alibaba/rtp-llm
Feb 2025 – Feb 2025
1 Month active
Languages Used
C++PythonShell
Technical Skills
C++Composable Kernel (CK)Deep LearningDistributed SystemsGPU ComputingInference Optimization
OscarXu
PROFILE
Oscarxu
Huaigu Xu developed scalable Mixture of Experts (MoE) inference capabilities for the alibaba/rtp-llm repository, focusing on ROCm device support. He integrated fused Composable Kernel (CK) MoE functionality with tensor parallelism, updating the device layer and weights loader to efficiently handle MoE workloads. Using C++ and Python, he implemented tensor-parallelism-aware weight shuffling and padding, optimizing distributed inference for large models. His work included adding build targets for fused MoE examples and refactoring ambiguous layer names to improve code clarity. The engineering effort addressed performance and scalability, enabling higher throughput and better resource utilization for MoE inference on ROCm hardware.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
881
Activity Months1
Your Network
1351 people
Work History
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 focused on enabling scalable Mixture of Experts (MoE) inference on ROCm devices within the alibaba/rtp-llm repository. Key deliverables include MoE integration with tensor parallelism support on ROCm, fused CK MoE path enabling, and build targets for fused MoE examples. Updates to the device layer and weights loader were made to support MoE workloads, along with tensor-parallelism-aware weight shuffling and padding to optimize distributed inference. This work drives higher throughput and better resource utilization for large MoE models on ROCm hardware, accelerating inference at scale while maintaining compatibility with existing CI/tests. No major bugs were recorded in this period; all changes are aligned with performance and scalability objectives.
3 Commits • 1 Features
Feb 1, 2025February 2025 focused on enabling scalable Mixture of Experts (MoE) inference on ROCm devices within the alibaba/rtp-llm repository. Key deliverables include MoE integration with tensor parallelism support on ROCm, fused CK MoE path enabling, and build targets for fused MoE examples. Updates to the device layer and weights loader were made to support MoE workloads, along with tensor-parallelism-aware weight shuffling and padding to optimize distributed inference. This work drives higher throughput and better resource utilization for large MoE models on ROCm hardware, accelerating inference at scale while maintaining compatibility with existing CI/tests. No major bugs were recorded in this period; all changes are aligned with performance and scalability objectives.
February 2025
Activity
Loading activity data...
Quality Metrics
Correctness83.4%
Maintainability80.0%
Architecture83.4%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++PythonShell
Technical Skills
C++Composable Kernel (CK)Deep LearningDistributed SystemsGPU ComputingInference OptimizationMachine LearningMixture-of-Experts (MoE)Model LoadingModel ParallelismPythonROCmRefactoringTensor Parallelism
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline