find_slot places a sequence's tokens at permuted non-contiguous blocks; greedy
generation is token-identical to stock (verified on Qwen3-0.6B at the pin),
branch confirmed firing. Default off. The placement substrate for the gather-read.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
First patch of the stacking series. Adds src/paged-kv-manager.{h,cpp} (the
CPU-verified vLLM-parity block manager) + CMake entry. No behavior change.
Generated against the pinned LLAMA_VERSION; applies clean.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Numbered patches under backend/cpp/llama-cpp/patches/ applied in order against
the pinned LLAMA_VERSION (build hook in the llama.cpp: target). Each phase is one
small, independently-buildable patch so the work rebases cleanly across llama.cpp
bumps (anti-drift). README defines the series (0001 vendor manager -> 0006 prefix
caching) + the regen workflow.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
No tcgen05/CUTLASS grouped-GEMM MoE kernel exists upstream (merged/in-flight/
draft); CUTLASS not a dep; no fork has one; activation-quant gather already
fused. Matching vLLM needs a from-scratch tcgen05 grouped GEMM (months,
maintainers deferring to cuTile). No tractable patch closes the 27x.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
On NVIDIA Blackwell consumer GPUs (sm_120/121, incl. GB10/DGX Spark) a larger
physical batch (n_ubatch) materially lifts MoE prefill throughput - measured on
a GB10 with Qwen3-30B-A3B to lift the prefill ceiling and saturate at ~2048.
When a model config leaves `batch:` unset, EffectiveBatchSize now picks 2048 on
Blackwell instead of 512; explicit `batch:` always overrides. Detection is a
shared, cached Go helper (xsysinfo.IsNVIDIABlackwell, nvidia-smi compute_cap
>= 12). Logic is isolated in core/backend/hardware_defaults.go and applied at
the common ModelOptions builder, so it covers the C++ llama.cpp backend too.
Measured (GB10, Qwen3-Coder-30B-A3B MXFP4): prefill ub512 2994 -> ub2048 3316
t/s; saturates past 2048. Also recorded in the DGX gap plan: 4-bit quant alone
captures the decode win (Q4_K_M 93.5 >= MXFP4 86.4 t/s), MXFP4's only edge is
prefill via Blackwell FP4 tensor cores.
Tests: hardware_defaults_internal_test.go; existing NBatch specs pinned to the
no-Blackwell branch for determinism.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Captures the full dgx.casa investigation: Q8/F16/vLLM baselines, concurrency
sweeps, paged-patch (no concurrency effect), nsys+code root-cause (MoE int8
MMQ on Ampere-class tensor cores = 74.5% compute, no FP8 path), and the
lever plan.
Measured wins:
- Lever 1 (MXFP4 / Blackwell FP4 path): decode +50-66% over Q8, prefill
plateau +66% (2200->3650). MXFP4 decode beats vLLM FP8 at B=1 (83 vs 48),
near-parity B=8. Prefill still plateaus (fused-MoE-GEMM gap).
- Lever 2 (ubatch): saturates at 2048; ceiling is the kernel, not batch.
Designed (not built): Lever 3 fused FP4/FP8 MoE grouped GEMM, Lever 4 FP8
GEMM (needs ggml_mul_mat_ext scale plumbing), Lever 5 tcgen05 kernels, and
the complete paged attention (on-demand alloc + gather-read + continuous
batching + prefix sharing). Honest scope: each is multi-week kernel/systems
work.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Wire paged, non-contiguous fixed-size BLOCK placement into the real
llama.cpp KV cache (find_slot), behind env LLAMA_KV_PAGED, and validate
Gate 0 on a real GGUF: Qwen3-0.6B greedy generation is TOKEN-IDENTICAL to
the contiguous cache while its KV is physically scattered across permuted
blocks (cells 0-15, 144-159, 32-47, ...). Proven non-contiguous via
LLAMA_KV_PAGED_DEBUG, not a silent fallback.
This retires the correctness premise of paged attention IN THE MODEL (not
just at the ggml-op level): attention is invariant to physical KV placement,
because reads use per-cell pos/seq metadata for masking. The patch lives at
patches/0001-paged-kv-block-placement.patch (against llama.cpp 0253fb21f).
Scope: storage/placement layer, single sequence. Remaining (P4): the
gather-read compute path (attend only a seq's own blocks) for the throughput
win, and the multi-sequence driver. README updated with repro + status.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Capture verified state (P0 manager parity, P1 ggml write/gather, P2 attention
numerics 7.5e-08, P3 capacity 9.2x + prefix-sharing 11.3x) and the exact
remaining work: wire build_attn_paged into llama-graph.cpp and validate
token-identical generation on Qwen3-0.6B (Gate 0), then win-2 throughput.
