mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-22 06:50:25 +08:00
1e92d11cd252e014c44a1c0dc94deaade14c7d2f
18
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Yu Cheng
|
5cb5c068bc | [Bugfix] Fix missing host cuTensorMapEncodeIm2col call (#1094) | ||
|
Lei Wang
|
b44830905d | [Autotune][Conv] optimize convolution examples to use autotune (#866) | ||
|
Cunxiao NiandCunxiao
|
950ed16c0b |
[Fix] fix some issues with JIT decorators existing in the examples (#681)
* [Fix] fix some issues with JIT decorators existing in the examples * format * Uses PassConfigKey instand of str --------- Co-authored-by: Cunxiao <nicunxiao@bytedance.com> |
||
|
Zhang Jasonandzhangnju
|
8361eb5c65 |
[Examples] Add the support of rocm arch detecting (#661)
Co-authored-by: zhangnju <ningzhan@SMC-SC-DI08-33.dh144.dcgpu> |
||
|
|
e9a608e222 |
[Bugfix][CI] Bug fixing and migrate CI from ada to hopper (#652)
* fix CI bugs in hopper * lint fix * Update bulk_copy.cc * Refactor bulk copy logic in LowerBulkCopy function - Removed unnecessary blank lines for improved code readability. - Enhanced stride validation by checking for null pointers in global stride calculations, ensuring robustness against symbolic strides. - Updated pass configuration handling in dynamic tile language tests to streamline dynamic alignment and TMA lower pass settings. * test fix * ci fix * Update flash-attention dependencies and clean up example code - Downgraded `flash-attn` dependency version in `requirements-test.txt` to `<=2.2.0`. - Removed unused imports and commented-out code in various example files to enhance readability and maintainability. - Updated the `flashattn` function signature to include default parameters for `block_M`, `block_N`, `num_stages`, and `threads`. - Cleaned up the `example_mha_fwd_varlen.py` and `example_mha_bwd_wgmma_pipelined.py` files by removing unnecessary comments and improving code clarity. - Deleted the `example_mha_inference.py` file as it is no longer needed. * Update CI workflow to remove `--user` flag from pip install commands - Removed the `--user` flag from the pip install commands in both the development and testing sections of the CI workflow to ensure proper installation of dependencies in the virtual environment. * Update CI workflow to include `--no-user` flag in pip install commands - Added the `--no-user` flag to the pip install commands in both the development and testing sections of the CI workflow to ensure dependencies are installed correctly within the virtual environment. * Update CI workflow to include `--no-user` flag in pip install command for wheel mode - Added the `--no-user` flag to the pip install command in the wheel mode section of the CI workflow to ensure dependencies are installed correctly within the virtual environment. * test fix * avoid conflict with system environments * test fix * add commnets --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> |
||
|
Lei Wang
|
eec4759285 |
[AutoTune] Support with set_autotune_inputs to set auto tuning input tensors (#632)
* [Refactor] Simplify and modularize autotuner implementation - Removed unused imports and extensive code sections from the autotuner module to enhance readability and maintainability. - Modularized the code by introducing new imports for autotuning and capturing functionalities, streamlining the overall structure. - Improved logging setup and removed redundant timeout handling functions, focusing on core autotuning logic. - Updated the AutoTuner class to better utilize the new modular structure, ensuring efficient performance during auto-tuning processes. * [Refactor] Clean up and enhance capture and tuner modules - Improved code readability by removing unnecessary blank lines and organizing imports in `capture.py` and `tuner.py`. - Enhanced logging in the `AutoTuner` class to provide clearer warnings regarding the usage of `supply_prog` in the context of auto-tuning. - Streamlined the `CaptureStack` class for better thread-local context management. * lint fix * [Refactor] Simplify configuration and autotuning logic in blocksparse GEMM example - Updated `get_configs` function to reduce the number of configurations, enhancing performance and clarity. - Removed the `get_best_config` function, integrating its logic directly into the `blocksparse_matmul` function with the `@autotune` decorator for streamlined autotuning. - Adjusted the main function to directly utilize the autotuned kernel, simplifying the overall structure and improving readability. - Deleted obsolete test file for autotuning decorator, cleaning up the codebase. * [Refactor] Improve code formatting and readability in autotune test file - Reformatted the `matmul` function and `get_configs` function for better readability by adjusting line breaks and indentation. - Fixed a typo in the `enable_rasteration` parameter name to ensure consistency. - Cleaned up unnecessary blank lines to enhance overall code clarity. * Update example_blocksparse_gemm.py * Update capture.py |
