GitLab

* [Enhancement] Introduce finalize_reducer operator and layout reducer support

- Added `FinalizeReducer` operator to handle reduction finalization in the TileLang framework, allowing for efficient reduction operations.
- Implemented layout inference for local.reducer buffers, enhancing the handling of layout mappings and reducing complexity in buffer management.
- Updated `setup.py` to include logging for build directory paths, improving build process visibility.
- Enhanced atomic operations with new functions for atomic max, min, load, and store, providing more robust atomicity control in memory operations.
- Refactored parallel loop handling to incorporate reducer information, ensuring proper management of reduction operations in parallel contexts.
- Cleaned up test cases by removing unnecessary cache disabling and optimizing test parameters for better performance.

* Refactor code formatting and improve readability in multiple files

- Cleaned up whitespace in `setup.py` to enhance logging clarity.
- Reformatted `AtomicMax` and `AtomicMin` functions in `common.h` for better alignment and readability.
- Adjusted `debug_print_var` function in `debug.h` to improve code structure and maintainability.
- Enhanced readability of the `atomic_add` function in `customize.py` by breaking long lines for better clarity.

* Remove debug print statements from `copy.cc` and `inject_tma_barrier.cc` to enhance code clarity and maintainability.

* [Enhancement] Disable reuse of small arrays in shared memory allocation

- Added logic to prevent the reuse of small arrays (<= 32 bits) in `merge_shared_memory_allocations.cc`, ensuring they are lowered to registers in LLVM for improved performance and memory management.

* Refactor `setup.py` to remove duplicate logging statements and enhance clarity. Update `finalize_reducer` function documentation in `reduce.py` to include detailed parameter and return descriptions, improving code readability and maintainability.

* Refactor `finalize_reducer` and `reduce` functions to remove redundant target checks. Simplified conditionals by retaining only the `TargetIsHopper` check, enhancing code clarity and maintainability.

* bug fix

* Add thread checks workaround for replicated cases

* Remove the is_one check

* fix lint error

* lint fix

* Update autotune tests to use smaller matrix sizes for improved performance and reliability

* [Refactor] Update FinalizeReducer to FinalizeReducerOp and adjust related methods

- Refactored FinalizeReducer class to FinalizeReducerOp, updating constructor and method signatures for consistency with the new TileOperator structure.
- Enhanced layout inference and cloning methods in FinalizeReducerOpNode.
- Updated test_example_flash_attention.py to call test_example_gqa_bwd instead of tilelang.testing.main.
- Adjusted header inclusions for improved organization and clarity across multiple files.

* [Refactor] Update atomic operations in common.h and modify test_example_flash_attention.py

- Enhanced atomic operations (Add, Min, Max) in common.h to handle half and bfloat16 types more efficiently.
- Updated test_example_flash_attention.py to call test_example_gqa_bwd instead of tilelang.testing.main, improving test organization.

* [Refactor] Simplify CopyNode::LowerBulkCopy logic and update test execution

- Removed redundant checks for contiguous memory access in CopyNode::LowerBulkCopy, streamlining the logic for TMA copy operations.
- Updated test_tilelang_kernel_gemm.py to comment out the main testing function and call a specific test for i8i8i32 tensor operations instead, improving test focus.

---------
Co-authored-by: Huanqi Cao <caohuanqi@deepseek.com>
Co-authored-by: Freebase6912 <amid-gauze-racing@duck.com>

* Introduce Barrier

* Enhance CUDA kernel with new barrier management and post-processing support

- Added a new CUDA kernel implementation in `example_mla_decode.py` for improved performance with shared memory barriers.
- Refactored barrier handling in `codegen_cuda.cc` and `codegen_hip.cc` to utilize a more flexible mbarrier structure.
- Updated intrinsic definitions from `ptx_stmatirx` to `ptx_stmatrix` across multiple files for consistency.
- Introduced additional print statements for debugging in the lowering phase of the TileLang engine.
- Enhanced the overall structure and readability of the codebase.

* Remove unused barrier handling code in CUDA and HIP code generators to streamline the implementation. This change enhances code clarity and reduces complexity in the barrier management logic.

* Enhance barrier management in TileLang

- Introduced a new intrinsic `allocate_barrier` for dynamic barrier allocation in the TileLang framework.
- Updated CUDA code generation to support the new barrier structure, allowing for improved synchronization in shared memory.
- Refactored existing barrier handling logic to accommodate the new intrinsic and streamline code.
- Added print statements for debugging purposes in various examples and the lowering phase of the TileLang engine.
- Removed deprecated memory scope handling code to enhance clarity and maintainability.

* lint fix

* lint fix

* Remove `allocate_barrier` intrinsic and related code from TileLang to streamline barrier management. This includes updates to CUDA code generation and the removal of associated Python wrappers, enhancing code clarity and maintainability.

