GitLab

* chore: misc cleanup

* feat: add pre-commit config

* chore: update lint dependencies

* style: fix lint issues

* feat: add pre-commit hooks

* fix: fix typos

* chore: update .gitattributes

* [Lint]: [pre-commit.ci] auto fixes [...]

* docs: update CONTRIBUTING.md

* chore: update default venv name

* chore: revert and exclude CUDA files

---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* remove debug print

* Remove inline let expressions from the LowerAndLegalize function in phase.py

* add test

* Update sparse MLA examples to support SKV adjustment and correctness checks

- Changed SKV parameter from 32768 to 8192 in sparse MLA backward and forward tests.
- Added check_correctness parameter to test functions for validation of outputs.
- Updated test cases to reflect new SKV values and correctness checks.

* reduce test shape

* Update documentation structure and refactor main function parameters in example_fusedmoe_tilelang.py

- Added a new section for compiler internals in the documentation.
- Refactored the main function in example_fusedmoe_tilelang.py to accept parameters for hidden dimensions, expert configurations, and batch/sequence sizes, improving flexibility and readability.

* Update buffer access checks in merge_shared_memory_allocations.cc

- Changed the condition for buffer access from less than (<) to less than or equal to (<=) to allow access at the same scope level.
- Adjusted the logic for determining the access level when touching buffers to ensure correct handling of scope levels.

* lint fix

* Support pipeline with LetStmt

* lint fix

* • Fix LowerTileOp let handling to avoid LetInline dependency

  - inline let-bound BufferLoad nodes via resolver helpers and structured return
  - remap layouts/buffers using original data vars and only rewrite when needed
  - update pipeline planner to understand let-bound address_of buffers
  - document the new inline behaviour in docs/let_inline_fix.md

* fix for wgmma pipeline with let binding

* lint fix

* test fix

* reduce smem usage.

* let binding enhancement

* fix for dpgm

* fix simplify

* lint fix

* use tilelang.Simplify instead of tir.Simplify

* • Add TL_FORCE_LET_INLINE pass config and gate eager LetInline usage

  - register the new config in builtin headers/registration
  - add helper to pipeline enabling LetInline based on pass context
  - document LetStmt inlining controls and usage

* [Feature] Introduce WGMMA support and enhance GEMM layout handling

- Added support for the WGMMA intrinsic in the TileLang framework, enabling efficient matrix multiplication on newer architectures.
- Refactored GEMM layout functions to accept a boolean parameter for K dimension handling, improving flexibility in layout generation.
- Updated layout inference logic to accommodate new WGMMA configurations and ensure compatibility with existing GEMM operations.
- Enhanced Python bindings for layout functions, allowing for better integration and usability in user-defined operations.
- Improved documentation for layout functions and GEMM operations to clarify usage and parameters.

These changes enhance the performance and usability of GEMM operations, particularly for advanced architectures, while maintaining backward compatibility with existing implementations.

* [Refactor] Clean up code formatting and enhance layout function readability

- Improved code formatting across multiple files for better readability, including consistent indentation and line breaks.
- Updated layout function signatures to enhance clarity, particularly in `gemm_layouts.cc`, `layout.cc`, and `layout.h`.
- Refactored lambda functions in `builtin.cc` and `gemm_py.cc` for improved structure and maintainability.
- Enhanced comments and documentation in layout-related files to clarify usage and parameters.

These changes contribute to a cleaner codebase and improved maintainability of layout functions in the TileLang framework.

* [Feature] Add descriptor initialization and offset manipulation for WGMMA

- Introduced new TileLang builtins `initialize_descriptor` and `increase_descriptor_offset` to facilitate descriptor management for WGMMA operations.
- Updated `builtin.cc` and `builtin.h` to define and document the new builtins, enhancing the framework's capabilities for descriptor handling.
- Modified `codegen_cuda.cc` and `ptx.cc` to integrate the new builtins into the code generation process, ensuring proper assembly generation for WGMMA operations.
- Enhanced the `GemmWGMMA` class to utilize the new descriptor functionalities, improving the efficiency of matrix multiplication operations.
- Updated related tests and documentation to reflect the new features and ensure comprehensive coverage.

These changes enhance the TileLang framework's support for advanced matrix operations on newer architectures, improving performance and usability.

* [Refactor] Improve code formatting and readability in various files

- Enhanced code formatting across multiple files for better readability, including consistent indentation and line breaks.
- Updated function signatures and comments in `builtin.h`, `codegen_cuda.cc`, and `ptx.cc` to improve clarity.
- Refactored descriptor initialization and offset manipulation functions in `builtin.py` and `wgmma_macro_generator.py` for improved structure.
- Cleaned up unnecessary whitespace and improved alignment in `common.h` and `allocate.py`.

These changes contribute to a cleaner and more maintainable codebase in the TileLang framework.

* [Update] Update subproject commit and refactor layout function call

- Updated the subproject commit for `cutlass` to indicate a dirty state.
- Refactored the `UpdateAnalyzer` function in `layout.cc` to call `LayoutNode::getVarMap()` instead of `getVarMap()`, improving clarity and ensuring proper context for variable mapping.

These changes enhance the maintainability and clarity of the layout handling in the TileLang framework.

* support more data types

* gemm_rs support

* lint fix

* wgmma wrapper

* Remove debug logging for wgmma assembly code and refactor swizzle byte size calculations in wgmma macro generator. Enhanced handling of leading and stride byte offsets based on swizzle mode, improving clarity and performance in tensor core intrinsic emissions.

* Refactor GEMM layout functions to replace 'kfactor' with 'k_inner' for improved clarity and consistency. Update includes necessary changes in error messages for Hopper and Sm100 layouts. Additionally, include a new header for CUTE utilities in common.h.

* Comprehensively support WGMMA GEMM SS

* remove debug print

* lint fix

* remove debug print

* reduce bwd test shape

* lint fix

* clear cache for pytest

* lint fix

* Update sparse MLA examples to support SKV adjustment and correctness checks

- Changed SKV parameter from 32768 to 8192 in sparse MLA backward and forward tests.
- Added check_correctness parameter to test functions for validation of outputs.
- Updated test cases to reflect new SKV values and correctness checks.

* test fix

* adjust test case

* test fix

* skip some test currently

[Example] Introduce split+sum template, and optimize `atomic_add` performance for bwd examples (#940)

* example fix

* lint fix

* bug fix

* reduce test size.

* Implements tcgen05.ld instruction support for copying from shared.tmem
  to local.fragment on SM100/Blackwell architecture. Adds layout inference
  and lowering logic for tensor memory operations with proper physical
  coordinate range analysis and warpgroup alignment checks.

