llvm/torch-mlir
Mar 2026 – Jun 2026
2 Months active
Languages Used
C++Python
Technical Skills
C++ developmentPython developmentmachine learningtestingC++Compiler Development
krotov01
PROFILE
Krotov01
Worked on the llvm/torch-mlir repository to enhance cross-framework consistency and correctness in machine learning compiler workflows. Addressed a key bug by aligning TOSA’s bilinear resize offset with PyTorch’s half-pixel mapping, ensuring resize operations produced consistent outputs across frameworks. Developed support for scalar (rank-0) tensor reductions in TorchToTosa, treating certain reductions as no-ops and updating mean decomposition logic to match PyTorch’s handling of scalar tensors. Leveraged C++, MLIR, and Python to implement these changes, expanding end-to-end test coverage and improving reliability for scalar-tensor operations, which reduced downstream debugging and strengthened production readiness for machine learning workloads.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
3Total
Bugs
2
Commits
3
Features
1
Lines of code
378
Activity Months2
Your Network
654 peopleSame Organization
@arm.com573
Work History
June 2026
2 Commits • 1 Features
Jun 1, 2026June 2026 (2026-06) – llvm/torch-mlir: Focused on scalar-tensor correctness to improve PyTorch parity and downstream reliability. Key features delivered: - TorchToTosa: Added support for scalar (rank-0) tensor reductions, handling dim=0 or dim=-1 as no-ops and adding end-to-end tests (commit 0165da299bdfbdb32a4f2b49dcf8c2a7513f055a). Major bugs fixed: - Scalar mean operation handling for rank-0 tensors in the mean decomposition, using a constant divisor of one to match PyTorch and avoid invalid operations on scalar tensors (commit 1e2ef9f6d363ad24e3462b8adad0546ae559938b). Overall impact and accomplishments: - Improved correctness and parity with PyTorch semantics across Torch-MLIR and TorchToTosa paths, enabling safer scalar-tensor workflows and reducing downstream debugging/friction. - Strengthened cross-backend compatibility and stability for scalar tensor operations, supporting production ML workloads more reliably. Technologies/skills demonstrated: - PyTorch semantics alignment, IR lowering and decomposition updates, TorchToTosa conversion improvements, and end-to-end test development with robust regression coverage.
2 Commits • 1 Features
Jun 1, 2026June 2026 (2026-06) – llvm/torch-mlir: Focused on scalar-tensor correctness to improve PyTorch parity and downstream reliability. Key features delivered: - TorchToTosa: Added support for scalar (rank-0) tensor reductions, handling dim=0 or dim=-1 as no-ops and adding end-to-end tests (commit 0165da299bdfbdb32a4f2b49dcf8c2a7513f055a). Major bugs fixed: - Scalar mean operation handling for rank-0 tensors in the mean decomposition, using a constant divisor of one to match PyTorch and avoid invalid operations on scalar tensors (commit 1e2ef9f6d363ad24e3462b8adad0546ae559938b). Overall impact and accomplishments: - Improved correctness and parity with PyTorch semantics across Torch-MLIR and TorchToTosa paths, enabling safer scalar-tensor workflows and reducing downstream debugging/friction. - Strengthened cross-backend compatibility and stability for scalar tensor operations, supporting production ML workloads more reliably. Technologies/skills demonstrated: - PyTorch semantics alignment, IR lowering and decomposition updates, TorchToTosa conversion improvements, and end-to-end test development with robust regression coverage.
June 2026
March 2026
1 Commits
Mar 1, 2026March 2026 (llvm/torch-mlir): Key bug fix improving resize correctness and cross-framework consistency. Implemented a TOSA resize offset adjustment to align with PyTorch's half-pixel mapping when align_corners is false, ensuring upsampling outputs match PyTorch behavior and MLIR/TOSA paths are consistent.
March 2026
1 Commits
Mar 1, 2026March 2026 (llvm/torch-mlir): Key bug fix improving resize correctness and cross-framework consistency. Implemented a TOSA resize offset adjustment to align with PyTorch's half-pixel mapping when align_corners is false, ensuring upsampling outputs match PyTorch behavior and MLIR/TOSA paths are consistent.
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Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture86.6%
Performance80.0%
AI Usage46.6%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++C++ developmentCompiler DevelopmentMLIRPyTorchPythonPython developmentmachine learningtesting
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