pytorch/FBGEMM
May 2025 – Oct 2025
2 Months active
Languages Used
C++CUDAPython
Technical Skills
C++CUDACUDA ProgrammingDebuggingGPU ProgrammingPyTorch
Zhou Fang
PROFILE
Zhou Fang
Worked on core tensor and CUDA infrastructure across PyTorch repositories, focusing on stability, performance, and compatibility. Delivered memory-safety improvements in FBGEMM’s CUDA InputCombine path, addressing illegal memory access with robust handling of empty per-sample weights using C++ and CUDA. Extended pack_segments_forward to support integer input tensors on both CPU and GPU, updating type checks and gradient logic for mixed dtype workflows. In torchrec, implemented a latency optimization for KeyedJaggedTensor.to_dict by enabling optional offset computation, reducing serialization overhead. Fixed a regression in PyTorch’s Triton kernel CUDA graph integration, ensuring correct execution paths and maintaining model compatibility and performance.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
5Total
Bugs
2
Commits
5
Features
2
Lines of code
253
Activity Months4
Your Network
4036 peopleSame Organization
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Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron PollackMember
Aaryaman SagarMember
Aashay GaikwadMember
Ajanthan AsogamoorthyMember
Work History
March 2026
1 Commits
Mar 1, 2026March 2026 monthly summary focused on stabilizing Triton kernel CUDA graph integration within PyTorch Inductor. Implemented a regression fix to ensure correct get_read_writes behavior when epilogue_fusion_user_defined_triton_kernel is disabled, preventing conflicts for models relying on the original behavior and preserving CUDA graph correctness and performance.
1 Commits
Mar 1, 2026March 2026 monthly summary focused on stabilizing Triton kernel CUDA graph integration within PyTorch Inductor. Implemented a regression fix to ensure correct get_read_writes behavior when epilogue_fusion_user_defined_triton_kernel is disabled, preventing conflicts for models relying on the original behavior and preserving CUDA graph correctness and performance.
March 2026
November 2025
1 Commits • 1 Features
Nov 1, 20252025-11 monthly summary: Delivered a latency optimization for KeyedJaggedTensor.to_dict in pytorch/torchrec by enabling optional skipping of offset computations when offsets are unnecessary. This performance-focused change reduces latency in the serialization path, enabling faster data pipelines for models that do not require offsets.
November 2025
1 Commits • 1 Features
Nov 1, 20252025-11 monthly summary: Delivered a latency optimization for KeyedJaggedTensor.to_dict in pytorch/torchrec by enabling optional skipping of offset computations when offsets are unnecessary. This performance-focused change reduces latency in the serialization path, enabling faster data pipelines for models that do not require offsets.
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly performance summary for 2025-10 focusing on features delivered, bugs fixed, impact, and skill demonstration for the pytorch/FBGEMM workstream.
1 Commits • 1 Features
Oct 1, 2025Monthly performance summary for 2025-10 focusing on features delivered, bugs fixed, impact, and skill demonstration for the pytorch/FBGEMM workstream.
October 2025
May 2025
2 Commits
May 1, 2025May 2025: Delivered stability improvements and verified fixes for the CUDA InputCombine path in FBGEMM. Focused on memory-safety correctness when per_sample_weights include empty tensors, and solidified test coverage around mixed empty/non-empty and all-empty scenarios. Resulted in safer memory handling, reduced risk of illegal memory access, and improved reliability of downstream models using FBGEMM.
May 2025
2 Commits
May 1, 2025May 2025: Delivered stability improvements and verified fixes for the CUDA InputCombine path in FBGEMM. Focused on memory-safety correctness when per_sample_weights include empty tensors, and solidified test coverage around mixed empty/non-empty and all-empty scenarios. Resulted in safer memory handling, reduced risk of illegal memory access, and improved reliability of downstream models using FBGEMM.
Activity
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Quality Metrics
Correctness96.0%
Maintainability80.0%
Architecture80.0%
Performance84.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
C++CUDACUDA ProgrammingCUDA programmingDebuggingDeep LearningGPU ProgrammingMachine LearningPyTorchPythonTensor operationsTestingdata structuresperformance optimizationunit testing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
pytorch/torchrec
Nov 2025 – Nov 2025
1 Month active
Languages Used
Python
Technical Skills
data structuresperformance optimizationunit testing
pytorch/pytorch
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
CUDADeep LearningMachine LearningPython