[Llava] Add support to cross compile llava_runner for Android

digantdesai · digantdesai · commit 85d7f9874f56 · 2024-09-05T21:44:03.000-05:00
Tested on M1 (non Android) and S23 (Android), Debug and Release build

Known issues:
- Dummy image hack should go
- ET_LOG is broken and doesn't print anything on adb shell
diff --git a/.ci/scripts/test_llava.sh b/.ci/scripts/test_llava.sh
@@ -9,48 +9,97 @@ set -exu
 # shellcheck source=/dev/null
 
 BUILD_TYPE=${1:-Debug}
+TARGET_OS=${2:-Native}
+BUILD_DIR=${3:-cmake-out}
 
-echo "Building with BUILD_TYPE: $BUILD_TYPE"
+echo "Building with BUILD_TYPE: $BUILD_TYPE, TARGET_OS: $TARGET_OS, BUILD_DIR: $BUILD_DIR"
 
 if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then
-  PYTHON_EXECUTABLE=python3
+    PYTHON_EXECUTABLE=python3
 fi
 
+TARGET_OS_lower="$(echo "${TARGET_OS}" | awk '{print tolower($0)}')"
+if [[ "${TARGET_OS_lower}" == "android" ]]; then
+    if [[ -z "${ANDROID_NDK}" ]]; then
+        echo "Set ANDROID_NDK environment variable to build for Android."
+        exit 1
+    fi
+fi
+
+# Number of processes for a parallel build
+NPROC=8
+if hash nproc &> /dev/null; then NPROC=$(nproc); fi
+
+EXECUTORCH_COMMON_CMAKE_ARGS="                      \
+        -DCMAKE_INSTALL_PREFIX=${BUILD_DIR}         \
+        -DCMAKE_BUILD_TYPE=${BUILD_TYPE}            \
+        -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON      \
+        -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \
+        -DEXECUTORCH_BUILD_EXTENSION_TENSOR=ON      \
+        -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON        \
+        -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON     \
+        -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON     \
+        -DEXECUTORCH_BUILD_XNNPACK=ON               \
+        -DEXECUTORCH_DO_NOT_USE_CXX11_ABI=ON        \
+        -DEXECUTORCH_XNNPACK_SHARED_WORKSPACE=ON"
+
 cmake_install_executorch_libraries() {
-    cmake                                               \
-        -DCMAKE_INSTALL_PREFIX=cmake-out                \
-        -DCMAKE_BUILD_TYPE=${BUILD_TYPE}                \
-        -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON     \
-        -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON          \
-        -DEXECUTORCH_BUILD_EXTENSION_TENSOR=ON          \
-        -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON            \
-        -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON         \
-        -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON         \
-        -DEXECUTORCH_BUILD_XNNPACK=ON                   \
-        -DEXECUTORCH_DO_NOT_USE_CXX11_ABI=ON            \
-        -DEXECUTORCH_XNNPACK_SHARED_WORKSPACE=ON        \
-        -Bcmake-out .
-
-
-    cmake --build cmake-out -j9 --target install --config ${BUILD_TYPE}
+    cmake                               \
+        ${EXECUTORCH_COMMON_CMAKE_ARGS} \
+        -B${BUILD_DIR} .
+
+    cmake --build ${BUILD_DIR} -j${NPROC} --target install --config ${BUILD_TYPE}
+}
+
+cmake_install_executorch_libraries_for_android() {
+    cmake                                                                       \
+        -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
+        -DANDROID_ABI=arm64-v8a                                                 \
+        -DANDROID_PLATFORM=android-23                                           \
+        ${EXECUTORCH_COMMON_CMAKE_ARGS}                                         \
+        -B${BUILD_DIR} .
+
+    cmake --build ${BUILD_DIR} -j${NPROC} --target install --config ${BUILD_TYPE}
 }
 
