The llama.cpp backend already accepts a free-form options: array in the
model config that maps to common_params fields, but a coverage audit
against upstream pin 7f3f843c flagged 12 user-visible knobs that were
neither set via the typed proto fields nor reachable via options:.
Wire them up under the existing if/else chain in params_parse, before
the speculative section. Each new option follows the file's prevailing
patterns (try/catch around numeric parses, the same true/1/yes/on bool
form used elsewhere, hardware_concurrency() fallback for thread counts,
mirror of draft_override_tensor for override_tensor).
Top-level / batching / IO:
- n_ubatch (alias ubatch) -- physical batch size; was previously
force-aliased to n_batch at line 482, blocking embedding/rerank
workloads that need independent control
- threads_batch (alias n_threads_batch) -- main-model batch threads;
mirrors the existing draft_threads_batch
- direct_io (alias use_direct_io) -- O_DIRECT model loads
- verbosity -- llama.cpp log threshold (line 479 had this commented
out)
- override_tensor (alias tensor_buft_overrides) -- per-tensor buffer
overrides for the main model; mirrors draft_override_tensor
Embedding / multimodal:
- pooling_type (alias pooling) -- mean/cls/last/rank/none; previously
only auto-flipped to RANK for rerankers
- embd_normalize (alias embedding_normalize) -- and the embedding
handler now reads params_base.embd_normalize instead of a hardcoded
2 at the previous embd_normalize literal in Embedding()
- mmproj_use_gpu (alias mmproj_offload) -- mmproj on CPU vs GPU
- image_min_tokens / image_max_tokens -- per-image vision token budget
Reasoning surface (the audit-focus three; LocalAI's existing
ReasoningConfig.DisableReasoning only feeds the per-request
chat_template_kwargs.enable_thinking and does not touch any of these):
- reasoning_format -- none/auto/deepseek/deepseek-legacy parser
- enable_reasoning (alias reasoning_budget) -- -1/0/>0 thinking budget
- prefill_assistant -- trailing-assistant-message prefill toggle
All 14 referenced fields exist on both the upstream pin and the
turboquant fork's common.h, so no LOCALAI_LEGACY_LLAMA_CPP_SPEC guard
is needed.
Docs: extend model-configuration.md with new "Reasoning Models",
"Multimodal Backend Options", "Embedding & Reranking Backend Options",
and "Other Backend Tuning Options" subsections; also refresh the
Speculative Type Values table to show the new dash-separated canonical
names alongside the underscore aliases LocalAI still accepts.
Assisted-by: claude-code:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* ⬆️ Update ggml-org/llama.cpp
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(llama-cpp): adapt to upstream COMMON_SPECULATIVE_TYPE_DRAFT rename
ggml-org/llama.cpp#22964 ("spec: update CLI arguments for better
consistency") renamed the speculative type enum values:
COMMON_SPECULATIVE_TYPE_DRAFT -> COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE
COMMON_SPECULATIVE_TYPE_EAGLE3 -> COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3
and the registered name strings flipped from underscore- to dash-
separated form (e.g. ngram_simple -> ngram-simple), with the bare
draft/eagle3 aliases replaced by draft-simple/draft-eagle3.
This broke the build with the new LLAMA_VERSION on every variant
(vulkan/arm64, darwin and likely all the rest) at grpc-server.cpp:461.
Update the upstream branch of the speculative-type fallback to use the
new identifier (the LOCALAI_LEGACY_LLAMA_CPP_SPEC fork branch keeps the
old name), and normalize spec_type option tokens before passing them to
common_speculative_types_from_names so existing model configs that say
spec_type:draft / spec_type:ngram_simple keep working.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: claude-code:claude-opus-4-7
---------
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* 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=${AMDGPU_TARGETS}` pair that
`builder-fromsource` already has (and that `Dockerfile.llama-cpp`
mirrors across both stages). When CI uses the prebuilt base image
(quay.io/go-skynet/ci-cache:base-grpc-*, the common path) the build-arg
passed by the workflow never reaches the env inside the compile stage.
backend/cpp/llama-cpp/Makefile:38 (introduced by #9626) errors out on
hipblas builds when AMDGPU_TARGETS is empty, and the turboquant
Makefile reuses backend/cpp/llama-cpp via a sibling build dir, so the
same check fires from turboquant-fallback under BUILD_TYPE=hipblas:
Makefile:38: *** AMDGPU_TARGETS is empty — set it to a comma-separated
list of gfx targets e.g. gfx1100,gfx1101. Stop.
make: *** [Makefile:66: turboquant-fallback] Error 2
The bug is latent on master because the docker layer cache stays warm
across builds — the compile step rarely re-runs from scratch. The
llama.cpp bump in this PR invalidates the cache, so the missing env var
becomes load-bearing and the hipblas turboquant CI job fails.
