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Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Lei Wang
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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 |
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zqh-wzandLei Wang
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c5bbc60855 |
[Bugfix] Fix mismatch of shared memory layout and mma atom on Hopper (#224)
* add test for issue 101 * use ss_smem_selector from cutlass * fix mismatch between smem layout and mma * only fix for sm90 * Add CUDA requirements to GEMM thread tests * lint fix --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> |
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Lei Wang
|
3471904f68 |
[JIT] Support Cython jit and make cython a default execution backend (#102)
* [Feature] Add CTypes JIT kernel support for dynamic shapes and multi-stream execution - Enhance CtypesKernelAdapter to handle dynamic symbolic shapes - Add support for multi-stream kernel execution in CTypes backend - Implement dynamic shape handling in test_tilelang_jit_gemm_ctypes.py - Add symbolic shape utility function in tilelang.language - Update profiler to improve flexibility in benchmark selection * Remove redundant thread binding in GEMM kernel implementations - Remove unnecessary `thread_binding` line in GEMM kernel functions - Clean up code in `examples/gemm/README.md` and `testing/python/kernel/test_tilelang_kernel_int4_gemm_mma.py` - Enhance code readability by removing redundant thread binding annotation * Fix indentation in int4 GEMM kernel test file - Correct indentation for function calls in `test_tilelang_kernel_int4_gemm_mma.py` - Remove extra indentation in `mma_emitter.ldmatrix_a()` and `mma_emitter.ldmatrix_b()` calls - Improve code formatting for better readability * [Feature] Add Cython JIT kernel support for dynamic shapes and multi-stream execution - Implement CythonKernelAdapter to handle dynamic symbolic shapes - Add support for multi-stream kernel execution in Cython backend - Create comprehensive test suite for Cython GEMM kernel in test_tilelang_jit_gemm_cython.py - Update JITKernel to include "cython" as a valid execution backend - Add Cython-specific wrapper and library generation modules - Update .gitignore to exclude Cython cache directory - Modify setup.py to include Cython source files in package data * lint fix * [Refactor] Replace JITKernel with compile() function for kernel compilation - Add new `compile()` function in tilelang/jit/__init__.py as a wrapper for JITKernel - Update multiple test files and examples to use `tilelang.compile()` instead of `tilelang.JITKernel()` - Modify kernel adapters to support optional kernel-only source retrieval - Update `__init__.py` to import the new `compile()` function - Improve kernel source retrieval for different execution backends * lint fix * remove debug print * Add C/C++ compiler utility module and update Cython JIT kernel support - Introduce new `tilelang/contrib/cc.py` module with cross-platform C/C++ compiler utilities - Add functions to detect and retrieve system C/C++ compilers - Implement cross-compilation and shared library creation support - Update Cython JIT kernel to validate C++ compiler availability - Modify Cython adapter to use detected C++ compiler for library generation * Refactor float8 dtype mapping in tensor utility module - Move float8_dtype_map inside adapt_torch2tvm function - Simplify global scope by localizing the dtype mapping - Maintain existing functionality for converting torch float8 tensors to TVM ndarray * Refactor float8 dtype mapping in tensor utility module - Move float8_dtype_map inside adapt_torch2tvm function - Simplify global scope by localizing the dtype mapping - Maintain existing functionality for converting torch float8 tensors to TVM ndarray * revert * Enhance Cython JIT adapter with Cython compiler detection - Add `get_cython_compiler()` function to dynamically locate Cython executable - Update Cython adapter to use detected Cython compiler instead of hardcoded command - Raise an exception if no Cython compiler is found - Update requirements.txt to specify minimum PyTorch version (>=2.2.0) * Fix Cython kernel wrapper stream handling and type annotations - Update stream parameter type to int64_t for better compatibility - Directly use torch.cuda.current_stream().cuda_stream instead of casting - Improve type safety and precision in Cython kernel wrapper |
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Lei Wang
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7cd6b3cd74 |
[Bugfix] Put InjectPtxAsyncCopy Pass behind ThreadSync Pass (#97)
* 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 * Add support for CPU device backend with C code generation - Introduce `is_cpu_device_backend` function to detect CPU backend with C code generation - Modify `lower` function to handle special case of CPU device backend - Update host and device call filtering for CPU backend - Add conditional source code generation for C host target - Extend JITKernel to support optional target_host parameter * lint fix * Enhance JIT kernel adapters with CTypes and Torch C++ backends - Add CtypesKernelAdapter with dynamic library generation and kernel wrapping - Implement TorchCPPKernelAdapter for CUDA kernel compilation - Refactor BaseKernelAdapter to support more flexible initialization - Improve error handling and argument processing in kernel adapters - Update adapter initialization to support various execution backends * Refactor and clean up code style in JIT CTypes adapter modules - Apply consistent code formatting and whitespace in CTypes adapter files - Remove unused imports and improve import organization - Enhance readability of code in adapter, libgen, and wrapper modules - Add missing whitespace and improve line breaks - Minor linting and code style improvements across CTypes adapter files * Add test for TileLang JIT GEMM with CTypes backend - Implement comprehensive test for matrix multiplication using CTypes execution backend - Create test functions for GEMM with float16 data type - Add kernel source verification with custom callback - Implement reference implementation using PyTorch for result validation - Support various matrix multiplication configurations (transposition, block sizes) * test fix * Update TileLang JIT callback registration with override parameter - Modify tilelang_callback_cuda_postproc to use @tvm.register_func(override=True) - Ensure proper function registration with ability to replace existing implementations * Reorder TileLang lowering passes for Hopper intrinsics and PTX async copy - Adjust the order of LowerHopperIntrin and InjectPTXAsyncCopy passes - Move these passes to ensure correct synchronization and device preparation * Rebase main * shared.dyn * lint fix * test fix * Add environment variable handling for TileLang template and CUTLASS paths - Introduce fallback logic for TL_TEMPLATE_PATH environment variable - Add support for optional TL_CUTLASS_PATH configuration - Include TODO comment for future environment variable renaming |