llvm/torch-mlir
May 2025 – Aug 2025
3 Months active
Languages Used
C++PythonMLIR
Technical Skills
C++ DevelopmentMLIRPython DevelopmentTensor OperationsBackend DevelopmentC++ development
Zahid Wakeel
PROFILE
Zahid Wakeel
Zahid Wakeel contributed to the llvm/torch-mlir repository by developing and integrating core backend features that expanded operator coverage and improved cross-framework compatibility. He implemented tensor flipping operations and advanced loss functions, such as KL-divergence, using C++ and MLIR, focusing on operation definitions, shape inference, and decomposition patterns to enable efficient code generation. Zahid also addressed ONNX-to-Torch IR translation gaps, notably lowering ONNX MeanVarianceNormalization directly to the Torch dialect, which streamlined interoperability and reduced downstream complexity. His work demonstrated depth in compiler design and testing, resulting in more robust, maintainable, and performant Torch MLIR workflows for machine learning models.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
5
Lines of code
1,263
Activity Months3
Your Network
90 people
Work History
August 2025
1 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for llvm/torch-mlir: Delivered a key feature to enhance cross-framework interoperability and runtime compatibility between ONNX and Torch dialects. Implemented lowering of the ONNX MeanVarianceNormalization operation to the Torch dialect, enabling direct translation and reducing downstream expansion overhead. No major bugs fixed this month. The work strengthens Torch-based workflows, accelerates codegen paths, and lays groundwork for future performance optimizations.
1 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for llvm/torch-mlir: Delivered a key feature to enhance cross-framework interoperability and runtime compatibility between ONNX and Torch dialects. Implemented lowering of the ONNX MeanVarianceNormalization operation to the Torch dialect, enabling direct translation and reducing downstream expansion overhead. No major bugs fixed this month. The work strengthens Torch-based workflows, accelerates codegen paths, and lays groundwork for future performance optimizations.
August 2025
June 2025
4 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary for llvm/torch-mlir focused on expanding Torch-MLIR coverage, stabilizing IR translations, and delivering test-backed features that enable broader PyTorch model execution and ONNX interoperability. Key features delivered include ops support, dialect lowering improvements, and robust loss function coverage, underpinned by targeted tests and clear commit history. Major bug fixes address compatibility gaps in ONNX-to-Torch IR lowering, strengthening reliability for downstream users and integrations.
June 2025
4 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary for llvm/torch-mlir focused on expanding Torch-MLIR coverage, stabilizing IR translations, and delivering test-backed features that enable broader PyTorch model execution and ONNX interoperability. Key features delivered include ops support, dialect lowering improvements, and robust loss function coverage, underpinned by targeted tests and clear commit history. Major bug fixes address compatibility gaps in ONNX-to-Torch IR lowering, strengthening reliability for downstream users and integrations.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for llvm/torch-mlir: Key features delivered: Implemented Tensor Flipping Operations Support in Torch MLIR (aten.fliplr, aten.flipud) with new operation definitions, shape inference, and decomposition patterns to enable tensor flipping along specified dimensions. Commit: 1cb25e9e66c4aca05a188e098e15aff8f9048d04. Major bugs fixed: No major bugs reported; minor fixes to integration of flip operators and decomposition pathways to improve stability. Overall impact: Expanded Torch MLIR operator coverage for common tensor transformations, enabling more models to run without custom ops, reducing manual translation effort, and improving optimization opportunities through MLIR passes. Technologies/skills demonstrated: MLIR/Ops definitions, shape inference, decomposition patterns, Torch MLIR integration, code review and commit discipline.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for llvm/torch-mlir: Key features delivered: Implemented Tensor Flipping Operations Support in Torch MLIR (aten.fliplr, aten.flipud) with new operation definitions, shape inference, and decomposition patterns to enable tensor flipping along specified dimensions. Commit: 1cb25e9e66c4aca05a188e098e15aff8f9048d04. Major bugs fixed: No major bugs reported; minor fixes to integration of flip operators and decomposition pathways to improve stability. Overall impact: Expanded Torch MLIR operator coverage for common tensor transformations, enabling more models to run without custom ops, reducing manual translation effort, and improving optimization opportunities through MLIR passes. Technologies/skills demonstrated: MLIR/Ops definitions, shape inference, decomposition patterns, Torch MLIR integration, code review and commit discipline.
May 2025
Activity
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Quality Metrics
Correctness93.4%
Maintainability83.4%
Architecture93.4%
Performance80.0%
AI Usage23.4%
Skills & Technologies
Programming Languages
C++MLIRPython
Technical Skills
Backend DevelopmentC++C++ DevelopmentC++ developmentMLIRPyTorchPython DevelopmentPython developmentTensor ManipulationTensor OperationsTestingcompiler designmachine learning
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