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fa9ffc181c |
chore: ⬆️ Update ggml-org/llama.cpp to f280b26983ad0fdb705a0d9ebf0503e76f2899b0 (#11646)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): adapt to the common JSON API The llama.cpp bump replaces its nlohmann JSON alias with common_json. Update the gRPC adapter for the new exception, iterator, conversion, and container APIs. Assisted-by: Codex:gpt-5.6 [systematic-debugging] * fix(turboquant): adapt the JSON exception type The shared gRPC source now follows the upstream common_json API. The TurboQuant fork still exposes nlohmann JSON and cannot compile the new exception type. Translate that exception in the fork-specific source patch so both llama.cpp variants compile from the shared adapter. Assisted-by: Codex:gpt-5.6 [systematic-debugging] * fix(bonsai): adapt the JSON exception type The shared gRPC source uses upstream's common_json wrapper. The Bonsai fork still exposes nlohmann JSON and cannot compile that exception type.\n\nTranslate the exception in the fork-specific preparation step and verify that repeated preparation stays idempotent.\n\nAssisted-by: Codex:gpt-5.6 [systematic-debugging] * fix(llama-cpp): let prepare register gRPC The score patch duplicated the gRPC CMake registration that prepare.sh already owns. Its stale context rejects the current upstream tools file on Darwin before compilation starts. Assisted-by: Codex:gpt-5 --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.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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mudler's LocalAI [bot]andEttore Di Giacinto
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a0f7faaa2a |
fix(sycl): stop building the ggml CPU variant matrix with icpx (#11321)
Since #11255 and #11276 every GPU image also builds ggml's CPU_ALL_VARIANTS matrix, so a partial offload uses the host's SIMD kernels. That works everywhere except SYCL, where the Makefile compiles the whole tree with icpx -fsycl: icpx never finishes ggml-cpu/arch/x86/repack.cpp at -march=sapphirerapids. In run 30765516644 both sycl_f16 and sycl_f32 stopped at that translation unit and sat there for 5h30m with a single compile in flight until GitHub killed the job at its 6h limit, and turboquant's f16 job lost its runner outright. gcc compiles the same file in seconds in the vulkan and CPU jobs of the same run, so the CPU variant matrix is only unbuildable under icpx. Route SYCL back to the portable fallback binary, which is what these images shipped before #11255. run.sh already prefers *-cpu-all when present and falls back otherwise, so nothing else has to change. Assisted-by: Claude Code:claude-opus-5[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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localai-org-maint-botandlocalai-org-maint-bot
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9fe1165f61 |
fix(turboquant): retain CPU variants in GPU builds (#11276)
Select the CPU_ALL_VARIANTS target for x86 GPU images so partial offload uses runtime-selected host kernels. Keep GPU arm64 builds on the portable fallback until their toolchains consistently provide gcc-14. 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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mudler's LocalAI [bot]andmudler
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7e4a60c701 |
chore: ⬆️ Update TheTom/llama-cpp-turboquant to 8a891f4b566efdbd3cea92fafee3227a0a267683 (#11258)
⬆️ Update TheTom/llama-cpp-turboquant Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Dimitris Karakasilisandlocalai-org-maint-bot
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c089caf320 |
feat(sycl): make the intel llama.cpp backend self-contained on any host (#10991)
* feat(sycl): make the intel llama.cpp backend self-contained on any host The SYCL backend shipped an incomplete oneAPI runtime AND relied on a host-provided GPU driver, so it only ran inside the build container. On a bare host it died with "libze_loader.so.1 / libdnnl.so.3: cannot open shared object file", and even with the host's Intel driver installed it SIGSEGV'd during SYCL init when the host driver was built against a newer glibc than the backend's bundled loader (rolling-release distros). package_intel_libs now bundles the complete, coherent oneAPI runtime (the missing MKL ILP64 / sycl_blas / tbb_thread + oneDNN + the dlopen'd UR adapters, plus a sweep of the backend binaries' own direct deps) and the Intel GPU userspace driver (libze_intel_gpu + libigdrcl + IGC + gmm) with its OpenCL ICD manifest, mirroring how package_vulkan_libs bundles Mesa. run.sh points the Level Zero and OpenCL loaders at the bundled driver, and install-base-deps.sh installs it in the SYCL build image. Bundling the driver is safe across kernels because it talks to the host i915/xe via the stable DRM UAPI (unlike NVIDIA's kernel-locked userspace). Validated on Arch (glibc 2.43, i915): the backend loads and runs on an Iris Xe with no host Intel packages installed. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me> * fix(sycl): install a driver that exists, and let the user choose their own The driver install added earlier in this branch asked apt for intel-level-zero-gpu, which is not a package in Ubuntu 24.04. apt fails outright on an unknown name, so neither driver was installed, nothing was there to copy, and the images carried no driver at all. It now comes from Intel's own repository, which has 25.18 for this Ubuntu release, against 23.43 from late 2023 in the Ubuntu archive. The archive driver does not know any card released since, so a machine with a recent Intel GPU would end up carrying a driver that cannot drive it. Anything that goes wrong during that install fails the build on purpose: an unreachable repository is a passing problem that a retry fixes, while quietly carrying a different driver, or none, is a difference nobody would notice until a user reports an idle GPU. run.sh used to overwrite whatever driver the user had chosen. Level Zero uses only the driver it is given, so on a machine with a card too new for the carried driver, the GPU would go unused with no way back. Both that setting and the OpenCL one are now left alone when already set, and the docs say how to point a backend at the machine's own driver. The OpenCL setting also used to be applied whenever the backend held a driver list, even when the driver it named had not been copied, which leaves OpenCL with nothing instead of falling back to the machine's own driver. It now requires the copied driver to be present, and the packaging leaves out the list entry of any driver it did not copy. The oneAPI images list a processor-only OpenCL library, which was being carried with nothing behind it. Two more corrections in the packaging. The scan for libraries a program is linked against only looked at files named llama-cpp-*, so turboquant and bonsai, which are also built for Intel GPUs, were left with the incomplete set of libraries this branch set out to fix; it now looks at every program in the directory. And a build that should carry a driver but ends up without one now says so, which is what a stale prebuilt base image looks like: such a backend still runs on a machine that has its own driver, so nothing fails and the only other symptom is a user reporting an idle GPU. Backends now also ask the driver to report how much graphics memory is free, without which llama.cpp reads zero on an integrated GPU, since such a chip shares the system memory instead of having its own. turboquant and bonsai get the same run.sh handling as llama.cpp. The driver is only carried by the builds that start through run.sh, because run.sh is what points Level Zero and OpenCL at it. The Python backends for Intel GPUs start differently and would never load it, so they keep using the machine's own driver rather than carrying several hundred megabytes they cannot use. Checked in a container on Ubuntu 24.04: the install brings driver 25.18 with the files where the packaging expects them, an unreachable repository fails the build, and the copied set resolves on its own once the machine's Intel packages are moved away. Assisted-by: Claude:claude-opus-5 Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me> * fix(ci): rebuild every Linux backend when the GPU packaging script changes scripts/build/package-gpu-libs.sh decides which GPU libraries end up inside an image. The filter that builds the backend matrix listed it as an input of the Python images only, so changing it rebuilt no Go and no C++ backend, even though those run it from their own package.sh. A packaging fix aimed at the Intel llama.cpp backend could merge and reach no image, which is the same failure this rule was written to prevent. Assisted-by: Claude:claude-opus-5 Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me> * fix(sycl): carry only the driver Level Zero uses, not the OpenCL one llama.cpp reaches an Intel GPU through Level Zero, which hands the driver programs that are already compiled and so needs only the back end of the graphics compiler. The OpenCL driver can be handed source code instead, so it needs the compiler's front end as well, and that arrives with its own copy of clang. Carrying it cost about 139 MB in every backend built for Intel GPUs, and took the carried set from 123 MB to 261 MB. Nothing here takes that path. No LocalAI code selects an OpenCL device, each backend image holds one backend, and the documentation never described OpenCL as a way to run models: the only mentions are a stale clblas row in the BUILD_TYPE table, for a llama.cpp backend that no longer exists and that no build matrix entry uses, and the sycl-ls troubleshooting hint. Before this branch the packaging carried the OpenCL loader and adapter but no driver, so the path could not work in a released image either. There is nobody to keep working. The driver list that OpenCL reads is no longer carried, and run.sh no longer sets OCL_ICD_VENDORS, so OpenCL inside a container keeps using whatever the image provides rather than being pointed at a directory with no driver in it. Checked in a container against the real 25.18 driver: the carried set is 123 MB with nothing unresolved, and Level Zero still reports the GPU with the machine's own Intel packages moved out of the way. Neither the Level Zero driver nor the compiler back end names the front end or clang among the libraries it opens by name, so the leaner set is complete for this path. Assisted-by: Claude:claude-opus-5 Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me> --------- Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me> Co-authored-by: localai-org-maint-bot <bot-opensource@localaisrl.com> |
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Leoyandlocalai-org-maint-bot
|
632c4b6db2 |
refactor(backends): extract package-system-libs.sh from 31 package.sh (#11095)
refactor(backends): extract shared package-system-libs.sh from package.sh
The arch-detect-and-copy-system-libs block (Darwin rpath / x86_64 / aarch64
loader + libc/libstdc++/libgcc_s/libm/libgomp/libdl/librt/libpthread) was
inlined verbatim in 31 backend package.sh scripts. Extract it into a single
sourced scripts/build/package-system-libs.sh, the CPU-side counterpart to
scripts/build/package-gpu-libs.sh and its sourcing contract.
