llvm/torch-mlir
May 2025 – May 2025
1 Month active
Languages Used
C++Python
Technical Skills
C++ DevelopmentMLIRPyTorchPython DevelopmentTensor OperationsTesting
um-urosmarkovic
PROFILE
Um-urosmarkovic
Uros Markovic developed an enhanced tensor analytics feature for the llvm/torch-mlir repository, focusing on implementing the Tensor count_nonzero operation optimized for Linalg-on-Tensors. He decomposed the operation into simpler components and established lowering paths for both count_nonzero and count_nonzero.dim_IntList, ensuring efficient integration with the MLIR and LLVM infrastructure. Uros wrote comprehensive verification logic and robust tests in C++ and Python to guarantee correctness and handle edge cases. His work expanded the Torch-MLIR feature set, enabling more efficient code generation and improving downstream ecosystem compatibility, demonstrating depth in C++ development, MLIR integration, and test-driven engineering practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
404
Activity Months1
Your Network
61 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025Month: 2025-05 — Focused on delivering enhanced tensor analytics within the Torch-MLIR pipeline for llvm/torch-mlir. Key accomplishments include implementing a new Tensor count_nonzero operation optimized for Linalg-on-Tensors, with decomposition into simpler components, lowering paths for count_nonzero and count_nonzero.dim_IntList, verification logic, and comprehensive tests to ensure correctness and edge-case handling. Major bugs fixed: None reported this month. Overall impact: expands the Torch-MLIR feature set with a performance-conscious tensor operation, enabling more efficient code generation and downstream ecosystem improvements, while providing strong correctness guarantees via tests. Technologies/skills demonstrated: TorchToLinalg lowering, Linalg-on-Tensors backend, MLIR/LLVM integration, test-driven development, verification logic, and robust test coverage for edge cases.
2 Commits • 1 Features
May 1, 2025Month: 2025-05 — Focused on delivering enhanced tensor analytics within the Torch-MLIR pipeline for llvm/torch-mlir. Key accomplishments include implementing a new Tensor count_nonzero operation optimized for Linalg-on-Tensors, with decomposition into simpler components, lowering paths for count_nonzero and count_nonzero.dim_IntList, verification logic, and comprehensive tests to ensure correctness and edge-case handling. Major bugs fixed: None reported this month. Overall impact: expands the Torch-MLIR feature set with a performance-conscious tensor operation, enabling more efficient code generation and downstream ecosystem improvements, while providing strong correctness guarantees via tests. Technologies/skills demonstrated: TorchToLinalg lowering, Linalg-on-Tensors backend, MLIR/LLVM integration, test-driven development, verification logic, and robust test coverage for edge cases.
May 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++ DevelopmentMLIRPyTorchPython DevelopmentTensor OperationsTesting
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