ESP-PPQ 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.
Installation (安装方法)
-
Install CUDA from CUDA Toolkit
-
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.
- 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-backendparameter, which will override the PyTorch URLs index configured in the project.
License
This project is distributed under the Apache License, Version 2.0.
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