Introduction to PPL Quantization Tool 0.6.6
PPL Quantization Tool, or PPQ, is a highly extensible, high-performance quantization tool designed for industrial applications of neural networks. The tool has emerged as a common method for accelerating neural networks since 2016.
Importance of Neural Network Quantization
Neural network quantization is an essential technique for enhancing computational efficiency. Unlike neural network pruning or architecture search, quantization offers greater versatility and practical value, particularly in scenarios where chip area and power consumption are constrained. It achieves this by converting floating-point operations, known for being resource-intensive, into fixed-point operations. This transformation significantly optimizes chip design, system power consumption, latency, and throughput.
Tackling Modern AI Model Challenges
In the current AI-driven era, advancements in image recognition, super-resolution, content generation, and model reconstruction are reshaping everyday life. However, these advancements bring complexity to model structures, posing challenges for quantization and deployment. PPQ addresses these complexities with a sophisticated graph logic system that parses and modifies intricate model structures. It distinguishes between quantized and non-quantized regions, thus enabling user control over the scheduling logic.
Encouraging User Involvement
PPQ advocates for user engagement in the network quantization and deployment process by providing educational resources on GitHub and designing flexible interfaces. The concept of a "quantizer" is introduced to initialize quantization strategies across different hardware platforms, allowing users to customize operator and tensor aspects such as bit width, granularity, and calibration algorithms. This flexibility enables users to explore innovative boundaries of quantization technology.
Advanced Execution Engine
Designed for handling complex quantization tasks, PPQ's execution engine is specialized and built for quantization. Currently, it supports 99 common ONNX operators and allows quantization simulations during execution. PPQ can independently perform ONNX model inference and quantization, supporting various platforms with customizable operator implementations via Python, PyTorch, or C++/CUDA.
Collaboration with Inference Frameworks
PPQ's close ties with inference frameworks like TensorRT, OpenPPL, OpenVINO, ncnn, MNN, ONNX Runtime, and others facilitate seamless integration and support through pre-built quantizers and export logic. This extensibility also allows users to expand PPQ’s quantization capabilities to additional hardware and inference libraries.
Key Features in Version 0.6.6
- Supports multiple FP8 quantization standards, including E4M3 and E5M2.
- Introduces a foundational API library for more flexible quantization tasks.
- Enhanced graph pattern matching and fusion capabilities.
- ONNX-based Quantization-Aware Training (QAT).
- New TensorRT quantization and export logic.
- The world's largest quantized model library, OnnxQuant.
Installation Guide
- Install CUDA from the CUDA Toolkit.
- Install build tools such as
ninja-build. - Clone the PPQ repository, install dependencies, and set up the package.
Optional installation methods include using a Docker image or installing PPQ via pip.
Learning and Resources
PPQ offers various learning materials and resources, including tutorials on model quantization, executors, error analysis, calibration, fine-tuning, network scheduling, optimization processes, and more. Additional resources include video tutorials on fundamental concepts like computer architecture, network performance analysis, and quantization principles.
Real-World Application
PPQ supports efficient quantization deployment across platforms like TensorRT, ONNX Runtime, OpenVINO, SNPE, NCNN, and OpenPPL, among others. These integrations provide comprehensive support for real-world implementations of neural network quantization, minimizing inference time and enhancing overall performance.
In summary, PPQ stands as a comprehensive framework for handling the complexities of neural network quantization, delivering significant performance improvements and fostering a community of users exploring the future of AI applications.