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* 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>
53 lines
1.8 KiB
Bash
Executable File
53 lines
1.8 KiB
Bash
Executable File
#!/bin/bash
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# Darwin/Metal build for the ds4 backend. Mirrors llama-cpp-darwin.sh:
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# native make, otool -L for dylib bundling, then assemble an OCI tar that
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# `local-ai backends install` can consume.
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set -ex
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IMAGE_NAME="${IMAGE_NAME:-localai/ds4-darwin}"
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pushd backend/cpp/ds4
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make NATIVE=false grpc-server package
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popd
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mkdir -p build/darwin
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mkdir -p build/darwin/lib
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mkdir -p backend-images
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cp -rf backend/cpp/ds4/grpc-server build/darwin/
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cp -rf backend/cpp/ds4/ds4-worker build/darwin/
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cp -rf backend/cpp/ds4/run.sh build/darwin/
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# Apple Silicon: pick up Homebrew-installed protobuf utf8_validity if present.
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if [[ "$(uname -s)" == "Darwin" && "$(uname -m)" == "arm64" ]]; then
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ADDITIONAL_LIBS=${ADDITIONAL_LIBS:-$(ls /opt/homebrew/Cellar/protobuf/**/lib/libutf8_validity*.dylib 2>/dev/null)}
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else
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ADDITIONAL_LIBS=${ADDITIONAL_LIBS:-""}
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fi
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for file in $ADDITIONAL_LIBS; do
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cp -rfv "$file" build/darwin/lib
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done
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# Walk dylibs via otool -L and bundle anything that isn't a system framework.
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for file in build/darwin/grpc-server build/darwin/ds4-worker; do
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LIBS="$(otool -L "$file" | awk 'NR > 1 { system("echo " $1) } ' | xargs echo)"
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for lib in $LIBS; do
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if [[ "$lib" == *.dylib ]] && [[ -e "$lib" ]]; then
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cp -rvf "$lib" build/darwin/lib
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fi
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done
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done
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echo "Bundled libraries:"
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ls -la build/darwin/lib
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# Build an OCI tar that local-ai backends install can consume.
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# scripts/build/oci-pack.sh is the existing helper used by llama-cpp-darwin
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# - if your tree doesn't have it, write one (5 lines: tar + manifest.json).
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if [ -f scripts/build/oci-pack.sh ]; then
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bash scripts/build/oci-pack.sh build/darwin backend-images/ds4.tar "$IMAGE_NAME"
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else
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# Fallback: simple tar - local-ai accepts a flat tar in dev environments.
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tar -C build/darwin -cvf backend-images/ds4.tar .
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fi
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