12 Commits

Author	SHA1	Message	Date
LocalAI [bot]	07f6c15a37	feat(ds4): layer-split distributed inference (#10098) ... * feat(ds4): add standalone ds4-worker distributed worker binary Add worker_main.c, a minimal standalone worker that owns a slice of the model's transformer layers and serves activations over ds4's own TCP transport via ds4_dist_run(). It links the same engine objects the backend already builds (including ds4_distributed.o) and has NO gRPC/protobuf dependency, so it builds even on hosts lacking protobuf/grpc dev headers. Launched by `local-ai worker ds4-distributed`. Wire the ds4-worker CMake target (mirrors grpc-server's object/GPU/native handling) and have the Makefile copy + clean the binary alongside grpc-server. Ignore the built ds4-worker artifact. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ds4): package ds4-worker alongside grpc-server Copy the standalone ds4-worker binary into the backend package (Linux package.sh) and the Darwin OCI tar (ds4-darwin.sh: both the explicit copy and the otool dylib-bundling loop) so distributed workers ship with the backend. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(ds4): tighten ds4-worker integer arg validation to match upstream Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ds4): wire grpc-server as distributed coordinator Add distributed COORDINATOR support to the ds4 backend's gRPC server. Distributed inference is an engine backend: when LoadModel receives 'ds4_role:coordinator', the process populates ds4_engine_options.distributed (role, layer slice, listen host/port) before ds4_engine_open, then the normal ds4_session_* generation path runs transparently once the worker route covers all layers. - New LoadModel options: ds4_role, ds4_layers (START:END or START:output), ds4_listen (host:port), ds4_route_timeout. - parse_layers_spec() maps the layer spec onto ds4_distributed_layers. - wait_route_ready() blocks generation until ds4_session_distributed_route_ready() reports full coverage (or timeout), gating both Predict and PredictStream; returns UNAVAILABLE on timeout/error. - No ds4_role => g_distributed stays false and wait_route_ready is a no-op, so single-node behavior is unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(ds4): don't block Status during route wait; validate coordinator opts Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(cli): add ds4-distributed worker exec helper Add the ds4WorkerArgs helper plus findDS4Backend/DS4Distributed.Run that resolve the ds4 backend via the gallery and exec the packaged ds4-worker binary. Unlike worker_llamacpp.go, ds4 bundles its own dynamic loader (lib/ld.so) for glibc compatibility, so when present we exec ds4-worker through that loader with LD_LIBRARY_PATH=<backend>/lib, mirroring backend/cpp/ds4/run.sh; otherwise we exec it directly. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(cli): register the ds4-distributed worker subcommand Wire DS4Distributed into the Worker kong command tree so `local-ai worker ds4-distributed` is available. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(ds4): document layer-split distributed inference Add a ds4 section to the distributed-mode feature docs (coordinator model YAML, manual worker command, layer-range semantics, the 'GGUF on every machine' requirement, coordinator-listens dial direction vs llama.cpp) and a terse Distributed mode section to the ds4 backend agent guide. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * test(ds4): opt-in hardware-gated distributed e2e spec Add a self-contained, opt-in Ginkgo spec to the backend e2e suite that spins a ds4 coordinator (via the packaged run.sh, loaded with ds4_role/ds4_layers/ds4_listen options) plus a ds4-worker process for the upper layers, then uses Eventually to assert a short successful Predict once the layer route forms, before tearing the worker down. Gated by BACKEND_TEST_DS4_DISTRIBUTED=1 (plus the existing BACKEND_BINARY + BACKEND_TEST_MODEL_FILE and optional layer/listen/accel knobs); compiles and skips cleanly with no env, hardware, or model. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * test(ds4): pass coordinator ctx to worker; lowercase error string Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(ds4): note distributed transport is plaintext/unauthenticated Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * style(ds4): replace em dashes in distributed docs/agent/test per repo convention Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(ds4): link ds4-worker with the C++ driver for CUDA/Metal builds The ds4-worker target is built from worker_main.c (C), so CMake linked it with the C driver. The nvcc-built ds4_cuda.o (and Obj-C++ ds4_metal.o) reference the C++ runtime, so the CUDA/Metal builds failed with undefined libstdc++ symbols (std::__throw_length_error). The CPU build passed because ds4_cpu.o is pure C. Force LINKER_LANGUAGE CXX so libstdc++ is linked. 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>	2026-05-31 00:09:55 +02:00
