Overview
FlashMLA implements high-performance multi-head latent attention kernels for large Transformer models. It focuses on lowering memory usage and improving throughput through optimized GPU code paths.
Key Features
- Memory-efficient multi-head latent attention implementation.
- Optimized C++/CUDA kernels for high throughput.
- Designed for easy integration with common Transformer training and inference pipelines.
Use Cases
- Replacing attention operators in large-scale model training to reduce memory pressure.
- Improving inference throughput and latency in constrained GPU environments.
- Serving as an optimization component in custom or open-source inference stacks.
Technical Details
- Implemented in C++/CUDA with careful memory layout and parallelization strategies.
- Supports FP16/FP8-friendly scaling strategies for efficient mixed-precision execution.
- Provides interfaces and examples to integrate with PyTorch and similar frameworks.