Mirror the existing pattern from Dockerfile.llama-cpp.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* ci(bump-deps): register ds4 + move version pin into the Makefile
The initial ds4 PR (#9758) put the upstream commit pin in
backend/cpp/ds4/prepare.sh as a shell variable. The auto-bump bot at
.github/bump_deps.sh greps for ^$VAR?= in a Makefile, so DS4_VERSION
was invisible to it - other backends (llama-cpp, ik-llama-cpp,
turboquant, voxtral, etc.) all pin in their Makefile.
This change:
- Moves DS4_VERSION?= and DS4_REPO?= to the top of
backend/cpp/ds4/Makefile.
- Inlines the git init/fetch/checkout recipe into the 'ds4:' target
(matches llama-cpp's 'llama.cpp:' target pattern). Directory acts
as the target so make only re-clones when missing.
- Deletes the now-redundant prepare.sh.
- Adds antirez/ds4 + DS4_VERSION + main + backend/cpp/ds4/Makefile to
the .github/workflows/bump_deps.yaml matrix so the daily bot opens
PRs against this pin.
- Updates .agents/ds4-backend.md to point at the Makefile.
Verified:
$ grep -m1 '^DS4_VERSION?=' backend/cpp/ds4/Makefile
DS4_VERSION?=ae302c2fa18cc6d9aefc021d0f27ae03c9ad2fc0
$ make -C backend/cpp/ds4 ds4 # clones into ds4/ at the pin
$ make -C backend/cpp/ds4 ds4 # no-op on second invocation
make: 'ds4' is up to date.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci: route backend/cpp/ds4/ changes through changed-backends.js
scripts/changed-backends.js:inferBackendPath has an explicit branch per
cpp dockerfile suffix (ik-llama-cpp, turboquant, llama-cpp). Without a
matching branch the function returns null, the backend never lands in
the path map, and PR change-detection cannot map "backend/cpp/ds4/X
changed" -> "rebuild ds4 image".
This is why PR #9761 produced zero ds4 jobs even though it directly
edits backend/cpp/ds4/Makefile.
Adds the missing branch (Dockerfile.ds4 -> backend/cpp/ds4/), placed
before the llama-cpp branch (since both share the .cpp ancestry but
ds4 is more specific - same ordering rule documented in
.agents/adding-backends.md).
Verified with a local Node simulation of the script against this PR's
diff: the path map now contains 'ds4 -> backend/cpp/ds4/' and a
'backend/cpp/ds4/Makefile' change correctly triggers the ds4 backend
in the rebuild set.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs(adding-backends): harden the two gotchas that bit ds4
Both omissions are silent at the time you ADD a backend - the failure
mode only appears later (the bump bot stays silent forever, or the path
filter shows up on the next PR that touches your backend with zero CI
jobs and looks broken for unrelated reasons). Expanding the
`scripts/changed-backends.js` paragraph from a one-liner to a fully
worked example, and adding a new sibling paragraph for the
`bump_deps.yaml` + Makefile-pin contract.
Both call out the specific mistakes from the ds4 timeline (#9758
→ #9761) so future contributors can pattern-match on the cause.
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>
* test(e2e-backends): allow BACKEND_BINARY for native-built backends
Adds an escape hatch for hardware-gated backends (e.g. ds4) where the
model is too large for Docker build context. When BACKEND_BINARY points
at a run.sh produced by 'make -C backend/cpp/<name> package', the suite
skips docker image extraction and drives the binary directly.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(e2e-backends): validate BACKEND_BINARY basename + log actual source
Two follow-ups from the cbcf5148 code review:
- BACKEND_BINARY now requires a path whose basename is `run.sh`. Without
this check, `filepath.Dir(binary)` silently discarded the filename, so
pointing the env var at an arbitrary binary failed later with a
confusing assertion that named a path the user never typed.