+
+LLAVA_COMMON_CMAKE_ARGS="                        \
+        -DPYTHON_EXECUTABLE="$PYTHON_EXECUTABLE" \
+        -DCMAKE_INSTALL_PREFIX=${BUILD_DIR}      \
+        -DCMAKE_BUILD_TYPE=${BUILD_TYPE}         \
+        -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON     \
+        -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON  \
+        -DEXECUTORCH_BUILD_XNNPACK=ON"
+
 cmake_build_llava_runner() {
     dir=examples/models/llava
     python_lib=$($PYTHON_EXECUTABLE -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')
 
-    cmake                                       \
-        -DCMAKE_INSTALL_PREFIX=cmake-out        \
-        -DCMAKE_BUILD_TYPE=${BUILD_TYPE}         \
-        -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON    \
-        -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \
-        -DEXECUTORCH_BUILD_XNNPACK=ON           \
-        -DCMAKE_PREFIX_PATH="$python_lib"       \
-        -Bcmake-out/${dir}                      \
+    cmake                                 \
+        ${LLAVA_COMMON_CMAKE_ARGS}        \
+        -DCMAKE_PREFIX_PATH="$python_lib" \
+        -B${BUILD_DIR}/${dir}             \
         ${dir}
 
+    cmake --build ${BUILD_DIR}/${dir} -j${NPROC} --config ${BUILD_TYPE}
+}
+
 
-    cmake --build cmake-out/${dir} -j9 --config ${BUILD_TYPE}
+cmake_build_llava_runner_for_android() {
+    dir=examples/models/llava
+    python_lib=$($PYTHON_EXECUTABLE -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')
+
+    cmake                                                                       \
+        -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
+        -DANDROID_ABI=arm64-v8a                                                 \
+        -DANDROID_PLATFORM=android-23                                           \
+        ${LLAVA_COMMON_CMAKE_ARGS}                                              \
+        -DCMAKE_PREFIX_PATH="$python_lib"                                       \
+        -DLLAVA_RUNNER_NO_TORCH_DUMMY_IMAGE=ON                                  \
+        -B${BUILD_DIR}/${dir}                                                   \
+        ${dir}
+
+    cmake --build ${BUILD_DIR}/${dir} -j${NPROC} --config ${BUILD_TYPE}
 }
 
 # only export the one without custom op for now since it's
@@ -81,13 +130,24 @@ run_and_verify() {
         echo "tokenizer.bin is missing."
         exit 1
     fi
-    RUNTIME_ARGS="--model_path=llava.pte \
-     --tokenizer_path=tokenizer.bin \
-     --image_path=image.pt \
-     --prompt=ASSISTANT: \
-     --temperature=0 \
-     --seq_len=650"
-    cmake-out/examples/models/llava/llava_main ${RUNTIME_ARGS} > result.txt
+
+
+
+    RUNTIME_ARGS="--model_path=llava.pte    \
+        --tokenizer_path=tokenizer.bin      \
+        --image_path=image.pt               \
+        --prompt=ASSISTANT:                 \
+        --temperature=0                     \
+        --seq_len=650"
+
+    if [[ "${TARGET_OS_lower}" == "android" ]]; then
+        echo "Transfer relevant files to the phone via ADB and run llava_main with following args,"
+        echo "$ llava_main ${RUNTIME_ARGS} "
+        exit 0;
+    fi
+
+    ${BUILD_DIR}/examples/models/llava/llava_main ${RUNTIME_ARGS} > result.txt
+
     # verify result.txt
     RESULT=$(cat result.txt)
     # set the expected prefix to be the same as prompt because there's a bug in sdpa_with_kv_cache that causes <unk> tokens.
@@ -110,8 +170,20 @@ run_and_verify() {
     fi
 }
 