* Refactor logging in JITKernel to improve kernel compilation tracking

- Removed unused import of `torch.backends` in the example file.
- Introduced logging for kernel compilation in `JITKernel`, replacing print statements with structured logging for better traceability and debugging.
- Added an assertion to ensure the presence of the `global_symbol` attribute in the kernel function.

* Refactor dequantization tests and update barrier function

- Removed the test for `example_dequant_gemm_bf16_fp4_hopper_serial` to streamline the testing suite.
- Updated the `mbarrier_cp_async_arrive` function to support both pointer and non-pointer types, enhancing flexibility in barrier management.

* Update CI configuration to increase pytest parallelism from 4 to 8 threads for improved test execution speed.

* Fix typos in rasterization parameters and update import path for cached module

- Corrected the spelling of `enable_rasteration` to `enable_rasterization` in the matmul function and its usage.
- Updated the import statement for the `cached` module to reflect the new path in the cache submodule.
- Added `StridedTensor` import in the language module for enhanced tensor functionality.

* Update ci.yml

* [Refactor] Simplify and modularize autotuner implementation

- Removed unused imports and extensive code sections from the autotuner module to enhance readability and maintainability.
- Modularized the code by introducing new imports for autotuning and capturing functionalities, streamlining the overall structure.
- Improved logging setup and removed redundant timeout handling functions, focusing on core autotuning logic.
- Updated the AutoTuner class to better utilize the new modular structure, ensuring efficient performance during auto-tuning processes.

* [Refactor] Clean up and enhance capture and tuner modules

- Improved code readability by removing unnecessary blank lines and organizing imports in `capture.py` and `tuner.py`.
- Enhanced logging in the `AutoTuner` class to provide clearer warnings regarding the usage of `supply_prog` in the context of auto-tuning.
- Streamlined the `CaptureStack` class for better thread-local context management.

* lint fix

* [Refactor] Simplify configuration and autotuning logic in blocksparse GEMM example

- Updated `get_configs` function to reduce the number of configurations, enhancing performance and clarity.
- Removed the `get_best_config` function, integrating its logic directly into the `blocksparse_matmul` function with the `@autotune` decorator for streamlined autotuning.
- Adjusted the main function to directly utilize the autotuned kernel, simplifying the overall structure and improving readability.
- Deleted obsolete test file for autotuning decorator, cleaning up the codebase.

* [Refactor] Improve code formatting and readability in autotune test file

- Reformatted the `matmul` function and `get_configs` function for better readability by adjusting line breaks and indentation.
- Fixed a typo in the `enable_rasteration` parameter name to ensure consistency.
- Cleaned up unnecessary blank lines to enhance overall code clarity.

* Update example_blocksparse_gemm.py

* Update capture.py

* [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] Update GEMM examples and autotuner for improved performance

- Modified `example_gemm_intrinsics.py` to enhance matrix multiplication configurations, increasing warp sizes and adjusting data types for better performance.
- Updated the kernel compilation process to utilize the new `tilelang.compile` method and improved latency measurement with the profiler.
- Refactored `example_gemm.py` to include a new autotuning configuration and ensure consistency in latency checks against reference results.
- Adjusted tensor supply generation in `tilelang/utils/tensor.py` to use `torch.randn` for better randomness in tensor initialization.
- Enhanced the `JITContext` in `tilelang/autotuner/__init__.py` to replace the profiler with a kernel instance for performance measurement, improving the overall structure of the autotuner.

* bug fix

* fix

* [Enhancement] Update convolution tests and profiling assertions

- Added a random seed setting for reproducibility in convolution tests.
- Removed several redundant convolution test cases to streamline the testing process.
- Updated the assertion in the matrix multiplication profiling to include a maximum mismatched ratio for improved accuracy in results.
- Enabled the main testing function for better test execution.

* lint fix

* [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 autotune to example_gemm.py

* format init.py

* add autotune to example_gemm.py

* add autotune to example_gemm.py

* add autotune to example_gemm.py

* add autotune to example_gemm.py

* remove llvm build

* [Refactor] Update kernel compilation and profiling in examples

- Replaced `tilelang.lower` with `tilelang.compile` in multiple example scripts to streamline kernel compilation.
- Updated profiling calls to utilize the new `get_profiler` method, enhancing performance measurement consistency.
- Adjusted assertions and benchmarking methods to align with the new profiling structure across various examples, ensuring correctness and clarity in performance evaluations.

* lint fix

* License Update

* [Refactor] Improve code formatting and documentation in CUDA header and HIP runtime files

- Adjusted formatting in `cuda.h` for better readability, including alignment of comments and struct fields.
- Cleaned up whitespace and improved comment clarity in `rt_mod_hip.cc` to enhance code maintainability.

