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
Worked on the ROCm/flash-attention repository to address a correctness issue in the backward pass of the Flash Attention kernel, specifically enabling support for distinct head dimensions between QK and V tensors. Refactored interfaces, APIs, templates, main loops, and epilogue logic to separate the handling of QK and V dimensions, ensuring accurate gradient computations in mixed-dimension scenarios. Utilized C++, CUDA, and deep learning techniques to enhance numerical stability and maintainability. The solution reduced the risk of dimension-related failures during training and aligned with repository standards, resulting in a more robust and traceable implementation for GPU-accelerated attention mechanisms.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
1Total
Bugs
1
Commits
1
Features
0
Lines of code
440
Activity Months1
Your Network
59 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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