llvm/torch-mlir
Mar 2025 – Jun 2025
3 Months active
Languages Used
C++MLIRPython
Technical Skills
C++MLIRQuantizationTensor Operationsmachine learningquantization
Muhammad Asif Manzoor
PROFILE
Muhammad Asif Manzoor
Over a three-month period, M. Manzoor enhanced the llvm/torch-mlir repository by developing four robust backend features focused on quantization, tensor operations, and model export compatibility. He implemented per-tensor and per-channel quantization across Torch-MLIR and ONNX, enabling stablehlo conversions and improving model optimization workflows. Manzoor also delivered StableHLO conversion support for aten.prod.dim_int reductions, ensuring correct handling of tensor shapes and integer dimensions. His work included new operations such as Aten.round.decimals and AtenAnyDims, with comprehensive test coverage and decomposition logic. Using C++, MLIR, and Python, he prioritized code quality, interoperability, and backend stability throughout each feature.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
4
Lines of code
937
Activity Months3
Your Network
357 people
Work History
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for llvm/torch-mlir focusing on feature delivery and stability. Delivered two user-facing Torch MLIR operations: Aten.round.decimals and AtenAnyDims with lowering to Linalg-on-Tensors, plus StableHLO conversion support. Included implementation, decomposition logic, and tests. No major bugs fixed this month; emphasis on test coverage, interoperability, and backend stability.
3 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for llvm/torch-mlir focusing on feature delivery and stability. Delivered two user-facing Torch MLIR operations: Aten.round.decimals and AtenAnyDims with lowering to Linalg-on-Tensors, plus StableHLO conversion support. Included implementation, decomposition logic, and tests. No major bugs fixed this month; emphasis on test coverage, interoperability, and backend stability.
June 2025
May 2025
1 Commits • 1 Features
May 1, 2025Monthly summary for 2025-05 focusing on llvm/torch-mlir. Key feature delivered: StableHLO conversion support for aten.prod.dim_int reduction, enabling reduction along specified dimensions while handling integer dimension values and keepdim semantics to preserve output shapes. This work improves model export compatibility and runtime behavior on MLIR backends. No major bugs fixed were documented this month within the provided scope. Overall impact includes broader StableHLO coverage for PyTorch models, enabling more efficient execution and interoperability with MLIR toolchains. Technologies demonstrated include MLIR/StableHLO, PyTorch dialects, and commit-based traceability (commit c675b2f354b44050cf416f384a088d0321c4a58b).
May 2025
1 Commits • 1 Features
May 1, 2025Monthly summary for 2025-05 focusing on llvm/torch-mlir. Key feature delivered: StableHLO conversion support for aten.prod.dim_int reduction, enabling reduction along specified dimensions while handling integer dimension values and keepdim semantics to preserve output shapes. This work improves model export compatibility and runtime behavior on MLIR backends. No major bugs fixed were documented this month within the provided scope. Overall impact includes broader StableHLO coverage for PyTorch models, enabling more efficient execution and interoperability with MLIR toolchains. Technologies demonstrated include MLIR/StableHLO, PyTorch dialects, and commit-based traceability (commit c675b2f354b44050cf416f384a088d0321c4a58b).
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 – Quantization improvements across Torch-MLIR and ONNX. Implemented per-tensor and per-channel quantization with conversion to stablehlo in Torch-MLIR and added per-channel QuantizeLinear support for ONNX, enabling faster model optimization and broader interoperability. Two targeted commits completed. No explicit bug fixes reported this month; focus on delivering robust features and code quality.
2 Commits • 1 Features
Mar 1, 2025March 2025 – Quantization improvements across Torch-MLIR and ONNX. Implemented per-tensor and per-channel quantization with conversion to stablehlo in Torch-MLIR and added per-channel QuantizeLinear support for ONNX, enabling faster model optimization and broader interoperability. Two targeted commits completed. No explicit bug fixes reported this month; focus on delivering robust features and code quality.
March 2025
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage36.8%
Skills & Technologies
Programming Languages
C++MLIRPython
Technical Skills
Backend DevelopmentC++C++ DevelopmentMLIRPython TestingQuantizationTensor ManipulationTensor Operationsmachine learningquantizationtensor operations
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