pytorch/FBGEMM
Sep 2025 – Sep 2025
1 Month active
Languages Used
C++Python
Technical Skills
Backend DevelopmentBenchmarkingC++Code RefactoringData AnalysisDebugging
Yan Xiong
PROFILE
Yan Xiong
Contributed to the pytorch/FBGEMM repository by enhancing benchmarking features and improving reporting accuracy for device performance analysis. Developed support for benchmarking with device speclists and custom batch sizes or sequence lengths, enabling more flexible and comprehensive performance testing. Refactored the bench parameters reporter to provide per-table metrics and corrected bag size calculations using offset arithmetic, resulting in more reliable data analysis. Addressed cross-language stability by implementing a Python-side fix for batch size handling, ensuring consistency between Python and C++ components. Additionally, reduced log noise by cleaning up unnecessary output, streamlining debugging and performance optimization workflows across deep learning frameworks.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
698
Activity Months1
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Aaryaman SagarMember
Work History
September 2025
4 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for pytorch/FBGEMM. Delivered benchmarking feature enhancements and improved reporting accuracy, along with log noise reduction. Key outcomes include: 1) TBE Bench Benchmarking Enhancements enabling benchmarking with device speclists and custom batch sizes/sequence lengths; 2) Bench Params Reporter Enhancements and Corrections adding per-table L reporting, Ls list, corrected bag_sizes using offsets, and improved L correctness; 3) EEG Estimator Logging Cleanup removing noisy std::cout output; 4) Cross-language data handling improvements including Python-side fix to convert batch size to float before torch.std for bench_params_reporter. Impact: faster, more reliable benchmarking across devices, higher quality performance metrics, and reduced log noise. Technologies: C++, Python, PyTorch, benchmarking frameworks, offset arithmetic, and robust reporting.
4 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for pytorch/FBGEMM. Delivered benchmarking feature enhancements and improved reporting accuracy, along with log noise reduction. Key outcomes include: 1) TBE Bench Benchmarking Enhancements enabling benchmarking with device speclists and custom batch sizes/sequence lengths; 2) Bench Params Reporter Enhancements and Corrections adding per-table L reporting, Ls list, corrected bag_sizes using offsets, and improved L correctness; 3) EEG Estimator Logging Cleanup removing noisy std::cout output; 4) Cross-language data handling improvements including Python-side fix to convert batch size to float before torch.std for bench_params_reporter. Impact: faster, more reliable benchmarking across devices, higher quality performance metrics, and reduced log noise. Technologies: C++, Python, PyTorch, benchmarking frameworks, offset arithmetic, and robust reporting.
September 2025
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Quality Metrics
Correctness92.6%
Maintainability90.0%
Architecture92.6%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Backend DevelopmentBenchmarkingC++Code RefactoringData AnalysisDebuggingDeep Learning FrameworksGPU ComputingNumerical ComputingPerformance OptimizationPython
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