PaddlePaddle/Paddle
Oct 2024 – Mar 2025
5 Months active
Languages Used
C++Python
Technical Skills
C++Compiler DevelopmentDeep Learning OptimizationMachine LearningSoftware DevelopmentCompiler Optimizations
Xinyi Li
PROFILE
Xinyi Li
Xinyi Li contributed to the PaddlePaddle/Paddle repository by developing and optimizing deep learning compiler features focused on OneDNN integration, bfloat16 quantization, and dynamic shape handling. Using C++ and Python, Xinyi enhanced inference performance by upgrading OneDNN versions, introducing quantization passes for operations like Concat, and generalizing shape checks to support partially undefined tensors. Their work included refactoring placement logic, improving pattern matching for quantized workflows, and resolving critical bugs affecting repeated input handling. Through careful testing and compatibility upgrades, Xinyi ensured stable, efficient execution paths, demonstrating depth in compiler optimization, deep learning frameworks, and performance-oriented software development.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
10Total
Bugs
1
Commits
10
Features
5
Lines of code
908
Activity Months5
Your Network
2139 people
Work History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025: PaddlePaddle/Paddle OneDNN integration stabilization and compatibility improvements. Stabilized the OneDNN execution path by ensuring OneDNNOperatorDialect is registered in IrContext and upgrading OneDNN to v3.6, with corresponding unit test adjustments to maintain compatibility. These changes reduce runtime errors when enabling OneDNN and improve CPU performance portability across platforms.
2 Commits • 1 Features
Mar 1, 2025March 2025: PaddlePaddle/Paddle OneDNN integration stabilization and compatibility improvements. Stabilized the OneDNN execution path by ensuring OneDNNOperatorDialect is registered in IrContext and upgrading OneDNN to v3.6, with corresponding unit test adjustments to maintain compatibility. These changes reduce runtime errors when enabling OneDNN and improve CPU performance portability across platforms.
March 2025
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for PaddlePaddle/Paddle. Focused delivery on bfloat16 handling optimizations within the oneDNN integration, with an emphasis on performance and correctness in transform passes. Completed targeted refactors and pattern enhancements, and resolved a critical bug affecting repeated inputs in the bf16 pass. These efforts align with delivering faster, more reliable bf16 conversions and quantization workflows in Paddle.
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for PaddlePaddle/Paddle. Focused delivery on bfloat16 handling optimizations within the oneDNN integration, with an emphasis on performance and correctness in transform passes. Completed targeted refactors and pattern enhancements, and resolved a critical bug affecting repeated inputs in the bf16 pass. These efforts align with delivering faster, more reliable bf16 conversions and quantization workflows in Paddle.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for PaddlePaddle/Paddle. Key feature delivered: OneDNN integration upgrades with bf16 quantization support for the Concat operation. Implemented a new inference pass that inserts quantize and dequantize around Concat to compute in bf16 with oneDNN, transforming inputs to bf16 for computation and converting back to the original dtype. This delivers faster inference and improved throughput for Concat-heavy models. Commits: d0f9dbb5440f95d8e7073fa58bb6924783ea0b51 (Upgrade oneDNN to v3.5), 1e9f2b363ce20c946b9790d388af72c701ee7b62 ([PIR][oneDNN] Add quantization pattern for Concatence).
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for PaddlePaddle/Paddle. Key feature delivered: OneDNN integration upgrades with bf16 quantization support for the Concat operation. Implemented a new inference pass that inserts quantize and dequantize around Concat to compute in bf16 with oneDNN, transforming inputs to bf16 for computation and converting back to the original dtype. This delivers faster inference and improved throughput for Concat-heavy models. Commits: d0f9dbb5440f95d8e7073fa58bb6924783ea0b51 (Upgrade oneDNN to v3.5), 1e9f2b363ce20c946b9790d388af72c701ee7b62 ([PIR][oneDNN] Add quantization pattern for Concatence).
December 2024
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for PaddlePaddle/Paddle. Key feature delivered: OneDNN optimization level tuning in the Paddle framework by lowering the opt level for several oneDNN passes from 3 to 2 across multiple fusion passes, aiming to improve stability with potential minimal impact on performance. Commit: c579ed0221ca5394488bd2b9e22e17d35fa6fc8e (Change opt level of onednn passes to 2) in PaddlePaddle/Paddle (PR #69524).
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for PaddlePaddle/Paddle. Key feature delivered: OneDNN optimization level tuning in the Paddle framework by lowering the opt level for several oneDNN passes from 3 to 2 across multiple fusion passes, aiming to improve stability with potential minimal impact on performance. Commit: c579ed0221ca5394488bd2b9e22e17d35fa6fc8e (Change opt level of onednn passes to 2) in PaddlePaddle/Paddle (PR #69524).
October 2024
2 Commits • 1 Features
Oct 1, 2024Concise monthly summary for Oct 2024 focused on PaddlePaddle/Paddle. Expanded dynamic shape handling for reshape and related onednn/bfloat16 paths, improving robustness in the presence of unknown or partially undefined shapes and generalizing shape checks to support reshape and slice operations in BF16 placement path.
2 Commits • 1 Features
Oct 1, 2024Concise monthly summary for Oct 2024 focused on PaddlePaddle/Paddle. Expanded dynamic shape handling for reshape and related onednn/bfloat16 paths, improving robustness in the presence of unknown or partially undefined shapes and generalizing shape checks to support reshape and slice operations in BF16 placement path.
October 2024
Activity
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Quality Metrics
Correctness82.0%
Maintainability84.0%
Architecture80.0%
Performance70.0%
AI Usage22.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Build SystemsC++Compiler DevelopmentCompiler OptimizationCompiler OptimizationsDeep LearningDeep Learning FrameworksDeep Learning OptimizationInference OptimizationMachine LearningOperator DialectsPIRPass OptimizationPerformance OptimizationPython
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