llvm/torch-mlir
May 2025 – Jun 2025
2 Months active
Languages Used
C++Python
Technical Skills
C++ DevelopmentMLIRPyTorchPython DevelopmentDeep LearningTensor Operations
sharavana
PROFILE
Sharavana
Over a two-month period, contributed to the llvm/torch-mlir repository by developing three core features that enhance Torch-to-MLIR compatibility and numerical operation coverage. Work included implementing the fix operation (an alias for trunc), with full support for parsing, printing, and end-to-end testing to ensure robust model compilation. Further contributions added logcumsumexp and RMS normalization (aten.rms_norm) operations, involving operator decomposition and comprehensive test coverage to improve numerical stability and model reliability. Leveraged C++, Python, and MLIR to deliver these features, focusing on deep learning workflows and advanced tensor operations without addressing bug fixes during this timeframe.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
538
Activity Months2
Your Network
117 peopleShared Repositories
79
Work History
June 2025
2 Commits • 2 Features
Jun 1, 2025Summary for Month: 2025-06 (llvm/torch-mlir): Delivered two major Torch MLIR dialect enhancements that expand numerical operation coverage and improve model stability. Key features delivered: logcumsumexp support and RMS normalization (aten.rms_norm) with decomposition into sub-ops and thorough tests. No major bugs fixed this month. Overall impact: extended numerical capabilities reduce risk in numerical computations, enable more robust model implementations, and strengthen the dialect's reliability and test coverage. Technologies/skills demonstrated: MLIR/Torch dialect, operator decomposition, focused testing, commit-driven development, and collaboration with core infra.
2 Commits • 2 Features
Jun 1, 2025Summary for Month: 2025-06 (llvm/torch-mlir): Delivered two major Torch MLIR dialect enhancements that expand numerical operation coverage and improve model stability. Key features delivered: logcumsumexp support and RMS normalization (aten.rms_norm) with decomposition into sub-ops and thorough tests. No major bugs fixed this month. Overall impact: extended numerical capabilities reduce risk in numerical computations, enable more robust model implementations, and strengthen the dialect's reliability and test coverage. Technologies/skills demonstrated: MLIR/Torch dialect, operator decomposition, focused testing, commit-driven development, and collaboration with core infra.
June 2025
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for llvm/torch-mlir: Delivered Torch fix operation support (alias for trunc), including implementation, parsing, printing, and end-to-end tests. The work enhances Torch-to-MLIR compatibility, improves model compilation reliability, and expands operation coverage across the Torch dialect in the LLVM-backed MLIR pathway.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for llvm/torch-mlir: Delivered Torch fix operation support (alias for trunc), including implementation, parsing, printing, and end-to-end tests. The work enhances Torch-to-MLIR compatibility, improves model compilation reliability, and expands operation coverage across the Torch dialect in the LLVM-backed MLIR pathway.
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++ DevelopmentDeep LearningMLIRPyTorchPython DevelopmentTensor Operations
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline