Skip to content

README.md

Latest commit

 

History

History
85 lines (57 loc) · 3.76 KB

README.md

File metadata and controls

85 lines (57 loc) · 3.76 KB

MediaTek Backend on ExecuTorch

MediaTek backend empowers ExecuTorch to speed up PyTorch models on edge devices that equips with MediaTek Neuron Processing Unit (NPU). This document offers a step-by-step guide to set up the build environment for the MediaTek ExecuTorch libraries.

Supported Chips

The examples provided in this repository are tested and supported on the following MediaTek chip:

  • MediaTek Dimensity 9300 (D9300)
  • MediaTek Dimensity 9400 (D9400)

Build Instructions

Prerequisites

Before you begin, ensure you have the following prerequisites installed and configured:

1. Buck2 Build Tool

  • Download Buck2: Obtain Buck2 from the official releases page.
  • Add to PATH: Extract the downloaded file and add the directory to your system's $PATH environment variable. 
    export PATH=<path_to_buck>:$PATH

2. Android NDK

  • Download Android NDK: Acquire the Android NDK version 26.3.11579264 from the Android developer site.
  • Set NDK Path: Ensure that the $ANDROID_NDK environment variable is set to the path where the NDK is located. 
    export ANDROID_NDK=<path_to_android_ndk>

3. MediaTek ExecuTorch Libraries

To get started with MediaTek's ExecuTorch libraries, download the NeuroPilot Express SDK from MediaTek's NeuroPilot portal. The SDK includes the following components:

  • libneuronusdk_adapter.mtk.so: This universal SDK contains the implementation required for executing target-dependent code on the MediaTek chip.

  • libneuron_buffer_allocator.so: This utility library is designed for allocating DMA buffers necessary for model inference.

  • mtk_converter-8.13.0+public-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl: This library preprocesses the model into a MediaTek representation.

  • mtk_neuron-8.2.19-py3-none-linux_x86_64.whl: This library converts the model to binaries.

Additionally, make sure to copy NeuronAdapter.h to the following directory: backends/mediatek/runtime/include/api/.

Setup

Follow the steps below to setup your build environment:

  1. Setup ExecuTorch Environment: Refer to the Setting up ExecuTorch guide for detailed instructions on setting up the ExecuTorch environment.

  2. Setup MediaTek Backend Environment

  • Install the dependent libs. Ensure that you are inside backends/mediatek/ directory 
    pip3 install -r requirements.txt
  • Install the two .whl downloaded from NeuroPilot Portal 
    pip3 install mtk_neuron-8.2.19-py3-none-linux_x86_64.whl
pip3 install mtk_converter-8.13.0+public-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
  • Set evironment variables for building backend 
    export NEURON_BUFFER_ALLOCATOR_LIB=<path_to_buffer_allocator>

Build

  1. Navigate to scripts/ directory.

  2. Build MediaTek Backend: Once the prerequisites are in place, run the mtk_build.sh script to start the build process, MediaTek backend will be built under cmake-android-out/backends/ as libneuron_backend.so

    ./mtk_build.sh

Run

  1. Push MediaTek universal SDK and MediaTek backend to the device: push libneuronusdk_adapter.mtk.so and libneuron_backend.so to the phone and export it to the $LD_LIBRARY_PATH environment variable before executing ExecuTorch with MediaTek backend.

    export LD_LIBRARY_PATH=<path_to_usdk>:<path_to_neuron_backend>:$LD_LIBRARY_PATH

Please refer to executorch/examples/mediatek/ for export and execution examples of various of models.