pytorch/FBGEMM
Nov 2024 – Jun 2025
5 Months active
Languages Used
C++PythonGitShellCUDAHIP
Technical Skills
C++CUDADeep LearningGPU ComputingLinear AlgebraPython
Josh Fromm
PROFILE
Josh Fromm
Josh Fromm contributed to the pytorch/FBGEMM repository by developing and integrating advanced GPU computing features, focusing on compatibility and performance for both NVIDIA and AMD hardware. He implemented support for new Cutlass and composable_kernel versions, enabling groupwise mixed data type GEMM operations and expanding GenAI kernel builds to AMD platforms. Using C++, CUDA, and Python, Josh managed complex submodule dependencies and optimized machine learning kernels for forward compatibility and reproducibility. His work included refactoring FP8 row-wise kernels, improving CI/CD reliability, and addressing broadcasting correctness in tensor operations, demonstrating depth in low-level programming and cross-platform build system management.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
11Total
Bugs
1
Commits
11
Features
8
Lines of code
4,103
Activity Months5
Work History
June 2025
4 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary for pytorch/FBGEMM highlighting key features delivered, major bug fixes, and impact.
4 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary for pytorch/FBGEMM highlighting key features delivered, major bug fixes, and impact.
June 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for pytorch/FBGEMM. Delivered composable_kernel integration to enable AMD GenAI builds in the open-source repository, expanding hardware support and improving build reproducibility. No major bugs fixed this month. Overall impact: broadened GenAI workload support on AMD hardware, enabling wider experimentation and deployment in open-source workflows. Technologies demonstrated: dependency management, submodule integration, fork management, and cross-platform build workflows for GenAI kernels in OSS.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for pytorch/FBGEMM. Delivered composable_kernel integration to enable AMD GenAI builds in the open-source repository, expanding hardware support and improving build reproducibility. No major bugs fixed this month. Overall impact: broadened GenAI workload support on AMD hardware, enabling wider experimentation and deployment in open-source workflows. Technologies demonstrated: dependency management, submodule integration, fork management, and cross-platform build workflows for GenAI kernels in OSS.
March 2025
3 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for pytorch/FBGEMM: Stabilized GPU build pipeline by updating Cutlass submodule to 3.8V2 and aligning CI configuration, and extended GEMM capabilities with groupwise mixed data type support to enable upcoming open-source model releases.
3 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for pytorch/FBGEMM: Stabilized GPU build pipeline by updating Cutlass submodule to 3.8V2 and aligning CI configuration, and extended GEMM capabilities with groupwise mixed data type support to enable upcoming open-source model releases.
March 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary focusing on key accomplishments, major fixes, and overall impact across pytorch/FBGEMM and intel/sycl-tla. Delivered targeted features and stability improvements that reduce MOE deployment risk and improve correctness in core tensor operations, enabling downstream productivity and performance.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary focusing on key accomplishments, major fixes, and overall impact across pytorch/FBGEMM and intel/sycl-tla. Delivered targeted features and stability improvements that reduce MOE deployment risk and improve correctness in core tensor operations, enabling downstream productivity and performance.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for pytorch/FBGEMM: Delivered Cutlass 3.6 compatibility for the FBGEMM library with forward-compatibility fixes; validation shows preserved correctness and potential minor speed improvements. No major bugs fixed this month; focused on maintainability and compatibility with cutting-edge CUDA libraries, enabling users to leverage Cutlass 3.6 with FBGEMM kernels.
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for pytorch/FBGEMM: Delivered Cutlass 3.6 compatibility for the FBGEMM library with forward-compatibility fixes; validation shows preserved correctness and potential minor speed improvements. No major bugs fixed this month; focused on maintainability and compatibility with cutting-edge CUDA libraries, enabling users to leverage Cutlass 3.6 with FBGEMM kernels.
November 2024
Activity
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Quality Metrics
Correctness92.8%
Maintainability91.0%
Architecture91.0%
Performance88.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDAGitHIPPythonShell
Technical Skills
Build SystemsC++CI/CDCUDADeep LearningDependency ManagementGPU ComputingGPU ProgrammingLibrary UpdatesLinear AlgebraLow-level ProgrammingMachine Learning KernelsPerformance OptimizationPythonSubmodule Management
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
intel/sycl-tla
Feb 2025 – Feb 2025
1 Month active
Languages Used
C++
Technical Skills
C++Low-level ProgrammingTemplate Metaprogramming