ml-explore/mlx
Oct 2024 – Sep 2025
9 Months active
Languages Used
C++Metal Shading LanguagePythonCMakeObjective-CCUDA
Technical Skills
C++Compute KernelsGPU ProgrammingLinear AlgebraMetal APIPerformance Optimization
Jagrit Digani
PROFILE
Jagrit Digani
Over nine months, Digani contributed to the ml-explore/mlx repository by engineering high-performance GPU kernels and backend optimizations for machine learning workloads. He developed and refactored Metal and CUDA kernels for matrix multiplication, attention mechanisms, and convolution, focusing on performance tuning, memory efficiency, and robust build systems. Using C++, Metal Shading Language, and CUDA, Digani introduced specialized kernels for small-batch and small-K scenarios, improved attention throughput, and enhanced API clarity and safety. His work addressed both Apple Silicon and NVIDIA GPU backends, resulting in faster inference, scalable model support, and maintainable code paths for critical ML primitives in production environments.
Overall Statistics
Feature vs Bugs
92%Features
Repository Contributions
14Total
Bugs
1
Commits
14
Features
11
Lines of code
7,941
Activity Months9
Your Network
566 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ml-explore/mlx: Delivered performance optimization for small-K workloads in GEMV and MATMUL kernels. The work focused on tuned parameters for the gemv kernel, adjustments to block sizes and Metal thread group dimensions, and a refined kernel selection logic in matmul.cpp to handle varying matrix shapes and K sizes. Resulting changes improve throughput and reduce latency for small-K matrix operations on GPU backends, contributing to faster inference and analytics workloads.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ml-explore/mlx: Delivered performance optimization for small-K workloads in GEMV and MATMUL kernels. The work focused on tuned parameters for the gemv kernel, adjustments to block sizes and Metal thread group dimensions, and a refined kernel selection logic in matmul.cpp to handle varying matrix shapes and K sizes. Resulting changes improve throughput and reduce latency for small-K matrix operations on GPU backends, contributing to faster inference and analytics workloads.
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — Focused on delivering GPU-accelerated attention for MLX. Key work centered on adding CUDA-accelerated Scaled Dot-Product Attention (SDPA) with one-pass and two-pass kernels, enhancing performance of attention computations on NVIDIA GPUs within ml-explore/mlx. Implemented updated CMake configuration and fallback logic to ensure robust builds and smooth deployment on GPU-enabled environments. The change set, anchored by commit 'Add CUDA sdpa vector (#2468)' (a9bdd67baa3c7f7b3353823eeff70ad690f5e2fd), positions MLX to unlock higher throughput for large-scale attention workloads and accelerates end-to-end model inference.
August 2025
1 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — Focused on delivering GPU-accelerated attention for MLX. Key work centered on adding CUDA-accelerated Scaled Dot-Product Attention (SDPA) with one-pass and two-pass kernels, enhancing performance of attention computations on NVIDIA GPUs within ml-explore/mlx. Implemented updated CMake configuration and fallback logic to ensure robust builds and smooth deployment on GPU-enabled environments. The change set, anchored by commit 'Add CUDA sdpa vector (#2468)' (a9bdd67baa3c7f7b3353823eeff70ad690f5e2fd), positions MLX to unlock higher throughput for large-scale attention workloads and accelerates end-to-end model inference.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 - Key feature delivered: Metal backend refactor and performance enhancements for matrix operations in ml-explore/mlx, including axpby support and optimized kernel dispatch; architecture generation detection updated for better runtime decisions. Major bugs fixed: none reported. Impact: faster ML workloads, reduced compute time, and better scalability for large experiments; maintainability improved through refactors. Technologies demonstrated: Metal shader backends, matrix math optimization, kernel dispatch tuning, no-copy improvements in normalization, architecture detection. Commit: fddb6933e1cdcb268467fc5d02be6b471bb232b9 (Collection of refactors #2274).
1 Commits • 1 Features
Jun 1, 2025June 2025 - Key feature delivered: Metal backend refactor and performance enhancements for matrix operations in ml-explore/mlx, including axpby support and optimized kernel dispatch; architecture generation detection updated for better runtime decisions. Major bugs fixed: none reported. Impact: faster ML workloads, reduced compute time, and better scalability for large experiments; maintainability improved through refactors. Technologies demonstrated: Metal shader backends, matrix math optimization, kernel dispatch tuning, no-copy improvements in normalization, architecture detection. Commit: fddb6933e1cdcb268467fc5d02be6b471bb232b9 (Collection of refactors #2274).
