270 lines
8.2 KiB
Nix
270 lines
8.2 KiB
Nix
{ config
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, stdenv
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, lib
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, fetchFromGitHub
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, Foundation
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, abseil-cpp_202401
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, cmake
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, cpuinfo
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, eigen
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, flatbuffers_23
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, gbenchmark
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, glibcLocales
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, gtest
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, libpng
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, nlohmann_json
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, nsync
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, pkg-config
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, python3Packages
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, re2
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, zlib
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, microsoft-gsl
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, libiconv
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, protobuf_21
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, pythonSupport ? true
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, cudaSupport ? config.cudaSupport
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, cudaPackages ? {}
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}@inputs:
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let
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version = "1.18.1";
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abseil-cpp = abseil-cpp_202401;
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stdenv = throw "Use effectiveStdenv instead";
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effectiveStdenv = if cudaSupport then cudaPackages.backendStdenv else inputs.stdenv;
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cudaArchitecturesString = cudaPackages.flags.cmakeCudaArchitecturesString;
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howard-hinnant-date = fetchFromGitHub {
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owner = "HowardHinnant";
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repo = "date";
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rev = "v3.0.1";
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sha256 = "sha256-ZSjeJKAcT7mPym/4ViDvIR9nFMQEBCSUtPEuMO27Z+I=";
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};
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mp11 = fetchFromGitHub {
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owner = "boostorg";
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repo = "mp11";
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rev = "boost-1.82.0";
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hash = "sha256-cLPvjkf2Au+B19PJNrUkTW/VPxybi1MpPxnIl4oo4/o=";
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};
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safeint = fetchFromGitHub {
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owner = "dcleblanc";
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repo = "safeint";
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rev = "ff15c6ada150a5018c5ef2172401cb4529eac9c0";
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hash = "sha256-PK1ce4C0uCR4TzLFg+elZdSk5DdPCRhhwT3LvEwWnPU=";
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};
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pytorch_clog = effectiveStdenv.mkDerivation {
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pname = "clog";
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version = "3c8b153";
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src = "${cpuinfo.src}/deps/clog";
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nativeBuildInputs = [ cmake gbenchmark gtest ];
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cmakeFlags = [
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"-DUSE_SYSTEM_GOOGLEBENCHMARK=ON"
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"-DUSE_SYSTEM_GOOGLETEST=ON"
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"-DUSE_SYSTEM_LIBS=ON"
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# 'clog' tests set 'CXX_STANDARD 11'; this conflicts with our 'gtest'.
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"-DCLOG_BUILD_TESTS=OFF"
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];
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};
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onnx = fetchFromGitHub {
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owner = "onnx";
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repo = "onnx";
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rev = "refs/tags/v1.16.1";
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hash = "sha256-I1wwfn91hdH3jORIKny0Xc73qW2P04MjkVCgcaNnQUE=";
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};
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cutlass = fetchFromGitHub {
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owner = "NVIDIA";
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repo = "cutlass";
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rev = "v3.1.0";
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hash = "sha256-mpaiCxiYR1WaSSkcEPTzvcREenJWklD+HRdTT5/pD54=";
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};
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in
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effectiveStdenv.mkDerivation rec {
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pname = "onnxruntime";
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inherit version;
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src = fetchFromGitHub {
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owner = "microsoft";
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repo = "onnxruntime";
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rev = "refs/tags/v${version}";
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hash = "sha256-+zWtbLKekGhwdBU3bm1u2F7rYejQ62epE+HcHj05/8A=";
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fetchSubmodules = true;
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};
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patches = [
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# If you stumble on these patches trying to update onnxruntime, check
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# `git blame` and ping the introducers.
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# Context: we want the upstream to
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# - always try find_package first (FIND_PACKAGE_ARGS),
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# - use MakeAvailable instead of the low-level Populate,
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# - use Eigen3::Eigen as the target name (as declared by libeigen/eigen).
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./0001-eigen-allow-dependency-injection.patch
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# Incorporate a patch that has landed upstream which exposes new
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# 'abseil-cpp' libraries & modifies the 're2' CMakeLists to fix a
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# configuration error that around missing 'gmock' exports.
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#
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# TODO: Check if it can be dropped after 1.19.0
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# https://github.com/microsoft/onnxruntime/commit/b522df0ae477e59f60acbe6c92c8a64eda96cace
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./update-re2.patch
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] ++ lib.optionals cudaSupport [
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# We apply the referenced 1064.patch ourselves to our nix dependency.
