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
Worked on integrating scalable Mixture of Experts (MoE) inference into the alibaba/rtp-llm repository, focusing on ROCm device support. Leveraged C++ and Python to enable fused Composable Kernel MoE paths, updating the device layer and weights loader for efficient MoE workloads. Implemented tensor parallelism-aware weight shuffling and padding to optimize distributed inference, supporting multi-tensor parallelism scenarios. The work improved throughput and resource utilization for large MoE models on ROCm hardware while maintaining compatibility with existing CI and tests. Refactored ambiguous MoE layer names to enhance code clarity and maintainability, aligning all changes with performance and scalability objectives.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
881
Activity Months1
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1714 peopleSame Organization
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Shared Repositories
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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
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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
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