GitLab

* [Refactor] Improve flash attention example and layout comparison logic

- Removed unnecessary annotation for `lse_local_split` in the flash attention example to streamline the code.
- Updated the handling of `lse_local_split` to utilize parallel processing for better performance.
- Refactored kernel compilation and profiling logic to enhance clarity and maintainability in the flash attention example.
- Added a condition in `FragmentNode::IsEqual` to handle broadcast cases, improving the robustness of layout comparisons.

* lint fix

* [Enhancement] Add support for shared memory scope in Fill operation

- Introduced handling for `shared.dyn` and `shared` memory scopes in the Fill operation.
- Implemented parallel operation and layout inference for improved performance in shared memory scenarios.
- Updated thread loop partitioning and vectorization logic to accommodate new memory scope handling.

* [Refactor] Remove deprecated decorator and enhance Cython kernel handling

- Removed the deprecated decorator from the main module and added a new implementation in the utils module for better organization.
- Introduced a pointer map in the Cython kernel adapter to manage pointer arguments, improving runtime shape resolution.
- Updated the Cython kernel wrapper to utilize the new pointer map for handling kernel arguments.
- Enhanced error checking in the tensor utility functions to ensure static shapes are enforced.
- Added a new proxy module for buffer and tensor handling, streamlining the interface for TIR programs.

* [Feature] Add matrix multiplication test and kernel implementation

- Introduced a new test file `test_tilelang_language_ptr.py` that implements a matrix multiplication function using TileLang's primitives.
- The `matmul_test` function defines a kernel for performing tile-level GEMM operations with customizable block sizes and data types.
- Added a `run_matmul` function to compile and execute the kernel, along with a test function to validate the implementation.
- Updated the `proxy.py` file to enhance type handling for buffer and tensor proxies, ensuring compatibility with TIR programs.
- Minor formatting improvements in `deprecated.py` for better readability.

* lint fix

* [Refactor] Update tensor creation in matrix multiplication test

- Replaced `T.Tensor.from_ptr` with `T.make_tensor` in `matmul_test` for improved clarity and consistency.
- Updated imports in `__init__.py` to include `make_tensor`.
- Added `make_tensor` function in `proxy.py` to streamline tensor creation from pointers.

* [Refactor] Update tensor definitions across multiple files

- Replaced instances of `T.Tensor` with updated tensor definitions in various benchmark and example files to enhance consistency and clarity.
- Adjusted tensor shapes and types in functions related to matrix multiplication, attention mechanisms, and other operations.
- Improved documentation in README and example files to reflect changes in tensor usage.

* lint fix

* [Refactor] Update tensor types in attention and matrix multiplication examples

- Replaced instances of `T.Tensor` with `T.SharedTensor` and `T.FragmentTensor` in various attention and matrix multiplication functions to improve consistency and clarity.
- Adjusted tensor definitions in benchmark and example files to align with the new tensor types.
- Enhanced the overall structure and readability of the code by standardizing tensor usage across multiple files.

* lint fix

* [Refactor] Update tensor types in GEMM example and test files

- Replaced instances of `T.Tensor` with `T.LocalTensor` and `T.Buffer` in the GEMM example and related test functions to improve consistency and clarity.
- Enhanced the overall structure of the code by standardizing tensor usage across multiple files, aligning with recent updates in tensor definitions.

* [Refactor] Update tensor usage in customize.py

- Replaced instances of `T.Tensor` with `T.Buffer` in the `reshape` and `view` functions to enhance consistency with recent tensor definitions.
- Improved code clarity by standardizing buffer usage across the file.

* [Refactor] Update tensor types in test_tilelang_transform_annotate_device_regions.py

- Replaced instances of `T.Tensor` with `T.Buffer` in the `before` and `expected` methods of the `TestAnnotateThreadExtent` and `TestAnnotateDeviceScope` classes to enhance consistency with recent tensor definitions.
- Improved code clarity by standardizing buffer usage across the test file.

* [Refactor] Update tensor types to SharedBuffer and FragmentBuffer

- Replaced instances of `T.SharedTensor` and `T.FragmentTensor` with `T.SharedBuffer` and `T.FragmentBuffer` across multiple benchmark, example, and test files to enhance consistency with recent tensor definitions.
- Improved code clarity and structure by standardizing buffer usage in attention and matrix multiplication functions.

* [Refactor] Introduce Tensor alias for Buffer in proxy.py

- Added a new alias `Tensor` for `Buffer` in `proxy.py` to facilitate JIT compilation, ensuring that inputs and outputs are mapped with `torch.Tensor`.
- This change enhances clarity and consistency in tensor usage across the codebase.

* Add GPU kernel for 2D continuous cumulative sum in TileLang example

- Introduced a new example script `example_tilelang_cumsum.py` that generates a GPU kernel for 2D continuous cumulative sum.
- Implemented functions to handle kernel configuration, memory allocation, and inclusive scan operations.
- Added a main execution block to demonstrate the kernel's functionality using PyTorch for tensor operations.
- Enhanced the example with error handling for power-of-two configurations and validation of results against PyTorch's built-in cumulative sum function.

* Refactor TileLang examples and enhance kernel compilation

- Updated `example_tilelang_cumsum.py` to improve GPU kernel generation for 2D continuous cumulative sum, including better parameter handling and error checking.
- Refactored `example_mha_bwd.py` to enhance kernel compilation readability and maintainability.
- Modified `kernel_cache.py` to prevent saving kernels to disk when using the DLPack backend, ensuring proper cache management.
- Added `get_block_bindings` function to `kernel.py` for improved access to block bindings in kernel launch frames.
- Cleaned up import statements in `__init__.py` for better organization and clarity.

* Enhance GPU kernel for 2D continuous cumulative sum in TileLang example

- Added additional spacing for improved readability in `example_tilelang_cumsum.py`.
- Refined kernel structure to enhance clarity and maintainability during GPU kernel generation for cumulative sum operations.

* Refactor CUDA post-processing callback registration in TileLang

- Introduced a new decorator `register_cuda_postproc_callback` for registering CUDA post-processing functions, enhancing usability and flexibility.
- Updated existing callback implementations to utilize the new decorator, improving code clarity and maintainability.
- Added debug prints to the CUDA code generation process for better traceability during development.
- Refactored the `OptimizeForTarget` function to streamline conditional statement handling in the pipeline transformation.
- Cleaned up the `inject_pipeline.cc` file by removing redundant code related to statement grouping and condition handling.

* lint fix

* Enhance BlockSparse GEMM Example with Autotuning and Configurable Parameters

- Added argument parsing to allow dynamic configuration of matrix dimensions and sparsity ratio.
- Implemented a function to generate various kernel configurations for autotuning.
- Refactored the main execution block to support both autotuned and default configurations.
- Improved the block mask generation to accommodate specified sparsity levels.
- Updated the kernel compilation process to utilize the new configurations and ensure accurate results verification.

* [CI] Clean up target repository before publishing documentation.

* [Doc] Convert docs from rst format to Markdown format.