  Changes:
  - Add kTMemLoad and kTMemStore to CopyInst enumeration
  - Implement CheckTMemLoad() and CheckTMemStore() validation functions
  - Add LowerTmemCopy() to generate tcgen05.ld/st/cp PTX intrinsics
  - Add tmem layout inference in InferLayout() using expandTcgen05Layout
  - Support multiple instruction variants (32dp32b/64b/128b/256b)
  - Add physical layout bounds analysis for tmem coordinates
  - Change clear_accum from bool to PrimExpr in GEMM operations
  - Fix std::optional access checks in layout_inference.cc
  - Add tmem_allocate/deallocate PTX intrinsic support
  - Fix cooperative_groups grid.sync() code generation

* fix

* pipeline fix

* bug fix

* bool fix

* [Layout] Add IsCompletedReplicated method and enhance layout inference in ParallelOpNode

- Introduced IsCompletedReplicated method in FragmentNode to check if a buffer is fully replicated.
- Enhanced InferLayout in ParallelOpNode to handle layout inference for replicated buffers, ensuring only fragment[0] access is allowed.
- Updated error handling for non-zero index access in fragment buffers to improve robustness.

* [Layout] Improve code formatting and readability in layout.cc and parallel.cc

- Enhanced formatting in FragmentNode's IsCompletedReplicated method for better clarity.
- Updated InferLayout method in ParallelOpNode to improve code readability by adjusting line breaks and indentation.
- Ensured consistent formatting across conditional statements and comments for improved maintainability.

* updt

* optimize const index related op

* bug fix

* reduce gdn test

* test fix

* lintfix

* lint fix

* test fix

* Fix CopyNode Lower method to include disable_tma flag in GetCopyInst call

* Refactor flash attention implementation to disable TMA for specific copy and allow TMA for other operations

* attempt to fix lint

* update sm100 related utcmma, tmem, ld/st256 in src
* update sm100 related utcmma, tmem, ld/st256 in tilelang
* Remove deprecated GEMM examples and related README documentation for SM100 architecture support
* Update GEMM implementation to replace UTCMMA with TCGEN5MMA across relevant files
* Remove gemm_umma.py example and update README to reflect TCGEN5MMA terminology changes
* Update README.md for gemm_sm100 example by removing outdated API sections and streamlining documentation
* Update README and source files to reflect TCGEN5.MMA terminology changes
* Refactor CUDA GEMM header for improved readability

[Layout] Introduce Flexible Parallel to Support T.serial and local buffers inside T.Parallel loop (#844)

* Support T.serial and local buffers inside T.Parallel loop.

* Fix reducer layout in T.Parallel nested inside other loops

* Debug output with LOG(INFO)

* Add disable option for WGMMA.

* fix

* Use DLOG; fix missing registration for new pass config

* bug fix

* lint fix

* Enhance GEMM instruction set with UTCMMA and improve local buffer handling in casting example

* Update format.sh shebang, improve logging in layout inference, and enhance buffer store wrapper with detailed comments

* Enhance GEMM instantiation logic and improve layout inference for local buffer detection

- Updated the GEMM instantiation logic to include a check for WGMMA compatibility, ensuring that the conditions for using WGMMA are more robust.
- Refined the layout inference process to better identify when loops manipulate only local buffers, improving the accuracy of thread binding decisions in parallel loops.

---------
Co-authored-by: Huanqi Cao <caohuanqi@deepseek.com>

* Add fast math operations for CUDA: exp, exp10, log, log2, log10, tan, cos, and sin (#865)

* Refactor fast math operation definitions for consistency and readability in CUDA code. Consolidated multiple definitions into single lines and improved formatting in related test files for better clarity.

* Remove unnecessary pass configurations for warp specialization and TMA lowering in fast math operation tests for CUDA. This simplifies the test setup while maintaining the focus on fast math functionality.

* Update fastmath tests to reflect that tl.* intrinsics generate no fastmath versions and disable cache in main execution.

* Fix formatting in fastmath test comments for clarity on tl.* intrinsics behavior.

* Add precision comparison tool for CUDA operations

This commit introduces a new Python script and CUDA source file for a precision comparison tool that evaluates the accuracy of various CUDA operations (including division, reciprocal, exponential, logarithmic, and trigonometric functions) across different implementations: CUDA Precise, CUDA Fast, Triton, Triton LibDevice, and TileLang. The tool generates test data, executes the operations, and summarizes the error statistics for each implementation against a double precision reference. Additionally, a README file is added to document the results of the comparisons for various operations.

* Add precision comparison tool for CUDA operations

This commit introduces a new precision comparison tool implemented in Python and CUDA, designed to evaluate the accuracy of various mathematical operations (division, reciprocal, exponential, logarithmic, trigonometric, square root, etc.) across different frameworks including CUDA Precise/Fast, Triton, Triton LibDevice, PyTorch, and TileLang. The tool includes functionality for generating test data, executing operations, and summarizing error statistics for each implementation. Additionally, it provides a comprehensive README with error metrics for each operation tested.

* Add IEEE-compliant mathematical operations and refactor fast math module

This commit introduces new high precision mathematical operations including ieee_add, ieee_sub, ieee_mul, ieee_fmaf, ieee_frcp, ieee_fsqrt, ieee_frsqrt, and ieee_fdiv to the TileLang framework. The fast math module has been refactored to remove the deprecated fastmath.py file and update the import paths accordingly. Additionally, the CUDA code generation has been enhanced to support these new operations, ensuring compatibility with IEEE standards for floating-point arithmetic.

* debug removed

* Refactor IEEE math tests for improved readability and consistency

This commit enhances the formatting of the `test_ieee_math.py` and `test_mathops_fastmath.py` files by adjusting line breaks for better clarity. It also removes unnecessary comments and ensures that the main execution of tests is streamlined. These changes aim to improve the overall maintainability of the test code.

* Update README.md to enhance formatting of precision comparison results

This commit reformats the precision comparison results in the README.md file, converting the error statistics tables into a more structured markdown format. This change improves readability and accessibility of the data for various mathematical operations across different implementations, including FP32 Precise, Triton, TileLang, and CUDA.

[FastMath] Disable default TVM fastmath intrinsic dispatch and add explicit fastmath op to invoke (#875)

* Add fast math operations for CUDA: exp, exp10, log, log2, log10, tan, cos, and sin (#865)

* Refactor fast math operation definitions for consistency and readability in CUDA code. Consolidated multiple definitions into single lines and improved formatting in related test files for better clarity.

* Remove unnecessary pass configurations for warp specialization and TMA lowering in fast math operation tests for CUDA. This simplifies the test setup while maintaining the focus on fast math functionality.

* Update fastmath tests to reflect that tl.* intrinsics generate no fastmath versions and disable cache in main execution.

* Fix formatting in fastmath test comments for clarity on tl.* intrinsics behavior.

