GitLab

* [Feature] Add FullyReplicated Fragment Layout and Enhance Layout Inference

* Introduced a new static method `FullyReplicated` in the `Fragment` class to create fully replicated fragment layouts, ensuring all threads hold identical copies of the buffer.
* Updated `CopyNode` to collect fragment layouts and mark them as fully replicated during layout inference.
* Enhanced `ParallelOpNode` to expand let bindings for fragment buffer accesses, improving layout inference accuracy.
* Added documentation for new methods and updated existing methods to support the new layout features.

* lint fix

* Remove debug logging statements from layout inference process to streamline output and improve performance.

* Refactor GEMM and Reduce operations by moving NormalizeToBufferRegion and MakeAccessPtrFromRegion to utils.{h,cc} for better code organization and reuse.

* lint fix

* Refactor region handling by removing the RegionOp and updating NormalizeToBufferRegion to only accept BufferLoad and BufferRegion. This change improves code organization and simplifies the handling of memory regions across various operations.

* fix

* Refactor memory region handling by introducing `tl.region` calls across various operations, including GEMM and fill functions. This change enhances the consistency of region management and improves code organization by utilizing utility functions for buffer region conversions.

* fix

* fix

* test fix

* lint fix

* Refactor GEMM operations to improve memory region handling by replacing `mbarPtr_` with `mbarRegion_` and updating related logic in both C++ and Python implementations. This change enhances the clarity and consistency of buffer region management.

* fix

* lint fix

* fix

* fix

* test fix

* lint fix

* lint fix

* minor fix

* fix

---------
Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk>

* Add kernel selection option for GEMM v1 in environment settings

- Introduced `TILELANG_USE_GEMM_V1` environment variable to control the selection of GEMM version.
- Added `use_gemm_v1` method in the `Environment` class to determine if GEMM v1 should be used based on the environment variable.
- Updated GEMM function assignment to default to v2, allowing for v1 to be forced via the new environment variable.

* bug fix

* Add kernel selection option for GEMM in environment settings

- Introduced `TILELANG_USE_GEMM_V1` environment variable to allow users to select between GEMM v1 and v2 implementations.
- Updated `gemm` function to default to v2 but switch to v1 if the environment variable is set to a truthy value.
- Added a method `use_gemm_v1` in the `Environment` class to facilitate this selection based on the environment variable.

* Refactor GEMM macro generator to use BufferRegion instead of Buffer

- Updated `wgmma` a...

* fix

* lint fix

* fix

* lint fix

* fix

* upd

* support n>256

* Remove unnecessary pass configurations for fast math in MHA forward BHSD latency script.

* lint fix

* lint fix

* 3rdparty tvm bump

* bump tvm into v0.22.0

* lint fix

* rebase tvm

* Update submodule tvm to latest commit 3085bc4

* Refactor: Update configuration retrieval in CopyNode and adjust test registration in tilelang

* test fix

* add requirement

* atomic_fix

* atomic_fix

* phaseout py39

* optimize

* optimize

* lint fix

* do not clean cache

* do not clean cache

* [Minor] Minor update for Python versions and dependencies

* [Lint] fix lint for py39

* [Lint] fix lint for ROCm

* [Build][CI] Sync CI changes from upstream/sdist

* [Lint] fix lint for ROCm

* [Build][CI] Update `repair-wheel-command`

* [Minor] update abi3audit result format

* [Lint] fix lint for ROCm

* [BugFix] fix build

* [Lint] fix lint for ROCm

* [BugFix] set rpath for libtvm and libtvm_runtime

* [Deps] pin apache-tvm-ffi version

* [Build] set Python 3.9 Limited API for Cython target

* [Build] set Python 3.9 Limited API for Cython target

* [Deps] Restore Python 3.8 support

* [Build] use `apache-tvm-ffi`'s `libtvm_ffi`

* [BugFix] use `;` as delimiter for RPATH on macOS

* [BugFix] use `--ignore-missing-dependencies` for `delocate-wheel`

* [Build] support `sccache` if available

* [Build] add CIBW import test

* [Build][CI] enable ccache for CIBW on Linux

* [BugFix] set rpath for libtvm and libtvm_runtime

* Revert "[Build][CI] enable ccache for CIBW on Linux"

This reverts commit cd9ab57bb5ddd2572c60bcbbebde81480a658fd3.

