GitLab

* feat: CuTeDSL backend

* fix: clang-tidy

* fix: clang-format

* fix: ci

* fix: revert example gemm fp8

* fix: remove duplicate code

* fix: switch-case

* fix: fp16 silence

* fix: TVM IR print

* fix: useless tir

* fix: clang-format

* fix: remove tilelang/contrib/cutedsl/.gitignore

* fix: use hexfloat

* fix: gsym guard

* fix: unknown storage sync type

* fix: string literal

* fix: add args guard

* fix: name hint dedup

* fix: better find_kernel_by_pattern

* fix: set libpath for from_database path

* fix: guard buffer.strides

* fix: from guard

* fix: eviction guard

* fix: use thread local tma descs

* fix: ruff

* fix: drop tma_init_cpp

* fix: exc_info

* fix: negative unmatch early return

* fix: rename postproc func and add test

* fix: handle fast math according to pass config

* fix: dyn_sym parse

* fix: wrap_forward

* fix: use tvm_ffi.libinfo instead of cli

* fix: keep signature

* fix: C++ string safety

* fix: mark tma_store_add as unsupported

* fix: tvm version

* resolve ldsm and cpasync issues.

* fix: minor fixes

* fix: parse signature using ast

* fix: guard global_addr

* fix: create tempfile only when necessary

* fix: use logger.execption for exceptions

* fix: guard lib_path and host_func

* fix: remove tma_cpp_init and add timeout for cpp compile

* add timeout for mbarrier_wait.

* fix: _load_kernel_from_disk signature

* resolve codegen issues.

* fix: logger.exception

* add comment for div_by=1

* merge

* fix: reserve cutlass,cute,tl

* fix: guard tma_store

* fix: allow int64 offset in make_tensor_at_offset

* fix: guard barrier

* fix: add comments for div_by=16

* fix: div_by=1 issue

* delete div_by when offset is 0

* use tl.make_tensor when offset is 0

* fix: explicitly check cutedsl target

* fix: use param.torch_dtype()

---------
Co-authored-by: yuxic <yuxic@nvidia.com>
Co-authored-by: Yong <yong@local>
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

* [Enhancement] Update examples and tests for improved type handling and functionality

- Enhanced various example scripts to support new data types and improve compatibility with PyTorch.
- Updated tests across multiple modules to ensure correct functionality with the latest changes in type handling.
- Refactored code in examples to streamline operations and improve clarity, particularly in tensor operations and memory management.
- Added comprehensive tests for new features and fixed existing issues related to type conversions and buffer handling.

* [Refactor] Update accumulation data type to float32 across examples

- Changed accumulation data type from "float" to T.float32 in multiple example scripts to ensure consistency and improve numerical stability.
- This update affects various modules including flash attention, GEMM analysis, convolution, and deepseek MLA examples, enhancing type handling across the board.

* [Refactor] Standardize data type usage across benchmark scripts

- Updated data type definitions in benchmark scripts to use T.float16 and T.float32 consistently, enhancing clarity and type handling.
- Adjusted dtype assignments in matmul functions and configuration setups to align with the new standard.
- Improved overall code consistency and maintainability by ensuring uniform data type usage across various modules.

* [Refactor] Standardize data type usage in templates and scripts

- Updated data type definitions in various templates and scripts to use string representations (e.g., "float16", "int32") instead of T.float16 and T.int32 for improved consistency and clarity.
- Enhanced overall code maintainability by ensuring uniform data type usage across multiple modules, including convolution, elementwise operations, and matrix multiplication templates.
- This change aims to streamline type handling and improve compatibility with existing workflows.

* [Refactor] Standardize data type usage in examples and benchmarks

- Updated data type definitions in various example and benchmark scripts to use T.float16 and T.int32 consistently, enhancing clarity and maintainability.
- Adjusted dtype assignments in kernel functions and configuration setups to align with the new standard.
- Improved overall code consistency by ensuring uniform data type usage across multiple modules, including attention mechanisms, matrix multiplication, and GEMM examples.

* [Refactor] Import dtypes from language.v2 module

- Added import statement for dtypes from the language.v2 module to enhance type handling and maintain consistency across the codebase.
- This change aims to streamline data type management and improve overall code clarity.

* fix

* [Refactor] Standardize data type usage across scripts

- Updated data type definitions in various scripts to use string representations (e.g., "float16", "int8") instead of T.float16 and T.int8 for improved consistency and clarity.
- Adjusted dtype assignments in functions and configuration setups to align with the new standard, enhancing overall code maintainability.
- This change affects multiple modules, including benchmark and attention mechanisms, ensuring uniform data type usage throughout the codebase.