* Add precision comparison tool for CUDA operations

This commit introduces a new Python script and CUDA source file for a precision comparison tool that evaluates the accuracy of various CUDA operations (including division, reciprocal, exponential, logarithmic, and trigonometric functions) across different implementations: CUDA Precise, CUDA Fast, Triton, Triton LibDevice, and TileLang. The tool generates test data, executes the operations, and summarizes the error statistics for each implementation against a double precision reference. Additionally, a README file is added to document the results of the comparisons for various operations.

* Add precision comparison tool for CUDA operations

This commit introduces a new precision comparison tool implemented in Python and CUDA, designed to evaluate the accuracy of various mathematical operations (division, reciprocal, exponential, logarithmic, trigonometric, square root, etc.) across different frameworks including CUDA Precise/Fast, Triton, Triton LibDevice, PyTorch, and TileLang. The tool includes functionality for generating test data, executing operations, and summarizing error statistics for each implementation. Additionally, it provides a comprehensive README with error metrics for each operation tested.

* Add atomic operations for CUDA templates in new atomic.h file

- Introduced atomic functions including AtomicMax, AtomicMin, AtomicAdd, and their return variants for various data types.
- Implemented support for half, bfloat16, and float types with appropriate memory ordering.
- Moved atomic-related utilities from common.h to the new atomic.h file for better organization.
- Added Python bindings for atomic operations in tilelang, including atomic_max, atomic_min, atomic_add, and their vectorized counterparts.
- Updated customize.py to utilize the new atomic functions, enhancing modularity and maintainability.

* Refactor atomic operations in CUDA templates for improved readability

- Reformatted atomic operation implementations in atomic.h for better code clarity.
- Adjusted function signatures in tilelang's atomic.py to enhance readability by aligning parameters.
- Cleaned up unnecessary whitespace and comments in customize.py to streamline the codebase.

* Add thread storage synchronization configuration option

- Introduced a new configuration option `tl.disable_thread_storage_sync` to control the automatic insertion of thread synchronization barriers in shared memory access.
- Updated the `ThreadSync` pass to check this configuration and bypass synchronization if disabled.
- Enhanced documentation in `builtin.h` and `pass_config.py` to clarify the purpose and usage of the new option.

* Refactor thread storage sync configuration retrieval

- Simplified the retrieval of the thread storage sync configuration in the `ThreadSync` pass by removing unnecessary intermediate variables.
- Ensured that the inclusion of `builtin.h` is consistent by moving it to the appropriate location in the file.

* test fix

* Update atomic operations and tests for improved functionality

- Updated atomic operations in CUDA templates to remove unnecessary address_of calls, enhancing performance and readability.
- Refactored atomic operation signatures in tilelang's atomic.py to accept references instead of pointers.
- Added new atomic operations and corresponding test cases for atomic add, max, min, and load/store functionalities in the testing suite.
- Updated the TVM subproject to the latest commit for better compatibility.

* Update attention sink examples to use 32 heads

- Modified the `heads` parameter in both `example_gqa_sink_fwd_bhsd_wgmma_pipelined.py` and `example_mha_sink_fwd_bhsd_wgmma_pipelined.py` from 1 to 32 to enhance performance in attention mechanisms.
- Ensured consistency across example scripts for improved usability and testing.

* Refactor atomic add handling in vectorization

- Simplified the extraction of buffer loads for atomic add operations by removing unnecessary address_of calls, improving code clarity and performance.
- Updated the data type retrieval for vectorization size calculation to directly access the buffer load node, enhancing efficiency.

* Add loop break functionality and enhance thread synchronization

- Introduced a new `loop_break` function in `customize.py` to allow breaking out of loops, returning a call to the `tl.loop_break` intrinsic.
- Updated the `sync_threads` function in `builtin.py` to accept optional parameters for `barrier_id` and `arrive_count`, improving its flexibility for thread synchronization.
- Added necessary imports in `__init__.py` to include the new `loop_break` function for broader accessibility.

* test fix

* [Enhancement] Enable fast math optimization in tilelang JIT configurations

- Updated multiple examples and kernel functions to include `pass_configs` for enabling fast math optimization.
- Added support for the `TL_ENABLE_FAST_MATH` configuration option in the built-in operations.
- Enhanced the `LibraryGenerator` to handle the new fast math configuration, ensuring compatibility with existing settings.
- Updated documentation to reflect the changes in fast math handling and deprecation of the `TL_DISABLE_FAST_MATH` option.

* lint fix

* [Refactor] Introduce deprecated_warning utility for improved deprecation handling

- Added a new `deprecated_warning` function to streamline deprecation messages.
- Updated the `LibraryGenerator` to utilize the new function for warning about the deprecated `TL_DISABLE_FAST_MATH` configuration.
- Enhanced the `deprecated` decorator to support phaseout version messaging, improving clarity for users.

[Refactor] Update TVM subproject and refactor BlockNode handling in warp_specialized_rewriter.cc (#812)

* [Feature] Introduce custom warp specialization attribute and enhance warp group register allocation

- Added a new attribute `kCustomWarpSpecialization` to support custom warp specialization in the TileLang framework.
- Updated the `Collect` method in `SetMaxNRegCollector` to handle cases where warp specialization is detected, returning an empty array accordingly.
- Enhanced the `SetMaxNRegInjector` to skip processing when no registers are needed, improving efficiency.
- Modified the `WarpSpecialized` pass to include the new attribute in the function body when warp specialization is enabled, ensuring proper handling in transformations.

* lint

* lint

* [feat] add an example mma atom

* [fix] fix typo naming

* [feat] add a template to enable compilation

* [feat] add print util

* [WIP] pass on single block tile

* [feat] add sm80 metadata layout

* [chore] clean codebase

* [CI] format.sh

* [feat] add sm80 compress utils

* [bugfix] fix C fragment layout

* [refactor] use nvcc version instead of str

* [test] add test cases

* [chore] add a param check

* [chore] format a bit

* [chore] rename func to satisfy PEP 8 and appease gemini

* [chore] add check

* [feat] support sm75 layout && add assertion && chore

* [bug] fix illegal memory access when using two warps over N=32

This could be a missing check related to cutlass 2.x implementation.
Using the cutlass example can't trigger this cause it's bypassed by
padding the input.

For now I think it might be safe to increase the atom size and inve-
sgate in the future.

* [chore] add example

* [chore] format

* [example] update benchmark

* [bugfix] fix namespace and format

* [bugfix] fix incorrect param passing

* [refactor] update variable declaration for clarity in gemm_layouts and gemm_sp

* [Cleanup] Remove unnecessary blank lines in metadata layout functions in gemm_sp.py

* [CI] fix arch

* [example] add torch sparse benchmark

* [misc] polish && add reference && apply review suggestionsi && format

* [CI] format with clang-tidy

* [Cleanup] Format and align template struct definitions in half.hpp, common.h, and gemm_sp_sm80.h

* [Update] Modify CUDA version requirements in test_gemm_sp_sm80 and mark cutlass subproject as dirty

---------
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

- Introduced a new intrinsic `ptx_cp_async_barrier_noinc` for handling the `cp.async.mbarrier.arrive.noinc` operation in TileLang.
- Updated the CUDA code generation to support the new barrier operation.
- Added a corresponding function in the TileLang Python API for ease of use.
- Enhanced the barrier handling in CUDA templates to include the new no-increment operation, improving synchronization capabilities in parallel execution contexts.