* [CI] fix perfbench bot

* [BugFix] use Python 3.9 to build wheel

* [Minor] update perfbench bot envs

* [BugFix] fix CIBW environment on Linux

* [CI] skip import test on CentOS 7

* [CI] use Python urllib to download file instead of Wget

---------
Co-authored-by: Xuehai Pan <XuehaiPan@pku.edu.cn>

* [Refactor] Refactor Pass  to support recursive load/store rewrite

* lint

* recursive collect conds for call_extern

* fix name

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

* lint

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

* lint

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

* address comment

* rename pad_value to safe_value

* lint

* add oob store test

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

* fix

* fix

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

* [Bugfix] Fix visit EvaluateNode in BufferGemmCollector

* address comment

* lint

* fix

* Add TileLang SplitHostDevice pass and tighten issue 830 test names

* lint fix

* enhance for kernel value unpacking.

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

* 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

* 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 `Let...

* [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.

[BugFix] Refactor the op check in LowerTileOp pass using the member function instead of string match (#771)

* [BugFix] Refactor the op check in LowerTileOp pass using the member function instead of string match

* [Lint]

* 📝 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>

* 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>

* [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

**Summarize part of the rebase pr:**

1. **Support T.thread_return() → CUDA return syntax**  
   Added support for translating `T.thread_return()` to CUDA's native `return` statement.

2. **Dynamic type support for function inputs**  
   Functions now accept dynamically typed parameters using `typing`:
   ```python
   dyn_type = T.int32 or T.float
   @T.prim_func
   def main(
       a: dyn_type,
   )
   ```

