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
Developed scalable paged attention support for FMHA forward kernels in the pytorch/FBGEMM repository, targeting both Blackwell (fixed-length) and CUTLASS (variable-length) implementations. The work involved integrating memory paging for key and value tensors, adjusting tensor dimensions and memory layouts, and creating comprehensive tests to ensure correctness. Leveraging C++, CUDA, and deep learning optimization techniques, the developer focused on efficient GPU programming and memory management to enable handling of longer context sequences. This feature enhanced the versatility and performance of FMHA kernels, laying a foundation for improved scalability and test coverage in deep learning inference and training workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
883
Activity Months1
Your Network
3284 peopleSame Organization
@meta.com3078
Peter RongMember
Zain RizviMember
Aaditya NairMember
Aahan AggarwalMember
Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron FarberMember
Aaron PollackMember
Aaryaman SagarMember
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