GitLab

* gemm_with_stride sm89

* fix offset issue

* bug fix

* format

* sm80 support

* add sm90

* add testing

* format

* add static_assert for wgmma

* Enhance error message for inner_box_dim validation in LowerBulkCopy

* lint fix

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

* [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>

* [Feature] Add Quarter Bank Swizzle Layout and Update GEMM Layout Logic

- Introduced a new `makeQuarterBankSwizzleLayout` function for layout swizzling of 32 bytes.
- Updated `makeGemmABLayout` to include an `enable_padding` parameter, allowing for conditional layout selection between padded and quarter bank swizzle layouts.
- Adjusted layout inference in GEMM operations to utilize the new quarter bank swizzle layout when appropriate.
- Enhanced bulk copy operations to recognize and handle the new layout type, improving memory access patterns.

* lint fix

* [Refactor] Update GEMM Layout Functions and Inference Logic

- Removed the `enable_padding` parameter from `makeGemmABLayout` to simplify its signature.
- Introduced `makeGemmABLayoutHopper` for enhanced layout handling specific to Hopper architecture.
- Updated layout inference in GEMM operations to utilize the new `makeGemmABLayoutHopper` function, improving clarity and maintainability in layout selection.
- Adjusted related layout functions to ensure consistent behavior across different architectures.

* [Refactor] Remove tf32 Casting Logic from GEMM Templates

- Eliminated the `cast_float_to_tf32` function from `gemm_sm80`, `gemm_sm89`, and `gemm_sm90` templates to streamline the code.
- Removed conditional casting logic for float32 to tfloat32 conversion, enhancing clarity and maintainability.
- Updated relevant sections in GEMM operations to reflect the removal of casting, ensuring consistent behavior across templates.
- Adjusted tensor view handling to improve performance and accuracy in matrix operations.

* Update bulk_copy.cc

* Fix profiler initialization in GEMM test by removing TensorSupplyType argument for improved flexibility.

* Add tf32 casting functionality to GEMM templates

- Introduced a `cast_float_to_tf32` function to convert float32 values to tfloat32 format across gemm_sm80, gemm_sm89, and gemm_sm90 templates.
- Implemented conditional casting in relevant sections of the GEMM operations to ensure compatibility with tfloat32 types.
- Enhanced the handling of tensor views to support the new casting logic, improving performance and accuracy in matrix operations.

* lint fix

* Refactor tfloat32 casting logic in GEMM templates

- Replaced the `is_tfloat32` boolean with `need_tfloat32_cast` to improve clarity and accuracy in determining when to cast float32 to tfloat32.
- Updated relevant sections in `gemm_sm80`, `gemm_sm89`, and `gemm_sm90` to utilize the new casting logic, enhancing compatibility with tfloat32 types.
- Ensured consistent application of casting across tensor views, improving performance and correctness in matrix operations.

* Refactor GEMM template functions for improved readability

- Simplified the function signature of `body_rs` in both `gemm_sm80` and `gemm_sm90` templates for better clarity.
- Adjusted the casting logic in `gemm_sm90` to ensure consistent application of `cast_float_to_tf32` across tensor views, enhancing performance and maintainability.

* Enhance tf32 casting logic in GEMM templates

- Updated the `cast_float_to_tf32` function in `gemm_sm80`, `gemm_sm89`, and `gemm_sm90` to conditionally apply the casting only if the input is finite, improving robustness.
- Simplified the `need_tfloat32_cast` logic to clarify the conditions under which tfloat32 casting is required, enhancing code readability and maintainability.

* Refactor GEMM template functions and layout inference logic

- Removed the `cast_float_to_tf32` function from `gemm_sm90` and updated the `body_sr` function to streamline the casting process for tensor views, enhancing code clarity and maintainability.
- Improved layout inference in `layout_inference.cc` by adding checks for the layout map's definition, ensuring robustness in handling layout annotations.
- Simplified the handling of layout maps in the `annotate_layout` function, allowing for more flexible layout definitions and error handling.

* [wip] feat: add nvrtc backend

* [wip] fix: handle out_idx

* [wip] refactor: move lib logic to libgen

* feat: cache for nvrtc backend

* fmt: run format

* fix: handle cuda bindings import error

* fix: handle cuda bindings import error

* fix: handle cuda bindings import error

* fix: handle cuda bindings import error

* fix: get kernel source

* refactor: speedup pyimport

* Improve error handling for missing cuda-python dependency in nvrtc backend. Raise ImportError with detailed installation instructions instead of logging a warning.

* Enhance nvrtc backend error handling by introducing a flag to check for cuda-python availability. Raise ImportError with detailed installation instructions during initialization if the nvrtc backend is unavailable, improving user experience and clarity.

* Update README.md to include recent NVRTC Backend addition, highlighting reduced compilation time for CUDA templates.

* fix tl_templates

* ensure CUDA context

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

* [Refactor] Enhance GEMM warp partitioning logic for improved performance and flexibility

* Updated the warp partitioning logic in `Gemm::ComputeWarpPartition` to better handle various GEMM policies, including FullRow, FullCol, and Square.
* Implemented checks to dynamically adjust warp allocation based on matrix dimensions, ensuring optimal performance.
* Introduced a new `SelectCopy` template to streamline memory access patterns in CUDA templates, enhancing compatibility across different architectures.
* Refactored the Python `GemmWarpPolicy` class to align with the updated C++ logic, improving clarity and maintainability in warp allocation strategies.