* [AMD] support fp8 T.gemm

* format

---------
Co-authored-by: tangxinsheng.txs <tangxinsheng.txs@alibaba-inc.com>

* 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.

* Enhance layout inference and copy operations with 1D TMA support

- Updated `CopyNode` to introduce separate handling for 1D bulk load/store operations, including new methods for checking and lowering these operations.
- Modified `InferLayout` and `GetCopyInst` to accommodate additional parameters for layout maps and analyzers.
- Enhanced `AtomicAddNode` and `FillNode` to utilize the updated layout inference logic.
- Improved buffer out-of-bounds checks during layout inference to ensure safe memory access.

This update improves the efficiency and correctness of memory operations in the TileLang framework.

* Refactor layout inference calls for improved readability

- Updated `InferLayout` calls in `AtomicAddNode`, `CopyNode`, and `FillNode` to enhance code clarity by formatting parameters across multiple lines.
- Cleaned up whitespace and formatting in `copy.h` and `layout_inference.cc` to adhere to coding standards and improve maintainability.

This refactor aims to streamline the layout inference logic and improve overall code organization.

* Fix shared tensor check in CopyNode for bulk copy operations

- Updated the condition in `CheckBulkCopy1D` to verify contiguity of `shared_tensor` instead of `dst`, ensuring correct handling of shared memory layouts during bulk copy operations.
- This change enhances the accuracy of memory operations in the TileLang framework.

* Update test_example_gdn_compilation.py to invoke test function directly

- Commented out the call to `tilelang.testing.main()` in `test_example_gdn_compilation.py` and replaced it with a direct call to `test_example_chunk_delta_bwd_compilation()`. This change simplifies the test execution flow and focuses on the specific test case.

* Enhance bulk load/store checks in CopyNode with last dimension validation

- Updated `CheckBulkLoad` and `CheckBulkStore` methods in `CopyNode` to include an optional parameter for validating the last dimension during bulk copy operations.
- Adjusted related methods `CheckBulkLoad1D` and `CheckBulkStore1D` to pass the new parameter, improving the accuracy of bulk copy checks.
- This change enhances the robustness of memory operations in the TileLang framework by ensuring compliance with dimensional requirements.

* Refactor CheckBulkLoad and CheckBulkStore methods for improved readability

- Reformatted the parameter lists of `CheckBulkLoad` and `CheckBulkStore` methods in `CopyNode` to enhance code clarity by aligning parameters across multiple lines.
- This change improves the maintainability of the code and adheres to coding standards.

* Implement Fill operator and related reflection methods in TileLang

- Added Fill operator implementation in `fill.cc` and `fill.h` for element-wise filling of buffers.
- Introduced reflection methods for Fill, AtomicAdd, Copy, Conv2DIm2Col, FinalizeReducer, Gemm, and Parallel operators to enhance introspection capabilities.
- Updated relevant files to register reflection methods and ensure proper initialization in static blocks.
- Removed outdated comments and unnecessary code in various operator files to improve clarity and maintainability.
- Added new Python bindings for the Fill operator in `tilelang/ir/fill.py` and updated the module imports accordingly.

* Refactor operator reflection methods and improve code clarity

- Updated reflection methods for AtomicAdd, Copy, FinalizeReducer, Gemm, and Parallel operators to enhance readability by using `empty()` instead of size checks.
- Consolidated static initialization blocks for various operators to a single line for improved consistency.
- Cleaned up whitespace and formatting in multiple files to adhere to coding standards and improve maintainability.
- Added new Python bindings for operators in the `tilelang/ir` module, ensuring proper registration and organization of imports.

* Refactor GEMM and AtomicAdd operations for improved clarity

- Updated the `GetArchInt` function in `atomic_add.cc` to use `std::string` and `std::stoi` for better readability and type safety.
- Removed unnecessary variables and comments in `gemm_sp.cc` and `gemm.cc` to streamline the `ComputeWarpPartition` method.
- Cleaned up the `layout_reducer.cc` file by removing unused variable declarations, enhancing code clarity.
- Added import for the `ir` module in `tilelang/__init__.py` to ensure proper organization of module imports.

* Remove deprecated operator files from the tilelang IR module

- Deleted files for Fill, AtomicAdd, Copy, Gemm, GemmSP, FinalizeReducer, Parallel, Reduce, and Region operators to streamline the codebase.
- This cleanup enhances maintainability by removing unused code and improving overall organization of the module.

* Refactor imports in tilelang IR module for improved organization

- Updated import statements in `tilelang/ir.py` to reflect changes in the TVM library structure, enhancing clarity and maintainability of the codebase.

* lint fix

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

- Updated the `Gemm` and `GemmSP` classes to utilize a new `GemmWarpPolicy` object for warp partitioning, improving encapsulation and readability.
- Removed deprecated `ComputeWarpPartition` methods and replaced them with calls to the new policy object, streamlining the code.
- Cleaned up comments and unnecessary code in `gemm.cc`, `gemm_sp.cc`, and related header files to enhance overall clarity.
- Introduced a new `GemmWarpPolicyNode` class to manage warp policy attributes and methods, facilitating better organization of related functionalities.
- Updated reflection methods to include the new policy structure, ensuring proper registration and introspection capabilities.

* Refactor Reduce operation to utilize ReduceType class for improved clarity and maintainability

- Replaced multiple conditional checks for reduce types with a single ReduceType object, simplifying the code structure.
- Introduced a new ReduceTypeNode class to encapsulate reduce type logic and methods, enhancing organization.
- Updated MakeInitValue, MakeReduce, and Lower methods to leverage the new ReduceType class, improving readability.
- Added Python bindings for the ReduceType class in tilelang IR module to ensure proper registration and usability.

* comment

* Refactor operator header files for improved readability

- Cleaned up formatting and whitespace in `atomic_add.h`, `copy.h`, `fill.h`, `reduce.cc`, and `reduce.h` to enhance code clarity.
- Consolidated comments and adjusted line breaks for better organization and maintainability across multiple operator definitions.

* Refactor MakeReduce method in ReduceOpNode for clarity

- Updated the parameter name in the MakeReduce method from `rhs` to `b` and assigned it to `rhs` for improved readability.
- This change enhances the clarity of the method's purpose and aligns with the overall refactoring efforts in the Reduce operation.

* Update Reduce operation type checks for consistency

- Changed string comparisons for reduce types in the MakeReduce method from "abs_sum" to "abssum" and "abs_max" to "absmax" for uniformity.
- This adjustment enhances the clarity and consistency of the reduce type handling in the codebase.