- The "Testing image=..." debug line printed an empty string when the
binary path was used, hiding the actual source in CI logs. The line
now reports whichever of BACKEND_IMAGE / BACKEND_BINARY is in effect
as `src=...`.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): scaffold ds4 backend dir
Adds prepare.sh, run.sh, and a .gitignore. CMakeLists, Makefile, and the
implementation arrive in follow-up commits.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): add backend Makefile
Drives ds4's upstream Makefile to produce engine .o files (CUDA on Linux
when BUILD_TYPE=cublas, Metal on Darwin, otherwise CPU debug path), then
invokes CMake on our wrapper.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): add CMakeLists for grpc-server
Generates protoc stubs from backend.proto, links grpc-server.cpp +
dsml_parser.cpp + dsml_renderer.cpp + kv_cache.cpp against pre-built
ds4 engine .o files. DS4_GPU=cuda|metal|cpu selects the backend.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): grpc-server skeleton + module stubs
The minimum that links: Backend service with Health + Free; other RPCs
default to UNIMPLEMENTED. Stub headers/sources for dsml_parser,
dsml_renderer, and kv_cache are in place so CMake links cleanly even
before those modules ship.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): implement LoadModel
Opens engine + creates session sized to ContextSize (default 32768).
Backend is compile-time: CPU when DS4_NO_GPU, Metal on __APPLE__, else
CUDA. MTP/speculative options are accepted via ModelOptions.Options[]
(mtp_path, mtp_draft, mtp_margin). kv_cache_dir option is captured into
g_kv_cache_dir for the cache module (Task 19 wires it in).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): implement TokenizeString
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): implement Predict (plain text)
Tool calls + thinking-mode split arrive in Task 13 once dsml_parser is in.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): implement PredictStream (plain text)
ChatDelta + reasoning/tool_calls split arrives in Task 14.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): implement Status RPC
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): add DSML streaming parser
Classifies raw model-emitted token text into CONTENT / REASONING /
TOOL_START / TOOL_ARGS / TOOL_END events. Markers it watches for are the
literal DSML strings rendered by ds4_server.c's prompt template
(<｜DSML｜tool_calls>, <｜DSML｜invoke name=...>, <think>, etc.) - these are
plain text the model emits, not special tokens.
Partial markers split across token chunks are buffered until a full marker
or a definitively-not-a-marker '<' is observed. RandomToolId() generates
the API-side tool call id (call_xxx) that exact-replay would key on.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(backend/cpp/ds4): split hex escapes in DSML markers + add cstring/cstdio includes
C++ \x hex escapes have no length cap. '\x9cD' was read as a single escape
producing byte 0xCD, eating the 'D'. The markers were never actually matching
the DSML text the model emits. Split each escape with adjacent string literal
concatenation so the byte sequence is exactly EF BD 9C 44 (｜D) at runtime.
Also adds <cstring> and <cstdio> includes (libstdc++ 13 does not transitively
expose std::strlen / std::snprintf via <string>).
The local plan file (uncommitted) was also updated with the same fixes so
Task 16's dsml_renderer.cpp does not re-introduce the bug.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): wire DsmlParser into Predict (ChatDelta)
Non-streaming Predict now emits one ChatDelta carrying content,
reasoning_content, and tool_calls[] parsed from the model's DSML output.
Reply.message still carries the raw model bytes for backends that prefer
the regex fallback path.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): wire DsmlParser into PredictStream
Per-token ChatDelta writes: content/reasoning_content go incrementally,
tool_calls emit TOOL_START as one delta (id + name) followed by
TOOL_ARGS deltas with incremental JSON. The Go-side aggregator
(pkg/functions/chat_deltas.go) reassembles them.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): chat template + reasoning_effort mapping
UseTokenizerTemplate=true + Messages -> ds4_chat_begin / append /
assistant_prefix. PredictOptions.Metadata['enable_thinking'] and
['reasoning_effort'] map to ds4_think_mode (DS4_THINK_HIGH default;
'max'/'xhigh' -> DS4_THINK_MAX; disabled -> DS4_THINK_NONE).
