[Dev][TL] Hardware Aware Tuning Examples with TL #201
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This pull request includes several changes to improve the scheduling and tuning capabilities in the
bitblasmodule, along with some code refactoring and cleanup. The most important changes include updating theThreadPoolExecutorusage, adding hardware-aware configuration methods, introducing a new fine-grained matrix multiplication scheduler, and making various code style improvements.Enhancements to Scheduling and Tuning:
bitblas/base/utils.py: Changed theThreadPoolExecutorto use a dynamic number of workers based on themax_workersparameter.bitblas/ops/base_scheduler.py: Added a method to get hardware-aware configurations for matrix multiplication schedulers.bitblas/ops/general_matmul/tilelang/dense/matmul_tensorcore.py: Added methods to get hardware-aware configurations for CUDA architectures.New Scheduler Introduction:
bitblas/ops/general_matmul/tilelang/dense/matmul_simt.py: IntroducedMatmulFineGrainSIMTScheduler, a new fine-grained matrix multiplication scheduler.Code Refactoring and Cleanup:
bitblas/ops/operator.py: Refactored multiple methods for better readability and maintainability, includingapply_fast_tuning,hardware_aware_finetune, and_build_default_module. [1] [2] [3]bitblas/ops/general_matmul/tilelang/dense/matmul.pytomatmul_tensorcore.py: Renamed the file for better clarity and organization.