* [Refactor] Update Clang-Tidy Checks and Improve Code Consistency

- Enhanced .clang-tidy configuration by adding specific checks for better bug detection and performance optimization.
- Refactored function signatures across multiple files to use `const` references for parameters, improving performance and code clarity.
- Updated various methods to ensure consistent handling of parameters, particularly in `AddPredicate`, `Substitute`, and `PlanLoopPartition` functions.
- Improved readability by replacing size checks with `empty()` method calls in several locations, ensuring clearer intent in the code.
- General code cleanup and adherence to best practices for better maintainability.

* [Refactor] Enhance Code Consistency and Clang-Tidy Configuration

- Updated .clang-tidy configuration to include additional checks for improved code quality and performance.
- Refactored function signatures across multiple files to use `const` references, enhancing performance and clarity.
- Replaced size checks with `empty()` method calls in various locations for clearer intent.
- Improved handling of parameters in several functions, ensuring consistent usage of `std::move` where applicable.
- General code cleanup to adhere to best practices and improve maintainability.

* [Refactor] Integrate Clang-Tidy Checks and Enhance Code Consistency

- Added clang-tidy checks to the format script for improved code quality assurance.
- Refactored function signatures across multiple files to consistently use `const` references, enhancing performance and clarity.
- Updated the requirements-lint.txt file to include clang-tidy as a dependency.
- General code cleanup to adhere to best practices and improve maintainability.

* [CI] Update AMD CI Workflow to Include Build Directory Creation

- Added steps to create a build directory and configure CMake with ROCm support during the format check process.
- Ensured cleanup of the build directory after the format check to maintain a clean workspace.

* [Refactor] Remove Unused Member Variables in AtomicAddNode and CopyNode

- Removed the `args_` member variable from both `AtomicAddNode` and `CopyNode` classes to streamline the code and eliminate unnecessary data members.
- This change enhances code clarity and maintainability by focusing on relevant attributes for each class.

* [Refactor] Update Clang-Tidy Integration and Code Improvements

- Modified the format script to include the `-fix` option in the clang-tidy command for automatic code fixes.
- Refactored the `AtomicAddVectorizePlanner` class to improve variable handling and consistency, including changes to member variable types and function signatures.
- Enhanced code clarity by removing unnecessary `std::move` calls and ensuring consistent usage of types across the class.
- General code cleanup to adhere to best practices and improve maintainability.

* [Refactor] Improve Parameter Handling and Consistency in AtomicAddVectorize

- Updated function signatures in `AtomicAddVectorizePlanResult` and `AtomicAddVectorizeRewriter` to use `const` references and `std::move` for better performance and clarity.
- Enhanced the `UpdateVectorSize` method to accept `const Array<PrimExpr>&` for improved efficiency.
- General code cleanup to maintain consistency and adhere to best practices.

* [CI] Add Git Submodule Initialization to CI Workflow

- Included a step to initialize and update git submodules recursively in the CI workflow.
- This change ensures that all necessary submodules are available during the format check process, improving build reliability.

* [CI] Add Git Submodule Update Step to Format Check

- Included a command to initialize and update git submodules recursively in the CI workflow during the format check process.
- This enhancement ensures that all required submodules are available, contributing to improved build reliability.

* [Refactor] Update Function Signatures in AtomicAddVectorize

- Modified the `VectorizeAtomicAdd` function signature to use `const` references for `thread_var` and `thread_bounds`, enhancing performance and code clarity.
- This change aligns with previous refactoring efforts to improve parameter handling and consistency across the codebase.

* Allow fill global buffer

* fix lint error

* 📝 Add docstrings to `reducer_0825`

Docstrings generation was requested by @LeiWang1999.

* https://github.com/tile-ai/tilelang/pull/757#issuecomment-3219088118



The following files were modified:

* `setup.py`
* `src/op/builtin.h`
* `src/op/finalize_reducer.cc`
* `src/op/finalize_reducer.h`
* `src/op/parallel.cc`
* `src/op/parallel.h`
* `src/op/reduce.cc`
* `src/target/codegen_cuda.cc`
* `src/tl_templates/cuda/common.h`
* `src/transform/layout_inference.cc`
* `src/transform/layout_reducer.cc`
* `src/transform/layout_reducer.h`
* `src/transform/merge_shared_memory_allocations.cc`
* `src/transform/storage_access.cc`
* `src/transform/warp_specialized_rewriter.cc`
* `testing/python/autotune/test_tilelang_autotune_with_inputs.py`
* `tilelang/engine/phase.py`
* `tilelang/language/customize.py`
* `tilelang/language/reduce.py`
* `tilelang/transform/__init__.py`

* lint fix

* lint fix

---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

* [Bugfix]:Fix atomic add auto vectorize negative optimization

* fixbug

* format

* fix bug

* 📝 Add docstrings to `pytile_0826`

Docstrings generation was requested by @LeiWang1999.

* https://github.com/tile-ai/tilelang/pull/763#issuecomment-3224197814



The following files were modified:

* `src/op/atomic_add.cc`
* `src/op/atomic_add.h`
* `src/op/copy.cc`
* `src/op/copy.h`
* `src/op/elem.cc`
* `src/op/elem.h`
* `src/op/gemm.cc`
* `src/op/gemm.h`
* `src/op/gemm_sp.cc`
* `src/op/gemm_sp.h`
* `src/op/operator.cc`
* `src/op/operator.h`
* `src/op/parallel.cc`
* `src/op/parallel.h`
* `src/op/reduce.cc`
* `src/op/reduce.h`
* `src/op/region.cc`
* `src/op/region.h`
* `src/transform/layout_inference.cc`
* `src/transform/lower_tile_op.cc`

* lint fix

---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

* [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>

* Refactor operator classes to inherit from TileOperator and update layout inference methods

- Changed base class of several operator classes (AtomicAdd, Copy, Gemm, etc.) from Operator to TileOperator for better alignment with tile operations.
- Updated InferLayout and Lower methods to use 'override' specifier for clarity and consistency.
- Adjusted header inclusions to replace "op.h" with "operator.h" across multiple files for improved organization.
- Added missing layout inference implementations for Fill and Conv2DIm2ColOp.
- Removed deprecated op.cc and op.h files to streamline the codebase.

* lint fix

* Refactor operator classes to use Node pattern and improve memory management

- Updated several operator classes (AtomicAdd, Copy, Gemm, etc.) to utilize the Node pattern for better memory management and encapsulation.
- Changed constructors to initialize member variables through a node object, enhancing clarity and reducing direct member access.
- Updated Clone methods to return TileOperator instances instead of unique pointers, aligning with the new design.
- Refactored InferLayout and Lower methods to ensure consistency across operator implementations.
- Adjusted header files to reflect the new class structure and removed deprecated code for a cleaner codebase.

