GitLab

* Refactor GEMM and GEMM-SP operations to enhance clarity and maintainability

- Removed deprecated prime factorization functions from `gemm.cc` and `gemm_sp.cc`.
- Introduced a new `GemmWarpPolicy` class to manage warp policy attributes and methods, improving encapsulation.
- Updated reflection methods to include the new policy structure, ensuring proper registration and introspection capabilities.
- Enhanced `GetArchInt` function in `utils.cc` for better readability and type safety.
- Added new `gemm_v2` function in `gemm.py` for improved GEMM operation with additional parameters and checks.

* Refactor GEMM and frontend legalize operations for improved clarity and functionality

- Updated `gemm_py.h` to include the correct header for GEMM operations.
- Renamed `FrontendLegalizer` class to `LetInliner` and updated related methods to reflect this change, enhancing code clarity.
- Modified the pass function from `FrontendLegalize` to `LetInline` for better alignment with its purpose.
- Updated test cases to utilize the new `gemm_v2` function and adjusted the testing framework for improved output and clarity.
- Removed obsolete test file `test_tilelang_transform_frontend_legalize.py` to streamline the test suite.
- Enhanced the `LowerAndLegalize` function to utilize the new `LetInline` pass, improving the overall transformation process.

* Enhance CUDA code generation and testing for GEMM operations

- Added indentation printing in `codegen_cuda.cc` for improved assembly code formatting.
- Updated `test_tilelang_tilelibrary_gemm.py` to include additional GEMM test cases and shared memory allocation with specified scope.
- Introduced new `matmul_sr` and `run_gemm_sr` functions for GEMM operations with shared and fragment memory layouts.
- Refactored layout inference in `mma_macro_generator.py` to improve clarity and correctness in shared memory handling.
- Enhanced `gemm/__init__.py` to support new GEMM operation combinations and layout inference logic.

These changes improve the clarity, functionality, and testing coverage of GEMM operations in the TileLang framework.

* Refactor GEMM layout and testing for improved clarity and functionality

- Updated `gemm_layouts.cc` to enhance the layout generation logic for transposed and non-transposed GEMM operations.
- Renamed and modified functions in `test_tilelang_tilelibrary_gemm.py` to reflect changes in GEMM function signatures and improve test coverage.
- Introduced new GEMM operation combinations in `gemm/__init__.py` to support additional layouts and configurations.
- Enhanced layout inference in `mma_layout.py` and `mma_macro_generator.py` for better handling of shared memory layouts.

These changes improve the clarity, functionality, and testing coverage of GEMM operations in the TileLang framework.

* Refactor GEMM layout and Python integration for improved functionality

- Updated `gemm_layouts.cc` to correct the order of layout replication and repetition for transposed and non-transposed GEMM operations.
- Enhanced `gemm_py.cc` to handle block realization more robustly, ensuring correct assignment of global symbols and block attributes.
- Refactored `inject_pipeline.cc` to streamline buffer read/write region handling, improving clarity and maintainability.
- Cleaned up test cases in `test_tilelang_tilelibrary_gemm.py` by removing unnecessary print statements and adjusting function calls for better test execution flow.

These changes enhance the clarity, functionality, and robustness of GEMM operations and their testing in the TileLang framework.

* Refactor GEMM layout and testing for improved clarity and functionality

- Updated `gemm_layouts.cc` to enhance layout generation logic for transposed and non-transposed GEMM operations.
- Improved block realization handling in `gemm_py.cc` for better assignment of global symbols.
- Streamlined buffer read/write region handling in `inject_pipeline.cc` for clarity.
- Enhanced test cases in `test_tilelang_tilelibrary_gemm.py` by adjusting function calls and adding new GEMM operation combinations.

These changes improve the clarity, functionality, and robustness of GEMM operations and their testing in the TileLang framework.

* tfloat32 support.

* lint fix

* lint fix

* Refactor shared memory allocation in GEMM tests

- Removed unnecessary scope specification in shared memory allocation for matrices A and B in `test_tilelang_tilelibrary_gemm.py`.
- This change simplifies the allocation process and aligns with the updated GEMM function signatures.

* fix CI bugs in hopper

* lint fix

* Update bulk_copy.cc

* Refactor bulk copy logic in LowerBulkCopy function

- Removed unnecessary blank lines for improved code readability.
- Enhanced stride validation by checking for null pointers in global stride calculations, ensuring robustness against symbolic strides.
- Updated pass configuration handling in dynamic tile language tests to streamline dynamic alignment and TMA lower pass settings.

