GitLab

* [Example] Update kernel compilation in examples to use @tilelang.jit

- Refactored multiple examples to eliminate the use of `tilelang.compile` for kernel creation, directly invoking the functions instead.
- Added `@tilelang.jit` decorators with appropriate output indices to enhance performance and maintainability.
- Improved code clarity by simplifying the kernel invocation process across various examples, ensuring consistency in how kernels are defined and executed.

* format

* Update example_tilelang_sparse_gqa_decode_varlen_indice.py

* Update example_dequant_gemm_fine_grained.py

* Update example_gemm_autotune.py

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Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>

* [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.

* [CI] Add elementwise and gemv examples to CI.

* fix lint

* test

* fix gemv lint

* fix lint

* [Enhancement] Add support for CUDA architecture 8.9 in GEMM template

- Introduced conditional inclusion of "gemm_sm89.h" for CUDA architectures 8.9 and above, enhancing compatibility with newer hardware.
- This change ensures that the GEMM template can leverage optimizations specific to the 8.9 architecture, improving performance for users with compatible GPUs.

* lintfix

* [Refactor] Clean up includes in gemm_sm89.h

- Removed duplicate inclusion of "common.h" and added "cuda_fp8.h" for improved clarity and organization.
- This change enhances the maintainability of the code by ensuring that header files are included only once and in a logical order.

* [Enhancement] Improve KernelCache with in-memory caching and detailed docstrings

- Added an in-memory cache to the KernelCache class to enhance performance by reducing disk access.
- Updated the __new__ method to initialize the memory cache and added logic to check the cache before loading from disk.
- Enhanced docstrings across multiple methods to provide clearer explanations of parameters and return values, improving code readability and maintainability.
- Implemented a clear_cache method to clear both in-memory and disk caches, ensuring efficient cache management.

* lint fix

* typofix

* [Refactor] Update matmul and flashattn function calls to return structured results

- Modified the matmul and flashattn function calls to return a single object containing latency, configuration, and reference latency, improving code clarity and reducing the number of returned variables.
- Updated all relevant instances in benchmark and example scripts to accommodate the new return structure, ensuring consistent usage across the codebase.

* lint fix

* [doc/example] init gemv doc and examples

* [example] add vectorized read

* [example] use local register instead of smem

* [example] add bench

* [doc] update doc

* [doc] refine doc

* [lint] format code

* [doc] add tips

* [doc/example] fix typo

* [example] use tmv_all_reduce

* [doc] update doc accordingly

* [doc] add benchmark table

* [lint] format code