pytorch/FBGEMM
Sep 2025 – Oct 2025
2 Months active
Languages Used
PythonC++
Technical Skills
Deep Learning OptimizationNumerical StabilityQuantizationDeep LearningPerformance OptimizationTriton
Yunjie Pan
PROFILE
Yunjie Pan
Worked on the pytorch/FBGEMM repository to enhance quantization accuracy and performance for deep learning inference. Delivered a new feature enabling MX4 FP8 local scale emulation by implementing a Triton kernel for FP32 to E8M0 conversion and updating the NVFP4 quantization path to better align with MX4 matrix multiplication behavior. Addressed a critical FP4 quantization scaling bug by correcting the scaled input calculation and enforcing FP64 precision for improved numerical stability. The work focused on deep learning optimization, quantization, and numerical stability, utilizing C++ and Python to deliver robust solutions for production-scale inference workloads in PyTorch environments.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
135
Activity Months2
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Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on feature delivery in pytorch/FBGEMM. Delivered MX4 FP8 local scale emulation with E8M0 scaling (NVFP4) by adding a new Triton kernel for FP32 to E8M0 conversion and updating the NVFP4 quantization path to mimic MX4 matrix multiplication behavior. This work improves performance and accuracy for FP8 quantization and aligns NVFP4 with MX4 expectations, enabling more accurate inference for production workloads.
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on feature delivery in pytorch/FBGEMM. Delivered MX4 FP8 local scale emulation with E8M0 scaling (NVFP4) by adding a new Triton kernel for FP32 to E8M0 conversion and updating the NVFP4 quantization path to mimic MX4 matrix multiplication behavior. This work improves performance and accuracy for FP8 quantization and aligns NVFP4 with MX4 expectations, enabling more accurate inference for production workloads.
October 2025
September 2025
1 Commits
Sep 1, 2025September 2025 performance summary focusing on stability and correctness of the FP4 quantization path in pytorch/FBGEMM. Delivered a critical FP4 Quantization Scaling Bug Fix that improves numerical stability and inference reliability for FP4 workloads.
September 2025
1 Commits
Sep 1, 2025September 2025 performance summary focusing on stability and correctness of the FP4 quantization path in pytorch/FBGEMM. Delivered a critical FP4 Quantization Scaling Bug Fix that improves numerical stability and inference reliability for FP4 workloads.
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Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture95.0%
Performance85.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Deep LearningDeep Learning OptimizationNumerical StabilityPerformance OptimizationQuantizationTriton
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