Consolidating the copies fixes three drift classes that had crept in:
- libgcc_s.so.1 and libstdc++.so.6 were listed twice in 9 backends
(acestep-cpp, crispasr, moss-tts-cpp, omnivoice-cpp, piper,
qwen3-tts-cpp, silero-vad, stablediffusion-ggml, whisper); the shared
script copies each once.
- libgomp.so.1 was omitted from opus. OpenMP consumers dlopen it rather
than link it, so the missing copy only failed at runtime; the shared
script always includes it.
- the Darwin @loader_path/lib rpath was applied only in piper and
silero-vad; both now pass their packaged binary to the shared script,
preserving that behavior. Every other backend passes an empty binary
path so no rpath is added, preserving its current behavior.
Each backend's pre/post packaging steps (binary copy, run.sh, ldd closure
walks, ggml variant bundling, espeak/OpenBLAS extras, the ds4 validate step)
are preserved verbatim; only the inline if/elif/else arch block is replaced
by a single source line.
Signed-off-by: supermario_leo <leo.stack@outlook.com>
Co-authored-by: localai-org-maint-bot <bot-opensource@localaisrl.com>
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Richard Palethorpe
|
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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9fbb8e89cf |
fix(turboquant): supersede stale dependency bump (#11064)
* ⬆️ Update TheTom/llama-cpp-turboquant Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(turboquant): refresh HIP compatibility patch The updated fork now carries its own HIP-safe peer-copy path, so the old hunk no longer applies. Keep only the event-creation compatibility change that the fork still needs. Assisted-by: Codex:gpt-5 [Codex] --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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LocalAI [bot]andEttore Di Giacinto
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1f53dff436 |
fix(turboquant,bonsai): do not apply vendored llama.cpp patches to fork trees (#10866)
The turboquant and bonsai backends copy backend/cpp/llama-cpp/ wholesale into their build directories and reuse its Makefile/prepare.sh against their own llama.cpp forks. When PR #10837 added backend/cpp/llama-cpp/patches/0001-add-minimax-m3-support.patch, the copied patches/ directory was mis-applied to the fork checkouts: the fork trees diverge from upstream, hunks rejected, and because the patch-apply loop in prepare.sh ran before set -e took effect the build kept going and died much later with a confusing compile error ("'LLM_ARCH_MINIMAX_M3' was not declared in this scope"). This broke tests-turboquant-grpc on that PR. Two hardening changes: - turboquant/bonsai Makefiles: delete the copied patches/ directory right after the cp -rf of backend/cpp/llama-cpp/. Patches vendored for upstream llama.cpp must never be applied to the forks; each fork carries its own patch series under backend/cpp/<backend>/patches/, applied by its apply-patches.sh. - llama-cpp prepare.sh: run the patch-apply loop under set -e so a rejecting patch fails fast and loudly at apply time instead of surfacing as a downstream compile error. A missing or empty patches/ directory remains a no-op success, so all existing callers (the llama-cpp Makefile targets and the turboquant/bonsai copies) are unaffected when no patches ship. Exposed by PR #10837. 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
|
348f3c87c0 |
fix(gpu-libs): bundle hipBLASLt TensileLibrary data so ROCm backends stop falling back (#10660) (#10672) the
The ROCm packager copied rocBLAS kernel data (rocblas/library/*.dat) into the
bundled lib/ dir and run.sh pointed ROCBLAS_TENSILE_LIBPATH at it, but the
parallel hipBLASLt data dir (hipblaslt/library/TensileLibrary_lazy_gfx*.dat)
was never packaged and no HIPBLASLT_TENSILE_LIBPATH was set. The bundled
libhipblaslt.so therefore resolved its per-arch kernel data relative to itself,
found nothing, and silently fell back to slow generic kernels, logging:
rocblaslt error: Cannot read "TensileLibrary_lazy_gfx1201.dat": No such file or directory
rocblaslt error: Could not load "TensileLibrary_lazy_gfx1201.dat"
Fix, mirroring the existing rocBLAS handling:
- package-gpu-libs.sh: extract the rocblas data-dir copy into a reusable
copy_rocm_data_dir helper and call it for both rocblas and hipblaslt.
- llama-cpp/turboquant run.sh: export HIPBLASLT_TENSILE_LIBPATH when the
bundled hipblaslt/library dir exists.
The helper takes an optional ROCM_BASE_DIRS override so the copy is unit
testable without a real ROCm install; add a regression test that runs
package_rocm_libs against a fabricated ROCm tree and asserts both data dirs
are bundled.
Note: this bundles whatever gfx*.dat the build image's ROCm provides. If a
given arch's tensile data is absent from the shipped ROCm, that arch still
needs a ROCm bump; the packaging gap itself is fixed for every supported arch.
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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74f07ecc35 |
fix(backends): quote $CURDIR in run.sh (fixes backends in paths with spaces) (#10519)
fix(backends): quote $CURDIR in run.sh so backends work in paths with spaces The backend launcher scripts derive their own directory with CURDIR=$(dirname "$(realpath $0)") and then referenced it unquoted as $CURDIR (e.g. [ -f $CURDIR/lib/ld.so ], export LD_LIBRARY_PATH=$CURDIR/lib:..., exec $CURDIR/<binary> "$@"). When a backend is installed under a path that contains a space - notably macOS's ~/Library/Application Support/... - bash word-splits the unquoted $CURDIR, so the test builtin fails with "binary operator expected" and exec tries to run ".../Library/Application", yielding "No such file or directory". The backend never starts, surfacing as a gRPC "service not ready" error and an HTTP 500. Quote $CURDIR (and the realpath "$0") in every affected run.sh; no logic changes. Co-authored-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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LocalAI [bot]andEttore Di Giacinto
|
4ac67d255d |
feat: single-build ggml CPU_ALL_VARIANTS for llama-cpp + turboquant (x86/arm64/apple) (#10497)
* feat(llama-cpp): single x86 CPU build via ggml CPU_ALL_VARIANTS
Replace the per-microarch avx/avx2/avx512/fallback multi-binary build on
x86 with a single grpc-server plus the dlopen-able libggml-cpu-*.so set
that ggml's backend registry selects at runtime by probing host CPU
features. One build instead of four, broader microarch coverage (adds
alderlake AVX-VNNI, zen4 AVX512-BF16, sapphirerapids AMX), and the
shell-side /proc/cpuinfo probing in run.sh goes away.
Build/link notes:
- CPU_ALL_VARIANTS requires GGML_BACKEND_DL + BUILD_SHARED_LIBS=ON, so
ggml/llama become shared objects. SHARED_LIBS is now a make variable
(default OFF) so the override survives the recursive sub-make into the
VARIANT build dir instead of being re-clobbered by the base flags.
- The cpu-all target also builds "--target ggml": the per-microarch
backends are runtime-dlopened, not link deps, so they only compile via
ggml's add_dependencies().
- hw_grpc_proto is pinned STATIC. Under BUILD_SHARED_LIBS=ON it would
otherwise become a DSO referencing hidden-visibility symbols in the
static libprotobuf.a, which fails to link ("hidden symbol ... is
referenced by DSO"). Keeping it static links gRPC/protobuf into the
executable while only ggml/llama stay shared, so no PIC or base-image
change is required.
