wehub-resource-sync
46 lines
1.9 KiB
Bash
Executable File
46 lines
1.9 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Shared compile logic for backend/Dockerfile.llama-cpp.
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# Sourced (via bind mount) from both builder-fromsource and builder-prebuilt stages.
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set -euxo pipefail
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export CCACHE_DIR=/root/.ccache
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ccache --max-size=5G || true
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ccache -z || true
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export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache"
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if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
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CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
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export CMAKE_ARGS="${CMAKE_ARGS} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
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echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
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rm -rf /LocalAI/backend/cpp/llama-cpp-*-build
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fi
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cd /LocalAI/backend/cpp/llama-cpp
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if [ -z "${BUILD_TYPE:-}" ]; then
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# Pure CPU image (BUILD_TYPE empty): one build with ggml CPU_ALL_VARIANTS replaces the
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# per-microarch binaries (x86: avx/avx2/avx512/fallback; arm64: armv8.x/armv9.x). ggml
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# dlopens the best libggml-cpu-*.so at runtime by probing host CPU features.
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#
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# arm64: the CPU_ALL_VARIANTS table includes armv9.2 SME variants whose -march=...+sme is
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# rejected by the Ubuntu 24.04 default gcc-13. gcc-14 accepts it, so build the arm64
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# variants with it (the host never *selects* SME unless it has it, but every variant must
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# still compile).
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if [ "${TARGETARCH}" = "arm64" ]; then
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apt-get update -qq && apt-get install -y -qq gcc-14 g++-14
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export CC=gcc-14 CXX=g++-14
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fi
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make llama-cpp-cpu-all
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else
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# GPU build (cublas/hipblas/sycl/vulkan/...): the accelerator does the compute, so a
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# single fallback CPU build is enough - no per-microarch CPU variants needed. (This also
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# keeps the heavy GPU backend compile from also building the whole CPU variant matrix,
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# and avoids the gcc-14 apt step on GPU base images such as nvidia l4t.)
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make llama-cpp-fallback
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fi
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make llama-cpp-grpc
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make llama-cpp-rpc-server
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ccache -s || true
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