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Quark 0.8rc3 documentation

Release Notes

Release Information

Getting Started with AMD Quark

Introduction to Quantization
Installation
Basic Usage
- AMD Quark for PyTorch
- AMD Quark for ONNX
Accessing PyTorch Examples
Accessing ONNX Examples

Advanced AMD Quark Features for PyTorch

Configuring PyTorch Quantization
Save and Load Quantized Models
Exporting Quantized Models
Best Practices for Post-Training Quantization (PTQ)
Debugging quantization Degradation
Language Model Optimization
Activation/Weight Smoothing (SmoothQuant)
Block Floating Point 16
Extensions
- Integration with AMD Pytorch-light (APL)
- Brevitas Integration
Using MX (Microscaling)
Two Level Quantization Formats

Advanced Quark Features for ONNX

Configuring ONNX Quantization
Data and OP Types
Accelerate with GPUs
Mixed Precision
Block Floating Point 16 (BFP16)
BF16 Quantization
Microscaling (MX)
Microexponents (MX)
Accuracy Improvement Algorithms
Automatic Search for Model Quantization
Auto search for quantizing a yolov3 ONNX model
Best Practice for Ryzen AI in AMD Quark ONNX
Best Practice of RyzenAI ONNX Model Quantization using Auto Search
Using ONNX Model Inference and Saving Input Data in NPY Format
Optional Utilities
Tools

APIs

PyTorch APIs
ONNX APIs

Troubleshooting and Support

PyTorch FAQ
ONNX FAQ

Accuracy...

Accuracy Improvement Algorithms

Accuracy Improvement Algorithms#

AMD Quark for ONNX provides several techniques to improve the accuracy for quantized models after PTQ.

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Introduction

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Quantizing Using CrossLayerEqualization (CLE)

Last updated on Feb 24, 2025.

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