* Enhance Clone methods in AtomicAdd and Copy classes to support parallel operation cloning

- Updated the Clone methods in AtomicAddNode and CopyNode to ensure that the parallel operation (par_op_) is properly cloned when defined, improving the integrity of cloned objects.
- Refactored the FillNode class to use ParallelOp directly instead of std::make_unique, streamlining the creation of parallel operations.
- Made minor adjustments in layout inference and other related methods for consistency and clarity.

* Refactor FillNode::Lower method to remove unused global function call

- Eliminated the call to the global function "tl.fill.lower" in the FillNode::Lower method, streamlining the code and improving clarity.
- Retained the core functionality of the method while enhancing maintainability by reducing unnecessary dependencies.

* [Feature] Add 1D TMA support
- Check the contiguous conditions of 1D TMA copy
- Add new interface and params order of `tma_load` and `tma_store` call
- Add 1D `tma_store` interface in sm90 template
- Add elementwise kernel for 1D TMA example

* [Lint]

* [BugFix] Add conditions for 1D TMA copy on non-swizzle shared tensors

* [Lint]

* [BugFix] 1D TMA load

* [README] Update GDN README for clarity and add acknowledgements (#758)

- Improved formatting and clarity of the GDN kernel implementation description.
- Updated requirement section to list dependencies in a clearer format.
- Added an acknowledgements section to credit the developers and the Xiaomi LLM-Core Team for their contributions.

* cutlass v4.2.0 supporting cuda 13 (#760)

* [Lint]

* [Lint]

* [MXFP4] Add test for bf16&mxfp4 gemm

* [BugFix]

* [Lint]

---------
Co-authored-by: Yu Cheng <54519279+chengyupku@users.noreply.github.com>
Co-authored-by: Johnny <johnnync13@gmail.com>

* Remove `thread_partial_sync.cc` and refactor `thread_storage_sync.cc` to streamline synchronization handling. Introduce `thread_sync_types.h` for thread-bound key definitions and reserved named barriers. Update related logic in `ThreadSyncInserter` and `TileLangThreadSync` for improved clarity and efficiency.

* Remove `sync_thread_partial` references and related documentation from the codebase. Update CUDA and HIP code generation files to eliminate calls to the removed function. Refactor `__sync_thread_partial` to `sync_thread_partial` in CUDA common header for consistency.

* Remove unused import of `bulk_copy.h` in `codegen_hip.cc` to enhance code clarity and maintainability.

* Add import of `bulk_copy.h` in `codegen_hip.cc` to support new functionality.

* typo fix

* Update data type in reduce_sum tests from float16 to float32 for consistency and clarity. Remove redundant dtype tests and streamline run functions. Enhance reshape kernel compilation with pass configurations to address shared memory layout issues.

* lint fix

* test fix

* Enhance CI configuration by adding verbose output to pip install command for better visibility during installation.

* use ninja instead of make

* Add CMake configuration step for Ninja build system in setup.py

* Update pyproject.toml to include additional build dependencies: build, torch, tox, auditwheel, patchelf, and ninja.

* Enhance CI configuration by adding verbose output to pytest commands for improved test visibility.

* Update pyproject.toml to add Cython as a build dependency. Enhance thread storage synchronization in thread_storage_sync.cc by introducing new thread variable handling and improving index disjointness checks.

* Update data type in cumulative sum tests from float16 to float32 for consistency. Modify run_cumsum function to utilize the updated dtype and enhance result validation with assertions. Adjust test cases accordingly.

* Refactor storage access handling by introducing buffer data mapping in TileLangStorageAccessVisitor. Enhance access entry structure to include pointer access flag. Update thread storage synchronization to accommodate new buffer data mappings. Adjust quickstart example to print kernel source for debugging purposes.

* Refactor linear index conversion in TileLangStorageAccessVisitor to utilize the analyzer for simplification. Update buffer index calculations to ensure consistent simplification of range expressions.

* bugfix

* Refactor buffer index calculation in TileLangStorageAccessVisitor to simplify access handling. Removed unused buffer mapping logic, ensuring consistent buffer index generation with a default ramp.

* Refactor TileLangStorageAccessVisitor to replace buffer indices with buffer ranges for improved pointer access handling. Update AccessEntry structure to include buffer_ranges and adjust thread storage synchronization logic to account for pointer access conflicts.

* Refactor thread storage synchronization to replace 'shared.dyn' with 'shared' for consistency in memory allocation. Update related test cases to reflect this change and ensure proper functionality.

* [Refactor] Merge bulk copy into copy and refactor layout inference for bulk copy

* Deleted the `bulk_copy` operator implementation and its header file as it is no longer needed.
* Introduced a new function `cuTensorMapType()` to return the data type for CUDA tensor mapping.
* Updated related files to reflect these changes, ensuring that the codebase remains clean and maintainable.

* lint fix

* Fix typos in intrinsic names and remove unused print statement in block_sparse_attn_tilelang.py. Updated references from `ptx_ldmatirx` to `ptx_ldmatrix` across multiple files for consistency.

* remove bulk copy

* Refactor copy and atomic add operations to support TMA lower configuration

- Updated `GetCopyInst` to accept a `disable_tma_lower` parameter, allowing for conditional usage of TMA in bulk load/store operations.
- Modified `Lower` method in `Copy` to incorporate the new TMA configuration.
- Refactored `AtomicAdd::Lower` to streamline layout inference and vectorization logic.
- Removed unused `disable_tma_lower` field from `LowerArgs` structure for clarity.
- Enhanced atomic add vectorization by replacing the buggy implementation with a more robust loop vectorization approach.

* Enhance TMA bulk copy logic in `LowerBulkCopy` method

- Added a condition to set `desc.swizzle` to `CU_TENSOR_MAP_SWIZZLE_NONE` when `shared_layout` matches `linear_layout`, improving clarity in layout handling.
- Updated warning log to provide more detailed information about fallback scenarios, including source and destination buffer names and shapes, enhancing debugging capabilities.

* lint fix

* Remove fallback logging for non-swizzled global layout in `LowerBulkCopy` method to streamline the bulk copy logic. This change enhances code clarity by eliminating unnecessary warning messages related to inner box dimensions.

* Enhance reshape kernel compilation in `run_reshape` and `run_reshape_smem_1d_2_2d` functions

- Updated the `tl.compile` method to include `pass_configs` that disable TMA lower and warp specialization, addressing shared memory layout transformation limitations.
- Added TODO comments to indicate the need for further improvements in shared memory handling.

* Update `native_sparse_attention` function to include TMA configuration options

- Added `pass_configs` to the JIT decorator to disable TMA lower and warp specialization, addressing potential issues with shared memory layout transformations.
- Updated comments to clarify modifications in tensor shapes for inference, specifically setting `q` sequence length to 1.

* Refactor JIT decorator formatting in `native_sparse_attention` function

- Improved readability by reformatting the JIT decorator parameters for `native_sparse_attention`, ensuring consistent style across the codebase.
- No functional changes were made; this update focuses on code clarity and maintainability.