3. **Device Function Codegen**  
   Added support for generating `__device__` functions in CUDA:
   ```python
   @I.ir_module
   class Module:
       @T.prim_func(private=True)
       def add(a: T.int32, b: T.int32) -> T.int32:
           return a + b

       @T.prim_func
       def main(
           A: T.Buffer((128, 128), "int32"),
           B: T.Buffer((128, 128), "int32"),
           C: T.Buffer((128, 128), "int32"),
       ):
           T.func_attr({"global_symbol": "main"})
           length: T.int32 = Module.add(64, 64)  # Host call
           for bx in...

* fix CI bugs in hopper

* lint fix

* Update bulk_copy.cc

* Refactor bulk copy logic in LowerBulkCopy function

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

* test fix

* ci fix

* Update flash-attention dependencies and clean up example code

- Downgraded `flash-attn` dependency version in `requirements-test.txt` to `<=2.2.0`.
- Removed unused imports and commented-out code in various example files to enhance readability and maintainability.
- Updated the `flashattn` function signature to include default parameters for `block_M`, `block_N`, `num_stages`, and `threads`.
- Cleaned up the `example_mha_fwd_varlen.py` and `example_mha_bwd_wgmma_pipelined.py` fil...

* [Refactor] Enhance GEMM Warp Partitioning Logic and Introduce Buffer Remapping (#516)

* Improved the warp partitioning logic in `Gemm::ComputeWarpPartition` to better accommodate various GEMM policies, including FullRow, FullCol, and Square, ensuring optimal performance based on matrix dimensions.
* Introduced a new `RemapBufferRewriter` class to handle buffer reference updates and padding annotations during statement transformations, enhancing memory access safety and clarity.
* Updated the `OptimizeForTarget` function to include a new step for configuring index bitwidth, improving the overall optimization process.
* Refactored existing code to utilize constants for warp sizes, enhancing maintainability and readability.
* Added checks to ensure correct warp allocation and padding map handling, improving robustness in memory management strategies.

* [Refactor] Update ConfigIndexBitwidthRewriter to Support Auto-Check Feature

* Modified the constructor of `ConfigIndexBitwidthRewriter` to include an `auto_check` parameter, allowing for dynamic bitwidth adjustments based on input conditions.
* Enhanced the `VisitExpr_` methods to apply the new auto-check logic, ensuring that integer types are upgraded to 64 bits when necessary, or to a specified index bitwidth otherwise.
* Updated the `ConfigIndexBitwidth` pass to determine the index bitwidth based on the presence of configuration, improving flexibility in handling different scenarios.

* Add dynamic matrix multiplication example and corresponding test

* Introduced `example_dynamic.py` to demonstrate dynamic matrix multiplication using TileLang and PyTorch, including a main function for execution and performance profiling.
* Added `test_example_dynamic.py` to validate the functionality of the dynamic matrix multiplication example.
* The example includes detailed parameter configurations and checks against PyTorch's implementation for correctness.

* lint fix

* Add get_num_sms function to retrieve the number of streaming multiprocessors on the CUDA device

* Implemented the `get_num_sms` function in `cuda_driver.py` to return the count of streaming multiprocessors for a specified CUDA device.
* Updated the `__init__.py` file to include the new function in the module exports.

* lint fix

* Modified `makeBufferWithLayout` to include a `var_remap` parameter for improved variable remapping during buffer creation.
* Enhanced buffer load and store operations to utilize the new variable remapping logic, ensuring correct buffer references.
* Commented out a check in `ThreadExtent` for clarity, maintaining functionality while improving code readability.

* [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

* [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.

* [Enhancement] Introduce thread range management in layout and operation handling

* Added `SetThreadRange` method to `FragmentNode` for managing thread ranges.
* Updated `LayoutNode::Inverse` to provide more informative error messages.
* Refactored layout inference and operation lowering to utilize `thread_bounds` instead of `block_size`, enhancing flexibility for thread management.
* Introduced new tests for tilelang operations to validate thread range functionality and ensure correctness in parallel execution scenarios.

* lint fix

* [Refactor] Improve thread variable handling in layout inference and operation lowering

* Removed workaround for undefined thread_var in layout inference, ensuring proper handling of thread bounds.
* Updated logic to define thread bounds based on the presence of thread_var, enhancing robustness in thread management.
* Refactored thread_var initialization in lower_tile_op to maintain consistency across the codebase.

* [Refactor] Update thread variable handling in layout inference and operation lowering

* Refactored thread variable checks to ensure bounds are only accessed when defined, improving safety and clarity.
* Initialized thread_var with a default range to prevent undefined behavior.
* Updated logic in lower_tile_op to align with new thread variable handling, enhancing consistency across the codebase.

* Add TMA lowering configuration option and update copyright notices

This commit introduces a new configuration option to disable TMA (Tensor Memory Access) lowering and updates copyright notices across multiple files. Key changes include:

- Add `kDisableTMALower` configuration option in builtin.h and builtin.cc
- Update copyright notices from Microsoft Corporation to Tile-AI Corporation
- Modify `LowerArgs` struct to include `disable_tma_lower` flag
- Update JIT compilation interfaces to support pass configuration
- Enhance error reporting in bulk copy lowering
- Propagate pass configuration through various adapter layers

* lint fix

* Add DeepSeek MLA decode example with Flash Attention implementation

* Add GEMM SplitK and StreamK example implementations

This commit introduces two new example scripts demonstrating advanced GEMM (matrix multiplication) techniques:
- `example_tilelang_gemm_splitk.py`: Implements a Split-K GEMM kernel using TileLang
- `example_tilelang_gemm_streamk.py`: Implements a Stream-K GEMM kernel using TileLang

Both examples showcase different parallel computation strategies for matrix multiplication, with comprehensive testing using PyTorch reference implementations.

* Refactor GEMM SplitK and StreamK example implementations

Clean up and improve code formatting for the SplitK and StreamK GEMM example scripts:
- Remove unused import (Profiler) in splitk example
- Simplify line breaks and improve code readability
- Standardize indentation and remove unnecessary whitespace
- Optimize atomic add and copy operations for better clarity

* Add block sparse attention benchmarks for multiple libraries

This commit introduces comprehensive block sparse attention benchmarks for different libraries:
- TileLang block sparse FMHA implementation
- Triton block sparse FMHA implementation
- PyTorch reference block sparse FMHA implementation
- FlashAttention dense FMHA reference implementation

The benchmarks include:
- Configurable benchmark parameters (batch size, heads, sequence length, etc.)
- Sparse mask generation using top-k and threshold methods
- Performance measurement for different sparse attention configurations
- Utility functions for mask generation and benchmarking

* Refactor block sparse attention benchmarks with code style improvements

- Add Ruff linter ignore comments to benchmark files
- Improve code formatting and line breaks
- Remove unused imports
- Standardize print statement formatting
- Enhance code readability across multiple library benchmarks

* lint fix

* Add CUDA atomic operations for BFLOAT16 and update function naming

- Implement AtomicAdd functions for BFLOAT16 and BFLOAT16x2 in CUDA common header
- Rename existing atomic add functions to use PascalCase (atomicAdd -> AtomicAdd)
- Add a new __pack_nv_bfloat162 function for packing BFLOAT16 values
- Update kernel and language customization to use new function names
- Add return type annotations in profiler module

* lint fix

* Add example for Group Query Attention (GQA) forward pass using Flash Attention in TileLang

This commit introduces a new example script `example_gqa_fwd_bshd.py` that demonstrates:
- Group Query Attention (GQA) implementation
- Flash Attention forward pass
- Performance benchmarking
- Configurable parameters for batch, heads, sequence length, and dimension
- Autotuning support
- Reference implementation comparison

* Refactor IR lowering pipeline into modular phases

This commit introduces a new module `phase.py` to modularize the IR lowering process by splitting the complex lowering pipeline into two distinct phases:
- `LowerAndLegalize`: Handles initial IR legalization and transformation
- `OptimizeForTarget`: Applies target-specific optimizations

The changes simplify the lowering logic in multiple files by extracting the transformation steps into reusable functions, improving code readability and maintainability.

* lintfix

* nas kernel

* Enhance Native Sparse Attention Examples with Code Improvements and Parameter Updates

- Updated example_tilelang_nsa.py and example_triton_nsa.py with code formatting and style improvements
- Increased default number of heads and selected blocks in TileLang NSA example
- Added Ruff linter ignore comments to reference.py
- Standardized function signatures and improved code readability across NSA implementations

* Add utility math functions for integer operations

- Implement `next_power_of_2()` to calculate the next power of 2 for an integer
- Add `cdiv()` function for ceiling division of integers

* Add utility math functions for integer operations

- Implement `next_power_of_2()` to calculate the next power of 2 for an integer
- Add `cdiv()` function for ceiling division of integers

* Refactor DeepSeek MLA Decode Example with Enhanced Flash Attention Implementation

- Update flash attention kernel to support positional embeddings (PE)
- Modify reference implementation to handle PE and group query attention
- Increase default batch size and adjust benchmarking parameters
- Improve kernel performance and readability
- Add einops and torch operations for more flexible tensor manipulation

* Update README.md with corrected Flash MLA Decoding example path

- Modify the example link for Flash MLA Decoding to point to the correct directory
- Ensure accurate navigation to the DeepSeek MLA decoding example

* Refactor Native Sparse Attention Kernel and Improve Utility Functions

This commit introduces several improvements:
- Simplified native sparse attention kernel by inlining macro functions in example_tilelang_nsa.py
- Enhanced error handling in loop_partition.cc with more informative error messages
- Updated print.py to support multi-dimensional buffer printing
- Improved torch_assert_close in testing/__init__.py with more detailed mismatch reporting
- Reduced default absolute tolerance in torch comparison from 1e-3 to 1e-2
- Added shape validation and detailed mismatch information in tensor comparison

* Refactor Code Formatting and Improve Utility Functions

This commit introduces several code formatting and utility improvements:
- Add Ruff linter ignore comment in example_tilelang_nsa.py
- Enhance code readability in loop_partition.cc and lower_tile_op.cc with improved line breaks
- Simplify print_flat_buffer_with_condition in print.py
- Refactor torch_assert_close in testing/__init__.py with improved line formatting

* README.md fixed

* update test ci

* Lint and Typo Fix

* Clang Format Lint Fix

* Add format.sh script for code formatting and linting

* docs update

* center align the title

* lint fix

* add ignore

* Add .gitignore for 3rdparty directory

* Add requirements-dev.txt, requirements-test.txt, and requirements.txt

* 3rdparty

* Add gemm.h, CMakeLists.txt, _ffi_api.py, __init__.py, runtime.h, reduce.h, loop_partition.h, utils.h, and loop_vectorize.h

* Refactor CMakeLists.txt and include statements

- Update CMakeLists.txt to use a newer version of CMake and add project name
- Remove unnecessary include directories

Fix include paths in layout.cc, codegen.cc, codegen.h, rt_mod.cc, frontend_legalize.cc, inject_pipeline.cc, layout_inference.cc, loop_vectorize.cc, and lower_tile_op.cc

- Update include paths to use relative paths instead of absolute paths

* Update submodule for 3rdparty/tvm

* update

* load dll first

* Refactor CMakeLists.txt and include statements

* Refactor CMakeLists.txt and include statements

* git keep update

* Refactor CMakeLists.txt and include statements

* Refactor CMakeLists.txt and include statements

* refactor code structure

* Update Readme

* CMakeLists Customized

* update readme

* update README

* update readme

* update usage

* with TVM_IMPORT_PYTHON_PATH to handle own tvm build python import

* annotate lower transform global func with `transform` prefix

* Migrate Simplify Pass from tilelang tvm branch

* enhance system environment handling with __init__ and CMake

* Initial commit

* CODE_OF_CONDUCT.md committed

* LICENSE committed

* README.md committed

* SECURITY.md committed

* SUPPORT.md committed

* CODE_OF_CONDUCT Commit

* LICENSE Commit

* SECURITY Commit

* SUPPORT Commit

* Modify Support

* Update README.md

* security ci update

* remove examples

* Update and implement clang-format

* add composable kernel components

* Migrate from latest update

* submodule update

* Test update

* Update License

* Spell check

* lint fix

* add clang-tidy to apply static analysis for c source

* update tilelang examples

* Update Install Docs

* Refactor filetree

* Enhance Install

* conflict resloved

* annotate_version

* Initial Update

* test fix

* install

* Implement setup.py

* lint fix

* Separate Init

* Separate test

* docker file commit

* add logo

* Update Readme and Examples

* update readme

* update logo

* Implement AMD Installation

* Add License

* Update AMD MI300x Benchmark

* update README

* update mi300 benchmark scripts

* update ignore

* enhance build scirpt

* update image

* enhance setup.py to remove duplicated libraries

* remove debug files

* update readme

* update image

* update gemm examples

* update flashattention README

* readme update

* add cmake into requirements

* libinfo fix

* auto update submodule

* lint fix

* Fix AMD Build and Test

* Update check for transpose attribute for CDNA Arch

* typo fix for amd

* Implement Matmul Benchmark

* Refactor Code

* [TypoFix] Fix GEMM Example

* [Docs] Init Linear Attention README

* [TYPO] Typo fix

* [Lint] Lint Fix

* enhance example with intrinsics

* [Enhancement] Improve Buffer Collection during IR Parser

* [Dev] Introduce Current classmethod to get current frame

* submodule update

* fake test pass update

* support thread_extent_api

* code optimize

* Add GEMM function implementation for matrix multiplication

* Update logging format to reflect TileLang in logger messages

* Refactor CMakeLists.txt for improved readability and set default build type to Release

* Support Gemm SS Primitives Implementation

* [README] Upload Tile Language Logo (#5)

* update logo

* Update README.md to enhance formatting and center the title

---------
Co-authored-by: microsoft-github-operations[bot] <55726097+microsoft-github-operations[bot]@users.noreply.github.com>
Co-authored-by: Microsoft Open Source <microsoftopensource@users.noreply.github.com>
Co-authored-by: Yu Cheng <yu.cheng@pku.edu.cn>