* [Refactor] Update data type handling for consistency and clarity

- Changed string representations of data types in the Hint class to use T.float32 and T.int32 for improved consistency.
- Added new data types "int4" and "int16" to the dtypes module, enhancing type support across the codebase.
- Updated function signatures and assertions in the lop3 and mxfp modules to utilize the new data types, ensuring uniformity in type handling.
- This refactor aims to streamline data type management and improve overall code clarity and maintainability.

* [Enhancement] Improve data type handling and error messaging

- Introduced a mapping for canonical data types to their display strings, enhancing clarity in type representation.
- Updated the dtype creation logic to utilize the new mapping, ensuring more intuitive handling of string inputs.
- Refined error messages in the lop3 module to provide clearer feedback on invalid source formats, improving debugging and user experience.

* [Fix] Correct boolean flag in GEMM SP test case

- Updated the boolean flag in the test_gemm_sp_sm90 function to ensure proper functionality in the test case.
- This change enhances the accuracy of the test and aligns it with expected behavior for the GEMM SP implementation.

* [Refactor] Standardize data type usage across scripts

- Updated data type definitions in various scripts to use T.float16 and T.bfloat16 consistently, enhancing clarity and maintainability.
- Adjusted dtype assignments in function signatures and argument parsing to align with the new standard, ensuring uniform data type usage throughout the codebase.
- This change affects multiple modules, including benchmarks and examples, improving overall code consistency and readability.

* [Refactor] Standardize data type usage in various modules

- Updated data type assignments in multiple scripts to utilize T.float32, T.int8, and T.int32 consistently, enhancing clarity and maintainability.
- Adjusted function signatures and parameter types across benchmarks, examples, and tests to align with the new standard, ensuring uniform data type usage throughout the codebase.
- This change improves overall code consistency and readability, impacting modules related to matrix multiplication, GEMM, and tensor operations.

* [Refactor] Update argument parsing for data types in benchmarks

- Changed argument parsing for data types in benchmark_matmul_intrinsic.py and benchmark_matmul_sp.py to use string representations ("float16", "int8", "float") instead of T.float16 and T.float.
- This update enhances consistency in data type handling across benchmark scripts, improving clarity and maintainability.

* [Refactor] Update data type handling in benchmark and example scripts

- Changed data type arguments in benchmark and example scripts to use string representations ("float16") instead of T.float16 for improved consistency.
- Updated function signatures and argument parsing to align with the new standard, enhancing clarity and maintainability across the codebase.
- This change affects multiple modules related to attention mechanisms and tensor operations, ensuring uniform data type usage throughout the examples.

* [Refactor] Fix data type conversion in multiple scripts

- Corrected the usage of the data type conversion method from dtype..as_torch() to dtype.as_torch() across various benchmark and example scripts.
- This change enhances consistency in data type handling and improves code readability, impacting modules related to attention mechanisms and tensor operations.

* [Refactor] Update float8 data type usage across multiple scripts

- Changed instances of T.float8_e4m3 to T.float8_e4m3fn in various benchmark, example, and test scripts to ensure consistency in data type handling.
- This update enhances clarity and maintainability across the codebase, particularly in modules related to matrix multiplication and tensor operations.

* [Refactor] Enhance float8 data type handling in CUDA code generation

- Updated the handling of float8 data types in the CUDA code generation to include additional float8 variants, improving type conversion logic.
- Adjusted conditions to ensure proper type checks for float8 conversions, enhancing clarity and maintainability in the codebase.
- Modified layout inference to streamline float8 type checks, ensuring consistency across the implementation.
- This change impacts modules related to matrix operations and CUDA code generation, improving overall type handling and conversion accuracy.

* [Refactor] Streamline float8 data type handling in CUDA and related modules

- Enhanced float8 data type handling in CUDA code generation by refining type conversion logic and ensuring consistent type checks.
- Updated layout inference for float8 types to improve clarity and maintainability across the implementation.
- This change impacts modules related to matrix operations and CUDA code generation, improving overall type handling and conversion accuracy.

* [Refactor] Remove unnecessary cache disabling in float8 example script

- Eliminated the call to tilelang.disable_cache() in example_group_per_split_token_cast_to_fp8.py to streamline the code.
- This change enhances clarity and maintainability of the example script without affecting its functionality.

* [Refactor] Update data type usage in debug print tests

- Changed the argument for dtype in the test_debug_print_buffer function from a string representation to the corresponding T.bool type.
- This update enhances consistency in data type handling within the test suite, improving clarity and maintainability.

