAI_LEGACY_LLAMA_CPP_SPEC, matching the existing fork-compat guards, so the
stock llama-cpp build is unchanged and the fork build skips them. Update
patch-grpc-server.sh's doc comment to record what the macro now gates out.
Verified by a local fallback-flavor turboquant build: grpc-server.cpp compiles
against the fork and the backend image builds.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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4aad97971c |
chore: ⬆️ Update ggml-org/llama.cpp to 35c9b1f39ebe5a7bb83986d64415a079218be78d (#9998)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): track upstream rename checkpoint_every_nt -> checkpoint_min_step Upstream llama.cpp renamed common_params::checkpoint_every_nt to checkpoint_min_step and changed its default from 8192 to 256. The semantics also shifted: it used to enforce a fixed checkpoint cadence during prefill, now it sets a minimum spacing between context checkpoints. Track the new field name in grpc-server.cpp and accept the old option names as backward- compatible aliases for users with existing configs. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:claude-opus-4-7 --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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6a80e23733 |
feat(middleware): Model routing, PII filtering, Cloud model proxies (#9802)
Add a routing middleware stack and a cloud-proxy backend. * cloud-proxy: a Go gRPC backend that forwards OpenAI- and Anthropic-shaped chat requests to upstream providers, with an optional translate mode (OpenAI request -> Anthropic /v1/messages -> OpenAI response) and full tool-calling support. * routing: admission control, content-aware model routing (embedding cache + classifier + rerank + Arch-Router score), PII detection/redaction (regex + NER) with streaming filter and OpenAI/Anthropic adapters, and a per-user/per-key billing recorder backed by GORM or in-memory storage. * middleware: UsageMiddleware records usage via the billing recorder, plus admission, route-model, usage-stamp and trace middlewares. * observability: BackendTrace ring buffer stores full request bodies (capped), MITM proxy emits structured trace events, and router classifier decisions surface at /api/router/decide. * gallery: Arch-Router-1.5B (Q4_K_M and Q8_0). * UI: cloud-proxy model-editor fields, classifier system-prompt and score-normalization config, and a Traces page rendering request bodies. Assisted-by: claude-code:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]andEttore Di Giacinto
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959de86761 |
feat(llama-cpp): make server-side prompt cache work by default (#9925)
Aligns LocalAI's llama-cpp gRPC backend with upstream's auto-on prompt cache path so repeated system prompts (agents, OpenAI/Anthropic-compatible CLIs, coding assistants) skip prefill on subsequent calls without any YAML changes. Reported in #9921. Upstream's server enables `kv_unified=true` (and bumps `n_parallel` to 4) when slot count is auto, which unlocks `cache_idle_slots`. LocalAI hardcodes `n_parallel=1` and so far also hardcoded `kv_unified=false`, which silently force-disables idle-slot saving at server init. The host prompt cache was allocated but never written across requests. Changes in backend/cpp/llama-cpp/grpc-server.cpp: - params.kv_unified: false -> true (single-slot path now benefits from the prompt cache; users can opt out with `kv_unified:false`) - params.n_ctx_checkpoints: 8 -> 32 (match upstream default) - params.cache_idle_slots = true initialized explicitly (upstream default) - params.checkpoint_every_nt = 8192 initialized explicitly (upstream default) - New option parsers: cache_idle_slots / idle_slots_cache, checkpoint_every_nt / checkpoint_every_n_tokens Docs: - features/text-generation.md: fix misleading `cache_ram` description (it's the host-side prompt cache, not the KV cache), document the kv_unified + cache_ram + cache_idle_slots interaction, add rows for the two newly-exposed options, and add a worked example for the agent/CLI workload from the issue. - advanced/model-configuration.md: mark the legacy `prompt_cache_path` / `prompt_cache_all` / `prompt_cache_ro` YAML fields as unused by the llama-cpp gRPC backend (they target upstream's CLI completion tool and are not consumed by grpc-server.cpp) and point readers at the new prompt-cache explainer. Closes #9921 Assisted-by: claude:opus-4.7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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c68818a62e |
fix(llama-cpp): terminate tensor_buft_overrides with sentinel (#9919)
llama.cpp's model loader asserts back().pattern == nullptr on params.tensor_buft_overrides (and on params.kv_overrides.back().key[0] == 0) before binding them into llama_model_params. PR #8560 attempted to satisfy llama_params_fit's placeholder requirement by pre-filling params.tensor_buft_overrides up to llama_max_tensor_buft_overrides() *before* the option-parse loop. Any subsequent push_back from override_tensor / draft_cpu_moe / draft_n_cpu_moe / draft_override_tensor then appended real entries after the placeholders, leaving back() with a real pattern and tripping the assert. The draft override vector likewise had no terminator at all. Mirror upstream common/arg.cpp:645-658 instead: real entries are pushed during option parsing, and after parsing we pad the main vector up to ntbo (placeholders land at the end, so back() is always nullptr) and append a single {nullptr, nullptr} to the draft vector when it is non-empty. The existing kv_overrides terminator block already matches upstream and stays. Verified against ggml-org/llama.cpp@5cbaa5e: only tensor_buft_overrides (main + draft) and kv_overrides are sentinel-terminated common_params fields; everything else is size-driven std::vector. Assisted-by: claude-code:claude-opus-4-7 Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]andEttore Di Giacinto
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6e1dbae256 |
feat(llama-cpp): expose 12 missing common_params via options[] (#9814)
The llama.cpp backend already accepts a free-form options: array in the
model config that maps to common_params fields, but a coverage audit
against upstream pin 7f3f843c flagged 12 user-visible knobs that were
neither set via the typed proto fields nor reachable via options:.