* [Refactor] Optimize matrix multiplication parameters and performance in quickstart example

* Updated thread count in the kernel context from 256 to 128 to enhance performance.
* Increased block sizes for matrix dimensions (M, N, block_M, block_N) to 1024 and 128 respectively, improving computational efficiency.
* Adjusted the pipeline stages in the GEMM loop from 0 to 3 for better parallel execution.
* Cleaned up comments for clarity and corrected a typo in the memory copy comment.

* [Refactor] Simplify Copy type selection in OperandTraits for improved clarity

* Replaced the conditional Copy type definition with a new SelectCopy template in OperandTraits, enhancing readability and maintainability of the code.
* This change streamlines the logic for selecting memory copy patterns based on matrix dimensions and warp configurations.

* Remove debug print statement from block_sparse_attn_triton.py and implement a timeout handler in autotuner for function execution. This enhances the robustness of the autotuner by allowing it to handle timeouts gracefully.

* Enhance the autotuner module by adding a timeout handler for function execution, improving robustness in handling long-running tasks. This change includes the introduction of a custom TimeoutException and updates to the run_with_timeout function for better signal management.

* Add merge shared memory allocations pass and related configurations

- Introduced a new pass for merging shared memory allocations in GPU kernels, allowing for more efficient memory usage.
- Registered configuration options for debugging and controlling the merging behavior.
- Updated relevant files to integrate the new pass into the TileLang engine and transform modules.
- Adjusted import paths and added documentation for the new functionality.

* Reduce num_stages parameter in GEMM functions from 3 to 1 for improved performance in test_tilelang_kernel_gemm.py

* Update Copy type in OperandTraits for GEMM templates to use conditional selection based on num_warp_n. This change enhances memory access patterns for different configurations in CUDA kernels.

* lint fix

* Update Copy type in OperandTraits for GEMM templates to use SM75_U16x4_LDSM_T and SM75_U16x8_LDSM_T for improved memory access patterns across CUDA architectures.

* Remove debug print statement from block_sparse_attn_triton.py and implement a timeout handler in autotuner for function execution. This enhances the robustness of the autotuner by allowing it to handle timeouts gracefully.

* Enhance the autotuner module by adding a timeout handler for function execution, improving robustness in handling long-running tasks. This change includes the introduction of a custom TimeoutException and updates to the run_with_timeout function for better signal management.

* Add merge shared memory allocations pass and related configurations

- Introduced a new pass for merging shared memory allocations in GPU kernels, allowing for more efficient memory usage.
- Registered configuration options for debugging and controlling the merging behavior.
- Updated relevant files to integrate the new pass into the TileLang engine and transform modules.
- Adjusted import paths and added documentation for the new functionality.

* Reduce num_stages parameter in GEMM functions from 3 to 1 for improved performance in test_tilelang_kernel_gemm.py

* Update Copy type in OperandTraits for GEMM templates to use conditional selection based on num_warp_n. This change enhances memory access patterns for different configurations in CUDA kernels.

* lint fix

* [Enhancement] Improve error handling in layout inference and update profiler type in tests

* Added a detailed error message in the layout inference for local.fragment to clarify the requirement for trans_B.
* Updated the profiler type in the cumulative sum test from TensorSupplyType.One to TensorDistributionType.Randn for better profiling accuracy.

* lint fix

* [Refactor] Update OperandTraits to include num_warp_n parameter

* Modified OperandTraits templates across gemm_sm80.h, gemm_sm89.h, and gemm_sm90.h to include an additional num_warp_n parameter for improved flexibility in layout and copy operations.
* Adjusted Copy type selection based on the new parameter to enhance performance and adaptability in various scenarios.

* lint fix

* [Refactor] Update DispatchInstruction templates to include N parameter

* Modified DispatchInstruction templates in gemm_sm80.h, gemm_sm89.h, and gemm_sm90.h to include an additional N parameter, enhancing flexibility in tile size calculations.
* Adjusted MMA_Group definitions to use std::min for improved handling of warp sizes, ensuring better performance and adaptability in various scenarios.

* [Enhancement] Add support for CUDA architecture 8.9 in GEMM template

- Introduced conditional inclusion of "gemm_sm89.h" for CUDA architectures 8.9 and above, enhancing compatibility with newer hardware.
- This change ensures that the GEMM template can leverage optimizations specific to the 8.9 architecture, improving performance for users with compatible GPUs.

* lintfix

* [Refactor] Clean up includes in gemm_sm89.h

- Removed duplicate inclusion of "common.h" and added "cuda_fp8.h" for improved clarity and organization.
- This change enhances the maintainability of the code by ensuring that header files are included only once and in a logical order.

* 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

* upgrade cutlass to upstream v3.8.0

* Implement fp8 gemm and add example script

* Fix dtype retrieval with map_torch_type for fp8 inputs

* Disable vectorization of fp8 values

* Make MMA declaration compatible with cutlass 3.4.0+

* Add test for fp8 T.gemm

* fix indent

* fix indent

* Add copyright and license header

* Add copyright and license header

* lint fix

* Refactor matmul_nt and assert_matmul_correctness functions for improved readability by consolidating parameter definitions and adjusting formatting.

* clang format lint

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

* 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>