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Lei Wang
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* [Add] Introduce deepseek_mla documentation for high-performance FlashMLA with TileLang - Added a comprehensive guide on writing high-performance kernels using TileLang, focusing on the Multi-Head Latent Attention (MLA) mechanism. - Included benchmark results comparing FlashMLA, TileLang, Torch, Triton, and FlashInfer, highlighting TileLang's efficiency and ease of use. - Detailed implementation strategies, including layout inference, threadblock swizzling, shared memory swizzling, and warp specialization. - Provided examples and explanations of optimization techniques to enhance performance in GPU kernel programming. * doc update * [Add] Enhance AMD FlashMLA implementation and documentation - Refactored variable names in `benchmark_mla_decode_amd_tilelang.py` for clarity, changing `Q_shared` and `Q_pe_shared` to `Q_local` and `Q_pe_local` to reflect their usage in register allocation. - Added a new `README.md` detailing the high-performance FlashMLA implementation on AMD MI300X accelerators, including architectural considerations, optimization strategies, and performance evaluation. - Introduced a performance comparison figure to illustrate the efficiency of the TileLang implementation against other frameworks. * lint fix * [Add] Expand deepseek_mla documentation for AMD MI300X optimization strategies - Introduced a new section detailing architectural differences and optimization strategies for implementing FlashMLA on AMD MI300X accelerators. - Highlighted key considerations such as instruction set variations, shared memory constraints, tile size flexibility, and memory bank conflict swizzling. - Included performance evaluation results demonstrating TileLang's efficiency compared to other frameworks. - Discussed future optimization opportunities for memory bank conflict mitigation and dimension parallelization.
