deepseek-ai/FlashMLA
FlashMLA
FlashMLA is an LLM attention kernel library and inference acceleration library providing a collection of high-performance CUDA kernels. It implements multi-head latent attention mechanisms designed to reduce memory overhead and increase throughput during the forward and backward passes of large language model inference.
The library utilizes quantized cache attention kernels to improve computation efficiency across both sparse and dense token processing. It specifically optimizes the prefill and decoding phases of model inference through these latent attention implementations.
The project covers GPU kernel development for NVIDIA hardware, focusing on KV cache memory reduction and sparse attention computation. It includes capabilities for executing multi-head attention, dense attention, and sparse attention across different inference stages.
Features
- GPU-Accelerated Inference - Implements high-performance CUDA kernels specifically to accelerate the inference phase of LLMs on NVIDIA GPUs.
- Latent Attention Mechanisms - Provides high-performance kernels for computing multi-head latent attention to increase inference throughput.
- Attention Kernel Libraries - Provides a high-performance collection of CUDA kernels for multi-head latent attention to increase LLM throughput.
- Latent Space Projections - Compresses attention keys and values into smaller latent representations to reduce memory bandwidth requirements.
- GPU Kernel Implementations - Implements high-performance CUDA kernels specifically for accelerating attention computations on NVIDIA hardware.
- Inference Optimization Kernels - Improves speed and memory efficiency of LLM decoding and prefill stages using specialized kernels.
- Attention Kernel Fusion - Combines multiple attention operations into single GPU kernels to minimize memory data movement.
- KV Cache Management - Lowers the memory footprint of long-context generation using quantized caches and sparse attention.
- Cache Quantization - Stores latent attention states in low-precision formats to reduce memory usage during decoding.
- Attention Mechanisms - Provides the capability to compute full attention across all tokens for complete contextual representation.
- Triton Kernels - Uses the Triton domain-specific language to generate high-performance GPU code and optimize memory tiling.
- Multi-Head Attention Mechanisms - Computes forward and backward attention operations for standard multi-head attention architectures.
- Prefill-Decode Disaggregation - Implements distinct computational paths to optimize the transition between prefill and decoding phases.
- Sparse Attention Kernels - Utilizes specialized kernels to compute attention only for a selective subset of tokens.
- Quantized Cache Kernels - Ships attention kernels that use quantized caches to improve efficiency for both sparse and dense token processing.
- AI & Machine Learning - Efficient MLA decoding kernels.
- Attention Optimization - Hardware-aligned attention optimization for latent attention models.
- Inference Kernels - Efficient decoding kernel for multi-head latent attention on Hopper GPUs.