* lint fix

* Update function parameter types from `str` to `T.dtype` for improved type safety in attention sink and related examples

* Refactor `gemv_alloc_reducer` function signature for improved readability by formatting parameters across multiple lines.

* [Dependency] Add torch-c-dlpack-ext to project requirements

* Added torch-c-dlpack-ext to both pyproject.toml and requirements.txt to provide prebuilt torch extensions, which may prevent JIT compilation on first import of TVM FFI.

* [Build] Update manylinux images in project configuration

* Changed the manylinux image for x86_64 from "manylinux2014" to "manylinux_2_28" in both pyproject.toml and the Dockerfile to align with updated standards for compatibility and performance.

* [Build] Update CUDA repository configuration in pyproject.toml

* Changed the package manager command from `yum-config-manager` to `dnf config-manager` for adding the CUDA repository, ensuring compatibility with newer systems.

* fix

* [Build] Update CUDA repository to RHEL 8

* Changed the CUDA repository configuration in both pyproject.toml and the manylinux Dockerfile from RHEL 7 to RHEL 8, ensuring compatibility with newer systems.

* test: run out of space

* use cu130 to reduce size

* upd

* upd comment

* upd

---------
Co-authored-by: Your Name <wenji.yyc@alibaba-inc.com>

* Update VERSION to 0.1.7

* Update Python version in distribution scripts to support CPython 3.9 and log output

* [Refactor] Improve assertion handling in CodeGenCHost and ArgBinder

This commit refines the assertion message generation in CodeGenCHost by optimizing the handling of equality checks and reducing buffer size for error messages. Additionally, it enhances the ArgBinder by introducing a nullable guard mechanism for assertions, allowing for more precise error handling when binding arguments. The changes improve the clarity and efficiency of assertion handling across the codebase.

* [Enhancement] Update matmul kernel and optimize argument binding

This commit enhances the matmul kernel by introducing additional tensor parameters and refining the pipeline stages for improved performance. It also updates the argument binding mechanism to include a flag indicating whether buffers are used, enhancing the efficiency of buffer management. Furthermore, the optimization phase in the engine is improved by adding a simplification step, ensur...

* Add correctness evaluation script for GEMM v2

- Introduced a new Python script `correctness_evaluation_tcgen05.py` for testing the correctness of GEMM v2 implementations using pytest.
- Implemented matrix multiplication and compilation checks, along with parameterized tests for various input configurations.
- Enhanced the testing framework to validate GEMM operations with different data types and configurations, ensuring robustness in the implementation.
- Updated logging in `legalize_negative_index.cc` to reduce verbosity by changing from WARNING to DLOG.
- Adjusted assertions in `tcgen05_macro_generator.py` to accommodate new warp size requirements for improved performance.
- Removed unused variable in `gemm_tcgen05.py` to streamline the codebase.

* lint 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...

* [Feature] Add support for SM70 tensor core MMA instructions

- Introduced new intrinsic `ptx_mma_sm70` for Volta GPUs, enabling m16n16k4 shape with FP16 inputs and FP16/FP32 accumulation.
- Added `GemmMMASm70` class for handling GEMM operations specific to SM70 architecture.
- Implemented layout functions for Volta swizzled layouts and updated existing GEMM layout inference logic.
- Updated `requirements-dev.txt` to include `apache-tvm-ffi` dependency.
- Added correctness evaluation script for testing GEMM operations on SM70.

* [Refactor] Update formatting and installation commands in scripts

- Modified `format.sh` to install `pre-commit` and `clang-tidy` with the `--user` flag for user-specific installations.
- Improved readability in `correctness_evaluation_sm70.py` by adjusting the formatting of pytest parameters.
- Cleaned up spacing and formatting in various C++ source files for better consistency and readability.
- Removed unnecessary comments and improved layout function definitions in `mma_sm70_layout.py` and `mma_sm70_macro_generator.py` for clarity.
- Ensured consistent formatting in layout initialization and swizzle functions.

* typo fix

[Refactor] Dynamic registration of FP8 data type for compatibility with older PyTorch versions (#1197)

* 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

* update exclude in sdist

* reuse cibw workflow in maint

* update

* fix

* fmt

* upload artifacts for [Release] PRs

* dot-prefix version file

* update

* fix

* lint fix

* fix

* lint fix

* fix

* upd

* [Release] Update README and VERSION for v0.1.6.post2 compatibility with Python 3.8

* [Enhancement] Update packaging configuration and Docker scripts for multi-architecture support

* Add allowlist for TVM, CUTLASS, and Composable Kernel items in pyproject.toml
* Enhance docker_local_distribute.sh to support cross-architecture builds using docker buildx
* Modify pypi.manylinux.Dockerfile to accept TARGETARCH argument for better architecture handling