* test fix

* ci fix

* Update flash-attention dependencies and clean up example code

- Downgraded `flash-attn` dependency version in `requirements-test.txt` to `<=2.2.0`.
- Removed unused imports and commented-out code in various example files to enhance readability and maintainability.
- Updated the `flashattn` function signature to include default parameters for `block_M`, `block_N`, `num_stages`, and `threads`.
- Cleaned up the `example_mha_fwd_varlen.py` and `example_mha_bwd_wgmma_pipelined.py` files by removing unnecessary comments and improving code clarity.
- Deleted the `example_mha_inference.py` file as it is no longer needed.

* Update CI workflow to remove `--user` flag from pip install commands

- Removed the `--user` flag from the pip install commands in both the development and testing sections of the CI workflow to ensure proper installation of dependencies in the virtual environment.

* Update CI workflow to include `--no-user` flag in pip install commands

- Added the `--no-user` flag to the pip install commands in both the development and testing sections of the CI workflow to ensure dependencies are installed correctly within the virtual environment.

* Update CI workflow to include `--no-user` flag in pip install command for wheel mode

- Added the `--no-user` flag to the pip install command in the wheel mode section of the CI workflow to ensure dependencies are installed correctly within the virtual environment.

* test fix

* avoid conflict with system environments

* test fix

* add commnets

---------
Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

* [experimental] add a draft gemm_sp

* [3rdparty] bump cutlass to v3.9.3

* [lint] run format.sh

* [chore] rebase

* [chore] use abs path

* [gemm_sp] add metadata layout

* [ci] add more example

* [lint] run format.sh

* [chore] polish

* [chore] move gemm_sp to experimental

* [chore] polish

* [lint] run format.sh

* [Enhancement] Improve bulk copy handling and update GEMM sparse tensor test

* Added a warning log for unsupported non-swizzled global layouts in the bulk copy operation, ensuring fallback to normal copy.
* Refactored the GEMM sparse tensor test by removing unnecessary imports and simplifying the kernel compilation process.
* Updated the test to directly call the `run_gemm_sp` function, enhancing clarity and functionality.

* Implement Test

* [Enhancement] Update GEMM SP and SM89 templates for improved functionality

* Refactored GEMM SP computation to enhance warp partitioning logic, ensuring compatibility with Hopper architecture.
* Updated layout inference to support new WGMMA conditions and improved error messaging for unsupported targets.
* Modified SM89 templates to utilize new MMA atom structures, enhancing performance and compatibility with fp8 types.
* Added conditional inclusion for GEMM SP header based on CUDA architecture version.

* lint fix

* [gemm_sp] support more layout and data types

* Enhancement: sync T.gemm_sp's layout inference with T.gemm

* Enhancement: support more block_k in compress util

* [Enhancement] enable block_k=64

* [Lint] run format.sh

* [Enhancement] compressor support more dtype

* Enhancement: enable block_K=32

* [Lint] format.sh

* [Fixbug] fix shape

* Refactor: sync gemm

* [Enhancement] enable transpose

* [Enhancement] enable fp8_e4m3

* [Enhancement] enable int8

* [Lint] run format.sh

* [Benchmark] add gemm_sp benchmark

* [Example] fix 256 threads hang

* [CI] fix ci

* [Chore] resolve gemini feedback

* [Benchmark] increase search space

* [Lint] format

* [CI] skip sparse tensor core related tests as only sm90 is supported

* [CI] pass local run

* Update gemm_sm89.h

* lint fix

* lint fix

* [Enhancement] Add support for sparse GEMM and initialize CUDA architecture flags

- Introduced a new boolean flag `enable_sparse_gemm_` to control the inclusion of sparse GEMM functionality in CUDA code generation.
- Updated the `Finish` method to conditionally include the sparse GEMM header based on the new flag.
- Implemented logic in `VisitStmt_` to enable sparse GEMM when the corresponding external call is detected.
- Added a function to initialize the `TORCH_CUDA_ARCH_LIST` environment variable based on the target compute version, enhancing compatibility with PyTorch.
- Refactored the initialization function into the appropriate module and ensured it is called in the sparse utilities module.

* Update test_compress_utils.py

---------
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>

* [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.