Wire them up under the existing if/else chain in params_parse, before
the speculative section. Each new option follows the file's prevailing
patterns (try/catch around numeric parses, the same true/1/yes/on bool
form used elsewhere, hardware_concurrency() fallback for thread counts,
mirror of draft_override_tensor for override_tensor).
Top-level / batching / IO:
- n_ubatch (alias ubatch) -- physical batch size; was previously
force-aliased to n_batch at line 482, blocking embedding/rerank
workloads that need independent control
- threads_batch (alias n_threads_batch) -- main-model batch threads;
mirrors the existing draft_threads_batch
- direct_io (alias use_direct_io) -- O_DIRECT model loads
- verbosity -- llama.cpp log threshold (line 479 had this commented
out)
- override_tensor (alias tensor_buft_overrides) -- per-tensor buffer
overrides for the main model; mirrors draft_override_tensor
Embedding / multimodal:
- pooling_type (alias pooling) -- mean/cls/last/rank/none; previously
only auto-flipped to RANK for rerankers
- embd_normalize (alias embedding_normalize) -- and the embedding
handler now reads params_base.embd_normalize instead of a hardcoded
2 at the previous embd_normalize literal in Embedding()
- mmproj_use_gpu (alias mmproj_offload) -- mmproj on CPU vs GPU
- image_min_tokens / image_max_tokens -- per-image vision token budget
Reasoning surface (the audit-focus three; LocalAI's existing
ReasoningConfig.DisableReasoning only feeds the per-request
chat_template_kwargs.enable_thinking and does not touch any of these):
- reasoning_format -- none/auto/deepseek/deepseek-legacy parser
- enable_reasoning (alias reasoning_budget) -- -1/0/>0 thinking budget
- prefill_assistant -- trailing-assistant-message prefill toggle
All 14 referenced fields exist on both the upstream pin and the
turboquant fork's common.h, so no LOCALAI_LEGACY_LLAMA_CPP_SPEC guard
is needed.
Docs: extend model-configuration.md with new "Reasoning Models",
"Multimodal Backend Options", "Embedding & Reranking Backend Options",
and "Other Backend Tuning Options" subsections; also refresh the
Speculative Type Values table to show the new dash-separated canonical
names alongside the underscore aliases LocalAI still accepts.
Assisted-by: claude-code:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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53bdb18d10 |
chore: ⬆️ Update ggml-org/llama.cpp to 7f3f843c31cd32dc4adc10b393342dfee071c332 (#9809)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): adapt to upstream COMMON_SPECULATIVE_TYPE_DRAFT rename ggml-org/llama.cpp#22964 ("spec: update CLI arguments for better consistency") renamed the speculative type enum values: COMMON_SPECULATIVE_TYPE_DRAFT -> COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE COMMON_SPECULATIVE_TYPE_EAGLE3 -> COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 and the registered name strings flipped from underscore- to dash- separated form (e.g. ngram_simple -> ngram-simple), with the bare draft/eagle3 aliases replaced by draft-simple/draft-eagle3. This broke the build with the new LLAMA_VERSION on every variant (vulkan/arm64, darwin and likely all the rest) at grpc-server.cpp:461. Update the upstream branch of the speculative-type fallback to use the new identifier (the LOCALAI_LEGACY_LLAMA_CPP_SPEC fork branch keeps the old name), and normalize spec_type option tokens before passing them to common_speculative_types_from_names so existing model configs that say spec_type:draft / spec_type:ngram_simple keep working. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:claude-opus-4-7 --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
|
bc4cd3dd85 |
feat(llama-cpp): bump to 1ec7ba0c, adapt grpc-server, expose new spec-decoding options (#9765)
* chore(llama.cpp): bump to 1ec7ba0c14f33f17e980daeeda5f35b225d41994
Picks up the upstream `spec : parallel drafting support` change
(ggml-org/llama.cpp#22838) which reshapes the speculative-decoding API
and `server_context_impl`.
Adapt the grpc-server wrapper accordingly:
* `common_params_speculative::type` (single enum) became `types`
(`std::vector<common_speculative_type>`). Update both the
"default to draft when a draft model is set" branch and the
`spec_type`/`speculative_type` option parser. The parser now also
tolerates comma-separated lists, mirroring the upstream
`common_speculative_types_from_names` semantics.
* `common_params_speculative_draft::n_ctx` is gone (draft now shares
the target context size). Keep the `draft_ctx_size` option name for
backward compatibility and ignore the value rather than failing.
* `server_context_impl::model` was renamed to `model_tgt`; update the
two reranker / model-metadata call sites.
Replaces #9763. Builds cleanly under the linux/amd64 cpu-llama-cpp
target locally.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(llama-cpp): expose new speculative-decoding option keys
Upstream `spec : parallel drafting support` (ggml-org/llama.cpp#22838)
adds the `ngram_mod`, `ngram_map_k`, and `ngram_map_k4v` speculative
families and beefs up the draft-model knobs. The previous bump only
adapted the API; this exposes the new fields through the grpc-server
options dictionary so model configs can drive them.
New `options:` keys (all under `backend: llama-cpp`):
ngram_mod (`ngram_mod` type):
spec_ngram_mod_n_min / spec_ngram_mod_n_max / spec_ngram_mod_n_match
ngram_map_k (`ngram_map_k` type):
spec_ngram_map_k_size_n / spec_ngram_map_k_size_m / spec_ngram_map_k_min_hits
ngram_map_k4v (`ngram_map_k4v` type):
spec_ngram_map_k4v_size_n / spec_ngram_map_k4v_size_m /
spec_ngram_map_k4v_min_hits
ngram lookup caches (`ngram_cache` type):
spec_lookup_cache_static / lookup_cache_static
spec_lookup_cache_dynamic / lookup_cache_dynamic
Draft-model tuning (active when `spec_type` is `draft`):
draft_cache_type_k / spec_draft_cache_type_k
draft_cache_type_v / spec_draft_cache_type_v
draft_threads / spec_draft_threads
draft_threads_batch / spec_draft_threads_batch
draft_cpu_moe / spec_draft_cpu_moe (bool flag)
draft_n_cpu_moe / spec_draft_n_cpu_moe (first N MoE layers on CPU)
draft_override_tensor / spec_draft_override_tensor
(comma-separated <tensor regex>=<buffer type>; re-implements upstream's
static parse_tensor_buffer_overrides since it isn't exported)
`spec_type` already accepted comma-separated lists after the previous
commit, matching upstream's `common_speculative_types_from_names`.
Docs: refresh `docs/content/advanced/model-configuration.md` with
per-family tables and a note about multi-type chaining.
Builds locally with `make docker-build-llama-cpp` (linux/amd64
cpu-llama-cpp AVX variant).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(turboquant): bridge new llama.cpp spec API to the legacy fork layout
The previous commits in this series adapted backend/cpp/llama-cpp/grpc-server.cpp
to the post-#22838 (parallel drafting) llama.cpp API. The turboquant build
reuses the same grpc-server.cpp through backend/cpp/turboquant/Makefile,
which copies it into turboquant-<flavor>-build/ and runs patch-grpc-server.sh
on the copy. The fork branched before the API refactor, so it errors out on:
* `ctx_server.impl->model_tgt` (fork still has `model`)
* `params.speculative.{ngram_mod,ngram_map_k,ngram_map_k4v,ngram_cache}.*`
(none of these sub-structs exist in the fork)
* `params.speculative.draft.{cache_type_k/v, cpuparams[, _batch].n_threads,
tensor_buft_overrides}` (fork uses the pre-#22397 flat layout)
* `params.speculative.types` vector / `common_speculative_types_from_names`
(fork has a scalar `type` and only the singular helper)
Approach:
1. backend/cpp/llama-cpp/grpc-server.cpp: introduce a single feature switch
`LOCALAI_LEGACY_LLAMA_CPP_SPEC`. When defined, the two `speculative.type[s]`
discriminations (the "default to draft when a draft model is set" branch
and the `spec_type` / `speculative_type` option parser) fall back to the
singular scalar form, and the entire new-option block (ngram_mod / map_k
/ map_k4v / ngram_cache / draft.{cache_type_*, cpuparams*,
tensor_buft_overrides}) is preprocessed out. The macro is *not* defined
in the source tree — stock llama-cpp builds get the full new API.
