VisualQuantization: A Visual Diagnostic Tool for Quantization Differences in ONNX Neural Networks

Neural network quantization is a key technology for edge device deployment, but locating the source of precision loss is challenging. VisualQuantization is a visualization tool focused on analyzing quantization differences in ONNX models, providing core features such as semantic comparison of heterogeneous computation graphs, operator fusion detection, and weight distribution analysis. It helps developers quickly locate precision loss issues during quantization and is suitable for multiple scenarios like quantization strategy tuning and deployment debugging.

Neural network quantization can significantly reduce model size and inference latency, making it a standard for edge device deployment. However, quantization involves complex calibration and inter-layer coordination. When precision drops, developers need to address issues like weight distribution changes, semantic changes in operator fusion, and establishing correspondence between heterogeneous computation graphs. Traditional debugging methods are inefficient and not intuitive.

1. Semantic Comparison of Heterogeneous Computation Graphs: Establish node correspondence between the original floating-point model and the quantized model via a semantics-aware graph alignment algorithm;
2. Operator Fusion Detection: Automatically identify operator fusion patterns during quantization and visualize the comparison of computation graphs before and after fusion;
3. Weight Distribution Analysis: Display the distribution comparison between original and quantized weights for each layer, histogram quantile analysis, and outlier detection;
4. Dual-Column Linked View: A dual-column layout shows the original and quantized models; selecting a node automatically highlights the corresponding node to enable cross-model navigation.

VisualQuantization is applicable to:

1. Quantization Strategy Tuning: Verify the impact of new quantization algorithms on model structure;
2. Model Deployment Debugging: Quickly locate layers with precision issues in quantized models;
3. Education and Research: Demonstrate the internal mechanisms of quantization technology;
4. Toolchain Evaluation: Compare model differences generated by different quantization tools (e.g., ONNX Runtime, TensorRT).

The project is developed based on the ONNX format and supports frameworks that can export ONNX (such as PyTorch and TensorFlow). The backend uses Python for graph analysis and data processing, while the frontend provides an intuitive interactive interface.

VisualQuantization fills the gap in the neural network quantization toolchain, helping developers intuitively understand and diagnose the quantization process, improving the success rate and efficiency of model deployment. It is a practical tool worth attention for edge AI development engineers.