TfLite-vx-delegate

TfLite-vx-delegate constructed with TIM-VX as an openvx delegate for tensorflow lite. Before vx-delegate, you may have nnapi-linux version from VeriSilicon, we suggest you move to this new delegate because:

1. without nnapi, it's flexible to enable more AI operators.
2. vx-delegate is opensourced, and will promised compatible with latest tensorflow release(currently v2.14.0).

Use tflite-vx-delegate

Prepare source code

mkdir wksp && cd wksp
# tim-vx is optional, it will be downloaded by CMake automatically for none-cross build
# if you want to do cross build with cmake, you have to build tim-vx firstly
git clone https://github.com/VeriSilicon/TIM-VX.git tim-vx
git clone https://github.com/VeriSilicon/tflite-vx-delegate.git
# tensorflow is optional, it will be downloaded automatically if not present
git clone https://github.com/tensorflow/tensorflow.git

Build from source with cmake

# default built for x86-64 simulator
cd tflite-vx-delegate
mkdir build && cd build
cmake ..
make vx_delegate -j12

# benchmark_model
make benchmark_model -j12
# label_image
make label_image -j12

If you would like to build with your own vivante driver sdk and tim-vx build, you need do cross-build as

cd tim-vx
mkdir build && cd build
cmake .. -DCMAKE_TOOLCHAIN_FILE=<toolchain.cmake> -DEXTERNAL_VIV_SDK=<sdk_root>
# we can also build from a specific ovxlib instead of use default one by set
# TIM_VX_USE_EXTERNAL_OVXLIB=ON
# OVXLIB_INC=<directory_to_ovxlib_include>
# OVXLIB_LIB=<full_path_to_libovxlib.so>

If you would like to build using local version of tensorflow, you can use FETCHCONTENT_SOURCE_DIR_TENSORFLOW cmake variable. Point this variable to your tensorflow tree. For additional details on this variable please see the official cmake documentation

cmake -DFETCHCONTENT_SOURCE_DIR_TENSORFLOW=/my/copy/of/tensorflow \
    -DOTHER_CMAKE_DEFINES...\
    ..

After cmake execution completes, build and run as usual. Beware that cmake process will apply a patch to your tensorflow tree. The patch is requred to enable the external delegate support and the NBG support.

Enable external delegate support in benchmark_model/label_image

For tensorflow v2.8.0, addtional patch pwd/patches/0001-TensorFlow-V280-Enable-External-Delegate.patch requred to enable enable external delegate in benchmark_model/label_image. For higher versions of TensorFlow, the benchmark_model has automatically enabled the external delegate mechanism, but it is still necessary to apply patch pwd/patches/label_image_support.patch to enable the external delegate in label_image. If tensorflow source code downloaded by cmake, you can find it in <build_output_dir>/_deps/tensorflow-src

The patch get merged into Tensorflow master branch, no patch required for master branch.

benchmark_model/label_image compatible with Tflite+NBG

With our Acuity Toolkit, you can generate tflite file with compiled NBG(Network Binary Graph) as a custom operator. To support this special format, you should build benchmark_model/label_image from our delegate repo and not use the offical one.

Run

# For default x86 build, you can find prebuilt sdk from tim-vx
# export VSIMULATOR_CONFIG=<your_target_npu_id> for x86-simulator
export VIVANTE_SDK_DIR=<direct_to_sdk_root>
# Please copy libtim-vx.so to drivers/ directory
export LD_LIBRARY_PATH=${VIVANTE_SDK_DIR}/drivers:$LD_LIBRARY_PATH # the "drivers" maybe named as lib
./benchmark_model --external_delegate_path=<path_to_libvx_delegate.so> --graph=<tflite_model.tflite>
# If you would like to use cache mode which save and load binary graph in local disk
./benchmark_model --external_delegate_path=<path_to_libvx_delegate.so> \
                  --external_delegate_options='allowed_cache_mode:true;cache_file_path:<cache_file>' \
                  --graph=<tflite_model.tflite>

Test

Introduced unit test with tensorflow keras api and convert it to tflite with quantized or none-quantized model, Golden generated from CPU implementation of tflite Details for run test

Model verification script to compare NPU result with CPU result

Examples

examples/python/label_image.py modified based on offical label_image

1. build tensorflow-lite runtime python package follow by [offical build instruction](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/pip_package#readme)
2. Added "-e" option to provide external provider, [Offical Label Image Instruction](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/python/README.md)

examples/minimal modified based on offical minimal

minimal <path_to_libvx_delegate.so> <tflite_model.tflite>
# If you would like to use cache mode which save and load binary graph in local disk
minimal <path_to_libvx_delegate.so> <tflite_model.tflite> use_cache_mode <cache_file>