* [Refactor] Enhance formatting and clarity in CUDA header and HIP runtime files

- Improved comment alignment and readability in `cuda.h`.
- Cleaned up whitespace and formatting in `rt_mod_hip.cc` to enhance maintainability.

* lint fix

* lint fix

* lint fix

* lint fix

* fix

* License update

* [Enhancement] Update JITKernel to use artifact for kernel source

- Assigned the generated artifact to `self.artifact` for better management.
- Updated kernel source references to use `artifact.kernel_source` for consistency in execution backend handling.

* lint fix

* Add @tilelang.testing.requires_llvm decorator to vectorization tests

* Enhance setup.py and env.py for library management

- Added functionality to remove original files after copying in CMakeBuild.
- Updated TVM_LIBRARY_PATH in env.py to include the PyPI build library path for better integration.

* Refactor TVM_LIBRARY_PATH assignment for improved readability in env.py

* Refactor CMakeBuild file handling in setup.py

- Added a check to ensure the target library directory exists before copying .so files.
- Improved the logic for creating the target directory and copying files to enhance robustness.

* bugfix

* Rename BuildTLDebug to BuildTileLangCUDAWithoutCompile and update registration. Add @tilelang.testing.requires_llvm decorator to multiple tests for LLVM requirement.

* lint fix

* Enhance TileLang code generation by adding support for device code generation without compilation. Updated `host_codegen` and `device_codegen` functions to include new transformations and registration for `tilelang_hip_without_compile`. Refactored JIT kernel adapters to accommodate host and device modules, improving overall integration and flexibility.

* lint fix

* Add support for C target in device code generation

- Updated `device_codegen_without_compile` to include handling for the C target by registering the `tilelang_cpp` function.

* [Enhancement] Implement auto-clear cache feature based on environment variable

* Added TILELANG_CLEAR_CACHE environment variable to control cache clearing.
* Updated CI workflow to set TILELANG_CLEAR_CACHE during testing.
* Modified cache initialization to clear cache if TILELANG_CLEAR_CACHE is set to true.

* [Refactor] Update kernel invocation and import paths in tests and cache

* Changed kernel invocation in `test_tilelang_kernel_dequantize_gemm.py` to return the result.
* Updated import statements in `test_tilelang_kernel_int4_gemm_mma.py` to use `bitblas` instead of `tilelang`.
* Refactored paths for artifact and parameters in `kernel_cache.py` for better maintainability.

* [Refactor] Clean up whitespace and improve code formatting in kernel_cache.py

* Removed unnecessary blank lines and adjusted spacing for better readability in the KernelCache class.
* Enhanced overall code formatting to align with project standards.

* [Enhancement] Add bfloat16 test case and improve kernel caching logic

* Introduced a new test case for bfloat16 matrix multiplication in `test_tilelang_kernel_gemm_mma_intrinsic.py`.
* Updated `KernelCache` to handle multiple kernel source files and improve error handling during saving and loading.
* Refactored `JITKernel` to support instantiation from a database, enhancing flexibility in kernel management.
* Adjusted `CtypesKernelAdapter` and `CythonKernelAdapter` to utilize the new kernel loading mechanism from the database.
* Improved code formatting and readability across several files.

* lint fix

* Update bfloat16 matrix multiplication test case to use larger dimensions for improved coverage

* [Dev] Adjust computation logic to avoid precision loss when casting acc_s from float to float16

- Remove redundant `acc_s_0` fragment in flash attention kernel
- Simplify memory copy and reduction operations
- Reorder memory copy and scaling steps for improved performance
- Add Hopper-specific synchronization method in CUDA reduce template
- Update reduce operation to use architecture-specific synchronization

* [Dev] Add DeepSeek MLA Decoding (Paged+Varlen) kernel and Performance Benchmark Script

- Implement comprehensive MLA (Multi-Head Latent Attention) decoding benchmark script
- Add support for multiple implementations: Torch, TileLang, FlashMLA, FlashInfer, and Triton
- Create flexible configuration for benchmarking different batch sizes, sequence lengths, and head configurations
- Implement performance comparison and CSV output for detailed performance analysis
- Add command-line argument support for targeted benchmarking and comparison

* [Dev] Refactor MLA Paged Decoding Kernel with Improved Block Handling and Precision

- Replace `d` parameter with `dv` to clarify value dimension in MLA decoding
- Enhance block distribution logic for split KV processing
- Improve handling of remaining blocks in split KV computation
- Add initialization of `lse_max_local` to prevent potential precision issues
- Optimize block start and range calculations for more accurate sequence processing

* lint

* implement jit test case

* [Dev] implement auto tune test case for matrix multiplication

* Implement test for legalize memory access and vectorized loop

* lint fix