2. backend/cpp/turboquant/patch-grpc-server.sh: two new patch steps applied
to the per-flavor build copy at turboquant-<flavor>-build/grpc-server.cpp:
- substitute `ctx_server.impl->model_tgt` -> `ctx_server.impl->model`
- inject `#define LOCALAI_LEGACY_LLAMA_CPP_SPEC 1` before the first
`#include`, so the guarded blocks above drop out for the fork build.
Both patches are idempotent and follow the existing sed/awk pattern in
this script (KV cache types, `get_media_marker`, flat speculative
renames). Stock llama-cpp's `grpc-server.cpp` is never touched.
Drop both legacy patches once the turboquant fork rebases past
ggml-org/llama.cpp#22397 / #22838.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(turboquant): close draft_ctx_size brace inside legacy guard
The previous turboquant fix wrapped the new option-handler blocks in
`#ifndef LOCALAI_LEGACY_LLAMA_CPP_SPEC ... #endif` but placed the guard
in the middle of an `else if` chain — the `} else if` openings of the
new blocks were responsible for closing the previous block's brace.
With the macro defined the new blocks vanish, draft_ctx_size's `{`
loses its closer, the for-loop's `}` is consumed instead, and the
file ends with a stray opening brace — clang reports it as
`function-definition is not allowed here before '{'` on the next
top-level `int main(...)` and `expected '}' at end of input`.
Move the chain split inside the draft_ctx_size branch:
} else if (... "draft_ctx_size") {
// ...
#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
} // legacy: chain ends here
#else
} else if (... "spec_ngram_mod_n_min") { // modern: chain continues
...
} else if (... "draft_override_tensor") {
...
} // closes last branch
#endif
} // closes for-loop
Brace count is now balanced under both preprocessor branches (verified
with `tr -cd '{' | wc -c` against the patched and unpatched outputs).
Local `make docker-build-turboquant` builds the linux/amd64 cpu-llama-cpp
`turboquant-avx` variant cleanly.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ci): forward AMDGPU_TARGETS into Dockerfile.turboquant builder-prebuilt
Dockerfile.turboquant's `builder-prebuilt` stage was missing the
`ARG AMDGPU_TARGETS` / `ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}` pair that
`builder-fromsource` already has (and that `Dockerfile.llama-cpp`
mirrors across both stages). When CI uses the prebuilt base image
(quay.io/go-skynet/ci-cache:base-grpc-*, the common path) the build-arg
passed by the workflow never reaches the env inside the compile stage.
backend/cpp/llama-cpp/Makefile:38 (introduced by #9626) errors out on
hipblas builds when AMDGPU_TARGETS is empty, and the turboquant
Makefile reuses backend/cpp/llama-cpp via a sibling build dir, so the
same check fires from turboquant-fallback under BUILD_TYPE=hipblas:
Makefile:38: *** AMDGPU_TARGETS is empty — set it to a comma-separated
list of gfx targets e.g. gfx1100,gfx1101. Stop.
make: *** [Makefile:66: turboquant-fallback] Error 2
The bug is latent on master because the docker layer cache stays warm
across builds — the compile step rarely re-runs from scratch. The
llama.cpp bump in this PR invalidates the cache, so the missing env var
becomes load-bearing and the hipblas turboquant CI job fails.
Mirror the existing pattern from Dockerfile.llama-cpp.
