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Showing content from https://github.com/espressif/esp-ppq below:

espressif/esp-ppq: PPL Quantization Tool (PPQ) is a powerful offline neural network quantization tool.

ESP-PPQ is a quantization tool based on PPQ, and its source code is fully open-sourced. Built upon PPQ, ESP-PPQ adds Espressif-customized quantizers and exporters, allowing users to select quantization rules compatible with ESP-DL for different chips and export standardized model files that can be directly loaded by ESP-DL. ESP-PPQ is fully compatible with all PPQ APIs and quantization scripts.

For more details on quantization principles, please refer to the PPQ documentation and videos. For instructions on using ESP-PPQ, see How to quantize model.

1. Install CUDA from CUDA Toolkit

2. Install Complier

apt-get install ninja-build # for debian/ubuntu user
yum install ninja-build # for redhat/centos user

For Windows User:

(1) Download ninja.exe from https://github.com/ninja-build/ninja/releases, add it to Windows PATH.

(2) Install Visual Studio 2019 from https://visualstudio.microsoft.com.

(3) Add your C++ compiler to Windows PATH Environment, if you are using Visual Studio, it should be like "C:\Program Files\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.16.27023\bin\Hostx86\x86"

(4) Update PyTorch version to 2.0.0+.

1. Install PPQ

Method 1: Install the package using pip

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
   pip install esp-ppq

Method 2: Install from source with pip to stay synchronized with the master branch

   git clone https://github.com/espressif/esp-ppq.git
   cd esp-ppq
   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
   pip install -e .

Method 3: Install the package using uv

   uv pip install "esp-ppq[cpu]" --torch-backend=cpu
   # GPU
   # uv pip install "esp-ppq[cpu]" --torch-backend=cu124
   # AMD GPU
   # uv pip install "esp-ppq[cpu]" --torch-backend=rocm6.2
   # Intel XPU
   # uv pip install "esp-ppq[cpu]" --torch-backend=xpu

Method 4: Install from source using uv to stay in sync with the master branch

   git clone https://github.com/espressif/esp-ppq.git
   cd esp-ppq
   uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
   uv pip install -e .

Method 5: Use esp-ppq with docker:

docker build -t esp-ppq:your_tag https://github.com/espressif/esp-ppq.git

Note

• The example code installs the Linux PyTorch CPU version. Please install the appropriate PyTorch version based on your actual needs.
• If installing the package with uv, simply modify the --torch-backend parameter, which will override the PyTorch URLs index configured in the project.

This project is distributed under the Apache License, Version 2.0.

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