* [Enhancement] Improve Docker scripts and build process for multi-architecture support

* Update .gitignore to include dist directories
* Refactor docker_local_distribute.sh for better cross-architecture handling and error management
* Enhance docker_pypi_distribute.sh to support multi-architecture builds with docker buildx
* Modify pypi_distribution.sh to clean up additional directories
* Update pypi.manylinux.Dockerfile for improved environment configuration and architecture handling

* fix

* Remove outdated classifier for Artificial Intelligence from pyproject.toml

* Update pyproject.toml classifiers and modify Docker distribution scripts for clarity

* Add new classifier for Artificial Intelligence in pyproject.toml
* Rename output directories in docker_local_distribute.sh and docker_pypi_distribute.sh for better context

* [Cleanup] Remove `tilelang.disable_cache()` calls from example scripts

* lint

* lint

* Donot lower ceildiv to >>

* lint fix

* test fix

* fallback ceildiv changes

* cleanup

* init

* build first wheel that may not work

* build cython ext

* fix tvm build

* use sabi

* update rpath to support auditwheel

* pass editible build

* update ci

* fix warnings

* do not use ccache in self host runner

* test local uv cache

* test pip index

* update lib search to respect new lib location

* fix

* update ci

* enable cuda by default

* update src map

* fix

* fix

* fix

* Generate version with backend and git information at build time

* copy tvm_cython to wheels

* fix tvm lib search

* fmt

* remove unused

* auto detect ccache

* add back backend-related files

* remove jit cython adaptor to simplify code

* fmt

* fix ci

* ci fix 2

* ci fix 3

* workaround metal

* ci fix 4

* fmt

* fmt

* Revert "ci fix 4"

This reverts commit d1de8291c3e40927955f3ad3cf87a75c78813676.

* tmp

* fix metal

* trivial cleanup

* add detailed build-time version for cuda

* add back mlc

* Restore wheel info and other trivial updates

*...

* Update MANIFEST.in and setup.py to include commit ID in versioning and adjust included files

- Modified MANIFEST.in to include shared library files `libtvm.so` and `libtvm_runtime.so`.
- Updated setup.py to conditionally include the commit ID in the package version based on the `WITH_COMMITID` environment variable.
- Enhanced versioning logic in version.py to use a truncated commit ID for better compatibility.

* Update setup.py and related scripts to enable commit ID inclusion in package metadata

- Changed the default value of the `WITH_COMMITID` environment variable in setup.py to "True".
- Updated tox.ini to set `WITH_COMMITID` to "TRUE" for the testing environment and "FALSE" for the build environment.
- Modified pypi_distribution.sh to pass `WITH_COMMITID=FALSE` during the wheel build process.

* Update MANIFEST.in to include additional files and directories for packaging

- Added VERSION, CMakeLists.txt, and various requirements files to the package.
- Included recursive inclusion of source files and third-party libraries, while excluding specific clang and llvm directories.

* 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 ope...

* bump version to 0.1.6

* phaseout py38

* py39

* Update submodule 'tvm' to latest commit adc0e48

* [Build] Update CMake and Python environment settings

- Added static linking flags for GCC and libstdc++ in CMakeLists.txt to enhance library linking.
- Removed the cmake version requirement from pyproject.toml to allow for broader compatibility.
- Updated the tox command in the Docker distribution script to include Python 3.8 for testing environments.

* [Build] Update Python version requirements in scripts and documentation

- Changed Python version requirement in README.md from 3.9+ to 3.8+.
- Updated installation and testing scripts to use Python 3.8 instead of 3.9, ensuring compatibility with the new minimum version.
- Adjusted tox commands in local and PyPI distribution scripts to include Python 3.8 in the testing environments.

* [Build] Update Python and CMake requirements in Dockerfile and pyproject.toml

- Added CMake version requirement (>=3.26) to pyproject.toml for build compatibility.
- Created a Python 3.8 environment in the Dockerfile and added a symlink for easier access to the Python 3.8 executable.

* [Build] Update CMake and Dockerfile for improved compatibility

- Removed static linking flags from CMakeLists.txt to simplify build configuration.
- Updated Dockerfile to use Ubuntu 20.04 and streamlined the installation of dependencies, removing gcc-9 and g++-9.
- Adjusted symlink creation for Python environments to use the `-sf` option for safer linking.

* [Build] Bump version to 0.1.6.post1 for post-release updates

* [Build] Remove static linking flags from CMakeLists.txt

- Eliminated static linking flags for GCC and libstdc++ to simplify build configuration and avoid potential conflicts with Python extensions.

