pytorch/FBGEMM
Dec 2024 – Dec 2025
7 Months active
Languages Used
C++HIPCUDA
Technical Skills
CUDA/HIPGPU ProgrammingMachine Learning LibrariesPerformance OptimizationCUDADeep Learning
Jing Zhang
PROFILE
Jing Zhang
Over seven months, contributed to the pytorch/FBGEMM repository by engineering robust FP8 and BF16 GPU kernels for deep learning inference and training. Focused on optimizing GEMM and convolution operations, the work addressed irregular input shapes, improved kernel dispatch heuristics, and introduced batch size-aware performance tuning. Leveraging C++, CUDA, and GPU programming expertise, implemented fallback mechanisms, shape-based lookup tables, and configurable kernel variants to enhance throughput, reliability, and hardware compatibility. These solutions reduced runtime failures, improved latency consistency, and simplified deployment for large-scale production workloads, demonstrating a deep understanding of algorithm optimization and performance engineering in machine learning systems.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
9Total
Bugs
3
Commits
9
Features
5
Lines of code
22,996
Activity Months7
Your Network
2993 peopleSame Organization
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Peter RongMember
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Aahan AggarwalMember
Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron PollackMember
Aaryaman SagarMember
Aashay GaikwadMember
Ajanthan AsogamoorthyMember
Shared Repositories
195
Work History
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary: Delivered batch size heuristic optimizations for FBGEMM and GB200 in pytorch/FBGEMM, focusing on performance, stability, and predictable scaling for production workloads. Key changes include skipping batch size in problem-size equality and hashing to reduce comparison overhead and improve hashing performance; extending GB200 with a robust fallback to the nearest tuned configuration when an exact match is unavailable; and expanding GB200’s considered batch sizes to 1, 2, 4, and 8. These changes reduce latency variance, improve throughput, and simplify configuration management for inference across diverse batch sizes.
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary: Delivered batch size heuristic optimizations for FBGEMM and GB200 in pytorch/FBGEMM, focusing on performance, stability, and predictable scaling for production workloads. Key changes include skipping batch size in problem-size equality and hashing to reduce comparison overhead and improve hashing performance; extending GB200 with a robust fallback to the nearest tuned configuration when an exact match is unavailable; and expanding GB200’s considered batch sizes to 1, 2, 4, and 8. These changes reduce latency variance, improve throughput, and simplify configuration management for inference across diverse batch sizes.
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for pytorch/FBGEMM: Delivered FP8 Convolution Performance Optimization and new kernel variants. Focuses on performance, configurability, and FP8 readiness for production-scale inference. No major bugs addressed in this repo this month; feature-focused delivery with measurable impact on throughput and efficiency.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for pytorch/FBGEMM: Delivered FP8 Convolution Performance Optimization and new kernel variants. Focuses on performance, configurability, and FP8 readiness for production-scale inference. No major bugs addressed in this repo this month; feature-focused delivery with measurable impact on throughput and efficiency.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered FP8 convolution support for WAN 2.2 in FBGEMM, featuring FP8 convolution kernels and a problem-size based kernel selection heuristic. This work enhances WAN 2.2 throughput on FP8 paths, broadens hardware applicability, and aligns with ongoing performance optimization efforts. No major bug fixes reported for this repository this month; the focus was on robust feature delivery, code quality, and cross-team collaboration.
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered FP8 convolution support for WAN 2.2 in FBGEMM, featuring FP8 convolution kernels and a problem-size based kernel selection heuristic. This work enhances WAN 2.2 throughput on FP8 paths, broadens hardware applicability, and aligns with ongoing performance optimization efforts. No major bug fixes reported for this repository this month; the focus was on robust feature delivery, code quality, and cross-team collaboration.
October 2025
April 2025
1 Commits
Apr 1, 2025In April 2025, delivered a robustness fix for FP8 row-wise GEMM in PyTorch FBGEMM (pytorch/FBGEMM). The change addresses irregular GEMM shapes by refining kernel dispatch heuristics and enabling MNKPadding by default, extending compatibility to input shapes that do not neatly align with kernel dimensions. The work reduces runtime failures, improves stability for FP8 workloads, and simplifies model deployment by eliminating manual shape workarounds.
April 2025
1 Commits
Apr 1, 2025In April 2025, delivered a robustness fix for FP8 row-wise GEMM in PyTorch FBGEMM (pytorch/FBGEMM). The change addresses irregular GEMM shapes by refining kernel dispatch heuristics and enabling MNKPadding by default, extending compatibility to input shapes that do not neatly align with kernel dimensions. The work reduces runtime failures, improves stability for FP8 workloads, and simplifies model deployment by eliminating manual shape workarounds.
March 2025
2 Commits • 1 Features
Mar 1, 2025Monthly work summary for 2025-03 focusing on FP8/BF16 path robustness and performance optimizations in the FBGEMM repository. The work delivered targeted fixes to irregular input sizes and a dispatch optimization that improves grouped GEMM performance, aligning with business goals for higher throughput and reliability in FP8/BF16 workloads.
2 Commits • 1 Features
Mar 1, 2025Monthly work summary for 2025-03 focusing on FP8/BF16 path robustness and performance optimizations in the FBGEMM repository. The work delivered targeted fixes to irregular input sizes and a dispatch optimization that improves grouped GEMM performance, aligning with business goals for higher throughput and reliability in FP8/BF16 workloads.
March 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01 — Focused on delivering high-impact FP8 GEMM optimizations for large-scale Prefill workloads in the pytorch/FBGEMM project, with emphasis on throughput, latency, and configurability.
January 2025
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01 — Focused on delivering high-impact FP8 GEMM optimizations for large-scale Prefill workloads in the pytorch/FBGEMM project, with emphasis on throughput, latency, and configurability.
December 2024
1 Commits
Dec 1, 2024December 2024: Focused on improving robustness and reliability of FP8 rowwise operations in FBGEMM when dealing with irregular shapes. Delivered a fallback mechanism, refined kernel dispatch for non-multiples of tile sizes, and refined CK GEMM handling by disabling atomicAdd for odd N to ensure correctness in edge cases. These changes reduce runtime failures in production workloads that use irregular shapes and broaden the supported input configurations, delivering tangible business value for production inference and research workflows.
1 Commits
Dec 1, 2024December 2024: Focused on improving robustness and reliability of FP8 rowwise operations in FBGEMM when dealing with irregular shapes. Delivered a fallback mechanism, refined kernel dispatch for non-multiples of tile sizes, and refined CK GEMM handling by disabling atomicAdd for odd N to ensure correctness in edge cases. These changes reduce runtime failures in production workloads that use irregular shapes and broaden the supported input configurations, delivering tangible business value for production inference and research workflows.
December 2024
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Quality Metrics
Correctness81.2%
Maintainability80.0%
Architecture80.0%
Performance85.6%
AI Usage26.6%
Skills & Technologies
Programming Languages
C++CUDAHIP
Technical Skills
BF16C++C++ developmentCUDACUDA ProgrammingCUDA/HIPConvolutional Neural NetworksDeep LearningFP8GEMM KernelsGPU ProgrammingGPU programmingLinear Algebra LibrariesMachine LearningMachine Learning Libraries
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