Tool-call rendering for assistant turns with tool_calls JSON arrives in
the next commit (dsml_renderer).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): render assistant tool_calls + tool results to DSML
Closes the round-trip: when an OpenAI client sends a multi-turn chat
where prior turns contain tool_calls or role=tool messages, build_prompt
serializes them back to the DSML shape the model was trained on. Mirrors
ds4_server.c's prompt renderer; uses nlohmann::json for parsing the
OpenAI tool_calls payload.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): disk KV cache module
Dir-based cache keyed by SHA1(rendered prompt prefix). File format:
'DS4G' magic + version + ctx_size + prefix_len + prefix + payload_bytes
+ ds4_session_save_payload output. NOT bit-compatible with ds4-server's
KVC files - that interop is a follow-up plan. LoadLongestPrefix walks
the dir picking the longest stored prefix that prefixes the incoming
prompt.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): wire KvCache into Predict/PredictStream
LoadModel reads 'kv_cache_dir' from ModelOptions.Options[], passes it to
g_kv_cache.SetDir. Each Predict/PredictStream computes a render text for
the request, tries LoadLongestPrefix to recover state, then Saves the
new state after generation. ds4_session_sync handles the live-cache
fast path internally, so the disk cache only matters for cold-starts
and cross-session reuse.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): add package.sh
Linux: bundles libc + ld + libstdc++ + libgomp + GPU runtime libs into
package/lib so the FROM scratch image boots without a host libc.
Darwin is handled by scripts/build/ds4-darwin.sh which uses otool -L.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(backend/cpp/ds4): rename namespace ds4_backend -> ds4cpp
ds4.h defines 'typedef enum {...} ds4_backend' which collides with our
C++ 'namespace ds4_backend' anywhere a TU includes both. kv_cache.h
includes ds4.h directly and surfaces the conflict immediately; other
TUs would hit it once gRPC dev headers are available.
Renames the C++ namespace to ds4cpp across all wrapper files and the
plan, leaving the upstream ds4 typedef untouched.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend): add Dockerfile.ds4
Single-stage builder (CUDA devel image for cublas, ubuntu:24.04 for cpu)
-> FROM scratch with packaged grpc-server + bundled runtime libs.
nlohmann-json3-dev is required for dsml_renderer's JSON handling.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(make): wire backend/cpp/ds4 + ds4-darwin into root Makefile
BACKEND_DS4 entry + generate-docker-build-target eval + docker-build-ds4
in docker-build-backends + .NOTPARALLEL guards. Also adds the
backends/ds4-darwin target which delegates to scripts/build/ds4-darwin.sh
(landed in Task 24).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci: add backend-matrix entries for ds4 (cpu + cuda13, per-arch)
Two entries per build (amd64 + arm64) so backend-merge-jobs assembles a
multi-arch manifest. Skipping cuda12 - ds4 was validated against CUDA 13.
Darwin Metal is handled outside this matrix by backend_build_darwin.yml.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/index): add ds4 meta + image entries
cpu + cuda13 x latest + master. Darwin Metal builds publish under
ds4-darwin via the existing llama-cpp-darwin OCI pipeline.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(scripts/build): add ds4-darwin.sh
Native macOS/Metal build for the ds4 backend. Mirrors llama-cpp-darwin.sh:
make grpc-server -> otool -L for dylib bundling -> OCI tar that
'local-ai backends install' consumes via the backends/ds4-darwin
Makefile target.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci(darwin): build ds4-darwin in backend_build_darwin
Adds a 'Build ds4 backend (Darwin Metal)' step that runs the
backends/ds4-darwin Makefile target on the macOS runner.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(import): auto-detect ds4 weights via DS4Importer
Adds core/gallery/importers/ds4.go which matches on the antirez/deepseek-v4-gguf
repo URI and the DeepSeek-V4-Flash-*.gguf filename pattern. Registered before
LlamaCPPImporter so ds4 weights route to backend: ds4 instead of falling
through to llama-cpp.
