pytorch/FBGEMM
Dec 2024 – May 2025
5 Months active
Languages Used
C++CUDAPythonMarkdown
Technical Skills
GPU ProgrammingNumerical ComputingPerformance OptimizationC++C++ DevelopmentCUDA
Shikai Li
PROFILE
Shikai Li
Shikai Li contributed to the pytorch/FBGEMM repository by engineering high-performance GPU kernels and APIs for deep learning workloads, focusing on GroupedGEMM, Mixture of Experts (MoE), and quantized operations. Leveraging C++, CUDA, and Python, Shikai refactored and optimized kernel code for reliability, modularity, and hardware adaptability, introducing features like FP8 quantization, fused activations, and cross-hardware support. Their work addressed numerical correctness, improved memory efficiency, and enabled robust PyTorch integration, including Torch.compile compatibility. Through benchmarking, code quality improvements, and expanded test coverage, Shikai delivered scalable solutions that enhanced inference and training throughput for large-scale distributed machine learning systems.
Overall Statistics
Feature vs Bugs
87%Features
Repository Contributions
41Total
Bugs
2
Commits
41
Features
13
Lines of code
9,192
Activity Months5
Your Network
2401 people
Work History
May 2025
13 Commits • 3 Features
May 1, 2025May 2025 monthly summary for pytorch/FBGEMM focusing on delivering scalable MoE performance improvements, FP8 support, and kernel-level optimizations, complemented by code quality enhancements. The work enhances large-scale MoE deployments, memory efficiency, and maintainability, driving business value through faster inference/training, better resource utilization, and robust APIs.
13 Commits • 3 Features
May 1, 2025May 2025 monthly summary for pytorch/FBGEMM focusing on delivering scalable MoE performance improvements, FP8 support, and kernel-level optimizations, complemented by code quality enhancements. The work enhances large-scale MoE deployments, memory efficiency, and maintainability, driving business value through faster inference/training, better resource utilization, and robust APIs.
May 2025
April 2025
15 Commits • 6 Features
Apr 1, 2025April 2025 — Summary of key business and technical achievements for pytorch/FBGEMM. Focused on delivering performance and stability improvements to the GroupedGEMM path, expanding API coverage for DeepGEMM, and enhancing open-source accessibility and visibility through public release and benchmarking. No separate bug-fix release was recorded this month; stability gains were achieved through broader feature work and safer indexing and memory setup. Key outputs include: GroupedGEMM performance enhancements with reduced recompilations across varying sequence lengths, Triton WS autodetection, FastAccum default on H100, wider kernel config search, and INT64 indexing; Masked DeepGEMM API with 128-byte alignment support and variable input sizes; open-source TokenShuffling MoE kernels released publicly with Python initializers and C++ index shuffling; Gather/Scatter enhancements with quantization (quantized gather/scale, FP8 row-wise quantization) and refactors; benchmarking tools for gather/scatter and index shuffling to gauge against PyTorch; and shuffling code refactors for better maintainability and Torch.compile compatibility.
April 2025
15 Commits • 6 Features
Apr 1, 2025April 2025 — Summary of key business and technical achievements for pytorch/FBGEMM. Focused on delivering performance and stability improvements to the GroupedGEMM path, expanding API coverage for DeepGEMM, and enhancing open-source accessibility and visibility through public release and benchmarking. No separate bug-fix release was recorded this month; stability gains were achieved through broader feature work and safer indexing and memory setup. Key outputs include: GroupedGEMM performance enhancements with reduced recompilations across varying sequence lengths, Triton WS autodetection, FastAccum default on H100, wider kernel config search, and INT64 indexing; Masked DeepGEMM API with 128-byte alignment support and variable input sizes; open-source TokenShuffling MoE kernels released publicly with Python initializers and C++ index shuffling; Gather/Scatter enhancements with quantization (quantized gather/scale, FP8 row-wise quantization) and refactors; benchmarking tools for gather/scatter and index shuffling to gauge against PyTorch; and shuffling code refactors for better maintainability and Torch.compile compatibility.
