GitLab

* [Refactor] Update barrier functions and remove argparse in example_warp_specialize_flashmla.py

* Refactored barrier functions to use new signatures for improved clarity and consistency.
* Replaced `mbarrier_arrive` and `mbarrier_wait_parity` with `barrier_arrive` and `barrier_wait` respectively.
* Removed argparse dependency and replaced it with hardcoded parameters for batch size and dimensions in the main function, simplifying the example script.

* [Refactor] Update warp_specialized_rewriter with license change and code cleanup

* Replaced Apache License header with MIT License in `warp_specialized_rewriter.cc`.
* Removed the `ThreadTagChecker` class to streamline the code, as it was no longer needed.
* Added `#include` for `common/collector.h` to support new functionality.
* Updated file documentation to reflect the correct filename and purpose.
* Improved overall code readability by removing unnecessary comments and sections.

* [Feature] Add thread synchronization functions in builtin.py and refine buffer region checks in copy.py

* Introduced `sync_threads` and `sync_thread_partial` functions in `builtin.py` for improved thread synchronization capabilities.
* Enhanced documentation for new synchronization functions to clarify usage and parameters.
* Updated buffer region validation in `copy.py` to ensure type checking for integer values, improving error handling for region extents.

* lint fix

* [Feature] Introduce TMA barrier injection and related utilities

* Added `inject_tma_barrier.cc` to implement TMA barrier rewriting for CUDA GPU (sm90+).
* Created `common/attr.h` and `common/collector.h` for attribute checks and information collection from the IR.
* Updated `ir.cc` to use a constant for the main block name instead of a hardcoded string.
* Cleaned up `warp_specialized_rewriter.cc` by removing unnecessary whitespace.
* Enhanced thread tag validation with `ThreadTagChecker` to ensure only `threadIdx.x` is used in TMA barrier contexts.

* lint fix

* [Refactor] Update KernelLaunch to clarify CPU and GPU kernel launch logic

* Added comments to distinguish between CPU and GPU kernel launch sections for better code readability.
* Changed the creation of empty blocks to use a consistent "root" identifier, enhancing clarity in frame management.

* [Refactor] Rename operations for consistency in lower_hopper_intrin and related files

* Updated function names from CamelCase to snake_case for better consistency across the codebase.
* Refactored calls to `CreateTMADescriptorOp`, `CreateListofMBarrierOp`, and similar functions to their new names: `create_tma_descriptor`, `create_list_of_mbarrier`, etc.
* Adjusted corresponding test cases to reflect these changes, ensuring compatibility with the new naming conventions.

* [Refactor] Rename operations to snake_case for consistency

* Updated function names from CamelCase to snake_case across various files, including `CreateTMADescriptorOp` to `create_tma_descriptor`, `GetMBarrierOp` to `get_mbarrier`, and others.
* Adjusted corresponding calls and definitions in the codebase to reflect these naming changes, ensuring uniformity and improved readability.
* Enhanced layout inference and loop partitioning logic to accommodate the new naming conventions.

* [Feature] Introduce Warp Specialization and Eliminate Storage Sync for MBarrier

* Added a new example `gemm_ws.py` demonstrating matrix multiplication with warp specialization using TileLang.
* Implemented `WarpSpecializeFrame` and `WarpSpecialize` functionality to manage warp group indices in TIR frames.
* Introduced `EliminateStorageSyncForMBarrier` transformation to optimize storage synchronization in mbarrier regions.
* Enhanced the TileLang API with new methods for retrieving block and thread extents.
* Updated the `LowerAndLegalize` and `OptimizeForTarget` functions to incorporate the new transformation.
* Improved layout inference and kernel launch logic for better performance and clarity.

* [Refactor] Clean up code formatting and improve readability

* Added blank lines for better separation of code blocks in `gemm_ws.py`, `phase.py`, `kernel.py`, and `warpgroup.py`.
* Reformatted the `tilelang.compile` call in `gemm_ws.py` for improved clarity.
* Updated comments in `warpgroup.py` to clarify the availability of the `WarpSpecialize` function for NVIDIA GPUs.
* Ensured consistent spacing and formatting across multiple files to enhance overall code readability.

* lint fix

* [Refactor] Update mbarrier functions for improved clarity and consistency

* Refactored `mbarrier_wait_parity` and `mbarrier_arrive` functions in `builtin.py` to accept explicit parameters for better readability.
* Updated calls in `gemm_ws.py` to use the new function signatures, enhancing code clarity.
* Adjusted `warpgroup.py` to remove unused thread extent variable, streamlining the code.
* Added detailed docstrings to clarify usage examples for memory barrier functions.

* Added blank lines in `mbarrier_wait_parity` and `mbarrier_arrive` functions in `builtin.py` for improved code readability and separation of logical sections.

* [Feature] Add examples for warp specialization and TMA barrier integration

* Introduced three new example scripts: `example_warp_specialize_gemm.py`, `example_warp_specialize_gemm_barrier4.py`, and `example_warp_specialize_mla.py` demonstrating matrix multiplication with warp specialization and TMA barriers.
* Implemented kernel functions with shared memory allocation and memory barrier synchronization for improved performance.
* Enhanced the TileLang API with new methods for compiling and testing kernels in Python using PyTorch.
* Updated the `phase.py` to include TMA barrier injection in the optimization process.
* Improved documentation and comments for better clarity on usage and functionality.

* [Feature] Add example for warp specialization in GEMM with TMA barriers

* Introduced a new example script `example_warp_specialize_gemm_stage2.py` demonstrating matrix multiplication using warp specialization and TMA barriers.
* Implemented a kernel function with shared memory allocation and memory barrier synchronization for enhanced performance.
* Included functionality to compile the kernel into a PyTorch-compatible function and validate its correctness against PyTorch's reference implementation.
* Enhanced documentation and comments for clarity on usage and functionality.

* lint fix

* [Feature] Implement WarpSpecializedDetector for TMA and MBarrier Detection

* Added the `WarpSpecializedDetector` class to identify the presence of TMA operations and memory barrier operations within a given TIR statement.
* Enhanced the `WarpSpecialized` pass to utilize the detector, allowing for conditional substitution based on the detection results.
* Improved code organization by including necessary headers and utilizing the `IRVisitorWithAnalyzer` for analysis.
* This addition aims to optimize warp specialization by ensuring that only relevant functions are transformed, enhancing performance and correctness.

* lint fix