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FlashKDA is an open-source library of high-performance CUDA kernels for Kimi Delta Attention, implemented on NVIDIA CUTLASS. It is intended to accelerate the forward pass used by KDA-based language models on modern NVIDIA GPUs. The package integrates with flash-linear-attention and can be selected automatically as the backend for chunk_kda during inference. It supports recurrent state input and output, variable-length batches, internal gating, query-key normalization, and beta activation. Builds can target the detected GPU architecture or multiple supported architectures for wheels and CI pipelines. The repository also provides correctness tests, benchmark material, a direct Python kernel API, and development helpers for CUDA and C++ tooling.
Features
- High-performance Kimi Delta Attention CUDA kernels
- NVIDIA CUTLASS-based implementation
- Automatic flash-linear-attention backend integration
- Stateful and variable-length sequence processing
- Configurable GPU architecture compilation
- Correctness tests and hardware benchmarks
Project Samples
