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haowen-han

PROFILE

Haowen-han

Worked on enhancing reliability and correctness in deep learning infrastructure, focusing on two core repositories. In AdvancedCompiler/FlagGems, addressed numerical stability issues in max reduction operations for non-contiguous tensors and large input shapes by implementing kernel-level corrections and expanding test coverage, using CUDA, PyTorch, and Triton. In kvcache-ai/sglang, delivered a targeted bug fix to the attention module, ensuring output tensors are properly initialized to zero in distributed setups, which improved model stability and reduced silent errors. Across both projects, emphasized robust testing and maintainable code, contributing to improved accuracy and reliability in large-scale machine learning workflows.

Overall Statistics

Feature vs Bugs

0%Features

Repository Contributions

3Total
Bugs
2
Commits
3
Features
0
Lines of code
67
Activity Months2

Work History

February 2026

1 Commits

Feb 1, 2026

February 2026 monthly summary for kvcache-ai/sglang: Primary focus on stabilizing attention computations in distributed configurations. Delivered a critical bug fix that initializes the attention output to zero, ensuring correct processing and preventing silent miscomputations in DP2/TP4 setups. The change improves model reliability, reduces downstream debugging, and supports robust training/inference. No new features released this month; the work centers on code health and correctness with a high business impact on stability and accuracy.

November 2024

2 Commits

Nov 1, 2024

November 2024 performance summary for AdvancedCompiler/FlagGems: Focused on improving numerical stability and correctness of the max reduction when faced with non-contiguous tensors and very large input shapes; implemented kernel-level corrections and expanded test coverage, with traceable commits addressing issues #273 and #304/#308. This work enhances reliability for real-world workloads that involve irregular memory layouts and large-scale data, contributing to improved downstream accuracy and stability in the compiler's optimization path.

Activity

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Quality Metrics

Correctness100.0%
Maintainability93.4%
Architecture86.6%
Performance93.4%
AI Usage20.0%

Skills & Technologies

Programming Languages

Python

Technical Skills

CUDADeep LearningMachine LearningPerformance OptimizationPyTorchPythonTestingTriton

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

AdvancedCompiler/FlagGems

Nov 2024 Nov 2024
1 Month active

Languages Used

Python

Technical Skills

CUDAPerformance OptimizationPyTorchTestingTriton

kvcache-ai/sglang

Feb 2026 Feb 2026
1 Month active

Languages Used

Python

Technical Skills

Deep LearningMachine LearningPython