-cmake_install_executorch_libraries
-cmake_build_llava_runner
+# Step1. Build stuff
+if [[ "${TARGET_OS_lower}" == "android" ]]; then
+    cmake_install_executorch_libraries_for_android
+    cmake_build_llava_runner_for_android
+elif [[ "${TARGET_OS_lower}" == "native" ]]; then
+    cmake_install_executorch_libraries
+    cmake_build_llava_runner
+else
+    echo "Invalid TARGET_OS ($2): ${TARGET_OS}"
+fi
+
+# Step2. Generate the PTE
 export_llava
+
+# Step3. Run
 prepare_image_tensor
 run_and_verify
diff --git a/examples/models/llava/CMakeLists.txt b/examples/models/llava/CMakeLists.txt
@@ -21,6 +21,9 @@ project(llava)
 # Duplicating options as root CMakeLists.txt
 option(EXECUTORCH_BUILD_KERNELS_OPTIMIZED "Build the optimized kernels" OFF)
 
+# This is a temporary hack to get around Torch dep so we can test this on android
+option(LLAVA_RUNNER_NO_TORCH_DUMMY_IMAGE "Hack option to feed dummy image to remove torch.load dep" OFF)
+
 include(CMakeDependentOption)
 #
 # pthreadpool: build pthreadpool library. Disable on unsupported platforms
@@ -70,7 +73,14 @@ set(_common_include_directories ${EXECUTORCH_ROOT}/..)
 set(gflags_DIR ${CMAKE_CURRENT_BINARY_DIR}/../../../third-party/gflags)
 find_package(gflags REQUIRED)
 
-find_package(Torch CONFIG REQUIRED)
+# Avoid torch dep from torch.load()-ing the image.
+# This is a temporary hack.
+if(LLAVA_RUNNER_NO_TORCH_DUMMY_IMAGE)
+  add_definitions(-DLLAVA_NO_TORCH_DUMMY_IMAGE=1)
+  message("Buidling the runner without Torch, feeding a dummy image!")
+else()
+  find_package(Torch CONFIG REQUIRED)
+endif()
 add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
 
 #
@@ -95,7 +105,11 @@ endif()
 # llava_runner library
 add_subdirectory(runner)
 
-set(link_libraries gflags torch)
+set(LINK_LIBS gflags)
+if(NOT LLAVA_RUNNER_NO_TORCH_DUMMY_IMAGE)
+  list(APPEND LINK_LIBS torch)
+endif()
+set(link_libraries ${LINK_LIBS})
 set(_srcs main.cpp)
 
 if(EXECUTORCH_BUILD_KERNELS_OPTIMIZED)
diff --git a/examples/models/llava/main.cpp b/examples/models/llava/main.cpp
@@ -8,7 +8,11 @@
 
 #include <executorch/examples/models/llava/runner/llava_runner.h>
 #include <gflags/gflags.h>
+#ifndef LLAVA_NO_TORCH_DUMMY_IMAGE
 #include <torch/torch.h>
+#else
+#include <algorithm> // std::fill
+#endif
 
 #if defined(ET_USE_THREADPOOL)
 #include <executorch/extension/threadpool/cpuinfo_utils.h>
@@ -80,6 +84,15 @@ int32_t main(int32_t argc, char** argv) {
 
   // read image and resize the longest edge to 336
   std::vector<uint8_t> image_data;
+
+#ifdef LLAVA_NO_TORCH_DUMMY_IMAGE
+  // Work without torch using a random data
+  image_data.resize(3 * 240 * 336);
+  std::fill(image_data.begin(), image_data.end(), 0); // black
+  std::array<int32_t, 3> image_shape = {3, 240, 336};
+  std::vector<torch::executor::Image> images = {
+      {.data = image_data, .width = image_shape[2], .height = image_shape[1]}};
+#else //  LLAVA_NO_TORCH_DUMMY_IMAGE
   //   cv::Mat image = cv::imread(image_path, cv::IMREAD_COLOR);
   //   int longest_edge = std::max(image.rows, image.cols);
   //   float scale_factor = 336.0f / longest_edge;
@@ -102,6 +115,8 @@ int32_t main(int32_t argc, char** argv) {
       {.data = image_data,
        .width = static_cast<int32_t>(image_tensor.size(2)),
        .height = static_cast<int32_t>(image_tensor.size(1))}};
+#endif // LLAVA_NO_TORCH_DUMMY_IMAGE
+
   // generate
   runner.generate(std::move(images), prompt, seq_len);
   return 0;
