ROCm/aiter
Jun 2026 – Jul 2026
2 Months active
Languages Used
C++Python
Technical Skills
CUDAGPU ProgrammingPerformance OptimizationUnit TestingC++GPU Kernel Development
Jack Hu
PROFILE
Jack Hu
Worked on the ROCm/aiter repository to optimize GPU kernel performance for non-contiguous KV cache layouts in fused QK-norm-RoPE-cache paths. Developed stride-aware indexing and layout logic in C++ and CUDA, enabling support for multiple KV cache memory layouts by considering block, token, and head strides. Enhanced reliability and maintainability by expanding unit tests to verify parity across kv-first, blocks-first, and packed-head-dim configurations. Addressed edge cases such as blocksize handling and improved code quality through formatting and linting. These efforts improved data access patterns, reduced layout-related risk, and enabled more flexible, robust deployment of quantized and shuffled fusion workloads.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
548
Activity Months2
Your Network
1985 peopleSame Organization
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Work History
July 2026
1 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for ROCm/aiter: Delivered stride-aware indexing and KV-cache layout parity for the fused QK-norm-RoPE-cache kernel, enabling support for multiple KV cache memory layouts (kv-first, blocks-first, packed-head-dim) by updating kernel logic to consider token and head strides in addition to block strides. Expanded unit tests to verify parity across layouts, strengthening reliability across layout configurations for quantized/shuffled fusion paths. This work reduces layout-related risk, enables more flexible deployment of KV cache layouts, and sets the stage for performance gains in inference workloads.
1 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for ROCm/aiter: Delivered stride-aware indexing and KV-cache layout parity for the fused QK-norm-RoPE-cache kernel, enabling support for multiple KV cache memory layouts (kv-first, blocks-first, packed-head-dim) by updating kernel logic to consider token and head strides in addition to block strides. Expanded unit tests to verify parity across layouts, strengthening reliability across layout configurations for quantized/shuffled fusion paths. This work reduces layout-related risk, enables more flexible deployment of KV cache layouts, and sets the stage for performance gains in inference workloads.
July 2026
June 2026
2 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for ROCm/aiter: Delivered stride-aware KV cache layout optimization for non-contiguous layouts in the fused_qk_norm_rope_cache_pts_quant_shuffle path. Implemented stride-aware offsets for the KV cache block dimension, added unit tests verifying parity and correctness against the original layout, and performed minor readability and formatting cleanups. Fixed a blocksize=0 edge-case in the non-shuffle KV write path, improving robustness. These efforts enhance data access patterns and reliability for non-contiguous workloads while maintaining CI stability and code quality.
June 2026
2 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for ROCm/aiter: Delivered stride-aware KV cache layout optimization for non-contiguous layouts in the fused_qk_norm_rope_cache_pts_quant_shuffle path. Implemented stride-aware offsets for the KV cache block dimension, added unit tests verifying parity and correctness against the original layout, and performed minor readability and formatting cleanups. Fixed a blocksize=0 edge-case in the non-shuffle KV write path, improving robustness. These efforts enhance data access patterns and reliability for non-contiguous workloads while maintaining CI stability and code quality.
Activity
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Quality Metrics
Correctness93.4%
Maintainability80.0%
Architecture86.6%
Performance80.0%
AI Usage46.6%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++CUDAGPU Kernel DevelopmentGPU ProgrammingPerformance OptimizationPyTorchPythonUnit Testing
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