Records the integration seams (create_memory, find_slot, get_k/get_v,
build_attn, mask) and the honest caveats (unified cache already shares a
pool; vLLM's classic kernel is deprecated) so the next session starts warm.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Quantify the two multi-tenant wins that are properties of the host-side
block model (vLLM-parity), independent of the in-model compute path:
WIN 1 concurrency capacity @ 512-block budget
contiguous (reserve n_ctx/seq): 4 sequences
paged (on-demand blocks): 37 sequences
--> 9.2x more concurrent sequences
WIN 3 cross-tenant prefix sharing (32 tenants, 1024-tok shared prefix)
prefix-cache OFF: 2176 physical blocks
prefix-cache ON: 192 physical blocks
--> 11.3x less KV memory
WIN 2 (throughput) is deliberately reported as PENDING: it requires the
paged gather-read path wired into llama-graph.cpp (Gate 0) and is not
measurable at the allocation layer. The win-1 baseline is per-sequence
n_ctx reservation (stream mode); llama.cpp's unified cache already shares
one pool, so the honest win there is on-demand sizing + prefix dedup.
Phase 3 (partial) of docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Retire the central numeric risk from the design: feeding gather-to-scratch
KV (a sequence whose blocks are non-contiguous in the shared pool, [2,1,5])
into ggml's standard attention ops produces correct attention.
Path under test: set_rows write -> get_rows gather (K and V) ->
mul_mat(K,Q) -> soft_max_ext -> mul_mat(V^T, probs). Result is compared
against an independent host-computed softmax attention over the same K/V/Q.
Max abs error ~7.5e-08 (n_kv=48, d=8, n_q=4).
This proves the paged read path is numerically sound on CPU with no new
ggml op. Remaining: wire build_attn_paged into llama-graph.cpp and validate
Gate 0 (token-identical greedy generation in a real model).
Phase 2 (core) of docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Validate the paged KV read/write path at the ggml-op level, driven by
PagedKVManager:
- write: ggml_set_rows(pool, k_src, slot_mapping) scatter K rows by slot
- read: ggml_get_rows(pool, gather_idx) gather a seq's slots into
contiguous scratch (the tensor an attention kernel consumes)
The test forces a non-contiguous, out-of-order physical block layout
(allocate seqA+seqB, free seqA, reallocate seqC -> blocks [2,1,5]) and
proves gather(write(x)) == x plus cross-sequence isolation in the shared
pool. This de-risks the central question (does slot-addressed paged storage
round-trip correctly through ggml) before the llama-graph integration.
Pool is statically allocated via ggml_backend_alloc_ctx_tensors, mirroring
how llama.cpp allocates its KV cache. CPU backend, no new ggml op.
Built against ggml from the vendored llama.cpp checkout.
Phase 1 of docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Squashed feat/pii-ner-tier-engine rebased onto master (was 45 commits; see
backup/pii-ner-tier-engine-prerebase). Net change:
- privacy-filter.cpp: standalone GGML engine for the openai-privacy-filter
PII/NER token classifier, wired as a LocalAI gRPC backend (CPU/CUDA/Vulkan).
TokenClassify moves off the patched llama.cpp path onto this backend.
- PII filter reworked to be NER-centric (encoder/NER detection tier scanning
whole conversations as one document), with a recreated bounded restricted-
regex secret-matching pattern detector tier alongside it (per-model
pii_detection.builtins / .patterns + core/services/routing/piipattern).
- Detection labelled by source (ner vs pattern); backend trace / confidence /
debug observability; analyze/redact exposed as a synchronous API.
- Instance-wide default detector policy + per-usecase default-on; request
filtering extended to completions, embeddings, edits & Ollama.
- React UI: NER-centric PII editor, detector-models table, pattern/builtins
editor, middleware default-policy UI.
- Gallery: privacy-filter-multilingual token-classify model + NER install
filter; token_classify known_usecase; batch sized to context for NER models.
privacy-filter backend registered in the backend gallery (cpu/vulkan/cuda-13
meta + image entries with a capabilities map) matching its CI matrix jobs,
and an /import-model auto-detect importer (PrivacyFilterImporter, narrow
privacy-filter GGUF detection) replacing the prior pref-only registration.
Reconciled against master's independent evolution:
- Dropped master's PIIPatternOverrides feature (global-pattern runtime
overrides + /api/pii/patterns API + runtime_settings.json persistence). The
per-model NER + pattern-detector design supersedes it; it was built on the
global redactor pattern set this branch replaced.
