alibaba/rtp-llm
Nov 2025 – Mar 2026
4 Months active
Languages Used
C++Python
Technical Skills
C++ developmentCUDAData ProcessingDeep LearningGPU ProgrammingGPU programming
xiebaijie.xbj
PROFILE
Xiebaijie.xbj
Baijie Xie contributed to alibaba/rtp-llm by engineering core enhancements for deep learning model deployment and inference. Over four months, he developed ROCm-based sampling kernels for AMD, integrated FlashInfer support, and optimized top-k/top-p sampling for reproducibility and performance. He refined the Qwen2.5 model architecture, introducing new activation functions and flexible Mixture-of-Experts configurations to improve deployment readiness. Using C++, CUDA, and Python, Baijie implemented dynamic vector-size optimizations in tensor operations and strengthened Deepep routing with auto-configuration and quantization-aware fixes. His work demonstrated depth in backend development, model optimization, and robust integration across diverse hardware and production environments.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
5
Lines of code
3,037
Activity Months4
Your Network
416 peopleSame Organization
@alibaba-inc.com333
Shared Repositories
83
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AMD-dtengMember
Work History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 — alibaba/rtp-llm: Key features delivered and bugs fixed focused on improving robustness and automation of the Deepep routing path. Key features delivered: - Deepep Deep_ep Auto-Configuration Enhancement (commit 2a8944d46260a82dde2d177fd95ac51ad8352120): Removed gating condition that prevented the use of deep_ep_config in auto-configuration, enabling better integration and functionality of the deep_ep module. Major bugs fixed: - Deepep Router End-to-End Robustness Fixes (commit 17bad45c662f298c29f0c55777f564e4bdfac5c6): Fixed issues in the Deepep Normal router end-to-end functionality, addressing quantization methods and configuration checks to ensure proper operation under different settings. Overall impact and accomplishments: - Increased reliability and stability of the Deepep routing path, enabling safer deployments across diverse settings. - Accelerated deployment and integration through improved auto-configuration, reducing manual configuration steps and enabling faster feature rollouts. Technologies/skills demonstrated: - Quantization-aware routing and configuration validation - Auto-configuration design and integration for the Deepep module - Git-based development, commit-driven delivery, and cross-functional collaboration
2 Commits • 1 Features
Mar 1, 2026March 2026 — alibaba/rtp-llm: Key features delivered and bugs fixed focused on improving robustness and automation of the Deepep routing path. Key features delivered: - Deepep Deep_ep Auto-Configuration Enhancement (commit 2a8944d46260a82dde2d177fd95ac51ad8352120): Removed gating condition that prevented the use of deep_ep_config in auto-configuration, enabling better integration and functionality of the deep_ep module. Major bugs fixed: - Deepep Router End-to-End Robustness Fixes (commit 17bad45c662f298c29f0c55777f564e4bdfac5c6): Fixed issues in the Deepep Normal router end-to-end functionality, addressing quantization methods and configuration checks to ensure proper operation under different settings. Overall impact and accomplishments: - Increased reliability and stability of the Deepep routing path, enabling safer deployments across diverse settings. - Accelerated deployment and integration through improved auto-configuration, reducing manual configuration steps and enabling faster feature rollouts. Technologies/skills demonstrated: - Quantization-aware routing and configuration validation - Auto-configuration design and integration for the Deepep module - Git-based development, commit-driven delivery, and cross-functional collaboration
March 2026
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary focused on performance optimization in the core tensor path of alibaba/rtp-llm. Implemented dynamic vector-size optimization for the Silu and Mul activation path, using a switch-case structure to adapt to different input dimensions and maintain consistent performance gains. Adjustments to related activation functions were made to preserve improvements across sizes. A targeted bug fix was included for inter_size vec_size handling in the flashinfer path (Q2.5VL VIT inter_size 3420) as captured in commit 13b1230d22e1a21670394dab0e1cf50296db89dc. Key achievements highlight the delivery of an optimized tensor operation path with improved runtime performance and stability across varying input sizes, under a single repository: alibaba/rtp-llm.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary focused on performance optimization in the core tensor path of alibaba/rtp-llm. Implemented dynamic vector-size optimization for the Silu and Mul activation path, using a switch-case structure to adapt to different input dimensions and maintain consistent performance gains. Adjustments to related activation functions were made to preserve improvements across sizes. A targeted bug fix was included for inter_size vec_size handling in the flashinfer path (Q2.5VL VIT inter_size 3420) as captured in commit 13b1230d22e1a21670394dab0e1cf50296db89dc. Key achievements highlight the delivery of an optimized tensor operation path with improved runtime performance and stability across varying input sizes, under a single repository: alibaba/rtp-llm.
