pytorch/pytorch
Jun 2025 – Oct 2025
5 Months active
Languages Used
C++MetalPython
Technical Skills
C++ DevelopmentDeep LearningGPU ProgrammingMetal APIPerformance OptimizationTensor Operations
Kurt Mohler
PROFILE
Kurt Mohler
Contributed to the pytorch/pytorch repository by expanding PyTorch’s Metal Performance Shaders (MPS) backend for Apple devices, focusing on GPU-accelerated deep learning operations. Developed and optimized pooling, unpooling, dropout, and embedding functionalities, including 3D max and average pooling, grid sampling, and EmbeddingBag support with forward and backward passes. Addressed numerical precision and consistency issues by refining kernel implementations and aligning behaviors across CPU and GPU backends. Leveraged C++, Metal, and Python to deliver efficient, numerically robust tensor operations, enabling broader model support and improved training performance on Apple hardware while reducing cross-backend discrepancies and enhancing developer experience for macOS deployments.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
20Total
Bugs
3
Commits
20
Features
9
Lines of code
5,268
Activity Months5
Your Network
938 peopleShared Repositories
928
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Focused on expanding MPS support by delivering EmbeddingBag backward pass with per-sample weights and support for SUM, MEAN, and MAX gradient modes, enabling correct and efficient training on Apple Silicon.
1 Commits • 1 Features
Oct 1, 2025October 2025: Focused on expanding MPS support by delivering EmbeddingBag backward pass with per-sample weights and support for SUM, MEAN, and MAX gradient modes, enabling correct and efficient training on Apple Silicon.
October 2025
September 2025
5 Commits • 3 Features
Sep 1, 2025September 2025 highlights: Expanded the Metal/MPS backend on Apple hardware with new mathematical ops, native dropout support, and embedding improvements, plus a critical NaN handling fix in grid_sampler_3d. These deliverables improve model training capabilities on Apple GPUs, enhance numerical robustness, and optimize embedding workflows, driving business value through broader hardware support and more reliable results.
September 2025
5 Commits • 3 Features
Sep 1, 2025September 2025 highlights: Expanded the Metal/MPS backend on Apple hardware with new mathematical ops, native dropout support, and embedding improvements, plus a critical NaN handling fix in grid_sampler_3d. These deliverables improve model training capabilities on Apple GPUs, enhance numerical robustness, and optimize embedding workflows, driving business value through broader hardware support and more reliable results.
August 2025
5 Commits • 2 Features
Aug 1, 2025Summary for 2025-08: Strengthened PyTorch's MPS backend with targeted pooling and sampling enhancements for Apple hardware, delivering broader model support, higher numeric accuracy, and improved performance parity with CPU. Key outcomes include new 1D/2D/3D max_unpool operations for MPS, a backward pass for avg_pool3d with opmath_t precision, and the adoption of opmath_t in avg_pool3d for improved numerical stability. Added grid_sampler_3d support for MPS enabling 3D grid sampling with bilinear interpolation, plus a targeted fix to align avg_pool2d ceil_mode behavior between Metal and CPU backends. These changes were implemented via kernel-level updates and new utilities, enabling more robust 3D pooling and sampling workloads on Apple hardware and reducing cross-backend discrepancies.
5 Commits • 2 Features
Aug 1, 2025Summary for 2025-08: Strengthened PyTorch's MPS backend with targeted pooling and sampling enhancements for Apple hardware, delivering broader model support, higher numeric accuracy, and improved performance parity with CPU. Key outcomes include new 1D/2D/3D max_unpool operations for MPS, a backward pass for avg_pool3d with opmath_t precision, and the adoption of opmath_t in avg_pool3d for improved numerical stability. Added grid_sampler_3d support for MPS enabling 3D grid sampling with bilinear interpolation, plus a targeted fix to align avg_pool2d ceil_mode behavior between Metal and CPU backends. These changes were implemented via kernel-level updates and new utilities, enabling more robust 3D pooling and sampling workloads on Apple hardware and reducing cross-backend discrepancies.