LocalAI [bot]	d892e4af80	feat: add ds4 backend (DeepSeek V4 Flash) with tool calls, thinking, KV cache (#9758) ... * 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>	2026-05-11 22:15:47 +02:00
Andreas Egli	1d0de757c3	fix: add hipblaslt library (#9541) ... Signed-off-by: Andreas Egli <github@kharan.ch>	2026-04-24 18:50:03 +02:00
Ettore Di Giacinto	151ad271f2	feat(rocm): bump to 7.x (#9323) ... feat(rocm): bump to 7.2.1 Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2026-04-12 08:51:30 +02:00
Ettore Di Giacinto	271cc79709	chore(backends): do not bundle cuda target directory (#7982) ... Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2026-01-12 07:51:09 +01:00
Copilot	06323df457	Optimize GPU library copying to preserve symlinks and avoid duplicates (#7931) ... * Initial plan * Optimize library copying to preserve symlinks and avoid duplicates Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> * Address code review feedback: extract get_inode helper, use file type detection for sorting Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> * Simplify implementation by removing inode tracking Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> * Add clarifying comment about basename deduplication Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>	2026-01-08 22:26:48 +01:00
Copilot	fd53978a7b	feat: package GPU libraries inside backend containers for unified base image (#7891) ... * Initial plan * Add GPU library packaging for isolated backend environments - Create scripts/build/package-gpu-libs.sh for packaging CUDA, ROCm, SYCL, and Vulkan libraries - Update llama-cpp, whisper, stablediffusion-ggml package.sh to include GPU libraries - Update Dockerfile.python to package GPU libraries into Python backends - Update libbackend.sh to set LD_LIBRARY_PATH for GPU library loading Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> * Address code review feedback: fix variable consistency and quoting Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> * Fix code review issues: improve glob handling and remove redundant variable Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> * Simplify main Dockerfile and workflow to use unified base image - Remove GPU-specific driver installation from Dockerfile (CUDA, ROCm, Vulkan, Intel) - Simplify image.yml workflow to build single unified base image for linux/amd64 and linux/arm64 - GPU libraries are now packaged in individual backend containers Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com>	2026-01-07 15:48:51 +01:00
Richard Palethorpe	976c159fdb	chore(ci): Build some Go based backends on Darwin (#6164) ... * chore(ci): Build Go based backends on Darwin Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(stablediffusion-ggml): Fixes for building on Darwin Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(whisper): Build on Darwin Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com>	2025-09-01 22:18:30 +02:00
Ettore Di Giacinto	4993df81c3	fix(metal-llama.cpp): add all libutf8_validity ... Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2025-08-26 10:19:55 +02:00
Ettore Di Giacinto	6971f71a6c	Add mlx-vlm (#6119) ... * Add mlx-vlm Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add to CI workflows Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add requirements-mps.txt Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Simplify Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2025-08-23 23:05:30 +02:00
Ettore Di Giacinto	1ba66d00f5	feat: bundle python inside backends (#6123) ... * feat(backends): bundle python Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test ci Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * vllm on self-hosted Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add clang Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Try to fix it for Mac * Relocate links only when is portable Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Make sure to call macosPortableEnv Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Use self-hosted for vllm Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * CI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2025-08-23 22:36:39 +02:00
Ettore Di Giacinto	1d830ce7dd	feat(mlx): add mlx backend (#6049) ... * chore: allow to install with pip Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * WIP Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Make the backend to build and actually work Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * List models from system only Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add script to build darwin python backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Run protogen in libbackend Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Detect if mps is available across python backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * CI: try to build backend Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Debug CI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Index mlx-vlm Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Remove mlx-vlm Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop CI test Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2025-08-22 08:42:29 +02:00