* [Refactor] Update KernelParam integration across modules

- Replaced instances of TensorType with KernelParam in various modules to standardize parameter handling.
- Updated JITKernel, BaseKernelAdapter, and CythonKernelAdapter to utilize KernelParam for improved type consistency.
- Enhanced Profiler class to include KernelParam in its parameters, ensuring better integration with the new parameter structure.
- Adjusted tensor handling in utility functions to accommodate the new KernelParam type, improving overall code clarity and maintainability.
- Updated copyright headers to reflect the correct organization.

* [Refactor] Clean up whitespace in kernel, profiler, and tensor modules

- Added blank lines for improved readability in kernel.py, __init__.py, and tensor.py.
- Enhanced code clarity by ensuring consistent formatting across these modules.

* [Enhancement] Add detailed docstrings to KernelParam and Profiler classes

- Enhanced KernelParam class with comprehensive docstrings for better understanding of its purpose and methods.
- Updated Profiler class to include detailed docstrings for its attributes and methods, improving code documentation and usability.
- Removed unused do_bench function to streamline the profiler module and improve clarity.

* [Refactor] Update type hints in do_bench function and clean up whitespace in profiler module

- Changed type hints for grad_to_none and quantiles parameters in do_bench function to use Optional for better clarity.
- Added a blank line in __init__.py for improved readability and consistency in the profiler module.

* [Refactor] Update type hint in do_bench function for consistency

- Changed the return type hint in the do_bench function from a union type to a more explicit List type for better clarity and consistency in type annotations.

* [Refactor] Update return type hint in do_bench function for clarity

- Changed the return type hint in the do_bench function from a union type to Union[float, List[float]] for improved clarity and consistency in type annotations.

* [Enhancement] Add func property to Profiler class for adapter access

- Introduced a new property `func` in the Profiler class to provide access to the adapter, ensuring that the adapter is set before retrieval. This enhancement improves the usability of the Profiler class by allowing easier access to the adapter functionality.

* [Refactor] Update kernel compilation and profiling in tests

- Replaced instances of `TL.lower` and `TL.Profiler` with `tilelang.compile` and the new profiler interface across multiple test files.
- Enhanced the kernel compilation process to utilize the updated API, improving consistency and maintainability in the testing framework.
- Updated assertions to use the new profiler methods for better clarity and functionality in performance testing.

* [Refactor] Simplify kernel invocation and remove unused parameters in tests

- Updated the kernel invocation in `test_tilelang_dynamic_symbolic.py` to directly assign the result to `C`, improving clarity.
- Removed the `execution_backend` parameter from `tilelang.compile` calls in `test_tilelang_jit_callback.py` and `test_tilelang_jit_gemm.py` for consistency with the updated API.
- Commented out the call to `tilelang.testing.main()` in `test_tilelang_jit_callback.py` and replaced it with a direct call to `test_gemm_jit_kernel()` to streamline test execution.
- Adjusted the dtype mapping in `TorchDLPackKernelAdapter` to use the parameter's dtype directly, enhancing code simplicity.

* [Refactor] Remove unused imports in test files for cleaner code

- Eliminated unnecessary imports of `tilelang` as `TL` in various test files to enhance code clarity and maintainability.
- Updated multiple test files to streamline the codebase and reduce potential confusion from unused references.

* [Refactor] Simplify kernel invocation in tilelang kernel test

- Updated the kernel invocation in `test_tilelang_kernel_bf16_gemm_mma.py` to directly assign the result to `C`, enhancing code clarity and consistency with recent changes in the API.

* [Refactor] Simplify kernel invocation in tilelang kernel tests

- Updated kernel invocations in multiple test files to directly assign the result to `C`, improving code clarity and consistency with the updated API.
- Removed unnecessary initialization of `C` as a zero tensor, streamlining the code further.

* [Refactor] Update kernel invocation in tilelang transform tests

- Replaced the use of `TL.Profiler` with `tilelang.compile` in `test_tilelang_transform_simplify.py`, enhancing code clarity and consistency with the updated API.
- Streamlined the kernel invocation process by directly assigning the result to `C`, improving readability and maintainability of the test code.

* [Doc] Update documentation structure and content: add overview section, revise project name, and change theme to Furo

* [Feature] Add device-side debug printing functions and integrate into kernel interface

* lint fix

* remove debug print

* implement test for debug

* lint fix

* add some comments

* Enhance fragment design and assert fragment print

* enhance debug print

* add test for msg

* lint fix

* format

* add flash decoding exmaples

* remove comment

* test simplified