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6
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 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. |
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|
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 |
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Wenhao Xie
|
71537ba567 |
[BugFix] Fix bug of mismatching dtype in testing (#245)
* [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 * fix bug of mismatching dtype in testing and set the default value of check_dtype in torch_assert_close to true * lint fix * fix bug * use map_torch_type |
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Lei Wang
|
de1ba1e4b3 |
[Refactor] Replace T.thread_binding with T.get_thread_binding in examples and test cases (#163)
* [Refactor] Update BitBLAS Benchmark with TileLang Carver Imports and Roller Hints Generation - Replace BitBLAS imports with TileLang Carver imports in benchmark_matmul.py - Modify roller hints generation using new TileLang Carver template and utility functions - Update get_roller_hints_from_func to handle None cases and improve return logic - Adjust DefaultPolicy to handle different codegen dictionary formats * [Refactor] Update Thread Binding and Import Statements in TileLang Kernels - Replace T.thread_binding() with T.get_thread_binding() across multiple kernel test files - Update import statements for MMA layout and macro generator in dequantize GEMM and FP8 examples - Move map_torch_type utility function to tilelang.utils.tensor - Remove unnecessary imports and improve code organization |
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Lei Wang
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d0434c3ede |
[Enhancement] Enable runtime tensor data type validation (#146)
* Fix debug print buffer template for unsigned char type - Update debug_print_buffer_value template specialization for unsigned char - Modify test_tilelang_debug_print.py to include additional dtype tests - Add test case for uint8 dtype in debug print buffer function * Refactor debug print buffer template formatting for unsigned char - Improve code formatting for debug_print_buffer_value template specialization - Adjust line breaks and indentation for better readability - Maintain consistent code style with other template specializations * Extract map_torch_type utility function to tilelang.utils.tensor - Move map_torch_type function from multiple test files to a centralized location - Import map_torch_type from tilelang.utils.tensor in kernel test files - Improve code reusability by creating a shared utility function for type mapping * Add buffer dtype mapping for Cython kernel adapter - Introduce buffer_dtype_map in CythonKernelAdapter to track buffer variable dtypes - Add _process_buffer_dtype method to extract dtype information from TIR function - Update CythonKernelWrapper to support setting and validating buffer dtypes - Enhance type checking during kernel execution with dtype verification - Improve logging message for Cython JIT adapter compilation * Add static shape mapping for Cython kernel adapter - Introduce static_shape_map in CythonKernelAdapter to track buffer variable static shapes - Add _process_static_shape method to extract static shape information from TIR function - Update CythonKernelWrapper to support setting and validating static shapes - Enhance type checking during kernel execution with static shape verification * Add Multi-Head Attention (MHA) Backward Pass Test for TileLang Kernel - Implement comprehensive test for Multi-Head Attention backward pass - Support both causal and non-causal attention scenarios - Add reference implementation for comparing kernel outputs - Test different batch sizes, head counts, sequence lengths, and head dimensions - Verify forward and backward pass correctness using torch.testing.assert_close * Set random seed for MHA backward pass test - Add random seed initialization for consistent test reproducibility - Use tilelang.testing.set_random_seed(42) to ensure deterministic test results |
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Lei Wang
|
61de528855 |
[Dev] Support FP8 Codegen for cuda backend (#64)
* [Enhancement] Add VectorizeLoop function and update imports for compatibility * [CI][Test] Improve test cases for vectorization and fix typos in parser comments * lint fix * Fix incorrect module reference for VectorizeLoop transformation * Refactor vectorize_loop transformation by removing unused extent mutation logic * [Enhancement] Add support for FP8 data types and global barriers in CUDA codegen * Fix formatting in CUDA FP8 header file for consistency * Refactor CI workflow to use 'tilelang_ci' virtual environment and update CUDA type printing for better clarity * Update submodule 'tvm' to latest commit for improved functionality * Refactor execution backend references from 'dl_pack' to 'dlpack' for consistency and clarity; add apply_simplify function to simplify PrimFunc or IRModule. * Refactor CUDA code for improved readability; clean up formatting and remove unnecessary whitespace in multiple files. * Refactor import statement in test_tilelang_kernel_dequantize_gemm.py to use 'tilelang.language' for consistency * Add CUDA requirements to FP8 test cases and update references for clarity * Add a blank line for improved readability in test_tilelang_kernel_fp8_gemm_mma.py * Fix data type in reference result calculation for consistency in test_tilelang_kernel_gemm_mma_intrinsic.py * Add CUDA requirements and FP8 test cases for matmul and gemv simulations * Remove debug print statements and use tilelang's testing assertion for result validation in test_tilelang_kernel_gemm_mma_intrinsic.py * Remove outdated comment regarding FP8 tests in test_tilelang_kernel_gemv_simt.py |