- package.sh bundles the libggml-*.so set into package/lib; ggml finds
them by scanning the bundled ld.so directory (/proc/self/exe), which
run.sh launches from.
Scope: x86 only. arm64/darwin keep the single fallback build. The
ik-llama-cpp / turboquant forks and the other ggml C++ backends are
unchanged; the same recipe applies but is out of scope here.
Validated with a full docker build plus a live inference smoke test:
the model loads, ggml selects the AVX512_BF16 variant on a Zen-class
host, and tokens generate correctly.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(llama-cpp,turboquant): extend CPU_ALL_VARIANTS to arm64 + turboquant
- llama-cpp: x86 AND arm64 now use the single llama-cpp-cpu-all build
(only hipblas keeps the fallback build). ggml's arm64 variant table
(armv8.x / armv9.x, plus apple_m* on darwin) is selected at runtime.
- turboquant: same recipe via a turboquant-cpu-all target. turboquant
copies backend/cpp/llama-cpp's CMakeLists.txt + Makefile per flavor, so
the hw_grpc_proto STATIC fix and the SHARED_LIBS / EXTRA_CMAKE_ARGS
make-vars are inherited; the target just passes SHARED_LIBS=ON, the DL
flags and --target ggml through, then collects the .so set. run.sh and
package.sh updated to ship/select turboquant-cpu-all.
- Makefile lib-collection find now also matches *.dylib (for the darwin
build, which emits dylibs rather than .so).
ik-llama-cpp is intentionally left unchanged: its pinned ggml has no
CPU_ALL_VARIANTS support and its IQK kernels require AVX2, so the
per-microarch dynamic backend set does not apply.
Scope still excludes the darwin packaging wiring (separate change).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(llama-cpp,turboquant): arm64 gcc-14 for SME variants + darwin cpu-all packaging
- arm64: ggml CPU_ALL_VARIANTS builds armv9.2 SME variants whose -march=...+sme
is rejected by the Ubuntu 24.04 default gcc-13. Build the arm64 variants with
gcc-14 (installed in the compile step). The host only selects a variant it
actually supports at runtime, but every variant must still compile.
- darwin: scripts/build/llama-cpp-darwin.sh builds llama-cpp-cpu-all instead of
the fallback binary, keeps Metal (GGML_METAL stays ON; --target ggml also builds
ggml-metal). The per-microarch libggml-cpu-*.dylib are placed in the package
root next to the binary (darwin has no bundled ld.so, so ggml's executable-dir
scan looks there), while the other shared dylibs go in lib/ for DYLD_LIBRARY_PATH.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(llama-cpp-darwin): distribute ggml backends by suffix (.so root, .dylib lib)
ggml emits its loadable backends (per-microarch CPU variants, metal, blas) with a
.so suffix even on darwin, while the core libraries (ggml-base/ggml/llama/
llama-common/mtmd) use .dylib. Split the distribution by suffix: .so DL backends
go in the package root for ggml's executable-directory scan, .dylib core libs go
in lib/ for DYLD_LIBRARY_PATH. The previous .dylib name-pattern matched none of the
variants.
Verified on an M4: ggml loads the apple_m4 CPU variant (SME=1) and Metal, model
loads and generates correct tokens.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(llama-cpp,turboquant): only CPU_ALL_VARIANTS for pure-CPU builds, GPU uses fallback
The previous gate sent every non-hipblas build through llama-cpp-cpu-all, so the
GPU image builds (cublas, sycl_f16/f32, vulkan, nvidia l4t) compiled the whole CPU
microarch variant matrix on top of their already-huge GPU backend - blowing the
build time (the sycl job was only 59% done after 2h11m) - and the arm64 l4t build
failed at `apt-get install gcc-14` (exit 100) on the Jetson base.
Gate on an empty BUILD_TYPE instead: only the pure CPU image (build-type: '' in
.github/backend-matrix.yml) builds the CPU_ALL_VARIANTS set; every GPU build gets a
single fallback CPU grpc-server, since the accelerator does the compute. This also
confines the arm64 gcc-14 step (needed for the armv9.2 SME variants) to the CPU
build, away from the GPU base images.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* docs(llama-cpp): correct run.sh comment for arm64/darwin cpu-all
arm64 and darwin CPU images now also ship llama-cpp-cpu-all (not fallback-only);
only GPU images ship fallback-only. Fix the stale comment to match.