Also lists ds4 in /backends/known so the /import-model UI surfaces it as a
manual choice for users who want to force the backend on a non-canonical URI.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(gallery): add deepseek-v4-flash-q2 (ds4 backend)
One-click install of the q2 weights with backend: ds4.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs(.agents): add ds4-backend.md
Documents the backend shape, DSML state machine, thinking-mode mapping,
disk KV cache, build matrix (cpu/cuda13/Darwin), and the BACKEND_BINARY
hardware-validation path.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(backend/cpp/ds4): pass UBUNTU_VERSION + arch env vars to install-base-deps
The .docker/install-base-deps.sh script needs UBUNTU_VERSION (defaults to
2404), TARGETARCH, SKIP_DRIVERS, and APT_MIRROR/APT_PORTS_MIRROR exported
into the environment so it can pick the right cuda-keyring / cudss / nvpl
debs and apt mirrors. Dockerfile.ds4 was declaring some of the ARGs but not
re-exporting them via ENV. Mirrors Dockerfile.llama-cpp's pattern.
Without this fix 'make docker-build-ds4 BUILD_TYPE=cublas CUDA_MAJOR_VERSION=13'
failed at:
/usr/local/sbin/install-base-deps: line 120: UBUNTU_VERSION: unbound variable
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/index): add Metal image entries for ds4
Adds metal-ds4 + metal-ds4-development image entries pointing at
quay.io/go-skynet/local-ai-backends:{latest,master}-metal-darwin-arm64-ds4
(built by scripts/build/ds4-darwin.sh on macOS arm64 runners), plus the
'metal' and 'metal-darwin-arm64' capability mappings on the ds4 meta and
ds4-development variant.
Closes a gap from the initial Task 23 landing - the Darwin Metal build
script and CI workflow step were already wired (Tasks 24-25), but the
gallery had no image entry for users to install the Metal variant.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ci): use ubuntu:24.04 base for ds4 cuda13 matrix entries
The initial Task 22 matrix landing used base-image: 'nvidia/cuda:13.0.0-devel-ubuntu24.04'
which clashes with install-base-deps.sh's cuda-keyring step:
E: Conflicting values set for option Signed-By regarding source
https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/sbsa/
The canonical pattern (llama-cpp, ik-llama-cpp, turboquant) uses plain
'ubuntu:24.04' + 'skip-drivers: false' so install-base-deps installs CUDA
from scratch via its own keyring setup. Adopting that here.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(backend/cpp/ds4): drop install-base-deps.sh dependency
The .docker/install-base-deps.sh pipeline is built around the llama-cpp
needs: NVIDIA keyring + cuda-toolkit apt + gRPC-from-source build at
/opt/grpc. For ds4 we don't need any of that:
- CUDA: nvidia/cuda:13.0.0-devel-ubuntu24.04 ships /usr/local/cuda
ready to go; install-base-deps's keyring step then conflicts with
the pre-installed Signed-By.
- gRPC: ds4's grpc-server.cpp only links against grpc++; system
libgrpc++-dev (apt) is sufficient, no source build needed.
Replaced the install-base-deps invocation in Dockerfile.ds4 with a
direct 'apt-get install libgrpc++-dev libprotobuf-dev protobuf-compiler-grpc
nlohmann-json3-dev cmake build-essential pkg-config git'. Matrix entries
back to nvidia/cuda base + skip-drivers=true so install-base-deps would
no-op even if some downstream tooling calls it.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(backend/cpp/ds4): correct proto accessors + alias grpc::Status as GStatus
Two compile bugs caught by the docker build:
1. proto::Message uses snake_case accessors. The build_prompt loop called
m.toolcalls() / m.toolcallid() - the protoc-generated names are
m.tool_calls() / m.tool_call_id(). Plan-text bug propagated to the
wrapper.
2. The Status RPC method shadowed the 'using grpc::Status' alias, so any
later method declaration using Status as a return type failed to parse
('Status does not name a type' starting at LoadModel). Solution: alias
grpc::Status as GStatus instead, with no 'using' clause that would
conflict. All RPC method declarations and return-statement constructions
now use GStatus.
Pre-existing code reviewer flagged the Status-shadow concern as 'minor'
in the original Task 10 commit; it turned out to be a real compile blocker
under libstdc++ 13 once the surrounding methods were filled in.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(backend/cpp/ds4): preserve TOOL_ARGS content in dsml_parser Flush
When the model emitted a parameter value that arrived in the same buffer
as the surrounding tool_call markers (e.g. the buffered tail after a
literal '</think>' opened the model output), the parser deferred all
buffered bytes to Flush() because looks_like_prefix() always returns
true while buf starts with '<'. Flush() then drained the buffer as
plain CONTENT/REASONING regardless of parser state, so the bytes
between the parameter open and close markers were classified as
CONTENT instead of TOOL_ARGS.
