
Worked on cross-platform enhancements and adaptive kernel features in open source deep learning infrastructure. In apache/tvm, delivered dynamic inline loading for C++ and CUDA code via the FFI, expanding the API surface and stabilizing load_inline across Windows and macOS. This involved API design, build system updates, and platform-aware adjustments to improve prototyping speed and reliability for on-the-fly compilation workflows. In flashinfer-ai/flashinfer, implemented adaptive sequence length support in the decode kernel for trtllm-gen attention, enabling variable-length batching and improved GPU utilization. Used C++, CUDA, and Python, with a focus on code generation, dynamic compilation, and cross-platform development.
December 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: 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.
For 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.
For 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.

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