* [Build] Update Docker distribution scripts for manylinux compatibility

- Changed base image from `tilelang-builder:18.04` to `tilelang-builder:manylinux` in both local and PyPI distribution scripts.
- Updated Dockerfile references to use `pypi.manylinux.Dockerfile`.
- Added `--gpus all` flag to the Docker run command to enable GPU support during execution.

* lint fix

* add cmake

* bump version to 0.1.6

* phaseout py38

* py39

* Update submodule 'tvm' to latest commit adc0e48

* [Build] Update CMake and Python environment settings

- Added static linking flags for GCC and libstdc++ in CMakeLists.txt to enhance library linking.
- Removed the cmake version requirement from pyproject.toml to allow for broader compatibility.
- Updated the tox command in the Docker distribution script to include Python 3.8 for testing environments.

* [Build] Update Python version requirements in scripts and documentation

- Changed Python version requirement in README.md from 3.9+ to 3.8+.
- Updated installation and testing scripts to use Python 3.8 instead of 3.9, ensuring compatibility with the new minimum version.
- Adjusted tox commands in local and PyPI distribution scripts to include Python 3.8 in the testing environments.

* [Build] Update Python and CMake requirements in Dockerfile and pyproject.toml

- Added CMake version requirement (>=3.26) to pyproject.toml for build compatibility.
- Created a Python 3.8 environment in the Dockerfile and added a symlink for easier access to the Python 3.8 executable.

* bugfix

* [Build] Update build dependencies and Dockerfile configuration

- Updated `pyproject.toml` and `requirements-build.txt` to specify Cython version as `Cython>=3.0.0`.
- Removed unnecessary dependencies from the build system.
- Enhanced `pypi.Dockerfile` to install gcc-9 and g++-9, and added ninja-build for improved build performance.
- Updated conda environment creation to include Python 3.9 to 3.12, while removing the Python 3.8 environment.

* cmake fix

* fix

* fix

* [Dequant] Add bit-twiddling dequantize cuda for fp4-->bf16

* [Dequant] Add extern call and serial dequantization

* [Dequant] Parallel Dequant wait for fence debug.

* [Scale] Add scale matrix to mxfp4 gemm

* [Remove] Remove fence-buggy example and some generated source cuda code

* [MXFP4] Update initial version of MXFP4 GEMM

* [Scale] Add scale to latest mxfp4 gemm

* [Lint]

* [BugFix] Load Scale, disabe TMA to recover performance

* [Lint]

* [Lint]

* [Scale] Use L2 to hold Scale and enable TMA will slightly boost performance

* [Lint]

* Update example_dequant_gemm_bf16_fp4_hopper_serial.py

* Remove deprecated dequantization examples for BF16 and MXFP4 in the dequantize_gemm directory.

* Refactor dequantization examples for improved readability and consistency. Adjusted formatting in matmul function and added spacing for clarity. Updated function signatures and comments for better understanding.

* Refactor index_to_coordinates usage in bitnet example and update dequantization example configurations. Removed the custom index_to_coordinates function and replaced it with the built-in version. Adjusted block_K parameter in dequantization example for consistency.

* lint fix

* ci fix

* Remove non-existent example

* [BugFix] Add smem swizzle to recover performance of TMA

* [BugFix] Enough reg for producer when threads=512

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

* [Bugfix][Docs] Update documentation build process and configurations for autoapi support

* lint fix

* Update VERSION to 0.1.5

* Add DEBUG_MODE support in setup.py and update CMake build type; enhance pypi.Dockerfile with git installation

* [Enhancement] Add commit ID to versioning and improve logging initialization

* Updated `get_tilelang_version` to include an optional commit ID in the version string.
* Enhanced the `TileLangBuilPydCommand` to write the version with commit ID to the VERSION file during the build process.
* Introduced a new function `get_git_commit_id` in `version.py` to retrieve the current git commit hash.
* Refactored logger initialization in `autotuner/__init__.py` to ensure handlers are set up only once, improving performance and clarity.
* Minor fixes in `flatten_buffer.cc` and `kernel_cache.py` for better handling of versioning and logging.

* [Refactor] Enhance AutoTuner and JITKernel for improved performance and caching

* Refactored the AutoTuner class to include new methods for setting compilation and profiling arguments, enhancing configurability.
* Introduced caching mechanisms for tuning results, allowing for faster retrieval of previously computed configurations.
* Updated JITKernel to store tuning results, including latency and configuration details, improving the kernel's performance tracking.
* Added new methods for generating cache keys and saving/loading results to/from disk, streamlining the tuning process.
* Enhanced the overall structure and readability of the autotuning logic, ensuring better maintainability and clarity.
* Minor adjustments in related modules to support the new caching and profiling features.