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
|
c02a50f2ab |
feat(llama-cpp): bump to d775992 and adapt to spec params refactor (#9618)
Bumps backend/cpp/llama-cpp/Makefile LLAMA_VERSION from 665abc6 to
d775992, picking up upstream PR ggml-org/llama.cpp#22397 which splits
common_params_speculative into nested draft / ngram_simple / ngram_mod
sub-structs. Renames every grpc-server.cpp reference to match:
speculative.mparams_dft.path -> speculative.draft.mparams.path
speculative.{n_max,n_min} -> speculative.draft.{n_max,n_min}
speculative.{p_min,p_split} -> speculative.draft.{p_min,p_split}
speculative.{n_gpu_layers,n_ctx} -> speculative.draft.{n_gpu_layers,n_ctx}
speculative.ngram_size_n -> speculative.ngram_simple.size_n
speculative.ngram_size_m -> speculative.ngram_simple.size_m
speculative.ngram_min_hits -> speculative.ngram_simple.min_hits
The "speculative.n_max" JSON key sent to the upstream server stays
unchanged — server-task.cpp still reads it and routes the value into
draft.n_max internally.
The turboquant fork (TheTom/llama-cpp-turboquant @ 11a241d) branched
before #22397 and still exposes the flat layout. Since turboquant
reuses the shared backend/cpp/llama-cpp/grpc-server.cpp, extend
patch-grpc-server.sh with an idempotent sed block that reverts the
ten field references back to the legacy flat names on the build copy
only — the original under backend/cpp/llama-cpp/ stays compiling
against vanilla upstream. Drop the block once the fork rebases.
ik-llama-cpp has its own grpc-server.cpp with no speculative refs
(0/2661 lines), so it is unaffected.
Validated locally with `make docker-build-llama-cpp` (avx, avx2,
avx512, fallback, grpc + rpc-server all built; image exported).
Assisted-by: Claude:claude-opus-4-7 [Bash Read Edit]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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Ettore Di Giacinto
|
21eace40ec |
feat(llama-cpp): expose split_mode option for multi-GPU placement (#9560)
Adds split_mode (alias sm) to the llama.cpp backend options allowlist, accepting none|layer|row|tensor. The tensor value targets the experimental backend-agnostic tensor parallelism from ggml-org/llama.cpp#19378 and requires a llama.cpp build that includes that PR, FlashAttention enabled, KV-cache quantization disabled, and a manually set context size. 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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ed648b3b4e |
fix(llama-cpp): include server-chat.cpp in grpc-server translation unit (#9511)
* fix(llama-cpp): include server-chat.cpp in grpc-server translation unit Upstream llama.cpp refactor (ggml-org/llama.cpp#20690) moved the OAI/Anthropic/Responses and transcription conversion helpers out of server-common.cpp into a new server-chat.cpp, and server-task.cpp and server-context.cpp now call those symbols (convert_transcriptions_to_chatcmpl, server_chat_convert_responses_to_chatcmpl, server_chat_convert_anthropic_to_oai, server_chat_msg_diff_to_json_oaicompat) via server-chat.h. grpc-server.cpp builds as a single translation unit by #include-ing the upstream .cpp files directly. Without including server-chat.cpp, the declarations are satisfied at compile time via server-chat.h but the link step fails with undefined references once LLAMA_VERSION crosses the refactor commit (134d6e54). Guard the include with __has_include so the same source stays buildable on older LLAMA_VERSION pins that predate the refactor (where prepare.sh won't copy server-chat.cpp into tools/grpc-server/). Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): bump LLAMA_VERSION to 0d0764dfd Bump to ggml-org/llama.cpp@0d0764dfd2. Paired with the preceding grpc-server server-chat.cpp include so the refactor at 134d6e54 links cleanly. Supersedes PR #9494. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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7809c5f5d0 |
fix(vision): propagate mtmd media marker from backend via ModelMetadata (#9412)
Upstream llama.cpp (PR #21962) switched the server-side mtmd media marker to a random per-server string and removed the legacy "<__media__>" backward-compat replacement in mtmd_tokenizer. The Go layer still emitted the hardcoded "<__media__>", so on the non-tokenizer-template path the prompt arrived with a marker mtmd did not recognize and tokenization failed with "number of bitmaps (1) does not match number of markers (0)". Report the active media marker via ModelMetadataResponse.media_marker and substitute the sentinel "<__media__>" with it right before the gRPC call, after the backend has been loaded and probed. Also skip the Go-side multimodal templating entirely when UseTokenizerTemplate is true — llama.cpp's oaicompat_chat_params_parse already injects its own marker and StringContent is unused in that path. Backends that do not expose the field keep the legacy "<__media__>" behavior. |
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Ettore Di Giacinto
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87e6de1989 |
feat: wire transcription for llama.cpp, add streaming support (#9353)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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9748a1cbc6 |
fix(streaming): skip chat deltas for role-init elements to prevent first token duplication (#9299)
When TASK_RESPONSE_TYPE_OAI_CHAT is used, the first streaming token produces a JSON array with two elements: a role-init chunk and the actual content chunk. The grpc-server loop called attach_chat_deltas for both elements with the same raw_result pointer, stamping the first token's ChatDelta.Content on both replies. The Go side accumulated both, emitting the first content token twice to SSE clients. Fix: in the array iteration loops in PredictStream, detect role-init elements (delta has "role" key) and skip attach_chat_deltas for them. Only content/reasoning elements get chat deltas attached. Reasoning models are unaffected because their first token goes into reasoning_content, not content. |