June 2025
April 2025
2 Commits • 2 Features
Apr 1, 2025Concise monthly summary for 2025-04 focused on delivered features, robustness, and business value for ml-explore/mlx. Key features delivered: - Depthwise 2D Convolution Kernel Optimization (Metal): Implemented a specialized kernel for depthwise 2D convolutions targeting small kernels and strides to boost performance on Metal-backed hardware. Includes Metal implementation and validation tests; optimization activates under specific conditions to maximize throughput with minimal overhead. Commit: 8777fd104f7c72d32cbb7ccf92754560ea35e7fa (Depthwise Conv2D optimization (#2036)). - Explicit mask handling in scaled_dot_product_attention: Refactored to separate mask_mode and mask_arrs parameters with a new overload, improving clarity, type safety, and robustness; enhanced validation for mask modes and array shapes. Commit: 3290bfa6902a8d55b217ebc50cc18fd4b08ac895 (Add new sdpa function overload (#2035)). Major bugs fixed: - No major bugs reported this month. Overall impact and accomplishments: - Achieved measurable performance improvements for depthwise convolutions on Metal-targeted environments, expanding efficient paths for small-kernel workloads. - Improved the attention path API, enabling safer usage and easier maintenance, with better validation guarantees. - Strengthened code quality through targeted refactors and test coverage for critical math/ML primitives. Technologies/skills demonstrated: - Metal kernel development and performance optimization, C++ API design and refactoring, test-driven development, input validation, and strong type-safety practices. Business value: - Faster inference for depthwise conv workloads on Apple hardware, with lower latency in small-kernel scenarios. - Safer, clearer API for attention mechanisms, reducing risk of misuse and enabling easier future enhancements.
April 2025
2 Commits • 2 Features
Apr 1, 2025Concise monthly summary for 2025-04 focused on delivered features, robustness, and business value for ml-explore/mlx. Key features delivered: - Depthwise 2D Convolution Kernel Optimization (Metal): Implemented a specialized kernel for depthwise 2D convolutions targeting small kernels and strides to boost performance on Metal-backed hardware. Includes Metal implementation and validation tests; optimization activates under specific conditions to maximize throughput with minimal overhead. Commit: 8777fd104f7c72d32cbb7ccf92754560ea35e7fa (Depthwise Conv2D optimization (#2036)). - Explicit mask handling in scaled_dot_product_attention: Refactored to separate mask_mode and mask_arrs parameters with a new overload, improving clarity, type safety, and robustness; enhanced validation for mask modes and array shapes. Commit: 3290bfa6902a8d55b217ebc50cc18fd4b08ac895 (Add new sdpa function overload (#2035)). Major bugs fixed: - No major bugs reported this month. Overall impact and accomplishments: - Achieved measurable performance improvements for depthwise convolutions on Metal-targeted environments, expanding efficient paths for small-kernel workloads. - Improved the attention path API, enabling safer usage and easier maintenance, with better validation guarantees. - Strengthened code quality through targeted refactors and test coverage for critical math/ML primitives. Technologies/skills demonstrated: - Metal kernel development and performance optimization, C++ API design and refactoring, test-driven development, input validation, and strong type-safety practices. Business value: - Faster inference for depthwise conv workloads on Apple hardware, with lower latency in small-kernel scenarios. - Safer, clearer API for attention mechanisms, reducing risk of misuse and enabling easier future enhancements.
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for ml-explore/mlx focusing on SDPA masking enhancements and robustness in the attention kernel, with targeted tests and correctness fixes. Delivered fused masking support for causal, additive, and boolean masks within the fused kernel, accompanied by a robustness refactor of mask logic and a correctness fix to the causal masking loop limit to ensure masking behaves properly with varying key-token counts. Also updated tests to reflect masking behavior and reduce false positives, improving maintainability and confidence in production deployments.
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for ml-explore/mlx focusing on SDPA masking enhancements and robustness in the attention kernel, with targeted tests and correctness fixes. Delivered fused masking support for causal, additive, and boolean masks within the fused kernel, accompanied by a robustness refactor of mask logic and a correctness fix to the causal masking loop limit to ensure masking behaves properly with varying key-token counts. Also updated tests to reflect masking behavior and reduce false positives, improving maintainability and confidence in production deployments.
