TensorFlow静态C库-如何链接到10个子依赖项？

Question

我试图链接到TensorFlow库的静态C版本。我使用以下命令构建了静态库：

// get the sources
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src

// create a build directory
mkdir builddir
cd builddir

// build the lib using CMake
cmake -S ../tensorflow_src/tensorflow/lite/c -DTFLITE_C_BUILD_SHARED_LIBS:BOOL=OFF
cmake --build . -j

这就构建了libtensorflow-lite.a。然而，libtensorflow-lite.a不是自给的，它有它自己的10组依赖项，在CMake文件中说。

# TensorFlow Lite dependencies.

find_package(absl REQUIRED)
find_package(eigen REQUIRED)
find_package(farmhash REQUIRED)
find_package(fft2d REQUIRED)
find_package(flatbuffers REQUIRED)
find_package(gemmlowp REQUIRED)
find_package(neon2sse REQUIRED)
find_package(clog REQUIRED)
find_package(cpuinfo REQUIRED)  #CPUINFO is used by XNNPACK and RUY library
find_package(ruy REQUIRED)

问题是，如何找到所需子库的.a名称？

我使用find ./builddir -type f -name "*.a"列出了由CMake构建的库，预计大约有10个库，但是实际的列表太长了：

./_deps/xnnpack-build/libXNNPACK.a
./_deps/ruy-build/ruy/libruy_pack_avx2_fma.a
./_deps/ruy-build/ruy/libruy_have_built_path_for_avx2_fma.a
./_deps/ruy-build/ruy/libruy_block_map.a
./_deps/ruy-build/ruy/libruy_system_aligned_alloc.a
./_deps/ruy-build/ruy/libruy_have_built_path_for_avx512.a
./_deps/ruy-build/ruy/profiler/libruy_profiler_instrumentation.a
./_deps/ruy-build/ruy/libruy_trmul.a
./_deps/ruy-build/ruy/libruy_cpuinfo.a
./_deps/ruy-build/ruy/libruy_blocking_counter.a
./_deps/ruy-build/ruy/libruy_pack_arm.a
./_deps/ruy-build/ruy/libruy_apply_multiplier.a
./_deps/ruy-build/ruy/libruy_kernel_avx2_fma.a
./_deps/ruy-build/ruy/libruy_prepacked_cache.a
./_deps/ruy-build/ruy/libruy_tune.a
./_deps/ruy-build/ruy/libruy_context_get_ctx.a
./_deps/ruy-build/ruy/libruy_have_built_path_for_avx.a
./_deps/ruy-build/ruy/libruy_ctx.a
./_deps/ruy-build/ruy/libruy_wait.a
./_deps/ruy-build/ruy/libruy_allocator.a
./_deps/ruy-build/ruy/libruy_context.a
./_deps/ruy-build/ruy/libruy_kernel_avx.a
./_deps/ruy-build/ruy/libruy_prepare_packed_matrices.a
./_deps/ruy-build/ruy/libruy_pack_avx512.a
./_deps/ruy-build/ruy/libruy_kernel_arm.a
./_deps/ruy-build/ruy/libruy_denormal.a
./_deps/ruy-build/ruy/libruy_kernel_avx512.a
./_deps/ruy-build/ruy/libruy_frontend.a
./_deps/ruy-build/ruy/libruy_pack_avx.a
./_deps/ruy-build/ruy/libruy_thread_pool.a
./_deps/flatbuffers-build/libflatbuffers.a
./_deps/fft2d-build/libfft2d_fftsg2d.a
./_deps/fft2d-build/libfft2d_fftsg.a
./_deps/farmhash-build/libfarmhash.a
./_deps/clog-build/libclog.a
./_deps/abseil-cpp-build/absl/synchronization/libabsl_graphcycles_internal.a
./_deps/abseil-cpp-build/absl/synchronization/libabsl_synchronization.a
./_deps/abseil-cpp-build/absl/strings/libabsl_strings.a
./_deps/abseil-cpp-build/absl/strings/libabsl_str_format_internal.a
./_deps/abseil-cpp-build/absl/strings/libabsl_cord.a
./_deps/abseil-cpp-build/absl/strings/libabsl_strings_internal.a
./_deps/abseil-cpp-build/absl/status/libabsl_status.a
./_deps/abseil-cpp-build/absl/hash/libabsl_city.a
./_deps/abseil-cpp-build/absl/hash/libabsl_wyhash.a
./_deps/abseil-cpp-build/absl/hash/libabsl_hash.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_reflection.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_program_name.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_internal.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_private_handle_accessor.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_marshalling.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_commandlineflag_internal.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_commandlineflag.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_config.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags.a
./_deps/abseil-cpp-build/absl/numeric/libabsl_int128.a
./_deps/abseil-cpp-build/absl/debugging/libabsl_symbolize.a
./_deps/abseil-cpp-build/absl/debugging/libabsl_debugging_internal.a
./_deps/abseil-cpp-build/absl/debugging/libabsl_demangle_internal.a
./_deps/abseil-cpp-build/absl/debugging/libabsl_stacktrace.a
./_deps/abseil-cpp-build/absl/base/libabsl_spinlock_wait.a
./_deps/abseil-cpp-build/absl/base/libabsl_raw_logging_internal.a
./_deps/abseil-cpp-build/absl/base/libabsl_malloc_internal.a
./_deps/abseil-cpp-build/absl/base/libabsl_throw_delegate.a
./_deps/abseil-cpp-build/absl/base/libabsl_exponential_biased.a
./_deps/abseil-cpp-build/absl/base/libabsl_base.a
./_deps/abseil-cpp-build/absl/base/libabsl_log_severity.a
./_deps/abseil-cpp-build/absl/time/libabsl_time_zone.a
./_deps/abseil-cpp-build/absl/time/libabsl_civil_time.a
./_deps/abseil-cpp-build/absl/time/libabsl_time.a
./_deps/abseil-cpp-build/absl/container/libabsl_hashtablez_sampler.a
./_deps/abseil-cpp-build/absl/container/libabsl_raw_hash_set.a
./_deps/abseil-cpp-build/absl/types/libabsl_bad_variant_access.a
./_deps/abseil-cpp-build/absl/types/libabsl_bad_optional_access.a
./_deps/cpuinfo-build/libcpuinfo.a

利布斯的情况似乎如下：

1. absl:找到30个图书馆
2. 本征:好，在标头中定义模板库。
3. 农哈希:好的，找到了一个库
4. fft2d:好的，找到了两个库
5. 平面缓冲器:好的，找到一个库
6. 好的，只有标题
7. neon2sse:好的，只有头
8. 卡格:好的，找到了一个图书馆
9. cpuinfo:好的，找到了一个库
10. ruy:找到了30个图书馆

总之，大多数库都是可以的，要么有一个库可以链接，要么库是标头的。剩下的问题是：

• absl
• 鲁伊

因为它们包含大约30个.a库。不知道我是不是要和所有这些联系起来？这将非常麻烦，因为我的构建系统是Meson，我正在使用目标()与TensorFlow链接。

Answer 1

一年后，但我亲自经历了这件事，所以我的回答是这样的。

根据我的经验(使用makefile和不使用-DTFLITE_C_BUILD_SHARED_LIBS:BOOL=OFF)，执行推理的程序不需要链接到Abseil。

您需要链接到您提到的所有其他库，除了ruy_kernel_arm和ruy_pack_arm，前提是您在x64平台上运行您的程序。(令人恼怒的是，-DTFLITE_ENABLE_RUY=OFF在构建TfLite时不受尊重，所以你被Ruy困住了)

详细步骤：

构建TfLite：

mkdir ~/my_tflite_project
cd ~/my_tflite_project/
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
mkdir tflite_build_x64
cd tflite_build_x64/
cmake ../tensorflow_src/tensorflow/lite/
    /* You may encounter two CMake messages:
    -- The Fortran compiler identification is unknown
    I believe a Fortran compiler is only necessary to build Fortran bindings for TfLite.

    -- Could NOT find CLANG_FORMAT: Found unsuitable version "0.0", but required is exact version "9" (found CLANG_FORMAT_EXECUTABLE-NOTFOUND)
    sudo apt install clang-format-9
    Annoyingly you need clang-format-9, plain clang-format (version 13, the newest) won't do. */
cmake --build . -j 4

相关的libs：

$ cd ~/my_tflite_project/tflite_build_x64
$ ls *.a
libtensorflow-lite.a
$ ls pthreadpool/*.a
pthreadpool/libpthreadpool.a
$ ls _deps/*/*.a
_deps/clog-build/libclog.a        _deps/farmhash-build/libfarmhash.a  _deps/fft2d-build/libfft2d_fftsg2d.a      _deps/xnnpack-build/libXNNPACK.a
_deps/cpuinfo-build/libcpuinfo.a  _deps/fft2d-build/libfft2d_fftsg.a  _deps/flatbuffers-build/libflatbuffers.a
$ ls _deps/ruy-build/ruy/*.a
_deps/ruy-build/ruy/libruy_allocator.a         _deps/ruy-build/ruy/libruy_ctx.a                           _deps/ruy-build/ruy/libruy_kernel_avx.a       _deps/ruy-build/ruy/libruy_prepacked_cache.a
_deps/ruy-build/ruy/libruy_apply_multiplier.a  _deps/ruy-build/ruy/libruy_denormal.a                      _deps/ruy-build/ruy/libruy_kernel_avx2_fma.a  _deps/ruy-build/ruy/libruy_prepare_packed_matrices.a
_deps/ruy-build/ruy/libruy_block_map.a         _deps/ruy-build/ruy/libruy_frontend.a                      _deps/ruy-build/ruy/libruy_kernel_avx512.a    _deps/ruy-build/ruy/libruy_system_aligned_alloc.a
_deps/ruy-build/ruy/libruy_blocking_counter.a  _deps/ruy-build/ruy/libruy_have_built_path_for_avx.a       _deps/ruy-build/ruy/libruy_pack_arm.a         _deps/ruy-build/ruy/libruy_thread_pool.a
_deps/ruy-build/ruy/libruy_context.a           _deps/ruy-build/ruy/libruy_have_built_path_for_avx2_fma.a  _deps/ruy-build/ruy/libruy_pack_avx.a         _deps/ruy-build/ruy/libruy_trmul.a
_deps/ruy-build/ruy/libruy_context_get_ctx.a   _deps/ruy-build/ruy/libruy_have_built_path_for_avx512.a    _deps/ruy-build/ruy/libruy_pack_avx2_fma.a    _deps/ruy-build/ruy/libruy_tune.a
_deps/ruy-build/ruy/libruy_cpuinfo.a           _deps/ruy-build/ruy/libruy_kernel_arm.a                    _deps/ruy-build/ruy/libruy_pack_avx512.a      _deps/ruy-build/ruy/libruy_wait.a

使用TfLite构建MWE：

$ cd ~/my_tflite_project
$ mkdir my_dev_x64
$ cd my_dev_x64/
/* Construct minimal.cpp and makefile below */
$ cat minimal.cpp
#include "tensorflow/lite/model.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include <iostream>

int main() {
    std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel::BuildFromFile("your_network_here.tflite");
    tflite::ops::builtin::BuiltinOpResolver resolver;

    std::cout "Done\n";
    return EXIT_SUCCESS;
}

$ cat makefile
COMPILER     := g++

LINKER       := g++

CXX_FILES    := minimal.cpp

OBJ_FILES    := $(CXX_FILES:.cpp=.o)

EXE_FILE     := app

INCLUDE_DIRS := -I../tensorflow_src -I../tflite_build_x64/flatbuffers/include

LIB_DIRS     := \
        -L../tflite_build_x64 \
        -L../tflite_build_x64/_deps/fft2d-build \
        -L../tflite_build_x64/_deps/flatbuffers-build \
        -L../tflite_build_x64/_deps/ruy-build/ruy \
        -L../tflite_build_x64/_deps/farmhash-build \
        -L../tflite_build_x64/_deps/xnnpack-build \
        -L../tflite_build_x64/_deps/cpuinfo-build \
        -L../tflite_build_x64/_deps/clog-build \
        -L../tflite_build_x64/pthreadpool

LIBS         := \
        -ltensorflow-lite \
        -lfft2d_fftsg \
        -lfft2d_fftsg2d \
        -lflatbuffers \
        -lruy_ctx \
        -lruy_allocator \
        -lruy_frontend \
        -lruy_context_get_ctx \
        -lruy_context \
        -lruy_apply_multiplier \
        -lruy_prepacked_cache \
        -lruy_tune \
        -lruy_cpuinfo \
        -lruy_system_aligned_alloc \
        -lruy_prepare_packed_matrices \
        -lruy_trmul \
        -lruy_block_map \
        -lruy_denormal \
        -lruy_thread_pool \
        -lruy_blocking_counter \
        -lruy_wait \
        -lruy_kernel_avx \
        -lruy_kernel_avx2_fma \
        -lruy_kernel_avx512 \
        -lruy_pack_avx \
        -lruy_pack_avx2_fma \
        -lruy_pack_avx512 \
        -lruy_have_built_path_for_avx \
        -lruy_have_built_path_for_avx2_fma \
        -lruy_have_built_path_for_avx512 \
        -lfarmhash \
        -lXNNPACK \
        -lpthreadpool \
        -lcpuinfo \
        -lclog

CXX_FLAGS    := -Wall #-pedantic

LINK_FLAGS   :=

#Do not print the output of the commands
.SILENT:

#Phony targets do not represent actual files, so files with the following names are ignored
.PHONY: clean depend

#Link object files to form an executable file
$(EXE_FILE): $(OBJ_FILES)
        $(LINKER) $(LINK_FLAGS) $(OBJ_FILES) -o $(EXE_FILE) $(LIB_DIRS) $(LIBS)

#Compile cpp files to object files
%.o: %.cpp
        $(COMPILER) $(CXX_FLAGS) $(INCLUDE_DIRS) -c $<

#Remove object files, executable, and possible linkinfo files
clean:
        -rm -f $(OBJ_FILES) $(EXE_FILE)

#Generate dependency file
depend:
        $(COMPILER) $(CXX_FLAGS) $(INCLUDE_DIRS) -MM $(CXX_FILES) > make.dep

#Include dependency file
-include make.dep

建立和运行MWE：

$ cd ~/my_tflite_project/my_dev_x64
$ make
$ ./app
Done

希望这能帮助您或其他可怜的家伙让TfLite C++工作。