Background
Our diffusion model for DIA-MS data deconvolution currently has a large memory footprint, which limits our ability to process larger datasets and deploy the model efficiently. Quantization techniques, which have been successfully applied to vision models, offer a promising approach to reduce model size while maintaining performance.
Task Objectives
- Implement post-training quantization for our existing diffusion model
- Evaluate multiple quantization strategies (e.g., INT8, FP16) for their impact on:
- Memory usage
- Inference speed
- Model accuracy/performance
- Implement the most effective quantization strategy in our training and inference pipelines
Technical Details
- The model is implemented in PyTorch with a U-Net-based architecture
- We need to balance memory efficiency with the precision required for accurate MS signal deconvolution
- Begin with static quantization of pre-trained models, then explore quantization-aware training if results are promising
Deliverables
- Implementation of quantization methods in the model code
- Comprehensive benchmarks comparing original vs. quantized models including:
- Memory usage measurements
- Inference time comparisons
- Quality metrics on test datasets
- Documentation of the quantization process and best practices
- Pull request with the optimized model implementation
Resources
Difficulty
Intermediate - Requires understanding of both the model architecture and quantization techniques.
Background
Our diffusion model for DIA-MS data deconvolution currently has a large memory footprint, which limits our ability to process larger datasets and deploy the model efficiently. Quantization techniques, which have been successfully applied to vision models, offer a promising approach to reduce model size while maintaining performance.
Task Objectives
Technical Details
Deliverables
Resources
Difficulty
Intermediate - Requires understanding of both the model architecture and quantization techniques.