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* 📝 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.

* 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>

* [experimental] add a draft gemm_sp

* [3rdparty] bump cutlass to v3.9.3

* [lint] run format.sh

* [chore] rebase

* [chore] use abs path

* [gemm_sp] add metadata layout

* [ci] add more example

* [lint] run format.sh

* [chore] polish

* [chore] move gemm_sp to experimental

* [chore] polish

* [lint] run format.sh

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

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

* Implement Test

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

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

* lint fix

* [gemm_sp] support more layout and data types

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

* Enhancement: support more block_k in compress util

* [Enhancement] enable block_k=64

* [Lint] run format.sh

* [Enhancement] compressor support more dtype

* Enhancement: enable block_K=32

* [Lint] format.sh

* [Fixbug] fix shape

* Refactor: sync gemm

* [Enhancement] enable transpose

* [Enhancement] enable fp8_e4m3

* [Enhancement] enable int8

* [Lint] run format.sh

* [Benchmark] add gemm_sp benchmark

* [Example] fix 256 threads hang

* [CI] fix ci

* [Chore] resolve gemini feedback

* [Benchmark] increase search space

* [Lint] format

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

* [CI] pass local run

* Update gemm_sm89.h

* lint fix

* lint fix

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

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

* Update test_compress_utils.py

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

* [Refactor] 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

* remove llvm build

* [Refactor] Update kernel compilation and profiling in examples

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

* lint fix

* License Update

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

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

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

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

* lint fix

* lint fix

* lint fix

* lint fix

* fix

* License update

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

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

* lint fix

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

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

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

* Refactor TVM_LIBRARY_PATH assignment for improved readability in env.py

* Refactor CMakeBuild file handling in setup.py

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

* bugfix

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

* lint fix

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

* lint fix

* Add support for C target in device code generation

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

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

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

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

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

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

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

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

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

* lint fix

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

* [Enhancement] Add zero initialization option to GEMM operations

- Introduced a new `zero_init` parameter to the GEMM function, allowing for optional zero initialization of the accumulator.
- Updated the GEMM implementation across various CUDA architectures to support the new parameter.
- Modified the Python interface for GEMM to include the `zero_init` argument, enhancing flexibility in kernel execution.
- Ensured compatibility with existing functionality while improving initialization control for performance optimization.

* rename zero_init to clear_accum

* lint

* [BugFix] Fix bug of missing MBarrierExpectTX

* [Dev] Implement FlashAttention3 Backward

- Added a new example for Flash Attention using pipelined WGMMA, including forward and backward pass implementations.
- Introduced functions for forward and backward processing, leveraging tilelang for optimized tensor operations.
- Enhanced the attention mechanism with support for both causal and non-causal configurations.
- Included command-line arguments for batch size, number of heads, context size, and head dimension for flexibility in testing.
- Updated GEMM operations to support a new `wg_wait` parameter for improved synchronization in kernel execution.

* Enhance error message for constant size stack allocation in CUDA codegen. Include the actual constant size and buffer variable name in the error output for better debugging.

* Refactor GEMM and Bulk Copy operations to enhance layout handling and support for Hopper architecture

- Update `ComputeWarpPartition` to include a new parameter for Hopper WGMMA support.
- Modify layout checks in `LowerBulkCopy` to accommodate new GEMM layout types.
- Enhance layout inference logic in `InferLayout` for better compatibility with Hopper architecture.
- Include necessary header files for built-in operations and layout inference improvements.

* lint fix

* Remove unused builtin.h include directive

* Update include path for builtin.h

* 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>