onnx/onnx-mlir
Oct 2024 – Oct 2025
13 Months active
Languages Used
PythonCC++MLIRTableGenCMakeLists.txtMarkdownJSON
Technical Skills
Command Line ArgumentsFile ManagementModel CompilationBuild SystemsCode RefactoringCommand-line Interface
Tung D. Le
PROFILE
Tung D. Le
Tung contributed to the onnx/onnx-mlir repository, building advanced compiler features and runtime optimizations for ONNX model deployment. He engineered parallel execution paths, robust shape inference, and quantization controls, leveraging C++ and MLIR to improve performance and model compatibility. His work included refactoring build systems with CMake, enhancing multi-accelerator support, and implementing OpenMP-based parallelism for accelerated workloads. Tung addressed edge-case bugs in tensor operations and improved error handling, while also expanding test coverage and documentation. His technical depth is reflected in modular code generation, configuration management, and cross-platform stability, resulting in a more reliable and scalable ONNX-MLIR backend.
Overall Statistics
Feature vs Bugs
76%Features
Repository Contributions
51Total
Bugs
9
Commits
51
Features
29
Lines of code
14,318
Activity Months13
Your Network
27 people
Work History
October 2025
7 Commits • 2 Features
Oct 1, 2025October 2025 highlights for onnx/onnx-mlir: Delivered enhanced ONNX support and MLIR compiler improvements, stabilized runtime behavior, and enabled parallel execution to boost throughput for accelerated workloads. Added robust shape inference for core ops, new ONNXDimOp rewrite patterns, support for merged decoder models in Python, and a compiler option to replace an operation with one of its operands. Fixed critical runtime bugs, including robust view creation in the zdnnx runtime and correct Abseil library linkage for PyRuntimeC. Enabled OpenMP parallel execution in the zdnnx accelerator to leverage multi-core hardware. These changes broaden model compatibility, improve stability, and unlock performance gains across the MLIR-backed ONNX backend.
7 Commits • 2 Features
Oct 1, 2025October 2025 highlights for onnx/onnx-mlir: Delivered enhanced ONNX support and MLIR compiler improvements, stabilized runtime behavior, and enabled parallel execution to boost throughput for accelerated workloads. Added robust shape inference for core ops, new ONNXDimOp rewrite patterns, support for merged decoder models in Python, and a compiler option to replace an operation with one of its operands. Fixed critical runtime bugs, including robust view creation in the zdnnx runtime and correct Abseil library linkage for PyRuntimeC. Enabled OpenMP parallel execution in the zdnnx accelerator to leverage multi-core hardware. These changes broaden model compatibility, improve stability, and unlock performance gains across the MLIR-backed ONNX backend.
October 2025
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for onnx/onnx-mlir: Delivered targeted ONNX operator optimizations and NNPA compiler improvements with configurability, enhancing performance, correctness, and deployment agility. Edge-case shape inference for Range, zero-dimension Concat operand elimination, NNPA reshape optimization, eraseOp ordering fix, and JSON configuration for device placement and quantization. These changes reduce runtime latency, improve stability, and enable easier multi-device deployment.
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for onnx/onnx-mlir: Delivered targeted ONNX operator optimizations and NNPA compiler improvements with configurability, enhancing performance, correctness, and deployment agility. Edge-case shape inference for Range, zero-dimension Concat operand elimination, NNPA reshape optimization, eraseOp ordering fix, and JSON configuration for device placement and quantization. These changes reduce runtime latency, improve stability, and enable easier multi-device deployment.