* Enhance thread management and logging in TileLang compilation

- Added a method to check if printing is enabled during compilation, improving control over logging behavior.
- Updated the JIT kernel class to utilize the new method for logging compilation status, ensuring consistent and clear output.
- Added comments to clarify the purpose of changes and improve code readability.

* Add warp specialization scope and refactor register management in TileLang

- Introduced a new constant `kWarpSpecializationScope` in `builtin.h` for better attribute management.
- Removed the `SetMaxNRegCollector` class and its related logic from `warp_specialized_rewriter.cc`, streamlining the warp specialization process.
- Added functions `annotate_producer_reg_dealloc` and `annotate_consumer_reg_alloc` in `builtin.py` to facilitate register management.
- Implemented `AnnotateWarpGroupRegAlloc` in `__init__.py` to inject register allocation calls into warp-specialized functions, enhancing the overall register handling in the compilation process.

* Refactor test for InjectSetMaxNReg pass in TileLang

- Improved readability by restructuring conditional checks and assertions in the test cases.
- Enhanced clarity in the collection of `set_max_nreg` calls by simplifying the logic.
- Ensured consistent formatting and spacing throughout the test functions for better maintainability.

* Enhance bulk copy and store checks in `Copy` class

- Updated scope validation for source and destination tensors in `CheckBulkLoad` and `CheckBulkStore` methods to include both `shared.dyn` and `shared` as valid options.
- Modified `CheckLDSMCopy` and `CheckSTSMCopy` methods to accommodate the new scope validation, ensuring compatibility with shared memory configurations.
- Improved logging in `LowerBulkCopy` to provide clearer warnings regarding unsupported swizzle layouts, including source and destination names for better debugging.

* lint fix

* 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

Docstrings generation was requested by @LeiWang1999.

* https://github.com/tile-ai/tilelang/pull/712#issuecomment-3190680851



The following files were modified:

* `src/op/gemm.cc`
* `src/tl_templates/cuda/gemm_sm90.h`
* `src/transform/warp_specialized_rewriter.cc`
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

[Enhancement][Bugfix] Fix bug in warp specialized pass and add gemm_sr fallback support for Hopper (#712)

* bug fix and support gemm_sr fallback for hopper

* Update gemm.cc

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

* [Enhancement] Refactor buffer index handling for improved precision and clarity (#668)

- Enhanced buffer index handling to address precision issues by removing redundant operations.
- Streamlined the logic for determining buffer overlaps, ensuring more accurate conflict detection.
- Updated related documentation to reflect changes in buffer management practices.

* Remove obsolete test script for AMD example, streamlining the examples directory.

* Remove unused dtype_size variable in AMD example script to streamline code.

* Add input configuration file and update AMD example script for enhanced flexibility

- Introduced a new input.txt file for configurable parameters.
- Modified the example_amd_flash_attn_fwd.py script to allow for a wider range of configurations, including additional options for num_stages, enable_rasterization, and k_pack.
- Streamlined the main function for better clarity and organization.
- Added a new test script to facilitate running the example with specified parameters.

* Remove input configuration file and obsolete test script; enhance AMD example with swizzle layout annotations

- Deleted input.txt and test.sh files as they are no longer needed.
- Updated example_amd_flash_attn_fwd.py to include swizzle layout annotations for shared memory, improving bank conflict avoidance.
- Reintroduced swizzle usage in the kernel for better performance.

* Refactor AMD example script for FlashAttention-2

- Updated function names for clarity, changing `get_v2_configs` to `get_configs` and `fast_flashattn_v2` to `fast_flashattn`.
- Streamlined the main function by renaming `main_v2` to `main` and adjusting the corresponding calls.
- Removed outdated comments and improved code organization for better readability.

* Refactor formatting in AMD FlashAttention example script

- Improved code readability by adjusting line breaks and indentation in the `fast_flashattn` function.
- Streamlined the `main` function parameter formatting for consistency.
- Removed unnecessary blank lines to enhance overall code organization.

* Update example_amd_flash_attn_fwd.py

* Update AMD FlashAttention example and TVM submodule

- Added a new example script `example_amd_flash_attn_fwd_k_block.py` for FlashAttention with K-blocking support.
- Enhanced `example_amd_flash_attn_fwd.py` by expanding configuration options for block sizes and threads.
- Updated the TVM submodule to the latest commit for improved functionality.
- Introduced a new test script `test.sh` to facilitate running the new example with specified parameters.

* Add CI workflow for automated format checking and testing

- Introduced a new GitHub Actions workflow in `amd_ci.yml` to automate format checks and testing for pull requests.
- The workflow includes steps for setting up a Python environment, running format checks, and executing tests.
- Removed obsolete example script `example_amd_flash_attn_fwd_k_block.py` and test script `test.sh` to streamline the examples directory.

* Rename CI workflow from "CI" to "AMD CI" for clarity and specificity.

* Update AMD CI workflow to include copying PyTorch, TorchVision, and Torchaudio packages to the virtual environment for improved dependency management.

* Update AMD CI workflow to install pytest directly instead of using requirements-test.txt

* Update AMD CI workflow to remove 'flash-attn' from requirements and install dependencies from requirements-test.txt

* Refactor AMD CI workflow to enhance clarity in removing 'flash-attn' from requirements-test.txt before installation

* Remove Torchaudio package copying from AMD CI workflow to streamline dependency management.

* Refactor AMD CI workflow to remove the format-check job and streamline the build-test process by directly copying PyTorch and TorchVision packages to the virtual environment.

* Add installation of ROCm in AMD CI workflow

- Included a step to execute the `install_rocm.sh` script for improved setup.
- Removed unnecessary blank line for better readability in the workflow script.

* Remove installation step for ROCm in AMD CI workflow to simplify the setup process.

* Update AMD CI workflow to run specific test file with verbose output instead of all tests.

* Add new tilelang built-in operations for AMD architecture

- Introduced `tvm_mfma`, `tvm_mfma_store`, `tvm_rdna_wmma`, and `tvm_rdna_wmma_store` built-in operations to enhance support for matrix multiplication and storage in tilelang.
- Each operation is configured with the appropriate number of inputs and marked as opaque in terms of call effects.

* Enhance autotuner configurations and GEMM operations in AMD example

- Updated block sizes and num_split_q parameters in `get_configs` for improved autotuning.
- Modified `T.gemm` calls in `fast_flashattn` to utilize `GemmWarpPolicy.FullRow`, optimizing performance for matrix multiplications.

* Update autotuner configurations in AMD example for enhanced performance

- Refined block sizes, thread counts, and added new parameters in `get_configs` to optimize autotuning.
- Adjusted `fast_flashattn` function to incorporate new parameters for panel size and coalesced widths, improving memory access patterns.