* [Refactor] Clean up code formatting and improve readability in AutoTuner and related modules

* Consolidated import statements and removed unnecessary line breaks for better readability.
* Standardized function argument formatting across the AutoTuner and CompileArgs classes.
* Enhanced consistency in the use of whitespace and indentation throughout the codebase.
* Minor adjustments in the Profiler and JITKernel classes to improve clarity and maintainability.
* Ensured that all changes adhere to the project's coding style guidelines.

* [Refactor] Remove redundant type hints in AutoTuner modules

* Simplified import statements in `__init__.py` and `param.py` by removing unnecessary duplicate type hints for `Any`.
* Improved code readability and maintainability by streamlining type imports across the AutoTuner module.

* [Refactor] Update AutoTuner configuration for improved profiling and target detection

* Enhanced the AutoTuner configuration across multiple examples by adding `set_profile_args` to better manage profiling settings.
* Standardized the use of `target="auto"` in compile arguments to ensure automatic target detection.
* Removed redundant target specifications in certain instances to streamline the configuration process.
* Improved overall clarity and maintainability of the autotuning logic in various example scripts.

* [Refactor] Simplify code formatting and improve readability in example scripts

* Consolidated function argument formatting in `benchmark_mla_decode_amd_tilelang.py`, `example_elementwise_add.py`, and `performance.py` for better clarity.
* Removed unnecessary line breaks and standardized argument placement across multiple files.
* Enhanced overall code readability and maintainability in autotuning examples and performance scripts.

* [Refactor] Update JIT decorator usage across multiple files

* Removed redundant parameters from the JIT decorator in various benchmark and example scripts, simplifying the code.
* Standardized the import of the JIT decorator from `tilelang`, enhancing consistency across the codebase.
* Improved overall readability and maintainability by consolidating import statements and cleaning up function definitions.

* [Refactor] Standardize JIT decorator formatting across benchmark and example scripts

* Simplified the formatting of the JIT decorator in multiple files by removing unnecessary line breaks.
* Enhanced code readability and consistency in the usage of the JIT decorator across benchmark and example scripts.
* Improved overall maintainability by ensuring uniformity in function definitions and decorator usage.

* fix bug

* update performance.py

* update python api docs

* test workflow

* fix dependency

* fix bug

* fix

* update correct git config

* test workflow

* clear cache

* lint fix

* fix exclude path

* [Refactor] Improve flash attention example and layout comparison logic

- Removed unnecessary annotation for `lse_local_split` in the flash attention example to streamline the code.
- Updated the handling of `lse_local_split` to utilize parallel processing for better performance.
- Refactored kernel compilation and profiling logic to enhance clarity and maintainability in the flash attention example.
- Added a condition in `FragmentNode::IsEqual` to handle broadcast cases, improving the robustness of layout comparisons.

* lint fix

* [Enhancement] Add support for shared memory scope in Fill operation

- Introduced handling for `shared.dyn` and `shared` memory scopes in the Fill operation.
- Implemented parallel operation and layout inference for improved performance in shared memory scenarios.
- Updated thread loop partitioning and vectorization logic to accommodate new memory scope handling.

* [Refactor] Remove deprecated decorator and enhance Cython kernel handling

- Removed the deprecated decorator from the main module and added a new implementation in the utils module for better organization.
- Introduced a pointer map in the Cython kernel adapter to manage pointer arguments, improving runtime shape resolution.
- Updated the Cython kernel wrapper to utilize the new pointer map for handling kernel arguments.
- Enhanced error checking in the tensor utility functions to ensure static shapes are enforced.
- Added a new proxy module for buffer and tensor handling, streamlining the interface for TIR programs.

* [Feature] Add matrix multiplication test and kernel implementation

- Introduced a new test file `test_tilelang_language_ptr.py` that implements a matrix multiplication function using TileLang's primitives.
- The `matmul_test` function defines a kernel for performing tile-level GEMM operations with customizable block sizes and data types.
- Added a `run_matmul` function to compile and execute the kernel, along with a test function to validate the implementation.
- Updated the `proxy.py` file to enhance type handling for buffer and tensor proxies, ensuring compatibility with TIR programs.
- Minor formatting improvements in `deprecated.py` for better readability.

* lint fix

* [Refactor] Update tensor creation in matrix multiplication test

- Replaced `T.Tensor.from_ptr` with `T.make_tensor` in `matmul_test` for improved clarity and consistency.
- Updated imports in `__init__.py` to include `make_tensor`.
- Added `make_tensor` function in `proxy.py` to streamline tensor creation from pointers.

