alibaba/rtp-llm
Feb 2026 – Apr 2026
3 Months active
Languages Used
C++Python
Technical Skills
C++Pythonasynchronous programmingbackend developmentstream processingAlgorithm design
weike.chw
PROFILE
Weike.chw
Worked on the alibaba/rtp-llm repository to enhance streaming inference scalability and reliability by developing batch group scheduling with isolation, enabling efficient grouped request processing while preserving individual contexts. Improved the FIFOScheduler with batch-aware timeout and isolation features, and introduced targeted unit tests to ensure robust cross-group scheduling. Addressed API migration challenges by adapting force batch tests to the updated KVCacheManager interface, maintaining CI stability after major refactors. Leveraged C++ and Python for backend development, asynchronous programming, and unit testing, focusing on code optimization and maintainability. Collaborated on API integration and test engineering to reduce regression risk and streamline iteration.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
3
Lines of code
557
Activity Months3
Your Network
438 peopleSame Organization
@alibaba-inc.com351
Shared Repositories
87
Work History
April 2026
1 Commits
Apr 1, 2026Summary for 2026-04 focusing on business value and technical achievements in alibaba/rtp-llm. Delivered an API-aligned test adaptation to KVCacheManager, ensuring post-rebase compatibility and stable CI. Major bug fix to adapt force batch tests to the updated API. This work reduces regression risk, speeds up iteration on API changes, and preserves test reliability across refactors. Technologies demonstrated include C++, PyTorch (torch::tensor), test engineering, API migration, and Git collaboration.
1 Commits
Apr 1, 2026Summary for 2026-04 focusing on business value and technical achievements in alibaba/rtp-llm. Delivered an API-aligned test adaptation to KVCacheManager, ensuring post-rebase compatibility and stable CI. Major bug fix to adapt force batch tests to the updated API. This work reduces regression risk, speeds up iteration on API changes, and preserves test reliability across refactors. Technologies demonstrated include C++, PyTorch (torch::tensor), test engineering, API migration, and Git collaboration.
April 2026
March 2026
3 Commits • 2 Features
Mar 1, 2026March 2026: Key reliability and maintainability improvements to the streaming batch scheduler. Delivered batch-aware FIFOScheduler enhancements with timeout and isolation, added targeted unit tests to verify cross-group scheduling, and performed a code cleanup to remove an unused include. These changes improve scheduling reliability, reduce cross-group contention, and streamline the codebase for faster builds and easier maintenance.
March 2026
3 Commits • 2 Features
Mar 1, 2026March 2026: Key reliability and maintainability improvements to the streaming batch scheduler. Delivered batch-aware FIFOScheduler enhancements with timeout and isolation, added targeted unit tests to verify cross-group scheduling, and performed a code cleanup to remove an unused include. These changes improve scheduling reliability, reduce cross-group contention, and streamline the codebase for faster builds and easier maintenance.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for alibaba/rtp-llm: Focused on scalability and efficiency for streaming inference. Delivered Batch Group Scheduling for Streams with batch isolation, enabling multiple requests to be processed in a single batch while preserving individual contexts. Added configuration options for batch grouping and per-batch timeout, and refined the scheduling logic to optimize grouped requests at scale. These changes improve throughput, reduce latency, and simplify operators' tuning in production.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for alibaba/rtp-llm: Focused on scalability and efficiency for streaming inference. Delivered Batch Group Scheduling for Streams with batch isolation, enabling multiple requests to be processed in a single batch while preserving individual contexts. Added configuration options for batch grouping and per-batch timeout, and refined the scheduling logic to optimize grouped requests at scale. These changes improve throughput, reduce latency, and simplify operators' tuning in production.
February 2026
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Quality Metrics
Correctness92.0%
Maintainability84.0%
Architecture84.0%
Performance84.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
API integrationAlgorithm designC++C++ developmentConcurrency handlingPythonPython scriptingUnit testingasynchronous programmingbackend developmentcode optimizationscheduling algorithmsstream processingunit testing
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