PaddlePaddle/Paddle2ONNX
Oct 2024 – Nov 2024
2 Months active
Languages Used
C++Python
Technical Skills
C++Model ConversionONNXPIRPaddlePaddlePython
qzylalala
PROFILE
Qzylalala
Worked on expanding Paddle2ONNX to improve Paddle-PIR to ONNX model export compatibility, focusing on operator mapping and exporter stability. Developed and integrated new operator support for core PIR operations, including cumulative sum, gather_nd, interpolate, and tensor manipulation functions, using C++ and Python. Enhanced test automation and coverage to reduce regressions and ensure reliable exports, while addressing critical bugs in tensor value retrieval. Refactored exporter mappings and headers to align with broader PIR scenarios, streamlining the conversion workflow. This work increased translation fidelity and scalability, enabling more efficient Paddle model deployment through ONNX within the PaddlePaddle/Paddle2ONNX repository.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
27Total
Bugs
2
Commits
27
Features
14
Lines of code
1,900
Activity Months2
Your Network
9 peopleShared Repositories
9
Work History
November 2024
22 Commits • 13 Features
Nov 1, 2024November 2024 performance for Paddle2ONNX focused on expanding PIR operator coverage and stabilizing tensor handling to accelerate Paddle-to-ONNX model exports and improve runtime reliability. Delivered initial core PIR operator support, added comprehensive cumulative sum (cumsum) support, introduced gather_nd, interpolate, and additional PIR operations, and completed critical bug fixes to improve tensor value retrieval. These changes increase translation fidelity, reduce manual intervention, and enable broader model support with improved test coverage. This work lays the foundation for more efficient and scalable ONNX export workflows and downstream optimization, driving faster time-to-value for customers deploying Paddle models through ONNX.
22 Commits • 13 Features
Nov 1, 2024November 2024 performance for Paddle2ONNX focused on expanding PIR operator coverage and stabilizing tensor handling to accelerate Paddle-to-ONNX model exports and improve runtime reliability. Delivered initial core PIR operator support, added comprehensive cumulative sum (cumsum) support, introduced gather_nd, interpolate, and additional PIR operations, and completed critical bug fixes to improve tensor value retrieval. These changes increase translation fidelity, reduce manual intervention, and enable broader model support with improved test coverage. This work lays the foundation for more efficient and scalable ONNX export workflows and downstream optimization, driving faster time-to-value for customers deploying Paddle models through ONNX.
November 2024
October 2024
5 Commits • 1 Features
Oct 1, 2024Month: 2024-10 — Paddle2ONNX: Paddle-PIR to ONNX operator support expansion and exporter mappings. Delivered broader PIR-mode compatibility via new operator mappings and exporter updates; strengthened tests and stability for production exports.
October 2024
5 Commits • 1 Features
Oct 1, 2024Month: 2024-10 — Paddle2ONNX: Paddle-PIR to ONNX operator support expansion and exporter mappings. Delivered broader PIR-mode compatibility via new operator mappings and exporter updates; strengthened tests and stability for production exports.
Activity
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Quality Metrics
Correctness86.2%
Maintainability85.8%
Architecture84.4%
Performance74.2%
AI Usage20.8%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++C++ DevelopmentCI/CDCode RefactoringCompiler DevelopmentDeep Learning FrameworksFramework IntegrationInference RuntimeMachine Learning OperationsMapper DevelopmentModel ConversionONNXONNX ConversionOperator MappingPIR
Repositories Contributed To
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