alibaba/rtp-llm
Mar 2026 – Apr 2026
2 Months active
Languages Used
C++PythonCUDA
Technical Skills
CUDADeep LearningDistributed ComputingGPU ProgrammingMachine LearningModel Optimization
hxy0118
PROFILE
Hxy0118
Contributed to alibaba/rtp-llm by developing advanced distributed training features and performance optimizations for large language models. Focused on enhancing ROCm-based Mixture-of-Experts support and introducing a fused AllReduce operator, the work improved throughput, memory efficiency, and deployment flexibility. Leveraged CUDA, Python, and PyTorch to implement BF16 fused MoE, FP8 quantization, and backend-agnostic L2 normalization, ensuring compatibility across AMD/ROCm and CUDA platforms. Addressed configuration validation bugs to strengthen system stability and broaden hardware support. The engineering approach emphasized modular kernel design, runtime adaptability, and robust unit testing, resulting in faster training, improved inference, and streamlined backend integration for scalable deployments.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
7Total
Bugs
1
Commits
7
Features
4
Lines of code
4,095
Activity Months2
Your Network
87 peopleShared Repositories
87
Work History
April 2026
4 Commits • 2 Features
Apr 1, 2026April 2026 monthly summary for alibaba/rtp-llm: Delivered targeted features, fixed a configuration bug, and demonstrated strong performance and backend portability. Key features delivered: TensorRT-based allreduce for distributed training with support for multiple hidden sizes and improved graph capture error handling. Fused L2 normalization optimization with backend gating applying the fused path on AMD/ROCm and CUDA fallback, plus runtime-path improvements to avoid per-shape recompiles. Major bug fixed: Router pure TP mode configuration validation bug to correctly identify applicability and prevent incorrect configurations. Impact: improved distributed training throughput and stability, broader backend hardware support (AMD/ROCm, CUDA), and significant performance gains (notably ~17x faster in the fused L2 norm path on a representative MI308X bf16 benchmark). Technologies/skills demonstrated: TensorRT, ROCm/AMD, CUDA fallback, fused L2 norm optimization, rsqrt-based math, BT-tiled kernel design, graph capture error handling, runtime-shape flexibility, testing updates.
4 Commits • 2 Features
Apr 1, 2026April 2026 monthly summary for alibaba/rtp-llm: Delivered targeted features, fixed a configuration bug, and demonstrated strong performance and backend portability. Key features delivered: TensorRT-based allreduce for distributed training with support for multiple hidden sizes and improved graph capture error handling. Fused L2 normalization optimization with backend gating applying the fused path on AMD/ROCm and CUDA fallback, plus runtime-path improvements to avoid per-shape recompiles. Major bug fixed: Router pure TP mode configuration validation bug to correctly identify applicability and prevent incorrect configurations. Impact: improved distributed training throughput and stability, broader backend hardware support (AMD/ROCm, CUDA), and significant performance gains (notably ~17x faster in the fused L2 norm path on a representative MI308X bf16 benchmark). Technologies/skills demonstrated: TensorRT, ROCm/AMD, CUDA fallback, fused L2 norm optimization, rsqrt-based math, BT-tiled kernel design, graph capture error handling, runtime-shape flexibility, testing updates.
April 2026
March 2026
3 Commits • 2 Features
Mar 1, 2026March 2026 monthly highlights for alibaba/rtp-llm: delivered major ROCm MoE enhancements and TRT-LLM AllReduce Fusion operator, enabling higher throughput, better memory efficiency, and more robust ROCm integration. Focused on business value: faster distributed training and inference, improved model loading and server configurability, and easier deployment at scale. The work strengthens ROCm-based MoE support and distributed training capabilities while laying groundwork for future FP8-based optimizations and broader hardware support.
March 2026
3 Commits • 2 Features
Mar 1, 2026March 2026 monthly highlights for alibaba/rtp-llm: delivered major ROCm MoE enhancements and TRT-LLM AllReduce Fusion operator, enabling higher throughput, better memory efficiency, and more robust ROCm integration. Focused on business value: faster distributed training and inference, improved model loading and server configurability, and easier deployment at scale. The work strengthens ROCm-based MoE support and distributed training capabilities while laying groundwork for future FP8-based optimizations and broader hardware support.
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Quality Metrics
Correctness88.6%
Maintainability80.0%
Architecture88.6%
Performance88.6%
AI Usage34.4%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
CUDADeep LearningDistributed ComputingGPU ProgrammingGPU programmingMachine LearningModel OptimizationParallel ComputingPerformance OptimizationPyTorchPython DevelopmentQuantizationROCmTensorRTUnit Testing
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