August 2025
July 2025
6 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary: On pytorch/pytorch, delivered substantial MPS (Metal Performance Shaders) backend improvements for Apple GPUs, including backward pass for 3D max pooling, 3D avg pooling addition, and an optimized 2D max pooling kernel for stride != 1, plus a bug fix preventing zeros in the MPS exponential function used by RNG. These changes improve GPU acceleration, compatibility, and RNG reliability, enabling faster training/inference and broader feature parity on MPS-backed deployments.
July 2025
6 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary: On pytorch/pytorch, delivered substantial MPS (Metal Performance Shaders) backend improvements for Apple GPUs, including backward pass for 3D max pooling, 3D avg pooling addition, and an optimized 2D max pooling kernel for stride != 1, plus a bug fix preventing zeros in the MPS exponential function used by RNG. These changes improve GPU acceleration, compatibility, and RNG reliability, enabling faster training/inference and broader feature parity on MPS-backed deployments.
June 2025
3 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for pytorch/pytorch focusing on Metal backend acceleration for Apple devices. Implemented support for abs, expm1, and 3D max pooling (max_pool3d) using Metal Performance Shaders (MPS), expanding on-device compute coverage and performance. Key commits enabling these changes include: - e7698ff5cf40729d11df6c32c6df0a163e5d0ce0: [MPS] Move abs op to Metal (#155474) - 013cf1e3302d27de36588cf7a7130d76a5686bad: [MPS] Move expm1 op to Metal (#155611) - e0447bb5f84dca38e7515d1b1fdea42c647e5acd: Add `max_pool3d` for MPS (#156467) Technologies/skills demonstrated: - Metal and Metal Performance Shaders (MPS) integration and kernel development - Kernel design, type registrations, and dispatch/registration improvements for backend flexibility - On-device performance optimization and cross-backend compatibility (CPU/GPU dispatch strategy) Business value and impact: - Broadened PyTorch’s on-device acceleration on Apple devices, enabling faster inference for CNNs and 3D workloads while reducing CPU offload and power consumption. - Improved hardware utilization for macOS/iOS deployments and improved developer experience with broader operation coverage on Metal backend.
3 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for pytorch/pytorch focusing on Metal backend acceleration for Apple devices. Implemented support for abs, expm1, and 3D max pooling (max_pool3d) using Metal Performance Shaders (MPS), expanding on-device compute coverage and performance. Key commits enabling these changes include: - e7698ff5cf40729d11df6c32c6df0a163e5d0ce0: [MPS] Move abs op to Metal (#155474) - 013cf1e3302d27de36588cf7a7130d76a5686bad: [MPS] Move expm1 op to Metal (#155611) - e0447bb5f84dca38e7515d1b1fdea42c647e5acd: Add `max_pool3d` for MPS (#156467) Technologies/skills demonstrated: - Metal and Metal Performance Shaders (MPS) integration and kernel development - Kernel design, type registrations, and dispatch/registration improvements for backend flexibility - On-device performance optimization and cross-backend compatibility (CPU/GPU dispatch strategy) Business value and impact: - Broadened PyTorch’s on-device acceleration on Apple devices, enabling faster inference for CNNs and 3D workloads while reducing CPU offload and power consumption. - Improved hardware utilization for macOS/iOS deployments and improved developer experience with broader operation coverage on Metal backend.
June 2025
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Quality Metrics
Correctness97.0%
Maintainability80.0%
Architecture91.0%
Performance85.0%
AI Usage21.0%
Skills & Technologies
Programming Languages
C++MetalPython
Technical Skills
3D GraphicsC++ DevelopmentC++ developmentCUDADeep LearningDeep learningGPU ProgrammingGPU programmingMPSMachine LearningMachine Learning Framework DevelopmentMathematical ComputationMetal APINumerical MethodsPerformance Optimization
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