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* 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>