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MSLK kernel reference (Website)
MSLK is a library of fused GPU kernels built on PyTorch primitives designed to accelerate transformer training and inference workloads.
What: MSLK (Meta Superintelligence Labs Kernels) provides high-performance kernels for transformer architectures, with release cycles synced to PyTorch versioning. It emphasizes automatic dispatch but allows for manual backend control for specialized tasks like split-K or custom KV layouts.
Deep dive
- Fused kernels: Combines multiple sequential GPU operations into a single kernel execution to reduce memory overhead and latency.
- Automatic dispatch: The library's default mode that selects the best kernel implementation based on the hardware and input configuration.
- Memory efficient attention: A specific implementation focus within MSLK to optimize transformer attention mechanisms.
- Paged KV layout: A technique for managing Key-Value cache memory to enable longer sequence generation without excessive memory fragmentation.
Decoder
- Fused GPU kernels: A technique where multiple small computational operations are combined into one single CUDA kernel to minimize global memory roundtrips.
- Split-K: A parallelization strategy in matrix multiplication where the computation is split across different thread blocks to utilize GPU resources more efficiently.
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