pytorch/FBGEMM
Jun 2025 – Jun 2025
1 Month active
Languages Used
C++CUDAPython
Technical Skills
C++CUDA ProgrammingDeep LearningGPU ComputingMachine LearningPyTorch
Summer Deng
PROFILE
Summer Deng
During June 2025, this developer contributed to the pytorch/FBGEMM repository by building NVFP4 quantization emulation kernels as a reference implementation for FP4 quantization-aware training on LLaMa3 8B. The work involved designing and implementing new CUDA kernels and C++ functions to enable accurate FP4 emulation, allowing researchers to prototype and benchmark quantization workflows efficiently. By integrating these components, the developer provided a ready-to-use foundation for FP4 QAT studies, supporting experimentation and performance benchmarking. The project demonstrated expertise in CUDA programming, C++, and quantization techniques, with a focus on enabling advanced deep learning research and GPU computing workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
486
Activity Months1
Your Network
3284 peopleSame Organization
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Aahan AggarwalMember
Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron FarberMember
Aaron PollackMember
Aaryaman SagarMember
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for pytorch/FBGEMM: Delivered NVFP4 quantization emulation kernels as a reference implementation for FP4 QAT on LLaMa3 8B. Implemented new CUDA kernels and C++ functions to support FP4 emulation, enabling researchers to prototype and benchmark quantization workflows. No major bugs fixed this month. Impact: provides a ready-to-use reference for FP4 QAT studies, accelerating experimentation and potential inference efficiency improvements. Technologies demonstrated: CUDA, C++, kernel development, quantization emulation, LLaMa3 8B integration, performance benchmarking readiness.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for pytorch/FBGEMM: Delivered NVFP4 quantization emulation kernels as a reference implementation for FP4 QAT on LLaMa3 8B. Implemented new CUDA kernels and C++ functions to support FP4 emulation, enabling researchers to prototype and benchmark quantization workflows. No major bugs fixed this month. Impact: provides a ready-to-use reference for FP4 QAT studies, accelerating experimentation and potential inference efficiency improvements. Technologies demonstrated: CUDA, C++, kernel development, quantization emulation, LLaMa3 8B integration, performance benchmarking readiness.
June 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
C++CUDA ProgrammingDeep LearningGPU ComputingMachine LearningPyTorchQuantization
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