ROCm/flash-attention
Apr 2025 – Apr 2025
1 Month active
Languages Used
C++Python
Technical Skills
Attention MechanismsCUDADeep LearningGPU ProgrammingPerformance Optimization
Sanghun Cho
PROFILE
Sanghun Cho
Sanghoon Cho contributed to the ROCm/flash-attention repository by implementing a correctness-critical fix in the backward pass to support distinct head dimensions for QK and V, addressing scenarios where hdimQK differs from hdimV. He refactored interfaces, APIs, templates, and main loop logic in C++ and CUDA to ensure accurate gradient computations in mixed-dimension configurations. This work improved numerical stability and reduced the risk of dimension-related failures during deep learning training. By aligning the changes with repository standards and focusing on performance optimization, Sanghoon enhanced both the maintainability and reliability of the Flash Attention backward kernel.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
1Total
Bugs
1
Commits
1
Features
0
Lines of code
440
Activity Months1
Your Network
58 peopleWork History
April 2025
1 Commits
Apr 1, 2025April 2025 performance summary for ROCm/flash-attention. Key features delivered and bugs fixed, impact, and tech stack. Implemented a correctness-critical fix in the backward pass to support distinct head dimensions for QK and V (hdimQK != hdimV) across interfaces, APIs, templates, main loops, and epilogue logic. This work stabilizes gradient computations, improves accuracy, and reduces risk of dimension-related failures in mixed-dimension configurations. The fix was committed as 37c816ab0d8fdfe90e8d50a756da8ef2b70ad2bc with message 'Support hdimQK != hdimV backward (#1604)'.
1 Commits
Apr 1, 2025April 2025 performance summary for ROCm/flash-attention. Key features delivered and bugs fixed, impact, and tech stack. Implemented a correctness-critical fix in the backward pass to support distinct head dimensions for QK and V (hdimQK != hdimV) across interfaces, APIs, templates, main loops, and epilogue logic. This work stabilizes gradient computations, improves accuracy, and reduces risk of dimension-related failures in mixed-dimension configurations. The fix was committed as 37c816ab0d8fdfe90e8d50a756da8ef2b70ad2bc with message 'Support hdimQK != hdimV backward (#1604)'.
April 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Attention MechanismsCUDADeep LearningGPU ProgrammingPerformance Optimization
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