March 2025
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 (ml-explore/mlx): Delivered reliability and performance improvements for Metal GPU backends with a focus on robust builds, efficient kernels, and expanded model support. Key contributions include a build-stability fix for kernels.h, a Winograd convolution kernel optimization for small batches, and an extension of fused attention to support 128 head dimension. These changes improve build reliability, accelerate small-batch inference, and broaden applicability for larger attention models on Metal GPUs.
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 (ml-explore/mlx): Delivered reliability and performance improvements for Metal GPU backends with a focus on robust builds, efficient kernels, and expanded model support. Key contributions include a build-stability fix for kernels.h, a Winograd convolution kernel optimization for small batches, and an extension of fused attention to support 128 head dimension. These changes improve build reliability, accelerate small-batch inference, and broaden applicability for larger attention models on Metal GPUs.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for ml-explore/mlx: Delivered a performance-focused optimization in the Scaled Dot-Product Attention kernel by refining the contiguity check to require only the 'headdim' stride 1, eliminating unnecessary matrix copies and boosting attention throughput. This work aligns with the product's focus on low-latency, scalable inference for attention-based models, and leverages a targeted code refactor to minimize memory bandwidth and kernel overhead.
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for ml-explore/mlx: Delivered a performance-focused optimization in the Scaled Dot-Product Attention kernel by refining the contiguity check to require only the 'headdim' stride 1, eliminating unnecessary matrix copies and boosting attention throughput. This work aligns with the product's focus on low-latency, scalable inference for attention-based models, and leverages a targeted code refactor to minimize memory bandwidth and kernel overhead.
December 2024
November 2024
1 Commits • 1 Features
Nov 1, 2024Month 2024-11 for ml-explore/mlx: Delivered a high-impact Matrix Attention kernel optimization for MLX SDPA, including refactored Metal GPU kernels to support efficient attention computations (fused and unfused variants) and comprehensive benchmarking for performance validation. No major bugs fixed were reported in this period. Result: enhanced attention throughput and efficiency on MLX hardware, with validated performance gains and a clear performance validation path for future optimization. Demonstrated proficiency in Metal GPU programming, kernel refactoring, and performance benchmarking to drive business value.
November 2024
1 Commits • 1 Features
Nov 1, 2024Month 2024-11 for ml-explore/mlx: Delivered a high-impact Matrix Attention kernel optimization for MLX SDPA, including refactored Metal GPU kernels to support efficient attention computations (fused and unfused variants) and comprehensive benchmarking for performance validation. No major bugs fixed were reported in this period. Result: enhanced attention throughput and efficiency on MLX hardware, with validated performance gains and a clear performance validation path for future optimization. Demonstrated proficiency in Metal GPU programming, kernel refactoring, and performance benchmarking to drive business value.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 — ml-explore/mlx: Delivered Metal backend GEMM performance optimization for Apple Silicon, including kernel refactor, tile-size tuning across device architectures, and improved data-type handling to accelerate matrix multiplication on macOS/iOS. No major bugs fixed this month in this repo. Impact: faster on-device ML workloads on Apple Silicon, enabling quicker inference/training and better energy efficiency. Skills: Metal backend programming, kernel optimization, performance benchmarking, cross-architecture tuning, data-type handling.
1 Commits • 1 Features
Oct 1, 2024October 2024 — ml-explore/mlx: Delivered Metal backend GEMM performance optimization for Apple Silicon, including kernel refactor, tile-size tuning across device architectures, and improved data-type handling to accelerate matrix multiplication on macOS/iOS. No major bugs fixed this month in this repo. Impact: faster on-device ML workloads on Apple Silicon, enabling quicker inference/training and better energy efficiency. Skills: Metal backend programming, kernel optimization, performance benchmarking, cross-architecture tuning, data-type handling.
October 2024
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Quality Metrics
Correctness87.8%
Maintainability81.4%
Architecture86.4%
Performance92.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakeCUDAMetal Shading LanguageObjective-CPython
Technical Skills
API DesignAttention MechanismsBackend DevelopmentBenchmarkingBuild SystemsC++C++ DevelopmentCUDA programmingCode RefactoringCompute KernelsConvolutional Neural NetworksDeep Learning KernelsGPU ComputingGPU ProgrammingGPU computing
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