apache/tvm
Sep 2025 – Sep 2025
1 Month active
Languages Used
C++CUDAPython
Technical Skills
API DesignBuild SystemsC++C++ DevelopmentCUDACUDA Development
Yaoyao Ding
PROFILE
Yaoyao Ding
During this period, Dingyaoyao enhanced cross-platform dynamic compilation workflows in the apache/tvm repository by delivering inline TVM FFI loading for C++ and CUDA code, enabling on-the-fly compilation from inline strings and refining the API for greater usability across Windows and macOS. This work involved API design, build system improvements, and robust test coverage to ensure reliability and portability. Additionally, in flashinfer-ai/flashinfer, Dingyaoyao implemented adaptive sequence length support in the decode kernel for trtllm-gen attention, using CUDA and Python to enable variable-length batching for deep learning inference, improving flexibility and GPU utilization for real-world machine learning workloads.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
6Total
Bugs
2
Commits
6
Features
2
Lines of code
1,421
Activity Months2
Your Network
218 peopleWork History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Delivered adaptive sequence length support in the decode kernel for trtllm-gen attention, enabling per-request max_q_len and cum_seq_lens_q to support variable input lengths. The change enhances flexibility for ragged batches, improves GPU utilization, and lays groundwork for more cost-efficient inference workloads. Included code changes, tests, and benchmarking artifacts, and prepared validation for deployment.
1 Commits • 1 Features
Dec 1, 2025December 2025: Delivered adaptive sequence length support in the decode kernel for trtllm-gen attention, enabling per-request max_q_len and cum_seq_lens_q to support variable input lengths. The change enhances flexibility for ragged batches, improves GPU utilization, and lays groundwork for more cost-efficient inference workloads. Included code changes, tests, and benchmarking artifacts, and prepared validation for deployment.
December 2025
September 2025
5 Commits • 1 Features
Sep 1, 2025For 2025-09, delivered cross-platform enhancements to TVM FFI inline loading, expanded the API surface, and stabilized load_inline across Windows and macOS. This work improves prototyping speed, portability, and reliability for inline C++/CUDA workflows with on-the-fly compilation and clearer export rules.
September 2025
5 Commits • 1 Features
Sep 1, 2025For 2025-09, delivered cross-platform enhancements to TVM FFI inline loading, expanded the API surface, and stabilized load_inline across Windows and macOS. This work improves prototyping speed, portability, and reliability for inline C++/CUDA workflows with on-the-fly compilation and clearer export rules.
Activity
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Quality Metrics
Correctness86.6%
Maintainability85.0%
Architecture85.0%
Performance73.4%
AI Usage23.4%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
API DesignBuild SystemsC++C++ DevelopmentCUDACUDA DevelopmentCode GenerationCode RefactoringCross-Platform DevelopmentDynamic CompilationFFIFFI (Foreign Function Interface)PyTorchPythonPython Development
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
flashinfer-ai/flashinfer
Dec 2025 – Dec 2025
1 Month active
Languages Used
C++Python
Technical Skills
CUDAPyTorchdeep learningmachine learning