August 2025
5 Commits • 2 Features
Aug 1, 2025Monthly summary for 2025-08 for onnx/onnx-mlir focusing on performance improvements, robustness, and developer experience. This month emphasized enabling parallel execution and more efficient graph patterns, along with targeted bug fixes and clearer user guidance, to deliver business value through higher potential throughput and easier maintainability. Key features delivered: - Parallelization Improvements across ONNX operations: Introduced a common helper tryCreateKrnlParallel for emitting krnl.parallel and enabled parallelization across ONNX operation paths; also lays groundwork for OpenMP/parallel execution of NNPA paths. Commits: 31c4749332e073906918526a295a5443eea15f62; 0571bb3d6a12ba8abf282751cd2254f772128f91 - Fusion optimization: MatMul and Div with scalar divisor: Added a pattern to fuse ONNXMatMul and ONNXDiv when the divisor is a scalar constant, simplifying the computation graph and potentially improving runtime performance. Commit: 59d8104f36ed21824a21768d40ec497e7950672a Major bugs fixed: - LayoutTransform bug fix: correct type usage for index expressions by ensuring a dimensional operand is not used as a symbol, improving robustness. Commit: 5387bdc87dd19b3038cb41aeb4adacdfa512d60d - User experience improvement: clearer error when constants.bin cannot be opened, guiding users to set OM_CONSTANT_PATH to resolve the issue. Commit: f21d2dff32423ce468cc94a8b658b71aebf9472c Overall impact and accomplishments: - Enhanced potential performance through parallelization readiness across ONNX ops, with a clear path toward OpenMP/NNPA parallel execution, which can translate to tangible throughput gains on parallel hardware. - Graph simplification via MatMul/Div fusion reduces runtime overhead and may improve cache efficiency. - Increased robustness and usability with correct LayoutTransform semantics and clearer error guidance, reducing troubleshooting time for users and reviewers. Technologies/skills demonstrated: - MLIR/Krnl dialect utilization and pattern-based optimizations - Parallelization strategies and groundwork for OpenMP/NNPA integration - Pattern fusion techniques and graph-level optimizations - Robustness fixes and user-centric error messaging
5 Commits • 2 Features
Aug 1, 2025Monthly summary for 2025-08 for onnx/onnx-mlir focusing on performance improvements, robustness, and developer experience. This month emphasized enabling parallel execution and more efficient graph patterns, along with targeted bug fixes and clearer user guidance, to deliver business value through higher potential throughput and easier maintainability. Key features delivered: - Parallelization Improvements across ONNX operations: Introduced a common helper tryCreateKrnlParallel for emitting krnl.parallel and enabled parallelization across ONNX operation paths; also lays groundwork for OpenMP/parallel execution of NNPA paths. Commits: 31c4749332e073906918526a295a5443eea15f62; 0571bb3d6a12ba8abf282751cd2254f772128f91 - Fusion optimization: MatMul and Div with scalar divisor: Added a pattern to fuse ONNXMatMul and ONNXDiv when the divisor is a scalar constant, simplifying the computation graph and potentially improving runtime performance. Commit: 59d8104f36ed21824a21768d40ec497e7950672a Major bugs fixed: - LayoutTransform bug fix: correct type usage for index expressions by ensuring a dimensional operand is not used as a symbol, improving robustness. Commit: 5387bdc87dd19b3038cb41aeb4adacdfa512d60d - User experience improvement: clearer error when constants.bin cannot be opened, guiding users to set OM_CONSTANT_PATH to resolve the issue. Commit: f21d2dff32423ce468cc94a8b658b71aebf9472c Overall impact and accomplishments: - Enhanced potential performance through parallelization readiness across ONNX ops, with a clear path toward OpenMP/NNPA parallel execution, which can translate to tangible throughput gains on parallel hardware. - Graph simplification via MatMul/Div fusion reduces runtime overhead and may improve cache efficiency. - Increased robustness and usability with correct LayoutTransform semantics and clearer error guidance, reducing troubleshooting time for users and reviewers. Technologies/skills demonstrated: - MLIR/Krnl dialect utilization and pattern-based optimizations - Parallelization strategies and groundwork for OpenMP/NNPA integration - Pattern fusion techniques and graph-level optimizations - Robustness fixes and user-centric error messaging
August 2025
July 2025
11 Commits • 6 Features
Jul 1, 2025July 2025 monthly summary for onnx/onnx-mlir highlighting work across multi-accelerator runtime, parallel codegen, shape inference, codegen improvements, and observability. Focused on delivering business value through performance, scalability, and maintainability while strengthening test coverage and reducing warnings.
July 2025
11 Commits • 6 Features
Jul 1, 2025July 2025 monthly summary for onnx/onnx-mlir highlighting work across multi-accelerator runtime, parallel codegen, shape inference, codegen improvements, and observability. Focused on delivering business value through performance, scalability, and maintainability while strengthening test coverage and reducing warnings.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for onnx/onnx-mlir. Major bugs fixed: none reported this month. Key features delivered include enhanced shape information input handling with support for a range of input indices and overwriting of -1 inputs. Overall impact: expanded input flexibility, broader compatibility with complex models, and reduced preprocessing effort for deployment. Technologies and skills demonstrated include C++ refactoring, parsing logic modularization, and expanded test coverage, contributing to higher reliability and faster model onboarding. Business value: lowers manual shape-preprocessing steps, improves model compatibility, and accelerates deployment cycles.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for onnx/onnx-mlir. Major bugs fixed: none reported this month. Key features delivered include enhanced shape information input handling with support for a range of input indices and overwriting of -1 inputs. Overall impact: expanded input flexibility, broader compatibility with complex models, and reduced preprocessing effort for deployment. Technologies and skills demonstrated include C++ refactoring, parsing logic modularization, and expanded test coverage, contributing to higher reliability and faster model onboarding. Business value: lowers manual shape-preprocessing steps, improves model compatibility, and accelerates deployment cycles.
June 2025
May 2025
2 Commits • 1 Features
May 1, 2025Monthly performance summary for May 2025 focused on onnx/onnx-mlir contributions. Delivered enhancements to ZDNN data format and type support, and improved user guidance for PyRuntimeC usage. Demonstrated strong cross-language collaboration (C++/Python), rigorous attention to compatibility and runtime flexibility, and improved developer experience through clearer messaging and initialization behavior.
May 2025
2 Commits • 1 Features
May 1, 2025Monthly performance summary for May 2025 focused on onnx/onnx-mlir contributions. Delivered enhancements to ZDNN data format and type support, and improved user guidance for PyRuntimeC usage. Demonstrated strong cross-language collaboration (C++/Python), rigorous attention to compatibility and runtime flexibility, and improved developer experience through clearer messaging and initialization behavior.
April 2025
4 Commits • 3 Features
Apr 1, 2025Monthly highlights for 2025-04: Delivered key features and fixes in onnx/onnx-mlir to advance correctness and performance of quantized paths and fusion, with new CPU support for QLinearMatMul and improved shape inference for Reshape. Emphasized reliability through targeted bug fix in ZHighConstantPropagation for QuantizedStick and added tests to validate IR ordering during fusion.
4 Commits • 3 Features
Apr 1, 2025Monthly highlights for 2025-04: Delivered key features and fixes in onnx/onnx-mlir to advance correctness and performance of quantized paths and fusion, with new CPU support for QLinearMatMul and improved shape inference for Reshape. Emphasized reliability through targeted bug fix in ZHighConstantPropagation for QuantizedStick and added tests to validate IR ordering during fusion.
April 2025
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 — Delivered a feature for onnx/onnx-mlir: NNPA saturation is enabled by default and the CLI flag was renamed from nnpa-saturation to nnpa-disable-saturation. Documentation and tests were updated accordingly. No major bugs fixed this month; the focus was on feature delivery, maintainability, and alignment with NNPA roadmap.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 — Delivered a feature for onnx/onnx-mlir: NNPA saturation is enabled by default and the CLI flag was renamed from nnpa-saturation to nnpa-disable-saturation. Documentation and tests were updated accordingly. No major bugs fixed this month; the focus was on feature delivery, maintainability, and alignment with NNPA roadmap.
February 2025
2 Commits • 2 Features
Feb 1, 2025February 2025 | ONNX-MLIR (onnx/onnx-mlir) contributed two focused feature improvements that improve tensor shape handling and reduce configuration noise, delivering tangible business value and preparing for upcoming capabilities.
2 Commits • 2 Features
Feb 1, 2025February 2025 | ONNX-MLIR (onnx/onnx-mlir) contributed two focused feature improvements that improve tensor shape handling and reduce configuration noise, delivering tangible business value and preparing for upcoming capabilities.