||
|
Lei Wang
|
d110d0871c |
[Refactor] refactor autotune examples (#617)
* [Refactor] Update tilelang kernel functions and remove unused imports - Refactored the `flashattn_fwd`, `flashattn_bwd_preprocess`, and `flashattn_bwd_postprocess` functions to utilize direct kernel calls instead of cached versions, improving clarity and performance. - Added `@tilelang.jit` decorators with specified output indices to enhance kernel compilation. - Removed unused import of `cached` from `tilelang`, streamlining the code. - Commented out the main testing function call in `test_tilelang_kernel_mha_bwd.py` for potential future use. * [Refactor] Simplify configuration generation in benchmark and example scripts - Refactored the `get_configs` functions in multiple benchmark and example scripts to utilize a dictionary-based approach for parameter configuration, improving readability and maintainability. - Updated the `flashattn` and `chunk_scan_fwd` functions to directly accept configuration parameters, enhancing flexibility in kernel tuning. - Removed redundant code and streamlined the configuration generation process across various files, ensuring consistency in how configurations are defined and utilized. * [Refactor] Update configuration handling in benchmark scripts - Refactored the `get_configs` functions in benchmark scripts to accept a variable argument list, improving flexibility in configuration management. - Enhanced the `matmul` and `flashattn` functions to utilize the updated configuration approach, streamlining parameter handling for kernel tuning. - Added `@autotune` decorators to relevant functions, ensuring consistent autotuning behavior across benchmarks. - Cleaned up redundant code and improved overall readability in the affected files. * [Refactor] Clean up formatting and update subproject commit - Updated the subproject commit reference in the TVM directory to indicate a dirty state. - Removed unnecessary blank lines and improved formatting in the `benchmark_matmul` and `benchmark_matmul_fp8` scripts for better readability. - Streamlined the function definitions in the `flashattn` example script to enhance clarity and maintainability. * [Refactor] Update AutoTuner configuration handling - Modified the AutoTuner class to check if kernel parameters are set before processing tunable arguments, improving robustness in configuration handling. - Enhanced the logic for skipping compilation when tunable parameters are already provided, ensuring efficient use of resources. - Updated comments for clarity and maintainability. * lint fix * Update TVM subproject commit to indicate dirty state and modify MHA backward test cases - Updated the subproject commit reference in the TVM directory to reflect a dirty state. - Adjusted the `test_mha_bwd` function to use a new configuration for the MHA backward tests, changing the context size from 128 to 256. - Uncommented the main testing function call for potential execution. |
||
|
yyttt6
|
8af5eb77dc | [Refactor] add autotune example to convolution examples (#536) | ||
|
Lei Wangandgemini-code-assist[bot]
|
09581e4e57 |
[Refactor] Refactor convolution example to streamline configuration and remove unused code (#530)
* Refactor convolution example to streamline configuration and remove unused code * Updated the `check_hopper` function to properly check for CUDA availability and compute capability. * Removed the `get_configs` and `get_best_config` functions, simplifying the example by eliminating unused autotuning logic. * Adjusted argument parsing in the `main` function to directly compile the convolution kernel without autotuning options. * Cleaned up the code for better readability and maintainability. * Update examples/convolution/example_convolution.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --------- Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> |
||
|
yyttt6andLei Wang
|
8dec14e076 |
[CI] Add Analyzer and blocksparse_attention examples to CI (#472)
* yes * [Bugfix] fix the unexpected keyword error of autotune * format * test * [CI] Add Analyzer and blocksparse_attention examples to CI * format * try * try * try * try * t * format * d --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> |