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
|
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> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
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>
|
||
|
LocalAI [bot]andmudler
|
ddbbdf45b9 |
chore: ⬆️ Update TheTom/llama-cpp-turboquant to 5aeb2fdbe26cd4c534c6fa15de73cb5749bd0403 (#9740)
⬆️ Update TheTom/llama-cpp-turboquant Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
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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|
LocalAI [bot]andmudler
|
a315c321c1 |
chore: ⬆️ Update TheTom/llama-cpp-turboquant to 69d8e4be47243e83b3d0d71e932bc7aa61c644dc (#9638)
⬆️ Update TheTom/llama-cpp-turboquant Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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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>
|
||
|
LocalAI [bot]andmudler
|
f5c268deac |
chore: ⬆️ Update TheTom/llama-cpp-turboquant to 11a241d0db78a68e0a5b99fe6f36de6683100f6a (#9571)
⬆️ Update TheTom/llama-cpp-turboquant Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]andmudler
|
806ea24ff4 |
chore: ⬆️ Update TheTom/llama-cpp-turboquant to 67559e580b10e4e47e9a6fd6218873997976886d (#9497)
⬆️ Update TheTom/llama-cpp-turboquant Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
Ettore Di Giacinto
|
369c50a41c |
fix(turboquant): drop ignore-eos patch, bump fork to b8967-627ebbc (#9423)
* fix(turboquant): drop ignore-eos patch, bump fork to b8967-627ebbc
The upstream PR #21203 (server: respect the ignore_eos flag) has been
merged into the TheTom/llama-cpp-turboquant feature/turboquant-kv-cache
branch. With the fix now in-tree, 0001-server-respect-the-ignore-eos-flag.patch
no longer applies (git apply sees its additions already present) and the
nightly turboquant bump fails.
Retire the patch and bump the pin to the first fork revision that carries
the merged fix (tag feature-turboquant-kv-cache-b8967-627ebbc). This matches
the contract in apply-patches.sh: drop patches once the fork catches up.
* fix(turboquant): patch out get_media_marker() call in grpc-server copy
CI turboquant docker build was failing with:
grpc-server.cpp:2825:40: error: use of undeclared identifier
'get_media_marker'
The call was added by
|
||
|
Ettore Di Giacinto
|
5837b14888 |
chore: ⬆️ Update TheTom/llama-cpp-turboquant to `45f8a066ed5f5bb38c695cec532f6cef9f4efa9d' (#9385)
chore: ⬆️ Update TheTom/llama-cpp-turboquant to `45f8a066ed5f5bb38c695cec532f6cef9f4efa9d`
Drop 0002-ggml-rpc-bump-op-count-to-97.patch; the fork now has
GGML_OP_COUNT == 97 and RPC_PROTO_PATCH_VERSION 2 upstream.
Fetch all tags in backend/cpp/llama-cpp/Makefile so tag-only commits
(the new turboquant pin is reachable only through the tag
feature-turboquant-kv-cache-b8821-45f8a06) can be checked out.
|
||
|
Ettore Di Giacinto
|
95efb8a562 |
feat(backend): add turboquant llama.cpp-fork backend (#9355)
* feat(backend): add turboquant llama.cpp-fork backend
turboquant is a llama.cpp fork (TheTom/llama-cpp-turboquant, branch
feature/turboquant-kv-cache) that adds a TurboQuant KV-cache scheme.
It ships as a first-class backend reusing backend/cpp/llama-cpp sources
via a thin wrapper Makefile: each variant target copies ../llama-cpp
into a sibling build dir and invokes llama-cpp's build-llama-cpp-grpc-server
with LLAMA_REPO/LLAMA_VERSION overridden to point at the fork. No
duplication of grpc-server.cpp — upstream fixes flow through automatically.