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# FIND_PACKAGE_ARGS for CUDA was added in https://github.com/microsoft/onnxruntime/commit/87744e5 so it might be possible to delete this patch after upgrading to 1.17.0
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./nvcc-gsl.patch
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];
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nativeBuildInputs = [
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cmake
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pkg-config
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python3Packages.python
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protobuf_21
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] ++ lib.optionals pythonSupport (with python3Packages; [
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pip
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python
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pythonOutputDistHook
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setuptools
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wheel
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]) ++ lib.optionals cudaSupport [
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cudaPackages.cuda_nvcc
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];
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buildInputs = [
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cpuinfo
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eigen
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glibcLocales
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libpng
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nlohmann_json
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microsoft-gsl
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pytorch_clog
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zlib
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] ++ lib.optionals pythonSupport (with python3Packages; [
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numpy
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pybind11
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packaging
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]) ++ lib.optionals effectiveStdenv.hostPlatform.isDarwin [
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Foundation
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libiconv
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] ++ lib.optionals cudaSupport (with cudaPackages; [
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cuda_cccl # cub/cub.cuh
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libcublas # cublas_v2.h
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libcurand # curand.h
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libcusparse # cusparse.h
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libcufft # cufft.h
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cudnn # cudnn.h
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cuda_cudart
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nccl
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]);
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nativeCheckInputs = [
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gtest
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] ++ lib.optionals pythonSupport (with python3Packages; [
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pytest
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sympy
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onnx
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]);
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# TODO: build server, and move .so's to lib output
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# Python's wheel is stored in a separate dist output
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outputs = [ "out" "dev" ] ++ lib.optionals pythonSupport [ "dist" ];
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enableParallelBuilding = true;
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cmakeDir = "../cmake";
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cmakeFlags = [
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"-DABSL_ENABLE_INSTALL=ON"
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"-DFETCHCONTENT_FULLY_DISCONNECTED=ON"
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"-DFETCHCONTENT_QUIET=OFF"
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"-DFETCHCONTENT_SOURCE_DIR_ABSEIL_CPP=${abseil-cpp.src}"
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"-DFETCHCONTENT_SOURCE_DIR_DATE=${howard-hinnant-date}"
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"-DFETCHCONTENT_SOURCE_DIR_FLATBUFFERS=${flatbuffers_23.src}"
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"-DFETCHCONTENT_SOURCE_DIR_GOOGLETEST=${gtest.src}"
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"-DFETCHCONTENT_SOURCE_DIR_GOOGLE_NSYNC=${nsync.src}"
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"-DFETCHCONTENT_SOURCE_DIR_MP11=${mp11}"
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"-DFETCHCONTENT_SOURCE_DIR_ONNX=${onnx}"
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"-DFETCHCONTENT_SOURCE_DIR_RE2=${re2.src}"
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"-DFETCHCONTENT_SOURCE_DIR_SAFEINT=${safeint}"
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"-DFETCHCONTENT_TRY_FIND_PACKAGE_MODE=ALWAYS"
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"-Donnxruntime_BUILD_SHARED_LIB=ON"
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(lib.cmakeBool "onnxruntime_BUILD_UNIT_TESTS" doCheck)
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"-Donnxruntime_ENABLE_LTO=ON"
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"-Donnxruntime_USE_FULL_PROTOBUF=OFF"
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(lib.cmakeBool "onnxruntime_USE_CUDA" cudaSupport)
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(lib.cmakeBool "onnxruntime_USE_NCCL" cudaSupport)
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] ++ lib.optionals pythonSupport [
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"-Donnxruntime_ENABLE_PYTHON=ON"
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] ++ lib.optionals cudaSupport [
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(lib.cmakeFeature "FETCHCONTENT_SOURCE_DIR_CUTLASS" "${cutlass}")
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(lib.cmakeFeature "onnxruntime_CUDNN_HOME" "${cudaPackages.cudnn}")
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(lib.cmakeFeature "CMAKE_CUDA_ARCHITECTURES" cudaArchitecturesString)
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(lib.cmakeFeature "onnxruntime_NVCC_THREADS" "1")
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];
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env = lib.optionalAttrs effectiveStdenv.cc.isClang {
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NIX_CFLAGS_COMPILE = toString [
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"-Wno-error=deprecated-declarations"
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"-Wno-error=deprecated-pragma"
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"-Wno-error=unused-but-set-variable"
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];
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};
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# aarch64-linux fails cpuinfo test, because /sys/devices/system/cpu/ does not exist in the sandbox
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doCheck = !(cudaSupport || effectiveStdenv.buildPlatform.system == "aarch64-linux");
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requiredSystemFeatures = lib.optionals cudaSupport [ "big-parallel" ];
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postPatch = ''
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substituteInPlace cmake/libonnxruntime.pc.cmake.in \
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--replace-fail '$'{prefix}/@CMAKE_INSTALL_ @CMAKE_INSTALL_
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'' + lib.optionalString (effectiveStdenv.hostPlatform.system == "aarch64-linux") ''
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# https://github.com/NixOS/nixpkgs/pull/226734#issuecomment-1663028691
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rm -v onnxruntime/test/optimizer/nhwc_transformer_test.cc
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'';
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postBuild = lib.optionalString pythonSupport ''
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${python3Packages.python.interpreter} ../setup.py bdist_wheel
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'';
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postInstall = ''
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# perform parts of `tools/ci_build/github/linux/copy_strip_binary.sh`
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install -m644 -Dt $out/include \
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../include/onnxruntime/core/framework/provider_options.h \
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../include/onnxruntime/core/providers/cpu/cpu_provider_factory.h \
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../include/onnxruntime/core/session/onnxruntime_*.h
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'';
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passthru = {
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inherit cudaSupport cudaPackages; # for the python module
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protobuf = protobuf_21;
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tests = lib.optionalAttrs pythonSupport {
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python = python3Packages.onnxruntime;
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};
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};
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meta = with lib; {
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description = "Cross-platform, high performance scoring engine for ML models";
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longDescription = ''
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ONNX Runtime is a performance-focused complete scoring engine
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for Open Neural Network Exchange (ONNX) models, with an open
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extensible architecture to continually address the latest developments
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in AI and Deep Learning. ONNX Runtime stays up to date with the ONNX
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standard with complete implementation of all ONNX operators, and
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supports all ONNX releases (1.2+) with both future and backwards
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compatibility.
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'';
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homepage = "https://github.com/microsoft/onnxruntime";
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changelog = "https://github.com/microsoft/onnxruntime/releases/tag/v${version}";
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# https://github.com/microsoft/onnxruntime/blob/master/BUILD.md#architectures
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platforms = platforms.unix;
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license = licenses.mit;
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maintainers = with maintainers; [ puffnfresh ck3d cbourjau ];
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};
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}
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