February 2025
January 2025
4 Commits • 4 Features
Jan 1, 2025In 2025-01, ONNX-MLIR delivered four focused features in onnx/onnx-mlir, fixed a key ReduceMin/ReduceMax bug, and enhanced documentation and build reliability, driving business value through correctness, configurability, and broader hardware support. Key features delivered: (1) NNPA ReduceMin/ReduceMax legality and API unification with a unified ZDNN_REDUCE path to ensure reductions occur on the innermost dimension; (2) NNPA quantization controls with new compile-time flags -nnpa-quant-dynamic and -nnpa-quant-op-types, removal of deprecated --nnpa-quanzation, enabling granular control over dynamic quantization for activations/weights and target operation types; (3) Accelerator build documentation and parsing improvements, including robust handling of semicolon-separated accelerator names in CMake via ONNX_MLIR_ACCELERATORS; (4) NNPA quantization on IBM Telum II documentation detailing 8-bit signed quantization approaches, including pre-quantized and on-the-fly quantization, dynamic options, and scale/zero-point formulas. Major bugs fixed: fixed ReduceMin/ReduceMax legality and edge-case handling (commit 0183ad9bf95a90144c8dad139d16718a8421c845). Overall impact: improved correctness and reliability in reductions, more configurable quantization pipelines, and smoother multi-accelerator builds and deployment. Technologies/skills demonstrated: CMake/build script improvements, compiler flag design for quantization control, and developer-oriented documentation for hardware-specific quantization.
January 2025
4 Commits • 4 Features
Jan 1, 2025In 2025-01, ONNX-MLIR delivered four focused features in onnx/onnx-mlir, fixed a key ReduceMin/ReduceMax bug, and enhanced documentation and build reliability, driving business value through correctness, configurability, and broader hardware support. Key features delivered: (1) NNPA ReduceMin/ReduceMax legality and API unification with a unified ZDNN_REDUCE path to ensure reductions occur on the innermost dimension; (2) NNPA quantization controls with new compile-time flags -nnpa-quant-dynamic and -nnpa-quant-op-types, removal of deprecated --nnpa-quanzation, enabling granular control over dynamic quantization for activations/weights and target operation types; (3) Accelerator build documentation and parsing improvements, including robust handling of semicolon-separated accelerator names in CMake via ONNX_MLIR_ACCELERATORS; (4) NNPA quantization on IBM Telum II documentation detailing 8-bit signed quantization approaches, including pre-quantized and on-the-fly quantization, dynamic options, and scale/zero-point formulas. Major bugs fixed: fixed ReduceMin/ReduceMax legality and edge-case handling (commit 0183ad9bf95a90144c8dad139d16718a8421c845). Overall impact: improved correctness and reliability in reductions, more configurable quantization pipelines, and smoother multi-accelerator builds and deployment. Technologies/skills demonstrated: CMake/build script improvements, compiler flag design for quantization control, and developer-oriented documentation for hardware-specific quantization.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for onnx-mlir: Key features delivered and bugs fixed with a focus on performance and memory efficiency. Key features delivered: - Full-tensor reduction to scalar with SIMD optimization in ONNXReductionOpLowering, enabling efficient reductions across all tensor dimensions and adding tests for sequential and parallel execution paths. (Commit: 40f501760ed65b0ae7f503847ed74b2a7b5807de) Major bugs fixed: - Improved ZHigh constant propagation memory management by reverting a prior memory reduction change and introducing a DisposableElementsAttr garbage collector, enhancing constant handling efficiency. (Commit: f3fec68fde6df9623a109adc7cae0355a51dc0fe) Overall impact and accomplishments: - Delivering SIMD-accelerated reduction paths and robust constant propagation improves runtime performance, reduces memory pressure, and strengthens the ONNX-MLIR compilation pipeline. The changes lay groundwork for broader optimization across reductions and dialects and support more reliable inference for reduction-heavy models. Technologies/skills demonstrated: - SIMD optimizations, ONNX-MLIR lowering, ZHigh dialect constant propagation, memory management, disposable attribute GC design, test automation across sequential/parallel execution paths. Business value: - Faster inference for reduction-heavy workloads, improved memory efficiency, and more maintainable code paths to support future optimization efforts.