Wires up the full matrix (CPU, CUDA 12/13, L4T, L4T-CUDA13, ROCm, SYCL
f32/f16, Vulkan) in backend.yml and the gallery entries in index.yaml,
adds a tests-turboquant-grpc e2e job driven by BACKEND_TEST_CACHE_TYPE_K/V=q8_0
to exercise the KV-cache config path (backend_test.go gains dedicated env
vars wired into ModelOptions.CacheTypeKey/Value — a generic improvement
usable by any llama.cpp-family backend), and registers a nightly auto-bump
PR in bump_deps.yaml tracking feature/turboquant-kv-cache.
scripts/changed-backends.js gets a special-case so edits to
backend/cpp/llama-cpp/ also retrigger the turboquant CI pipeline, since
the wrapper reuses those sources.
* feat(turboquant): carry upstream patches against fork API drift
turboquant branched from llama.cpp before upstream commit 66060008
("server: respect the ignore eos flag", #21203) which added the
`logit_bias_eog` field to `server_context_meta` and a matching
parameter to `server_task::params_from_json_cmpl`. The shared
backend/cpp/llama-cpp/grpc-server.cpp depends on that field, so
building it against the fork unmodified fails.
Cherry-pick that commit as a patch file under
backend/cpp/turboquant/patches/ and apply it to the cloned fork
sources via a new apply-patches.sh hook called from the wrapper
Makefile. Simplifies the build flow too: instead of hopping through
llama-cpp's build-llama-cpp-grpc-server indirection, the wrapper now
drives the copied Makefile directly (clone -> patch -> build).
Drop the corresponding patch whenever the fork catches up with
upstream — the build fails fast if a patch stops applying, which
is the signal to retire it.
* docs: add turboquant backend section + clarify cache_type_k/v
Document the new turboquant (llama.cpp fork with TurboQuant KV-cache)
backend alongside the existing llama-cpp / ik-llama-cpp sections in
features/text-generation.md: when to pick it, how to install it from
the gallery, and a YAML example showing backend: turboquant together
with cache_type_k / cache_type_v.
Also expand the cache_type_k / cache_type_v table rows in
advanced/model-configuration.md to spell out the accepted llama.cpp
quantization values and note that these fields apply to all
llama.cpp-family backends, not just vLLM.
* feat(turboquant): patch ggml-rpc GGML_OP_COUNT assertion
The fork adds new GGML ops bringing GGML_OP_COUNT to 97, but
ggml/include/ggml-rpc.h static-asserts it equals 96, breaking
the GGML_RPC=ON build paths (turboquant-grpc / turboquant-rpc-server).
Carry a one-line patch that updates the expected count so the
assertion holds. Drop this patch whenever the fork fixes it upstream.
* feat(turboquant): allow turbo* KV-cache types and exercise them in e2e
The shared backend/cpp/llama-cpp/grpc-server.cpp carries its own
allow-list of accepted KV-cache types (kv_cache_types[]) and rejects
anything outside it before the value reaches llama.cpp's parser. That
list only contains the standard llama.cpp types — turbo2/turbo3/turbo4
would throw "Unsupported cache type" at LoadModel time, meaning
nothing the LocalAI gRPC layer accepted was actually fork-specific.
Add a build-time augmentation step (patch-grpc-server.sh, called from
the turboquant wrapper Makefile) that inserts GGML_TYPE_TURBO2_0/3_0/4_0
into the allow-list of the *copied* grpc-server.cpp under
turboquant-<flavor>-build/. The original file under backend/cpp/llama-cpp/
is never touched, so the stock llama-cpp build keeps compiling against
vanilla upstream which has no notion of those enum values.
Switch test-extra-backend-turboquant to set
BACKEND_TEST_CACHE_TYPE_K=turbo3 / _V=turbo3 so the e2e gRPC suite
actually runs the fork's TurboQuant KV-cache code paths (turbo3 also
auto-enables flash_attention in the fork). Picking q8_0 here would
only re-test the standard llama.cpp path that the upstream llama-cpp
backend already covers.
Refresh the docs (text-generation.md + model-configuration.md) to
list turbo2/turbo3/turbo4 explicitly and call out that you only get
the TurboQuant code path with this backend + a turbo* cache type.
* fix(turboquant): rewrite patch-grpc-server.sh in awk, not python3
The builder image (ubuntu:24.04 stage-2 in Dockerfile.turboquant)
does not install python3, so the python-based augmentation step
errored with `python3: command not found` at make time. Switch to
awk, which ships in coreutils and is already available everywhere
the rest of the wrapper Makefile runs.
* Apply suggestion from @mudler
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
---------
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
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