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
Yanxiong contributed to the pytorch/FBGEMM repository by enhancing benchmarking features and improving reporting accuracy for performance metrics. He developed support for benchmarking across multiple devices with customizable batch sizes and sequence lengths, leveraging C++ and Python to ensure robust cross-language data handling. His work included refining the bench_params_reporter to provide more granular per-table reporting and correcting calculations using offset arithmetic, which improved the reliability of numerical results. Additionally, Yanxiong addressed log noise by cleaning up unnecessary output in the EEG estimator, resulting in clearer logs. These contributions deepened the repository’s benchmarking capabilities and streamlined backend performance analysis.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
698
Activity Months1
Your Network
2401 people
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
Activity
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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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