Performance degradation from 2.8.1 to 2.9.2

[caiotoledo-lunasystems]

平台(如果交叉编译请再附上交叉编译目标平台):

Platform(Include target platform as well if cross-compiling):

Processor SoC QCM2290.

Github版本:

Github Version:

Version tags:

• Version 2.8.1: https://github.com/alibaba/MNN/tree/d284430f92557aa8b4cc435752b1dff3309f2e38
• Version 2.9.2: https://github.com/alibaba/MNN/tree/e1011161ed0382e1a33a65bfdde8bee931dbcfaf

编译方式:

Compiling Method

cmake \
  -DCMAKE_INSTALL_PREFIX=${SCRIPTPATH}/.install_android/ \
  -DCMAKE_TOOLCHAIN_FILE=~/Android/Sdk/ndk/25.2.9519653/build/cmake/android.toolchain.cmake \
  -DCMAKE_BUILD_TYPE=Release \
  -DANDROID_ABI="arm64-v8a" \
  -DANDROID_STL=c++_shared \
  -DMNN_USE_LOGCAT=ON \
  -DMNN_ARM82=ON \
  -DMNN_SUPPORT_BF16=ON \
  -DMNN_OPENCL=ON \
  -DMNN_VULKAN=ON \
  -DMNN_BUILD_OPENCV=ON \
  -DMNN_IMGCODECS=ON \
  -DMNN_JNI=ON \
  -DANDROID_NATIVE_API_LEVEL=android-21 \
  -DMNN_BUILD_FOR_ANDROID_COMMAND=true \
  -DNATIVE_LIBRARY_OUTPUT=. -DNATIVE_INCLUDE_OUTPUT=. \
  -DMNN_BUILD_TEST=ON \
  -DMNN_BUILD_CONVERTER=ON \
  -DMNN_BUILD_BENCHMARK=ON \
  ../

---

The performance of version 2.9.2 is worse than the 2.8.1, using the benchmark tool:

• Version 2.9.2

bengal_2w:/data/local/tmp/mnn-2.9.2-lib-arm64 # LD_LIBRARY_PATH=./:../cpp_shared/arm64-v8a/ ./benchmark.out ../ai-models/ 10 3 3
MNN benchmark
Forward type: OpenCL thread=4 precision=2 sparsity=0 sparseBlockOC=1 testQuantizedModel=0
--------> Benchmarking... loop = 10, warmup = 3
[ - ] yolov8n_160.mnn      max =   87.178 ms  min =   84.706 ms  avg =   85.698 ms

• Version 2.8.1:

bengal_2w:/data/local/tmp/MNN # LD_LIBRARY_PATH=./lib/ bin/benchmark.out models/ 10 3 3
MNN benchmark
Forward type: OpenCL thread=4 precision=2 sparsity=0 sparseBlockOC=1 testQuantizedModel=0
--------> Benchmarking... loop = 10, warmup = 3
[ - ] yolov8n_160.mnn         max =   65.977 ms  min =   62.635 ms  avg =   63.617 ms

The model used for this test is the YoloV8 Nano from Ultralytics, using an image input size of 160x160.

[jxt1234]

收到，我们排查一下

[jxt1234]

内部代码修正，近期同步

[jxt1234]

2.9.5 已经修正，可以更新并测试下，内部验证结果是快于 2.8.1 版本

[caiotoledo-lunasystems]

@jxt1234,

I tested again the same model in the board QCM2290, but I'm still getting worse numbers than the version 2.8.1:

bengal_2w:/data/local/tmp/mnn-2.9.5 # LD_LIBRARY_PATH=./lib/ ./bin/benchmark.out ../mnn-models/ 10 3 3
MNN benchmark                                                                                  
Forward type: OpenCL thread=4 precision=2 sparsity=0 sparseBlockOC=1 testQuantizedModel=0
--------> Benchmarking... loop = 10, warmup = 3
[ - ] yolov8n_160.mnn         max =   83.413 ms  min =   81.695 ms  avg =   82.418 ms

However, the performance is indeed better than the 2.9.2.

Using our internal applications I see around the same performance numbers as your benchmark binary.