- Reverted the llama.cpp Score carry-patch (0006-server-task-type-score):
removed the patch and restored master's grpc-server.cpp Score RPC (direct
llama_decode, slot-loop bypass) and LLAMA_VERSION pin, plus master's
model_config validation forbidding score + chat/completion/embeddings on
llama-cpp. token_classify is unaffected (it runs on the privacy-filter
backend, not llama-cpp).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
feat(ds4): wire SSD streaming + quality engine options, add 128GB DeepSeek gallery models
The ds4 backend zero-initialized ds4_engine_options and exposed none of the
engine's tunable knobs, so SSD streaming (run a model larger than RAM by
streaming routed MoE experts from the GGUF on SSD) and the quality/perf knobs
were unreachable from LocalAI model YAMLs.
Map ModelOptions.Options onto ds4_engine_options through a declarative table
(kEngineOptSpecs + apply_engine_option) instead of per-field branches: the
struct is fixed C with no reflection, so the field set is enumerated once and a
future knob is a one-line table row. Two fields use ds4's own typed parsers
(GiB budgets, cache-experts count-or-NGB). Bare flags (e.g. "ssd_streaming")
mean true; path-type options (mtp_path, expert_profile_path,
directional_steering_file) resolve relative to the model directory so a gallery
entry can reference a companion file by bare filename. mtp_draft/mtp_margin are
now validated rather than parsed with throwing std::stoi/std::stof.
Add gallery entries for the 128 GB class:
- deepseek-v4-flash-q2-q4 (~91 GB, mixed q2/q4, fits RAM, higher quality)
- deepseek-v4-flash-q4-ssd (~153 GB full 4-bit, runs on 128 GB via SSD streaming)
- deepseek-v4-flash-q2-mtp (~81 GB + MTP speculative draft weights)
- deepseek-v4-pro-q2-ssd (~433 GB Pro, experimental SSD streaming)
SSD streaming is Metal (Darwin) only; the options are inert on CUDA/CPU.
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>
* 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>
* ⬆️ Update antirez/ds4
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(ds4): add Homebrew include/lib prefix for Darwin grpc-proto build
The darwin/metal ds4 backend job runs for the first time on this bump
(it was skipped on prior ds4 PRs) and fails compiling backend.pb.cc with
'google/protobuf/runtime_version.h' file not found.
hw_grpc_proto links neither protobuf::libprotobuf nor gRPC::grpc++, so
the generated proto sources rely on default system include paths. That
works on Linux (/usr/include) but not on macOS, where Homebrew installs
under /opt/homebrew. Add the Homebrew prefix to include/link dirs on
Darwin, mirroring the llama-cpp backend that already builds on Darwin CI.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(ds4): install nlohmann-json on Darwin CI for ds4 backend
After the protobuf include-path fix the ds4 darwin build advances to
compiling dsml_renderer.cpp, which includes <nlohmann/json.hpp> and
#errors when absent. On Linux the header comes from apt nlohmann-json3-dev
in the build image; the macOS runner had no equivalent. Add the
header-only nlohmann-json formula to the shared Darwin backend brew
install/link list and Homebrew cache, alongside the existing deps.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(ds4): build proper OCI image tar for Darwin backend
The darwin packaging referenced scripts/build/oci-pack.sh, which was
never added to the tree, so it fell back to a plain 'tar' that omits
manifest.json. 'local-ai backends install' then rejects the tarball
with 'file manifest.json not found in tar'.
Use './local-ai util create-oci-image' (already built by the 'build'
prerequisite of the backends/ds4-darwin target), mirroring
llama-cpp-darwin.sh, to emit a real OCI image the installer accepts.
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>
* 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>
* 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>
* ⬆️ Update antirez/ds4
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(ds4): link ds4_ssd.o into the backend build
Upstream antirez/ds4 splits the SSD expert-cache into its own ds4_ssd.c
translation unit, whose symbols (ds4_ssd_memory_lock_acquire/release,
ds4_ssd_cache_experts_for_byte_budget, ds4_ssd_auto_cache_plan) are
referenced by ds4.c/ds4_cpu.o. The dependency-bump automation regenerated
this branch from clean master and dropped the prior linkage fix, so the
cpu-ds4 / cublas-ds4 backend builds fail again with undefined references.
Re-apply the ds4_ssd.o linkage GPU-agnostically (mirroring ds4_distributed.o)
in both the backend Makefile (DS4_OBJ_TARGET + the engine-object build rule
for every GPU mode) and CMakeLists.txt (list(APPEND DS4_OBJS ds4_ssd.o)).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: 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>
* 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>
* 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>
