pytorch/FBGEMM
Sep 2025 – Sep 2025
1 Month active
Languages Used
C++Python
Technical Skills
Deep LearningMachine LearningOptimizer ImplementationPerformance OptimizationTesting
Dongsu Du
PROFILE
Dongsu Du
Developed and integrated a new mode for the AdagradW optimizer in the pytorch/FBGEMM repository, introducing a counter-based linear learning rate schedule with a capped maximum rate to enhance training flexibility and stability. The feature was implemented as a high-performance C++ kernel and validated through comprehensive Python testing, ensuring robust integration and minimizing regression risk. This work focused on optimizer implementation and performance optimization, targeting large-scale deep learning workflows that rely on FB-GEMM. By expanding test coverage and embedding the feature within existing infrastructure, the contribution aimed to improve scalability and maintainability for machine learning model training in production environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
70
Activity Months1
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Shared Repositories
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Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09. Focused on delivering a high-value feature for the AdagradW optimizer with a counter-based linear learning rate mode, along with test coverage and integration within pytorch/FBGEMM. No formal bugs fixed in scope this month. The work enhances training stability and scalability for FB-GEMM workflows.
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09. Focused on delivering a high-value feature for the AdagradW optimizer with a counter-based linear learning rate mode, along with test coverage and integration within pytorch/FBGEMM. No formal bugs fixed in scope this month. The work enhances training stability and scalability for FB-GEMM workflows.
September 2025
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Deep LearningMachine LearningOptimizer ImplementationPerformance OptimizationTesting
Repositories Contributed To
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