Symptom: the model emitted
<|DSML|parameter name="location" string="true">Paris, France</|DSML|parameter>
and the assembled tool_call arguments came out as {"location":""} -
the opener and closer were emitted into the args stream but the
"Paris, France" content went to the assistant message instead.
Fix:
1. Flush() now uses the same state-aware emit logic as DrainPlain:
PARAM_VALUE bytes become TOOL_ARGS (json-escaped when string),
THINK bytes become REASONING, TEXT bytes become CONTENT, and
INVOKE / TOOL_CALLS structural whitespace is discarded.
2. looks_like_prefix() restricts its leading-'<' fallback to buffers
that have not yet seen a '>'. Without that change, char-by-char
feeds would discard the '<' of '<|DSML|invoke name="..."' once
the marker prefix length was reached but the closing quote/'>'
were still in flight.
Verified with a standalone harness that runs the failing input three
ways (single Feed, split-after-'>', and char-by-char) and aggregates
TOOL_ARGS for tool index 0: all three now produce
{"location":"Paris, France"}.
Assisted-by: Claude:opus-4.7 [Read,Edit,Bash]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(backend/cpp/ds4): use ds4_session_sync + manual generation loop for KV persistence
ds4_engine_generate_argmax() is a self-contained helper that doesn't take or
update a ds4_session - it manages its own internal state. Our Predict and
PredictStream methods created g_session via ds4_session_create() but then
called ds4_engine_generate_argmax(), so g_session's KV state never advanced.
ds4_session_payload_bytes(g_session) returned 0 and the disk KV cache save
correctly rejected with 'session has no valid checkpoint to save'.
Switch both RPCs to the proper session API:
ds4_session_sync(g_session, &prompt, ...)
loop:
int token = ds4_session_argmax(g_session)
if token == eos: break
emit(token)
ds4_session_eval(g_session, token, ...)
After the loop the session has a real checkpoint and ds4_session_save_payload
writes the KV state to disk. Verified end-to-end on a DGX Spark GB10: three
.kv files (15-30 MB each) are written when BACKEND_TEST_OPTIONS sets
kv_cache_dir, and the e2e tool-call assertion still passes.
Also added stderr diagnostics to KvCache (enabled/disabled at SetDir; per-save
path + payload_bytes + result) so future failures are visible instead of
silent. The 'wrote ok' lines are low-volume - one per Predict/PredictStream
when the cache is enabled - and skipped entirely when the option is unset.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): use ds4_session_eval_speculative_argmax when MTP loaded
Wires MTP (Multi-Token Prediction) speculative decoding into the manual
generation loop in both Predict and PredictStream. When the upstream MTP
weights are loaded via 'mtp_path:' option AND we're on CUDA / Metal,
ds4_engine_mtp_draft_tokens() returns >0 and we switch the inner loop to
ds4_session_eval_speculative_argmax(), which can accept N>1 tokens per
verifier step. When MTP is not loaded (no option, CPU backend, or weights
absent), we fall through to the simple ds4_session_argmax + ds4_session_eval
path with no behavior change.
Validated on a DGX Spark GB10 with the optional MTP GGUF
(DeepSeek-V4-Flash-MTP-Q4K-Q8_0-F32.gguf, ~3.6 GB). LoadModel logs
'ds4: MTP support model loaded ... (draft=2)' on stderr.
Caveat per upstream README: 'currently provides at most a slight speedup,
not a meaningful generation-speed win'. Wired now mainly to track the
upstream API; bigger speedups arrive when ds4 improves the speculative path.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend/cpp/ds4): honor PredictOptions sampling with DSML-aware override
Mirrors ds4_server.c:7102-7115 sampling-policy semantics on the LocalAI
gRPC side. The generation loop now consults compute_sample_params() per
token to pick the effective (temperature, top_k, top_p, min_p), based on:
1. Request defaults: PredictOptions.temperature / .topk / .topp / .minp
2. Thinking-mode override: when enable_thinking != false, force T=1.0,
top_k=0, top_p=1.0, min_p=0.0 (creativity for the reasoning pass and
the trailing content)
3. DSML structural override: when DsmlParser::IsInDsmlStructural()
returns true (we are between tool-call markers but NOT in a param
value payload), force T=0.0 so protocol bytes parse cleanly
When the effective temperature is 0, we keep using ds4_session_argmax +
MTP speculative path (matches ds4-server's gate that only enables MTP for
greedy positions). When > 0, we call ds4_session_sample(s, T, ...) with
a per-thread RNG seeded from system_clock and fall back to single-token
ds4_session_eval.
