vllm-project/vllm-ascend
Oct 2025 – Oct 2025
1 Month active
Languages Used
C++Python
Technical Skills
Ascend AIC++CUDA/Ascend ProgrammingCUDA/ROCm ProgrammingDeep LearningMachine Learning
shaopeng-666
PROFILE
Shaopeng-666
Lishaopeng worked on the vllm-ascend repository, where they developed and integrated a fused MRotaryEmbedding operation for the Qwen2.5-VL model. Using C++ and Python, they implemented the MRotaryEmbedding class and incorporated it into Ascend custom operations, supporting both 1D and 2D positional encodings. Lishaopeng also addressed NZ-format weight compatibility for VL float models by adding format casting for QKV and projection weights, ensuring correct operation when NZ is enabled. Their work included comprehensive end-to-end testing and operator registration, resulting in a robust, deployment-ready solution that improves model optimization and reliability within the deep learning pipeline.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
296
Activity Months1
Your Network
247 people
Work History
October 2025
3 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for vllm-ascend: Delivered the fused MRotaryEmbedding operation for the Qwen2.5-VL model, integrated into Ascend custom operations, and added end-to-end tests for 1D/2D positions. Fixed NZ-format weight support for VL float models by implementing format casting for QKV and projection weights when NZ is enabled. Strengthened operator registration and end-to-end validation to pave the way for deployment and future performance optimizations.
3 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for vllm-ascend: Delivered the fused MRotaryEmbedding operation for the Qwen2.5-VL model, integrated into Ascend custom operations, and added end-to-end tests for 1D/2D positions. Fixed NZ-format weight support for VL float models by implementing format casting for QKV and projection weights when NZ is enabled. Strengthened operator registration and end-to-end validation to pave the way for deployment and future performance optimizations.
October 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture86.6%
Performance83.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Ascend AIC++CUDA/Ascend ProgrammingCUDA/ROCm ProgrammingDeep LearningMachine LearningModel OptimizationPyTorchPythonTesting
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