Summary of Changes

Hello @xiezhq-hermann, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a critical performance bottleneck in the HiCache implementation by introducing highly efficient GPU-assisted KV cache I/O. My changes involve integrating custom CUDA kernels for data transfer, refactoring the underlying memory layout for better contiguity, and simplifying the overall I/O management by removing outdated mechanisms. The goal is to significantly improve bandwidth utilization, especially for workloads with small KV cache page sizes.

Highlights

• Optimized KV Cache I/O: I've introduced GPU-assisted KV cache transfer using custom CUDA kernels. This new approach is specifically designed for small page sizes (less than 64), which previously suffered from inefficient I/O due to memory fragmentation. For larger page sizes, a copy_-based direct implementation is used as a fallback.
• Memory Layout Refactoring: I've made significant modifications to the KV cache buffer memory layout. Instead of lists of individual tensors per layer, the K and V buffers (and combined KV buffer for MLA) are now stored as single contiguous tensors with an added layer dimension. This change facilitates more efficient data access and transfer by the new CUDA kernels.
• Streamlined I/O Logic: I've removed deprecated and less efficient I/O buffering and direct transfer functions from the cache_controller.py and memory_pool_host.py modules. This simplification reduces code complexity and focuses the I/O architecture on the new, optimized transfer mechanisms.
• New CUDA Kernels and Python Bindings: I've added new CUDA kernels in sgl-kernel/csrc/kvcacheio/transfer.cu that perform the core KV cache data transfers. These kernels are exposed to Python via new PyTorch custom operators, with high-level Python wrappers provided in sgl-kernel/python/sgl_kernel/kvcacheio.py to abstract the backend choice.
• Dynamic I/O Backend Selection: The cache_controller.py now includes logic to dynamically select the appropriate I/O backend ('kernel' or 'direct') based on the configured page_size, ensuring the most efficient transfer method is used for different scenarios.

Code Review

This pull request introduces custom CUDA kernels for more efficient KV cache I/O, particularly for small page sizes, and refactors the memory layout of K/V buffers to be contiguous per layer. The changes simplify the existing I/O logic in the CacheController and MemoryPool classes. Key areas for review include the pass implementation for I/O methods in DoubleSparseTokenToKVPool and the magic number used for I/O backend selection.