mozilla/onnxruntime
Nov 2024 – Apr 2025
4 Months active
Languages Used
C++
Technical Skills
C++ DevelopmentMachine LearningTensor OperationsWebNNC++C++ development
Peishen Yan
PROFILE
Peishen Yan
Peishen Yan contributed to mozilla/onnxruntime by developing advanced features for the WebNN Execution Provider, focusing on deep learning and efficient tensor operations in C++. Over four months, Peishen implemented support for Einstein Summation, GroupQueryAttention, and Multi-Head Attention, enabling more complex and performant AI workloads on web platforms. Their work included optimizing model partitioning, enhancing shape inference, and introducing usage-count tracking for ONNX initializers to improve runtime stability. By leveraging C++ and algorithm optimization, Peishen addressed both performance and reliability, delivering features that expanded WebNN’s capabilities and improved the integration of ONNX models for web-based machine learning.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
5
Lines of code
2,172
Activity Months4
Your Network
1966 people
Work History
April 2025
2 Commits • 2 Features
Apr 1, 2025April 2025 — Key WebNN enhancements to ONNX Runtime (mozilla/onnxruntime): GroupQueryAttention (GQA) and Multi-Head Attention (MHA) support in the WebNN Execution Provider, strengthening web-based AI deployment with higher throughput and efficiency. No explicit bug fixes documented for this period.
2 Commits • 2 Features
Apr 1, 2025April 2025 — Key WebNN enhancements to ONNX Runtime (mozilla/onnxruntime): GroupQueryAttention (GQA) and Multi-Head Attention (MHA) support in the WebNN Execution Provider, strengthening web-based AI deployment with higher throughput and efficiency. No explicit bug fixes documented for this period.
April 2025
March 2025
1 Commits • 1 Features
Mar 1, 2025Concise monthly summary for month 2025-03 focusing on ONNX Runtime development work for mozilla/onnxruntime. The primary focus this month was a feature enhancement to improve WebNN compatibility for GroupQueryAttention through advanced shape inference, enabling static shape requirements and compatibility with current sequence length constraints. No major bugs fixed this period; work concentrated on delivering a feature with clear business value for WebNN deployment and downstream integrations.
March 2025
1 Commits • 1 Features
Mar 1, 2025Concise monthly summary for month 2025-03 focusing on ONNX Runtime development work for mozilla/onnxruntime. The primary focus this month was a feature enhancement to improve WebNN compatibility for GroupQueryAttention through advanced shape inference, enabling static shape requirements and compatibility with current sequence length constraints. No major bugs fixed this period; work concentrated on delivering a feature with clear business value for WebNN deployment and downstream integrations.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 – mozilla/onnxruntime: WebNN execution provider (EP) enhancements and stability fixes. Key feature delivered: optimized model partitioning and node grouping to improve the efficiency of executing connected nodes supported by WebNN EP. Major bug fix: added usage-count tracking for ONNX initializers to prevent crashes when multiple operations reuse the same initializer, ensuring initializers are skipped only when unused by all operations. Impact: improved runtime throughput and stability for WebNN EP workloads, reducing crash scenarios and smoothing model execution. Demonstrated strong capabilities in runtime optimization, edge-case handling, and cross-component collaboration to advance WebNN integration.
2 Commits • 1 Features
Jan 1, 2025January 2025 – mozilla/onnxruntime: WebNN execution provider (EP) enhancements and stability fixes. Key feature delivered: optimized model partitioning and node grouping to improve the efficiency of executing connected nodes supported by WebNN EP. Major bug fix: added usage-count tracking for ONNX initializers to prevent crashes when multiple operations reuse the same initializer, ensuring initializers are skipped only when unused by all operations. Impact: improved runtime throughput and stability for WebNN EP workloads, reducing crash scenarios and smoothing model execution. Demonstrated strong capabilities in runtime optimization, edge-case handling, and cross-component collaboration to advance WebNN integration.
January 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Delivered Einstein Summation (Einsum) support in the WebNN Execution Provider for mozilla/onnxruntime, enabling advanced tensor operations (matrix multiplication, transposition, reductions) using Einstein summation convention. Implemented via commit 59280095539aa721096cb85045a4a4b267de33a1 and PR #19558. This enhances the WebNN path, broadening hardware-accelerated workloads with minimal changes to downstream models. No major bugs reported during the integration and validation of this feature.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Delivered Einstein Summation (Einsum) support in the WebNN Execution Provider for mozilla/onnxruntime, enabling advanced tensor operations (matrix multiplication, transposition, reductions) using Einstein summation convention. Implemented via commit 59280095539aa721096cb85045a4a4b267de33a1 and PR #19558. This enhances the WebNN path, broadening hardware-accelerated workloads with minimal changes to downstream models. No major bugs reported during the integration and validation of this feature.
Activity
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Quality Metrics
Correctness100.0%
Maintainability83.4%
Architecture96.6%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++C++ DevelopmentC++ developmentMachine LearningTensor OperationsWebNNWebNN frameworkalgorithm designalgorithm optimizationdeep learninggraph theorymachine learningsoftware debuggingtensor operations
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