TODO.md

Quanta MVP Roadmap

Phase 1: Core Quantization (DONE)

• Implement 8-bit quantization algorithms
• Implement 4-bit quantization algorithms
• Add support for different quantization schemes (symmetric/asymmetric)
• Implement base classes for quantization

Phase 2: Quantization State Management (CURRENT)

• Implement quantization state management
• Create tensor conversion utilities
• Implement serialization/deserialization for quantized tensors
• Add conversion between different precision formats

Phase 3: Model Quantization (MVP)

• Create model quantization wrapper API
• Implement per-layer configuration for mixed precision
• Add weight-only quantization for inference
• Create model export/import functionality
• Implement tensor packing for efficient storage

Phase 4: Post-Training Quantization (MVP)

• Implement calibration methods for quantization (min/max, entropy, percentile)
• Add support for representative dataset calibration
• Create outlier handling for activation quantization
• Add quantization-aware activation clipping
• Implement static vs dynamic quantization modes

Phase 5: Pre-Training Quantization Setup (MVP)

• Create quantization-aware training helpers
• Implement fake quantization for training
• Add gradient scaling for low-precision training
• Create QAT (Quantization-Aware Training) module wrappers
• Add support for custom quantization configurations during training

Phase 6: Integration & Example Models (MVP)

• Add quantization examples for common architectures (ResNet, BERT, etc.)
• Create benchmarking scripts for quantized vs. full-precision
• Add documentation for integration patterns
• Create simple CLI for model quantization
• Implement visualization tools for quantization statistics

Future Roadmap (Post-MVP)

• Optimizers (8-bit Adam, Lion)
• Hardware-specific optimizations (CUDA kernels)
• Additional hardware support (AMD, Intel)
• Advanced features (LoRA integration, mixed-precision)
• Performance optimization (kernel fusion, caching)
• Deployment utilities (export to various formats)
• Distributed training support