* [Refactor] Update tensor definitions across multiple files

- Replaced instances of `T.Tensor` with updated tensor definitions in various benchmark and example files to enhance consistency and clarity.
- Adjusted tensor shapes and types in functions related to matrix multiplication, attention mechanisms, and other operations.
- Improved documentation in README and example files to reflect changes in tensor usage.

* lint fix

* [Refactor] Update tensor types in attention and matrix multiplication examples

- Replaced instances of `T.Tensor` with `T.SharedTensor` and `T.FragmentTensor` in various attention and matrix multiplication functions to improve consistency and clarity.
- Adjusted tensor definitions in benchmark and example files to align with the new tensor types.
- Enhanced the overall structure and readability of the code by standardizing tensor usage across multiple files.

* lint fix

* [Refactor] Update tensor types in GEMM example and test files

- Replaced instances of `T.Tensor` with `T.LocalTensor` and `T.Buffer` in the GEMM example and related test functions to improve consistency and clarity.
- Enhanced the overall structure of the code by standardizing tensor usage across multiple files, aligning with recent updates in tensor definitions.

* [Refactor] Update tensor usage in customize.py

- Replaced instances of `T.Tensor` with `T.Buffer` in the `reshape` and `view` functions to enhance consistency with recent tensor definitions.
- Improved code clarity by standardizing buffer usage across the file.

* [Refactor] Update tensor types in test_tilelang_transform_annotate_device_regions.py

- Replaced instances of `T.Tensor` with `T.Buffer` in the `before` and `expected` methods of the `TestAnnotateThreadExtent` and `TestAnnotateDeviceScope` classes to enhance consistency with recent tensor definitions.
- Improved code clarity by standardizing buffer usage across the test file.

* [Refactor] Update tensor types to SharedBuffer and FragmentBuffer

- Replaced instances of `T.SharedTensor` and `T.FragmentTensor` with `T.SharedBuffer` and `T.FragmentBuffer` across multiple benchmark, example, and test files to enhance consistency with recent tensor definitions.
- Improved code clarity and structure by standardizing buffer usage in attention and matrix multiplication functions.

* [Refactor] Introduce Tensor alias for Buffer in proxy.py

- Added a new alias `Tensor` for `Buffer` in `proxy.py` to facilitate JIT compilation, ensuring that inputs and outputs are mapped with `torch.Tensor`.
- This change enhances clarity and consistency in tensor usage across the codebase.

* [Typo] Fix formatting in installation instructions in README.md

* [Enhancement] Improve CUDA path detection and update configuration handling

* fix typo

* remove IS_WINDOWS constant

* lint fix

* Improve error messages for CUDA detection failure

* lint fix

* lint fix

* Fix .gitignore to correctly include venv directory

* [Doc] Add instructions for installing nightly version of TileLang

* update installation instructions

* update install instruction

* update performance ci

* update

* update

* update

* update ci workflow

* delete test.yml

* lint fix

* update bot.yml

* update bot.yml

* remove changes in ci.yml

* Bump version to 0.1.3

* Refactor Docker script to streamline installation commands

- Removed the installation of the Python environment and CMake from the Docker run command, simplifying the execution process.
- Updated the command to focus on pip installation and running tox for testing across multiple Python versions.

* use auditwheel to get correct manylinux wheels

* fix

* make py3.8 happy

* trivial updates

* Add typing.Tuple import and update annotations

* fmt

* Remove unused import and update type hints

* lint fix

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

* 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

* [Build] Improve build configuration and package distribution support

- Add `build` to requirements-build.txt for package building
- Update MANIFEST.in to include Cython wrapper source file
- Enhance setup.py to improve Cython file copying logic
- Update build scripts to support multi-Python version distribution

* [Build] Improve Cython file handling in setup.py and MANIFEST.in

- Remove Cython wrapper from MANIFEST.in
- Enhance setup.py to create target directory if it doesn't exist when copying Cython files
- Improve file copying logic for Cython source files during build process

* [Build] Remove Cython file copying logic in setup.py

- Comment out Cython file copying code in TileLangBuilPydCommand
- Simplify setup.py build process by removing redundant Cython file handling

* [Build] Enhance Docker distribution scripts for multi-Python version support

- Refactor local and PyPI distribution Docker scripts
- Replace hardcoded Python installation with Miniconda-based multi-version Python environment
- Improve Docker image setup with dynamic Python version creation
- Simplify build process by using Miniconda for Python environment management