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Ettore Di Giacinto
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2b05420f95 |
chore(llama.cpp): bump to 'd12cc3d1ca6bba741cd77887ac9c9ee18c8415c7' (#9282)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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773489eeb1 |
fix(chat): do not retry if we had chatdeltas or tooldeltas from backend (#9244)
* fix(chat): do not retry if we had chatdeltas or tooldeltas from backend Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: use oai compat for llama.cpp Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: apply to non-streaming path too Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * map also other fields Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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06fbe48b3f |
feat(llama.cpp): wire speculative decoding settings (#9238)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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53deeb1107 |
fix(reasoning): suppress partial tag tokens during autoparser warm-up
The C++ PEG parser needs a few tokens to identify the reasoning format (e.g. "<|channel>thought\n" for Gemma 4). During this warm-up, the gRPC layer was sending raw partial tag tokens to Go, which leaked into the reasoning field. - Clear reply.message in gRPC when autoparser is active but has no diffs yet, matching llama.cpp server behavior of only emitting classified output - Prefer C++ autoparser chat deltas for reasoning/content in all streaming paths, falling back to Go-side extraction for backends without autoparser (e.g. vLLM) - Override non-streaming no-tools result with chat delta content when available - Guard PrependThinkingTokenIfNeeded against partial tag prefixes during streaming accumulation - Reorder default thinking tokens so <|channel>thought is checked before <|think|> (Gemma 4 templates contain both) |
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Ettore Di Giacinto
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1ed6b9e5ed |
fix(llama.cpp): correctly parse grpc header for bearer token auth
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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59108fbe32 |
feat: add distributed mode (#9124)
* feat: add distributed mode (experimental) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix data races, mutexes, transactions Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix events and tool stream in agent chat Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * use ginkgo Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(cron): compute correctly time boundaries avoiding re-triggering Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * enhancements, refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * do not flood of healthy checks Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * do not list obvious backends as text backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * tests fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop redundant healthcheck Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * enhancements, refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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031a36c995 |
feat: inferencing default, automatic tool parsing fallback and wire min_p (#9092)
* feat: wire min_p Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: inferencing defaults Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(refactor): re-use iterative parser Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: generate automatically inference defaults from unsloth Instead of trying to re-invent the wheel and maintain here the inference defaults, prefer to consume unsloth ones, and contribute there as necessary. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: apply defaults also to models installed via gallery Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: be consistent and apply fallback to all endpoint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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c3174f9543 |
chore(deps): bump llama-cpp to 'a0bbcdd9b6b83eeeda6f1216088f42c33d464e38' (#9079)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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b24ca51287 |
fix(llama-cpp): Set enable_thinking in the correct place (#8973)
Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Ettore Di Giacinto
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b2f81bfa2e |
feat(functions): add peg-based parsing and allow backends to return tool calls directly (#8838)
* feat(functions): add peg-based parsing Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: support returning toolcalls directly from backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: do run PEG only if backend didn't send deltas Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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580517f9db |
feat: pass-by metadata to predict options (#8795)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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1c5dc83232 |
chore(deps): bump llama.cpp to 'ecbcb7ea9d3303097519723b264a8b5f1e977028' (#8672)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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9e692967c3 |
fix(llama-cpp): Pass parameters when using embedded template (#8590)
Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Austen
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42cb7bda19 |
fix(llama-cpp): populate tensor_buft_override buffer so llama-cpp properly performs fit calculations (#8560)
fix auto-fit for llama-cpp |
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Ettore Di Giacinto
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48e08772f3 |
chore(llama.cpp): bump to 'f6b533d898ce84bae8d9fa8dfc6697ac087800bf' (#8275)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |