
Jichen contributed to performance-critical GPU and deep learning infrastructure, focusing on kernel and backend development for the pytorch/FBGEMM and ROCm/aiter repositories. He optimized embedding forward kernels using C++ and CUDA, introducing vec4-based data processing and subwarp tuning to accelerate embedding lookups. On ROCm/aiter, Jichen enhanced multi-head attention backward passes by precomputing dot products and developing new assembly kernels, improving both efficiency and reliability. He also removed dependencies on Composable Kernel, streamlined build flows, and implemented device identification via PCI chip IDs. His work demonstrated depth in GPU programming, algorithm optimization, and robust integration of Python scripting for flexible deployment.
March 2026: Delivered stability and build-time improvements on ROCm/aiter with a focus on FMHA reliability, CK-dependency management, and runtime device visibility. Key outcomes include FMHA backward overflow fixes for gfx942/gfx950, a CK-free backward pass (bwd v3), FMHA forward CK removal with ENABLE_CK flag, and PCI chip ID-based device name identification. These changes reduce crashes, enable broader platform support, and simplify builds and deployment.
March 2026: Delivered stability and build-time improvements on ROCm/aiter with a focus on FMHA reliability, CK-dependency management, and runtime device visibility. Key outcomes include FMHA backward overflow fixes for gfx942/gfx950, a CK-free backward pass (bwd v3), FMHA forward CK removal with ENABLE_CK flag, and PCI chip ID-based device name identification. These changes reduce crashes, enable broader platform support, and simplify builds and deployment.
February 2026 for ROCm/aiter: Delivered performance-focused enhancements to backward multi-head attention compute with new assembly kernels and Python integration for the hd192_128 branch kernel. Implemented mha bwd hd192_128 bottom-right a32/a16 assembly kernels, added causal br a16 kernel, refined kernel naming and NaN handling, and enabled hd192_128 br kernel in Python. Improved dimension validation for the new branch to ensure robust, flexible usage and to unlock broader model support.
February 2026 for ROCm/aiter: Delivered performance-focused enhancements to backward multi-head attention compute with new assembly kernels and Python integration for the hd192_128 branch kernel. Implemented mha bwd hd192_128 bottom-right a32/a16 assembly kernels, added causal br a16 kernel, refined kernel naming and NaN handling, and enabled hd192_128 br kernel in Python. Improved dimension validation for the new branch to ensure robust, flexible usage and to unlock broader model support.
Monthly work summary for 2025-12 focusing on key accomplishments in ROCm/aiter, highlighting delivered features, critical fixes, impact, and technical skills demonstrated.
Monthly work summary for 2025-12 focusing on key accomplishments in ROCm/aiter, highlighting delivered features, critical fixes, impact, and technical skills demonstrated.
Month 2025-11: Delivered a performance-focused optimization for the embedding forward kernel on ROCm MI350 within pytorch/FBGEMM. Implemented vec4-based data processing and subwarp optimization when embedding dimension ranges 32–64, resulting in faster embedding lookups and higher throughput. PR 5064 merged after review; validated against ROCm targets with no regressions.
Month 2025-11: Delivered a performance-focused optimization for the embedding forward kernel on ROCm MI350 within pytorch/FBGEMM. Implemented vec4-based data processing and subwarp optimization when embedding dimension ranges 32–64, resulting in faster embedding lookups and higher throughput. PR 5064 merged after review; validated against ROCm targets with no regressions.

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