ROCm/FBGEMM
Dec 2024 – Dec 2024
1 Month active
Languages Used
C++Python
Technical Skills
Deep LearningGPU ComputingGPU ProgrammingMemory ManagementPyTorchQuantization
Jingyuan Fan
PROFILE
Jingyuan Fan
Worked on ROCm/FBGEMM and pytorch/FBGEMM, focusing on deep learning quantization and build system reliability. Enhanced FP8 quantization by extending support for non-contiguous 4D tensors and updating Triton kernels to ensure robust memory access, reducing runtime risks for large-tensor workloads. Improved the MX4 quantization kernel by addressing integer overflow issues and adding validation tests for safer GPU memory management. In pytorch/FBGEMM, broadened CMake source discovery to include all relevant C++ and CUDA files, preventing build failures and streamlining CI processes. Demonstrated expertise in C++, Python, and build system configuration while prioritizing reliability and maintainability in complex codebases.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
4Total
Bugs
2
Commits
4
Features
1
Lines of code
305
Activity Months2
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3338 peopleSame Organization
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Arjun ChaturvediMember
Aaron FarberMember
Aaron PollackMember
Aaryaman SagarMember
Shared Repositories
260
Work History
April 2025
1 Commits
Apr 1, 2025April 2025: Focused on improving build reliability and feature completeness for pytorch/FBGEMM. Implemented broader source discovery in the CMake build to include all .cpp and .cu files under fb/src and subdirectories, addressing issues where features could be dropped during compilation. This work centers on reducing CI failures, accelerating downstream integration, and stabilizing builds for PyTorch dependencies.
1 Commits
Apr 1, 2025April 2025: Focused on improving build reliability and feature completeness for pytorch/FBGEMM. Implemented broader source discovery in the CMake build to include all .cpp and .cu files under fb/src and subdirectories, addressing issues where features could be dropped during compilation. This work centers on reducing CI failures, accelerating downstream integration, and stabilizing builds for PyTorch dependencies.
April 2025
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 ROCm/FBGEMM monthly review emphasizing robust FP8 quantization expansion and safer quantization kernels. Key work focused on delivering higher-dimensional support for FP8 quantization and hardening memory access paths in the MX4 kernel, with added tests to prevent regressions. These efforts extend device-side precision capabilities while reducing runtime risk for large-tensor workloads, directly aligning with reliability and performance goals for FP8 workflows.
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 ROCm/FBGEMM monthly review emphasizing robust FP8 quantization expansion and safer quantization kernels. Key work focused on delivering higher-dimensional support for FP8 quantization and hardening memory access paths in the MX4 kernel, with added tests to prevent regressions. These efforts extend device-side precision capabilities while reducing runtime risk for large-tensor workloads, directly aligning with reliability and performance goals for FP8 workflows.
Activity
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Quality Metrics
Correctness92.4%
Maintainability80.0%
Architecture75.0%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakePython
Technical Skills
Build System ConfigurationDeep LearningGPU ComputingGPU ProgrammingMemory ManagementPyTorchQuantizationTensor OperationsTestingTriton
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
pytorch/FBGEMM
Apr 2025 – Apr 2025
1 Month active
Languages Used
CMake
Technical Skills
Build System Configuration