* Enhance autotuner configurations and memory handling in AMD example

- Expanded block sizes and thread counts in `get_configs` for improved autotuning capabilities.
- Updated `fast_flashattn` to utilize a new shared memory allocation strategy, optimizing memory access patterns during GEMM operations.

* Refine autotuner configurations and memory usage in AMD example

- Reduced block sizes and adjusted thread counts in `get_configs` for optimized autotuning.
- Updated `fast_flashattn` to utilize register fragments for accumulation, minimizing LDS usage and enhancing performance during GEMM operations.

* Update autotuner configurations in AMD example for enhanced performance

- Expanded block sizes and thread counts in `get_configs` to improve autotuning capabilities.
- Adjusted `num_split_q` and `v_coalesced_width` parameters for better optimization during GEMM operations.

* Enhance autotuner configurations and GEMM operations in AMD example

- Expanded thread counts in `get_configs` to include higher values for improved autotuning.
- Updated `fast_flashattn` to adjust accumulation logic and ensure proper handling of causal conditions, optimizing performance during matrix multiplications.

* Update AMD CI workflow and remove obsolete test script

- Modified the CI workflow to run on multiple environments: self-hosted, amd, and gpu.
- Deleted the outdated `test.sh` script from the examples directory, streamlining the project structure.

* Remove TVM subproject from 3rdparty directory

* Refactor configuration generation and accumulation logic in AMD example

- Reformatted the `get_configs` function for improved readability by aligning parameters.
- Adjusted the `fast_flashattn` function to enhance clarity in the conditional logic for accumulation, ensuring better handling of causal conditions.

* Enhance AMD CI workflow with additional logging and setup steps

- Added echo statements to provide feedback during the CI process, indicating when the environment is running on an AMD GPU, copying necessary packages, and installing requirements.
- Improved clarity in the workflow by explicitly stating when the project is being installed and when tests are being executed.

* Comment out package copying in AMD CI workflow to prevent potential issues during environment setup

* Update AMD CI workflow to install nightly versions of PyTorch and remove obsolete package copying steps

* Enhance BuildTileLangHIP function by adding whitespace for improved readability

* Refactor kTVMGridConstant definition for clarity and remove unnecessary comment

* Update TVM subproject to latest commit a64a5926a6e59f5417ef2501f9d88b467337cf6a

* lint fix

* Update AMD CI workflow to use requirements-rocm.txt for dependency installation

* fix ci

* Remove dependency on format-check from AMD CI workflow

* fix ci

* fix ci

* fix ci

* Remove format-check job from AMD CI workflow

* Add torch to requirements-rocm.txt and remove explicit pip install commands from AMD CI workflow

* Add dependency on format-check job in AMD CI workflow

* Add format-check job to AMD CI workflow

* Update format-check job in AMD CI workflow to run on self-hosted environment

* Enhance format-check job in AMD CI workflow with improved Python environment setup and automatic commit of lint changes

* Update amd_ci.yml

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

* chore: fix typos

* chore: fix ruff

* chore: fix clang-format

* Init support for sm120

* fmt

* resolve comments

* unify mma gemm

* fmt

---------
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

[Enhancement] Add eviction policy support for TMA operations, enhance CUDA codegen, and introduce new pass config (#690)

* Enhance TMA and barrier handling in CUDA code generation

- Updated `CodeGenTileLangCUDA` to support eviction policies for TMA operations, allowing for more flexible memory management.
- Introduced a new `CacheHintSm90` enum to define eviction strategies in `copy_sm90.h`.
- Modified TMA load/store functions to accept eviction policies, improving performance on different architectures.
- Enhanced `TmaBarrierCollector` and `TmaBarrierRewriter` to account for SIMT copies, ensuring correct barrier insertion.
- Refactored thread synchronization logic to utilize barrier IDs, improving the efficiency of partial thread synchronization.
- Updated Python interface for `copy` and `c2d_im2col` to include optional eviction policy parameters, enhancing usability.

* update shuffle and elect optimization

* fix bug

* fix bug

* fix potential bug

* lint fix

* lint fix

* update shuffle_elect template

* fix bug

* fix bug

* fix template

* lint and fix

* fix typo

* gemm_with_stride sm89

* fix offset issue

* bug fix

* format

* sm80 support

* add sm90

* add testing

* format

* add static_assert for wgmma

* Enhance error message for inner_box_dim validation in LowerBulkCopy

* lint fix

---------
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

* Implement new free stage layout inference.

* Fix bug

* Make replication upcasting and unnormalizable iterators safe.

* Better handling of updating with more replica

* Remove unnecessary check.

* Fix compilation.

* Fix setup.py.

* Simplify development mode.

* Allow ParallelOp layout when there's already a compatible layout specified

* lint fix

* Add ProveFragmentContains function to validate thread access between small and large fragments

This function checks if the threads accessing elements of a smaller fragment are a subset of those accessing a larger fragment, ensuring valid access during updates. The implementation includes deriving thread indices, computing logical indices, and verifying thread mappings.

* Update dependencies in requirements files

* Remove 'thefuzz' from requirements-dev.txt
* Specify exact versions for 'torch' and add 'flash_attn' in requirements-test.txt

* Update CI workflow to use SHA256 hash for requirements file

* Update requirements and CI workflow for flash attention

* Removed specific version for 'torch' in requirements-test.txt
* Added installation of 'flash_attn==2.5.8' in CI workflow to ensure compatibility

* Refactor flash attention import handling in examples

* Removed availability checks for 'flash_attn' in multiple example scripts.
* Simplified import statements for 'flash_attn' to ensure consistent usage across examples.

---------
Co-authored-by: Huanqi Cao <caohuanqi@deepseek.com>

* [Enhancement] Refactor GEMM operations for improved warp partitioning and target instruction handling

- Introduced a new `GetGemmInst` method to determine the appropriate GEMM instruction based on block size and target architecture.
- Updated `ComputeWarpPartition` to accept the GEMM instruction type, enhancing flexibility in warp partitioning logic.
- Added `TargetGetWarpSize` utility to streamline warp size retrieval based on target architecture.
- Refactored layout inference and lowering methods to utilize the new GEMM instruction handling, improving clarity and maintainability of the codebase.

* bug fix

* test fix

* lint fix

* phase out Canonialize

* add option --expt-relaxed-constexpr

* [Enhancement] Introduce tilelang intrinsic operations for GEMM

- Added `tl_gemm` and `tl_gemm_sp` built-in operations to support general and sparse matrix multiplication in tilelang.
- Updated the lowering logic in `Gemm` and `GemmSP` to utilize the new tilelang operations.
- Enhanced CUDA and HIP code generation to handle the new GEMM operations, ensuring proper argument validation and external call printing.
- Implemented shared memory alignment planning for GEMM operations to optimize performance on supported architectures.

* lint fix

* lint fix

* test fix

* test fix

* rebase

* Update builtin.cc