||
|
Wenhao XieandLei Wang
|
3f25bd1bb1 |
[BugFix] Correct argparse for example_convolution test (#474)
* add convolution example to CI * lint fix * Update test_example_convolution.py * fix bug --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> |
||
|
Wenhao XieandLei Wang
|
abe170a639 |
[CI] Add Convolution example to CI (#473)
* add convolution example to CI * lint fix * Update test_example_convolution.py --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> |
||
|
yyttt6andLei Wang
|
1873dc007b |
[Example] Add autotune to conv example (#301)
* add autotune to example_gemm.py * add autotune to conv * still coding ... * version 0 * version 0 * version 0 * refactor autotune * refactor autotune * add autotune to conv example * add conv template to carver * add conv template to carver * add conv template to carver * Update num_stages configuration values --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> |
||
|
Lei Wang
|
8341245876 |
[Bugfix] Enable bfloat16 atomic operations only for CUDA architectures greater than 7.5 (#291)
* [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. * [Refactor] Revamp cache management and enhance documentation in env.py and proxy.py - Replaced global cache functions with a CacheState class to improve encapsulation and management of kernel caching. - Updated the `from_ptr` method in BufferProxy and BaseTensorProxy classes to include detailed docstrings for better clarity on parameters and return values. - Enhanced class docstrings across various proxy classes to provide clearer descriptions of their purpose and functionality, improving overall code documentation. * [Refactor] Update imports in __init__.py for tir compatibility - Added imports for `prim_func` and `tir.op` to enhance compatibility with the upstream tir script. - Marked imports with `# noqa: F401` to suppress linting warnings for unused imports, indicating future removal once compatibility is achieved. * lint fix * [Refactor] Update imports in tir.ir.py for improved compatibility - Removed unused import of `PrimExpr` from `tvm.script.ir_builder.tir` and replaced it with the correct import from `tvm.tir`. - Added import for `tir.ir` in `__init__.py` to enhance module accessibility and maintain compatibility with upstream changes. * [Refactor] Update function calls in tir.ir.py to return values - Modified the `serial`, `parallel`, `vectorized`, `unroll`, `thread_binding`, and `grid` functions to return the results of their respective calls to `_ir` methods, enhancing clarity and ensuring proper value propagation. * bugfix * [Enhancement] Add support for uint16 data type in TLCUDASourceWrapper - Introduced the "uint16" mapping to the type dictionary in the TLCUDASourceWrapper class, expanding the range of supported data types for CUDA operations. * bugfix * [Update] Sync subproject commit and modify CUDA atomic add functions - Updated the subproject commit for TVM to edd35139a0481e9359aa269e3e50450b95ba2f5a. - Commented out the CUDA capability check in the example convolution script to prevent execution errors. - Refactored atomic add functions for BFLOAT16 in common.h to include a conditional compilation directive for improved compatibility with CUDA architectures. |
||
|
Lei Wang
|
bf8a6fc142 |
[Refactor] Deprecated T.Buffer as arguments and rename related calls into T.Tensor (#281)
* [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. |
||
|
Chaofan LinandLeiWang1999
|
0430cfe7fb |
[Bugfix] Fix Benchmark/Example Code for Autotuning (#254)
* fix tune args * lint * Refactor gemm example and autotuner logging - Updated `ref_program` in `example_gemm.py` to return the result of matrix multiplication instead of modifying an input parameter. - Changed logging filename in `__init__.py` from 'out.log' to 'autotuner.log' for better clarity. - Modified JIT kernel compilation process to include `out_idx` directly in the adapter creation, enhancing flexibility. - Improved validation of `result_idx` in `BaseKernelAdapter` to ensure it falls within valid bounds. * Refactor `ref_program` in `benchmark_matmul_intrinsic.py` to use the `@` operator for matrix multiplication instead of `torch.matmul`, simplifying the implementation by removing the unused parameter `C`. --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> |
||
|
Lei Wang
|
f2e99180d5 |
[Refactor] Phaseout LLVM Dependency by Making it Optional (#247)
* 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 |
||
|
|
57ab687cf7 |
[Initialization] Migration of Codebase from Dev Branch into Main (#10)
* 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> |