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for onnx-mlir: Key features delivered and bugs fixed with a focus on performance and memory efficiency. Key features delivered: - Full-tensor reduction to scalar with SIMD optimization in ONNXReductionOpLowering, enabling efficient reductions across all tensor dimensions and adding tests for sequential and parallel execution paths. (Commit: 40f501760ed65b0ae7f503847ed74b2a7b5807de) Major bugs fixed: - Improved ZHigh constant propagation memory management by reverting a prior memory reduction change and introducing a DisposableElementsAttr garbage collector, enhancing constant handling efficiency. (Commit: f3fec68fde6df9623a109adc7cae0355a51dc0fe) Overall impact and accomplishments: - Delivering SIMD-accelerated reduction paths and robust constant propagation improves runtime performance, reduces memory pressure, and strengthens the ONNX-MLIR compilation pipeline. The changes lay groundwork for broader optimization across reductions and dialects and support more reliable inference for reduction-heavy models. Technologies/skills demonstrated: - SIMD optimizations, ONNX-MLIR lowering, ZHigh dialect constant propagation, memory management, disposable attribute GC design, test automation across sequential/parallel execution paths. Business value: - Faster inference for reduction-heavy workloads, improved memory efficiency, and more maintainable code paths to support future optimization efforts.
December 2024
November 2024
6 Commits • 3 Features
Nov 1, 2024During November 2024, the ONNX-MLIR initiative delivered reliability improvements, workflow enhancements, and cross‑platform stability that strengthen developer experience and operational efficiency. The work focused on accurate progress feedback, configurable output naming, selective optimization opportunities, secure and stable file handling on z/OS, and streamlined data loading to reduce unnecessary network activity. These contributions collectively improve product reliability, reduce time to value for users, and enable broader platform support.
November 2024
6 Commits • 3 Features
Nov 1, 2024During November 2024, the ONNX-MLIR initiative delivered reliability improvements, workflow enhancements, and cross‑platform stability that strengthen developer experience and operational efficiency. The work focused on accurate progress feedback, configurable output naming, selective optimization opportunities, secure and stable file handling on z/OS, and streamlined data loading to reduce unnecessary network activity. These contributions collectively improve product reliability, reduce time to value for users, and enable broader platform support.
October 2024
1 Commits • 1 Features
Oct 1, 2024Monthly summary for 2024-10 focused on delivering deployment-ready artifacts for onnx/onnx-mlir. Key feature delivered: Enhanced model export artifacts when saving compiled models with --save-model, now including the shared library, constants file, and a compilation log in the target directory. This improves deployment reliability, traceability, and speeds up downstream integration. Major bugs fixed: None reported this month. Overall impact: Improved model deployment readiness, reduced post-deploy investigation, and stronger alignment with CI/CD workflows. Technologies/skills demonstrated: Build tooling, artifact packaging, and deployment automation for model exports.
1 Commits • 1 Features
Oct 1, 2024Monthly summary for 2024-10 focused on delivering deployment-ready artifacts for onnx/onnx-mlir. Key feature delivered: Enhanced model export artifacts when saving compiled models with --save-model, now including the shared library, constants file, and a compilation log in the target directory. This improves deployment reliability, traceability, and speeds up downstream integration. Major bugs fixed: None reported this month. Overall impact: Improved model deployment readiness, reduced post-deploy investigation, and stronger alignment with CI/CD workflows. Technologies/skills demonstrated: Build tooling, artifact packaging, and deployment automation for model exports.
October 2024
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Quality Metrics
Correctness89.4%
Maintainability85.6%
Architecture85.4%
Performance81.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++CMakeCMakeLists.txtJSONMLIRMarkdownPythonTableGen
Technical Skills
AI AccelerationBuild System ConfigurationBuild System ManagementBuild SystemsCC ProgrammingC++C++ DevelopmentCMakeCPU OptimizationCode GenerationCode OptimizationCode RefactoringCommand Line ArgumentsCommand-Line Interface
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