pytorch/FBGEMM
Oct 2025 – Oct 2025
1 Month active
Languages Used
C++Python
Technical Skills
Attention MechanismsCUDACUDA ProgrammingDeep LearningDeep Learning OptimizationGPU Programming
Saritha Dwarakapuram
PROFILE
Saritha Dwarakapuram
During October 2025, Saritha D. developed scalable paged attention support for FMHA forward kernels in the pytorch/FBGEMM repository, targeting both Blackwell (fixed-length) and CUTLASS (variable-length) implementations. She engineered memory paging for key and value tensors, adjusted tensor dimensions and memory layouts, and created comprehensive tests to validate the new functionality. Leveraging C++, Python, and CUDA programming, Saritha’s work addressed the challenge of efficiently handling longer context sequences in deep learning models. Her contributions improved kernel versatility and test coverage, demonstrating depth in attention mechanisms, GPU programming, and performance engineering while laying a foundation for future optimization in this area.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
883
Activity Months1
Your Network
2401 people
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Focused on delivering scalable paged attention support for FMHA forward kernels across Blackwell (fixed-length) and CUTLASS (variable-length), with memory paging for K/V tensors, dimension/memory layout adjustments, and comprehensive tests. This work lays groundwork for efficient handling of longer contexts in pytorch/FBGEMM and improves overall kernel versatility and test coverage.
2 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Focused on delivering scalable paged attention support for FMHA forward kernels across Blackwell (fixed-length) and CUTLASS (variable-length), with memory paging for K/V tensors, dimension/memory layout adjustments, and comprehensive tests. This work lays groundwork for efficient handling of longer contexts in pytorch/FBGEMM and improves overall kernel versatility and test coverage.
October 2025
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Attention MechanismsCUDACUDA ProgrammingDeep LearningDeep Learning OptimizationGPU ProgrammingMemory ManagementPerformance EngineeringTesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline