## Flowchart: Hybrid Model Architecture for Computer Vision and Natural Language Processing ### Overview The image depicts a technical flowchart illustrating a hybrid model architecture that integrates computer vision (CV) and natural language processing (NLP) components. The system processes calibration and training data through specialized modules (Quantized ViTs for CV, Quantized PLMs/LLMs for NLP) before feeding into a Transformer-based architecture. The lower section details the Transformer's internal operations, including layer normalization, attention mechanisms, and quantization steps. ### Components/Axes #### Main Sections 1. **Computer Vision (Green)** - **Inputs**: Calibration images, Training images - **Output**: Quantized ViTs (via PTQ/QAT) - **Key Techniques**: Self-Attention Rank Consistency, Post-SoftMax/GELU/LayerNorm Rectification, Weight Oscillation/Data Variations 2. **Natural Language Processing (Pink)** - **Inputs**: Calibration sentences, Training sentences - **Output**: Quantized PLMs/LLMs (via PTQ/QAT) - **Key Techniques**: Outliers Clipping/Scale, Module-wise Reconstruction, Parameter-Efficient Fine-Tune 3. **Transformer Architecture (Blue/Purple)** - **Core Components**: LayerNorm, Quantize, Self-Attention, SoftMax, GELU, FCI, Dequantize - **Operations**: INT8/INT32 quantization, matrix projections (INT8→INT32), cross-module interactions #### Legend Colors - **Green**: Computer Vision elements - **Purple**: Training data inputs - **Pink**: NLP elements - **Yellow**: Quantized model outputs - **Blue**: Transformer architecture components ### Detailed Analysis #### Upper Flowchart - **Calibration vs. Training Data**: Both CV and NLP modules receive separate calibration and training inputs, suggesting a two-stage optimization process. - **Quantization Methods**: - **PTQ (Post-Training Quantization)**: Applied to CV's ViTs and NLP's PLMs/LLMs. - **QAT (Quantization-Aware Training)**: Implied for model optimization. - **Model-Specific Techniques**: - CV: Focus on attention rank consistency and weight rectification. - NLP: Emphasis on outlier handling and parameter efficiency. #### Lower Transformer Diagram - **Sequential Operations**: 1. **LayerNorm** → **Quantize** (INT8) → **Query-Key Value Projection** (INT8) 2. **Self-Attention** → **SoftMax** → **FP32** normalization 3. **Cross-Module Interactions**: - **INT8→INT32** projections between modules - **GELU activation** and **FCI** (Feed-Forward Component) 4. **Final Output**: FP32 normalization after cross-module processing #### Key Technical Details - **Quantization Levels**: - INT8 (8-bit integer) for initial layers - FP32 (32-bit float) for critical operations (e.g., SoftMax, final output) - **Attention Mechanisms**: - Self-Attention Rank Consistency ensures stable feature relationships. - Cross-module attention via **INT8→INT32** projections. - **Efficiency Techniques**: - Parameter-Efficient Fine-Tune (NLP) - Module-wise Reconstruction (NLP) ### Key Observations 1. **Hybrid Architecture**: Combines CV (ViTs) and NLP (PLMs/LLMs) models, unified under a Transformer framework. 2. **Quantization Focus**: Heavy emphasis on INT8 quantization for efficiency, with FP32 reserved for precision-critical steps. 3. **Attention Complexity**: Multiple attention variants (Self-Attention, Cross-Module) with rectification techniques. 4. **Data Handling**: - Outliers Clipping/Scale (NLP) - Weight Oscillation/Data Variations (CV) ### Interpretation This architecture demonstrates a **multi-modal optimization strategy** where CV and NLP models are independently quantized but integrated via a shared Transformer backbone. The use of **Post-SoftMax/GELU/LayerNorm Rectification** in CV and **Module-wise Reconstruction** in NLP suggests tailored approaches to maintain model stability after quantization. The Transformer's **INT8→INT32** projections indicate careful balancing between computational efficiency and precision. The **Parameter-Efficient Fine-Tune** in NLP implies adaptability to new data with minimal resource overhead. Overall, the system prioritizes **cross-modal efficiency** while maintaining performance through specialized quantization and attention mechanisms.