January 2026
2 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for alibaba/rtp-llm focused on delivering performance and deployment flexibility enhancements. Key features delivered include: (1) Qwen2.5 Model Architecture and Activation Enhancements, introducing a new activation function and updating the MLP with a merge gate mechanism to boost performance and flexibility. (2) FP4 MoE Configuration and Execution Flexibility, adding a configuration parameter to select FP4 MoE operation (trtllm or cutedsl) to improve integration with TensorRT-LLM and execution flexibility, along with adjustments to model configuration handling and device operations for compatibility. Overall impact: these changes strengthen the model’s deployment readiness, enabling more efficient and flexible inference in production environments and reducing integration friction with TensorRT-LLM frameworks. Technologies/skills demonstrated: deep learning architecture refinement, activation/Mlp design, Mixture-of-Experts configuration, TensorRT-LLM integration, model configuration management, and device operation handling.
2 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for alibaba/rtp-llm focused on delivering performance and deployment flexibility enhancements. Key features delivered include: (1) Qwen2.5 Model Architecture and Activation Enhancements, introducing a new activation function and updating the MLP with a merge gate mechanism to boost performance and flexibility. (2) FP4 MoE Configuration and Execution Flexibility, adding a configuration parameter to select FP4 MoE operation (trtllm or cutedsl) to improve integration with TensorRT-LLM and execution flexibility, along with adjustments to model configuration handling and device operations for compatibility. Overall impact: these changes strengthen the model’s deployment readiness, enabling more efficient and flexible inference in production environments and reducing integration friction with TensorRT-LLM frameworks. Technologies/skills demonstrated: deep learning architecture refinement, activation/Mlp design, Mixture-of-Experts configuration, TensorRT-LLM integration, model configuration management, and device operation handling.
January 2026
November 2025
7 Commits • 1 Features
Nov 1, 2025November 2025: Delivered ROCm-based sampling enhancements for AMD in alibaba/rtp-llm, consolidating kernel-level performance improvements, reliability, and reproducibility. Key work includes FlashInfer kernel support, top-k/top-p sampling, seeded reproducibility, buffer/type optimizations, removal of deprecated AMD sampler, warp-size standardization, and mask logits functionality. These changes reduce maintenance burden and broaden hardware coverage while improving inference throughput and determinism.
November 2025
7 Commits • 1 Features
Nov 1, 2025November 2025: Delivered ROCm-based sampling enhancements for AMD in alibaba/rtp-llm, consolidating kernel-level performance improvements, reliability, and reproducibility. Key work includes FlashInfer kernel support, top-k/top-p sampling, seeded reproducibility, buffer/type optimizations, removal of deprecated AMD sampler, warp-size standardization, and mask logits functionality. These changes reduce maintenance burden and broaden hardware coverage while improving inference throughput and determinism.
Activity
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture81.6%
Performance81.6%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
BazelC++ developmentCUDAData ProcessingDeep LearningGPU ProgrammingGPU programmingMachine LearningModel OptimizationParallel ComputingPyTorchPythonPython DevelopmentRandom number generationUnit testing
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