New public method on DsmlParser: IsInDsmlStructural() encodes which states
need protocol-byte determinism. PARAM_VALUE is excluded (payload uses user
sampling); TEXT and THINK are excluded (no tool-call context to protect).
Verified on the DGX Spark GB10: the e2e suite still passes with all 5
specs including tools, and the Predict output now varies between runs
(creative sampling active) while the tool-call args remain a clean
'{"location":"Paris, France"}' because the parser-state check forces
greedy on the structural bytes.
UX note: thinking mode is ON by default (matching ds4-server). Users who
want deterministic output should set Metadata.enable_thinking = false.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(gallery): add sha256 to deepseek-v4-flash-q2 entry
Per HF LFS metadata for antirez/deepseek-v4-gguf:
size: 86720111200 bytes (~80.76 GiB)
sha256: 31598c67c8b8744d3bcebcd19aa62253c6dc43cef3b8adf9f593656c9e86fd8c
LocalAI's downloader verifies sha256 when present, so users who install
deepseek-v4-flash-q2 from the gallery get integrity-checked weights and
the partial-download issue (an 81 GB file is easy to truncate) becomes
recoverable instead of silently producing a broken backend.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ci): fix AMDGPU_TARGETS empty-string bypass in hipblas builds
399c1dec wired amdgpu-targets through the backend_build workflow_call
interface, intending the input's default value to cover matrix entries
that don't specify targets. However, GitHub Actions only applies a
workflow_call input default when the caller omits the input entirely.
When backend.yml passes `amdgpu-targets: ${{ matrix.amdgpu-targets }}`
and the matrix entry has no amdgpu-targets key, the expression evaluates
to an empty string, which is treated as an explicit value — bypassing
the default. The result is Docker receiving AMDGPU_TARGETS="" which in
turn causes Make's ?= default to be skipped (since the variable is
already set in the environment, even to empty), and cmake gets
-DAMDGPU_TARGETS= with no targets, so the HIP backend compiles for an
indeterminate target rather than the intended GPU list.
Fix this at two levels:
1. backend.yml: use a || fallback in the expression so that an undefined
matrix.amdgpu-targets never reaches the reusable workflow as an empty
string. The target list is the canonical default and lives here.
2. backend_build.yml: remove the now-misleading default value from the
input declaration. The default never fired due to the above bug, so
keeping it implied a guarantee that didn't exist.
3. backend/cpp/llama-cpp/Makefile: add an explicit $(error ...) guard
after the ?= assignment so that if AMDGPU_TARGETS is empty (whether
from environment or any future CI wiring mistake) the build fails
immediately with a clear message rather than silently producing a
binary compiled for an unknown GPU target.
Assisted-by: Claude Code:claude-sonnet-4-6
Signed-off-by: Russell Sim <rsl@simopolis.xyz>
* fix(build): plumb AMDGPU_TARGETS through to Docker builds
The docker-build-backend Makefile macro and Dockerfile.golang did not
pass AMDGPU_TARGETS to the inner make invocation, so hipblas builds
always used the backend Makefile's hardcoded default GPU targets
regardless of what was specified via environment or CI inputs.
Signed-off-by: Russell Sim <rsl@simopolis.xyz>
---------
Signed-off-by: Russell Sim <rsl@simopolis.xyz>
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>
Adds split_mode (alias sm) to the llama.cpp backend options allowlist,
accepting none|layer|row|tensor. The tensor value targets the experimental
backend-agnostic tensor parallelism from ggml-org/llama.cpp#19378 and
requires a llama.cpp build that includes that PR, FlashAttention enabled,
KV-cache quantization disabled, and a manually set context size.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
