Worked on optimizing benchmarking tests for the deepseek-ai/FlashMLA repository, focusing on improving test reliability and maintainability. The main contribution involved simplifying the benchmarking workflow by removing the fast_flush parameter from the do_bench function in test_flash_mla.py. This change aligned the codebase with upstream Triton updates, resulting in faster and more consistent test runs while reducing the maintenance overhead for future development. The work was implemented using Python and emphasized skills in benchmarking and testing. The update addressed a specific workflow bottleneck, streamlining the process for developers working with FlashMLA and supporting ongoing improvements to the testing infrastructure.