pytorch/executorch
Jan 2025 – Jan 2025
1 Month active
Languages Used
Python
Technical Skills
Deep LearningMachine LearningPyTorchPythonQuantizationback end development
Thibaut Goetghebuer-Planchon
PROFILE
Thibaut Goetghebuer-planchon
Thibaut Goetghebuer-Planchon enhanced quantization workflows in the pytorch/executorch repository by generalizing quantization annotators and implementing activation fusion, which improved inference efficiency and reduced runtime errors. His approach involved developing a parameterizable annotator framework and optimizing graph-level quantization transforms using Python and PyTorch, resulting in a more robust and extensible backend. In the tensorflow/tensorflow repository, Thibaut refactored the codebase to improve TOSA/MLIR integration by relocating dequantization logic, clarifying module boundaries, and aligning with future maintainability goals. His work demonstrated depth in C++, MLIR, and quantization, addressing both performance and code organization challenges.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
1,581
Activity Months2
Your Network
786 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for the tensorflow/tensorflow repository focused on a targeted codebase refactor to improve TOSA/MLIR integration. Key work involved relocating dequantize_tfl_softmax.cc into the tfl_passes target to enhance code organization and future extension. This aligns with MLIR/TOSA initiatives and sets groundwork for more scalable integration and maintainability.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for the tensorflow/tensorflow repository focused on a targeted codebase refactor to improve TOSA/MLIR integration. Key work involved relocating dequantize_tfl_softmax.cc into the tfl_passes target to enhance code organization and future extension. This aligns with MLIR/TOSA initiatives and sets groundwork for more scalable integration and maintainability.
May 2025
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for pytorch/executorch focused on quantization workflow improvements and robustness. Delivered notable feature work around quantization annotation generalization and activation fusion, plus a robustness fix in the quantized activation type-check. The changes improved inference efficiency, reduced runtime errors in the quantization path, and laid groundwork for easier extension of annotators.
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for pytorch/executorch focused on quantization workflow improvements and robustness. Delivered notable feature work around quantization annotation generalization and activation fusion, plus a robustness fix in the quantized activation type-check. The changes improved inference efficiency, reduced runtime errors in the quantization path, and laid groundwork for easier extension of annotators.
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Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++Deep LearningMLIRMachine LearningPyTorchPythonQuantizationTensorFlowback end developmentmachine learningquantizationsoftware engineeringtorch
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
tensorflow/tensorflow
May 2025 – May 2025
1 Month active
Languages Used
C++
Technical Skills
C++MLIRTensorFlow