GitLab

* [Refactor] Update tilelang kernel functions and remove unused imports

- Refactored the `flashattn_fwd`, `flashattn_bwd_preprocess`, and `flashattn_bwd_postprocess` functions to utilize direct kernel calls instead of cached versions, improving clarity and performance.
- Added `@tilelang.jit` decorators with specified output indices to enhance kernel compilation.
- Removed unused import of `cached` from `tilelang`, streamlining the code.
- Commented out the main testing function call in `test_tilelang_kernel_mha_bwd.py` for potential future use.

* [Refactor] Simplify configuration generation in benchmark and example scripts

- Refactored the `get_configs` functions in multiple benchmark and example scripts to utilize a dictionary-based approach for parameter configuration, improving readability and maintainability.
- Updated the `flashattn` and `chunk_scan_fwd` functions to directly accept configuration parameters, enhancing flexibility in kernel tuning.
- Removed redundant code and streamlined the configuration generation process across various files, ensuring consistency in how configurations are defined and utilized.

* [Refactor] Update configuration handling in benchmark scripts

- Refactored the `get_configs` functions in benchmark scripts to accept a variable argument list, improving flexibility in configuration management.
- Enhanced the `matmul` and `flashattn` functions to utilize the updated configuration approach, streamlining parameter handling for kernel tuning.
- Added `@autotune` decorators to relevant functions, ensuring consistent autotuning behavior across benchmarks.
- Cleaned up redundant code and improved overall readability in the affected files.

* [Refactor] Clean up formatting and update subproject commit

- Updated the subproject commit reference in the TVM directory to indicate a dirty state.
- Removed unnecessary blank lines and improved formatting in the `benchmark_matmul` and `benchmark_matmul_fp8` scripts for better readability.
- Streamlined the function definitions in the `flashattn` example script to enhance clarity and maintainability.

* [Refactor] Update AutoTuner configuration handling

- Modified the AutoTuner class to check if kernel parameters are set before processing tunable arguments, improving robustness in configuration handling.
- Enhanced the logic for skipping compilation when tunable parameters are already provided, ensuring efficient use of resources.
- Updated comments for clarity and maintainability.

* lint fix

* Update TVM subproject commit to indicate dirty state and modify MHA backward test cases

- Updated the subproject commit reference in the TVM directory to reflect a dirty state.
- Adjusted the `test_mha_bwd` function to use a new configuration for the MHA backward tests, changing the context size from 128 to 256.
- Uncommented the main testing function call for potential execution.

* [Enhancement] Add commit ID to versioning and improve logging initialization

* Updated `get_tilelang_version` to include an optional commit ID in the version string.
* Enhanced the `TileLangBuilPydCommand` to write the version with commit ID to the VERSION file during the build process.
* Introduced a new function `get_git_commit_id` in `version.py` to retrieve the current git commit hash.
* Refactored logger initialization in `autotuner/__init__.py` to ensure handlers are set up only once, improving performance and clarity.
* Minor fixes in `flatten_buffer.cc` and `kernel_cache.py` for better handling of versioning and logging.

* [Refactor] Enhance AutoTuner and JITKernel for improved performance and caching

* Refactored the AutoTuner class to include new methods for setting compilation and profiling arguments, enhancing configurability.
* Introduced caching mechanisms for tuning results, allowing for faster retrieval of previously computed configurations.
* Updated JITKernel to store tuning results, including latency and configuration details, improving the kernel's performance tracking.
* Added new methods for generating cache keys and saving/loading results to/from disk, streamlining the tuning process.
* Enhanced the overall structure and readability of the autotuning logic, ensuring better maintainability and clarity.
* Minor adjustments in related modules to support the new caching and profiling features.

* [Refactor] Clean up code formatting and improve readability in AutoTuner and related modules

* Consolidated import statements and removed unnecessary line breaks for better readability.
* Standardized function argument formatting across the AutoTuner and CompileArgs classes.
* Enhanced consistency in the use of whitespace and indentation throughout the codebase.
* Minor adjustments in the Profiler and JITKernel classes to improve clarity and maintainability.
* Ensured that all changes adhere to the project's coding style guidelines.

* [Refactor] Remove redundant type hints in AutoTuner modules

* Simplified import statements in `__init__.py` and `param.py` by removing unnecessary duplicate type hints for `Any`.
* Improved code readability and maintainability by streamlining type imports across the AutoTuner module.

* [Refactor] Update AutoTuner configuration for improved profiling and target detection

* Enhanced the AutoTuner configuration across multiple examples by adding `set_profile_args` to better manage profiling settings.
* Standardized the use of `target="auto"` in compile arguments to ensure automatic target detection.
* Removed redundant target specifications in certain instances to streamline the configuration process.
* Improved overall clarity and maintainability of the autotuning logic in various example scripts.

* [Refactor] Simplify code formatting and improve readability in example scripts

* Consolidated function argument formatting in `benchmark_mla_decode_amd_tilelang.py`, `example_elementwise_add.py`, and `performance.py` for better clarity.
* Removed unnecessary line breaks and standardized argument placement across multiple files.
* Enhanced overall code readability and maintainability in autotuning examples and performance scripts.

* [Refactor] Update JIT decorator usage across multiple files

* Removed redundant parameters from the JIT decorator in various benchmark and example scripts, simplifying the code.
* Standardized the import of the JIT decorator from `tilelang`, enhancing consistency across the codebase.
* Improved overall readability and maintainability by consolidating import statements and cleaning up function definitions.

* [Refactor] Standardize JIT decorator formatting across benchmark and example scripts

* Simplified the formatting of the JIT decorator in multiple files by removing unnecessary line breaks.
* Enhanced code readability and consistency in the usage of the JIT decorator across benchmark and example scripts.
* Improved overall maintainability by ensuring uniformity in function definitions and decorator usage.

* [Enhancement] Introduce CUDA driver module and refactor CUDA device handling

- Added a new `cuda_driver` module to encapsulate CUDA device properties and functionalities.
- Updated `CUDA` class in `cuda.py` to utilize the new driver for fetching device name and shared memory capabilities.
- Introduced `get_device_name` and `get_shared_memory_per_block` functions in the `cuda_driver` for improved device property management.
- This refactor enhances code organization and maintainability while improving the handling of CUDA device attributes.

* [Refactor] Clean up whitespace in CUDA-related files

- Removed unnecessary blank lines in `cuda.py`, `__init__.py`, and `cuda_driver.py` to improve code readability and maintainability.
- This change enhances the overall organization of the codebase without altering functionality.

* [Benchmark] Add FP8 Matrix Multiplication Benchmark Script

- Introduced a new benchmark script for FP8 matrix multiplication in `benchmark/matmul_fp8/benchmark_matmul.py`.
- The script includes functions for reference matrix multiplication, configuration generation for autotuning, and an autotuned kernel for performance measurement.
- Added command-line argument parsing for matrix dimensions and the option to enable BitBLAS roller for search space exploration.
- The benchmark computes and prints the best latency and performance metrics, enhancing the benchmarking capabilities for FP8 operations.

* lint fix

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Co-authored-by: LeiWang1999 <wyatuestc@gmail.com>