llvm/torch-mlir
Oct 2024 – Aug 2025
6 Months active
Languages Used
C++Python
Technical Skills
C++ DevelopmentMLIRTensor ManipulationPython ScriptingTensor OperationsPyTorch
Yuanqiang Liu
PROFILE
Yuanqiang Liu
Yuanqiang Liu contributed to the llvm/torch-mlir repository by developing and extending backend features for PyTorch model compilation, focusing on tensor operations and operator coverage. Over six months, he implemented new operations such as bilinear upsampling, fractional value math utilities, and argsort, using C++ and MLIR to enable optimized lowering and decomposition paths. He improved testing infrastructure and debugging visibility, unified JIT importer test configurations, and fixed shape handling for scaled dot product attention. Liu also enabled 1D reflection padding support in StableHLO, broadening hardware compatibility. His work demonstrated depth in backend development, code generation, and test-driven validation.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
7
Lines of code
1,367
Activity Months6
Your Network
309 people
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focusing on delivering 1D reflection padding support in StableHLO for the llvm/torch-mlir project, enabling AtenReflectionPad1dOp lowering and expanding tensor operation compatibility within the StableHLO conversion framework. This work strengthens model deployment readiness and hardware/accelerator support by broadening operation coverage and reducing workaround needs across the Torch-MLIR pipeline.
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focusing on delivering 1D reflection padding support in StableHLO for the llvm/torch-mlir project, enabling AtenReflectionPad1dOp lowering and expanding tensor operation compatibility within the StableHLO conversion framework. This work strengthens model deployment readiness and hardware/accelerator support by broadening operation coverage and reducing workaround needs across the Torch-MLIR pipeline.
August 2025
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for llvm/torch-mlir: Delivered a robust fix for scaled dot product attention shape handling across varying batch sizes, improving correctness and robustness for diverse configurations. The shape function now derives the output shape directly from inputs, reducing shape-related errors during training and inference. Commit included: [Torch] fix sdpa's shape function when different batch size (#4137) - 60379d72f3494dcabc54dbc4086f8888a8ca584c.
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for llvm/torch-mlir: Delivered a robust fix for scaled dot product attention shape handling across varying batch sizes, improving correctness and robustness for diverse configurations. The shape function now derives the output shape directly from inputs, reducing shape-related errors during training and inference. Commit included: [Torch] fix sdpa's shape function when different batch size (#4137) - 60379d72f3494dcabc54dbc4086f8888a8ca584c.
March 2025
3 Commits • 3 Features
Mar 1, 2025March 2025 — Focused improvements in testing infrastructure, debugging visibility, and operator decomposition across llvm/torch-mlir. Key outcomes: unified JIT importer test configuration to streamline test setup and reduce maintenance; added verbose IR output in fx_importer_backend to accelerate debugging; decomposed aten.adaptive_max_pool2d into equivalent max pooling ops and tightened backend ops management to ensure decomposed ops are correctly recognized. These changes reduce test churn, improve visibility into compilation stages, and enable more flexible backend optimization, delivering faster iteration cycles and higher reliability for JIT import flows and adaptive pooling workflows.
3 Commits • 3 Features
Mar 1, 2025March 2025 — Focused improvements in testing infrastructure, debugging visibility, and operator decomposition across llvm/torch-mlir. Key outcomes: unified JIT importer test configuration to streamline test setup and reduce maintenance; added verbose IR output in fx_importer_backend to accelerate debugging; decomposed aten.adaptive_max_pool2d into equivalent max pooling ops and tightened backend ops management to ensure decomposed ops are correctly recognized. These changes reduce test churn, improve visibility into compilation stages, and enable more flexible backend optimization, delivering faster iteration cycles and higher reliability for JIT import flows and adaptive pooling workflows.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for llvm/torch-mlir: Delivered a new aten.argsort operation in the Torch MLIR dialect with a decomposition path to aten.sort, accompanied by tests. No major bugs fixed this month. Impact: extends Torch MLIR sorting capabilities with a performance-friendly decomposition, enabling dimension-wise sorting for models and downstream optimizations. Technologies/skills demonstrated: Torch MLIR dialect development, MLIR decomposition patterns, and test-driven validation.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for llvm/torch-mlir: Delivered a new aten.argsort operation in the Torch MLIR dialect with a decomposition path to aten.sort, accompanied by tests. No major bugs fixed this month. Impact: extends Torch MLIR sorting capabilities with a performance-friendly decomposition, enabling dimension-wise sorting for models and downstream optimizations. Technologies/skills demonstrated: Torch MLIR dialect development, MLIR decomposition patterns, and test-driven validation.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for llvm/torch-mlir: Key focus on delivering fractional value operations and related math utilities in the Torch MLIR dialect, enabling improved numeric fidelity and optimization opportunities. Implemented aten.frac, aten.signbit, aten.ldexp, and aten.copysign with definitions and decompositions to support optimized lowering. Shipped emission and lowering support via commit 70e089802a02f7c0b2541f6ccb1ceba9e9f9e1fd (PR #3851). No major bugs fixed this month in this repo. Impact: broader operator coverage, higher numeric fidelity, and stronger optimization paths for Torch MLIR workloads. Technologies/skills demonstrated: MLIR dialect development, Torch integration, op emission/lowering, op decomposition for optimization, codegen, C++/LLVM tooling.
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for llvm/torch-mlir: Key focus on delivering fractional value operations and related math utilities in the Torch MLIR dialect, enabling improved numeric fidelity and optimization opportunities. Implemented aten.frac, aten.signbit, aten.ldexp, and aten.copysign with definitions and decompositions to support optimized lowering. Shipped emission and lowering support via commit 70e089802a02f7c0b2541f6ccb1ceba9e9f9e1fd (PR #3851). No major bugs fixed this month in this repo. Impact: broader operator coverage, higher numeric fidelity, and stronger optimization paths for Torch MLIR workloads. Technologies/skills demonstrated: MLIR dialect development, Torch integration, op emission/lowering, op decomposition for optimization, codegen, C++/LLVM tooling.
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024Month: 2024-10 summary focusing on key accomplishments in llvm/torch-mlir; delivered bilinear upsampling support for Torch with scalar and vector variants; prepared for enhanced MLIR Torch backend performance and operator coverage.
October 2024
1 Commits • 1 Features
Oct 1, 2024Month: 2024-10 summary focusing on key accomplishments in llvm/torch-mlir; delivered bilinear upsampling support for Torch with scalar and vector variants; prepared for enhanced MLIR Torch backend performance and operator coverage.
Activity
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Quality Metrics
Correctness92.4%
Maintainability82.6%
Architecture87.6%
Performance82.6%
AI Usage35.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Backend DevelopmentC++C++ DevelopmentC++ developmentMLIRPyTorchPythonPython ScriptingPython TestingPython developmentTensor ManipulationTensor OperationsTorchbackend developmentmachine learning
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