* [Build] Separate lint requirements into a dedicated file

- Create new requirements-lint.txt for formatting and linting tools
- Update format.sh to use requirements-lint.txt instead of requirements-dev.txt
- Update requirements-dev.txt and requirements-test.txt to reference requirements-lint.txt
- Improve dependency management by isolating lint-specific requirements

* [Build] Restore Cython file copying logic in setup.py

- Re-add Cython file copying mechanism in TileLangBuilPydCommand
- Implement robust file search across multiple potential directories
- Add warning for cases where Cython source files cannot be found
- Improve build process reliability for Cython source files

* [Build] Refactor Cython file copying logic in setup.py

- Simplify Cython file copying mechanism in TileLangBuilPydCommand
- Improve directory creation and file copying for Cython source files
- Relocate potential directories list to a more logical position
- Enhance robustness of file and directory handling during build process

* [Build] Refine Cython file copying logic in setup.py

- Improve file existence check when copying Cython source files
- Use os.path.join to construct full path for more robust file checking
- Enhance file copying mechanism in TileLangBuilPydCommand

* bump version into v0.1.0

* [Enhancement] Add custom develop command for editable installs and update .gitignore

* [Documentation] Update README to include system dependencies installation instructions

* [Build] Update setup.py to support library file copying for both release and develop modes

* [Build] Refactor library file copying logic in setup.py

* [Documentation] Remove unnecessary install section header in Installation.md

* [Build] Add tox configuration and local distribution script for multi-Python version support

* [Build] Improve git submodule update function with better error handling

* [Build] Update LLVM configuration path in ROCm installation script

* [Build] Add .tox/ to .gitignore for tox testing environment

* [Build] Add support for TVM prebuild path configuration in CMakeLists.txt

* [Cleanup] Remove unused TVM runtime error codes header

* [Cleanup] Fix TVM grid constant type reference in CUDA module

* [Cleanup] Remove unused customized_code function from IR module

* [Feature] Add TileLang thread synchronization and storage access analysis passes

* [Build] Reorder DLL search path directories for more flexible library loading

* [Refactor] Improve thread synchronization and library path handling

- Rename ThreadSync and TileLangThreadSync functions in C++ code
- Update Python docstring for ThreadSync with more detailed description
- Reorder library path detection in tilelang environment setup
- Minor comment and code cleanup in CUDA and warp specialization modules

* [Refactor] Improve thread synchronization code style and formatting

- Standardize pointer type spacing in storage_access.h and storage_access.cc
- Update whitespace and indentation in thread_storage_sync.cc
- Reorder include statements in thread_partial_sync.cc
- Minor code formatting improvements across thread synchronization files

* [Refactor] Fix global function registration for ThreadSync

- Correct global function registration to use ThreadSync instead of TileLangThreadSync
- Update TVM global registration to match recent refactoring efforts

* [Refactor] Simplify ThreadSync global function registration

- Remove unnecessary whitespace in global function registration
- Compact the TVM global registration line for ThreadSync

* [Feature] Add WebGPU code generation support in TileLang

- Implement WebGPU code generator (codegen_webgpu.cc and codegen_webgpu.h)
- Add WebGPU target support in lower.py and target.py
- Update CMakeLists.txt to include WebGPU codegen source files
- Introduce WebGPU-specific code generation for WGSL shader language

* [Refactor] Improve WebGPU code generation formatting and readability

- Enhance code formatting in codegen_webgpu.cc and codegen_webgpu.h
- Standardize pointer type spacing and indentation
- Improve line breaks and reduce line length for better readability
- Minor code style improvements in WebGPU code generation

* [Test] Add WebGPU matrix multiplication code generation test

- Implement test_webgpu_codegen.py for WebGPU matrix multiplication
- Add assert_gemm_codegen function to validate WebGPU code generation
- Include basic matrix multiplication kernel test case

* Update README with WebGPU codegen support announcement

* Support multi version pypi package build via tox

* bump version into v0.1.0

* [Enhancement] Add custom develop command for editable installs and update .gitignore

* [Documentation] Update README to include system dependencies installation instructions

* [Build] Update setup.py to support library file copying for both release and develop modes

* [Build] Refactor library file copying logic in setup.py

* [Documentation] Remove unnecessary install section header in Installation.md

* [Build] Add tox configuration and local distribution script for multi-Python version support

* [Build] Improve git submodule update function with better error handling

* [Doc][CI] Update GitHub Actions workflow for documentation build and deployment.

* [Doc][CI] Remove redundant git add command in GitHub Actions workflow.

* [CI] Add workflow_dispatch trigger to publish_docs workflow

* remove code ql ci

* update comprehensive tilelang-benchmark link

* Bump Version into 0.0.1

* fix setup sdist issues