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025: FBGEMM Grouped GEMM improvements were delivered to bolster reliability, configurability, and PyTorch integration. By pruning suboptimal configurations, introducing a tunable fast accumulation option, and aligning kernel naming with PyTorch, the path to using grouped GEMM within PyTorch was made more robust, predictable, and hardware-aware. This directly enhances model throughput and reduces debugging time across deployments, while easing OSS compatibility for gathering dense tokens.
3 Commits • 1 Features
Mar 1, 2025March 2025: FBGEMM Grouped GEMM improvements were delivered to bolster reliability, configurability, and PyTorch integration. By pruning suboptimal configurations, introducing a tunable fast accumulation option, and aligning kernel naming with PyTorch, the path to using grouped GEMM within PyTorch was made more robust, predictable, and hardware-aware. This directly enhances model throughput and reduces debugging time across deployments, while easing OSS compatibility for gathering dense tokens.
March 2025
February 2025
9 Commits • 3 Features
Feb 1, 2025February 2025 performance summary for pytorch/FBGEMM. Focused on delivering performance-oriented GEMM enhancements, cross-hardware memory management, and a codebase refactor to improve maintainability. Key outcomes include a Triton-based GroupedGEMM with on-device shape information and controlled TMA usage, ongoing AMD HIP adaptation, and tighter PyTorch integration for gather/scatter workflows with Torch.compile readiness. A targeted codebase refactor moved utilities to a dedicated utils.py module, preserving functionality while improving modularity. Additionally, a stability-related rollback was executed to back out on-device TMA store, with corresponding test updates to guard against regressions. The month also advanced test coverage and shapes handling to enable robust Torch.compile pipelines.
February 2025
9 Commits • 3 Features
Feb 1, 2025February 2025 performance summary for pytorch/FBGEMM. Focused on delivering performance-oriented GEMM enhancements, cross-hardware memory management, and a codebase refactor to improve maintainability. Key outcomes include a Triton-based GroupedGEMM with on-device shape information and controlled TMA usage, ongoing AMD HIP adaptation, and tighter PyTorch integration for gather/scatter workflows with Torch.compile readiness. A targeted codebase refactor moved utilities to a dedicated utils.py module, preserving functionality while improving modularity. Additionally, a stability-related rollback was executed to back out on-device TMA store, with corresponding test updates to guard against regressions. The month also advanced test coverage and shapes handling to enable robust Torch.compile pipelines.
December 2024
1 Commits
Dec 1, 2024Monthly summary for December 2024 focusing on business value and technical achievements across pytorch/FBGEMM. This month included a critical correctness fix in the GroupedGEMM kernel for TP2EP, addressing a numerical issue that could cause incorrect token processing and shape mismatches. Key changes: added a guard against zero_start_index_M dimension, ensuring the kernel processes all tokens without skipping any, associated with commit 38bf23e419d0c79230df9d31fd69d8014e2b5ab0 (TP2EP + GroupedGEMM numerics fix. (#3449)). Result: improved correctness and stability for FP/GEMM paths, reducing risk in downstream training/inference.
1 Commits
Dec 1, 2024Monthly summary for December 2024 focusing on business value and technical achievements across pytorch/FBGEMM. This month included a critical correctness fix in the GroupedGEMM kernel for TP2EP, addressing a numerical issue that could cause incorrect token processing and shape mismatches. Key changes: added a guard against zero_start_index_M dimension, ensuring the kernel processes all tokens without skipping any, associated with commit 38bf23e419d0c79230df9d31fd69d8014e2b5ab0 (TP2EP + GroupedGEMM numerics fix. (#3449)). Result: improved correctness and stability for FP/GEMM paths, reducing risk in downstream training/inference.
December 2024
Activity
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Quality Metrics
Correctness91.8%
Maintainability85.2%
Architecture86.4%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDAMarkdownPython
Technical Skills
BenchmarkingC++C++ DevelopmentCUDACUDA ProgrammingCode RefactoringDeep LearningDeep Learning FrameworksDeep Learning OptimizationDistributed SystemsDocumentationFP8 QuantizationGPU ComputingGPU OptimizationGPU Programming
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