- 23 Feb, 2025 3 commits
-
-
Lei Wang authored
* Add DeepSeek MLA decode example with Flash Attention implementation * Add GEMM SplitK and StreamK example implementations This commit introduces two new example scripts demonstrating advanced GEMM (matrix multiplication) techniques: - `example_tilelang_gemm_splitk.py`: Implements a Split-K GEMM kernel using TileLang - `example_tilelang_gemm_streamk.py`: Implements a Stream-K GEMM kernel using TileLang Both examples showcase different parallel computation strategies for matrix multiplication, with comprehensive testing using PyTorch reference implementations. * Refactor GEMM SplitK and StreamK example implementations Clean up and improve code formatting for the SplitK and StreamK GEMM example scripts: - Remove unused import (Profiler) in splitk example - Simplify line breaks and improve code readability - Standardize indentation and remove unnecessary whitespace - Optimize atomic add and copy operations for better clarity
-
Yu Cheng authored
* [CI][Test] Add test cases for tilelang transform MultiVersionBuffer and WarpSpecialized * Relax the mismatch ratio restrictions in the flash_linear_attention and mha tests * [Dev] Add mha backward example * [Dev] Add mla decode example * bug fix * Add triton impl * Add gqa decode example * [Dev] Add GQA decode example * lint * delete unused triton example * set default profiler to 'auto'
-
Lei Wang authored
* Remove Torch CPP backend and update execution backend options - Remove TorchCPPKernelAdapter and related code from JIT modules - Update execution backend options in jit/__init__.py, kernel.py, and adapter/__init__.py - Remove "torch_cpp" from supported execution backend literals - Simplify backend validation and remove unused torch_cpp-related code 。 * lint fix * Add block sparse attention implementations for TileLang and Triton - Implement block sparse attention kernels for TileLang and Triton - Add example scripts for block sparse attention with top-k and threshold-based masking - Include utility functions for generating sparse attention masks - Demonstrate causal attention with block-level sparsity - Add test cases to validate sparse attention implementations against PyTorch reference * Bump version to 0.1.1 * Refactor block sparse attention examples for improved code quality - Apply consistent code formatting and style in TileLang and Triton block sparse attention implementations - Add ruff linter ignore comment for specific line in Triton implementation - Improve readability by adjusting indentation and line breaks - Standardize sparse mask generation and test function implementations - Minor optimizations in test case configurations * lint
-
- 22 Feb, 2025 3 commits
-
-
Lei Wang authored
* Remove Torch CPP backend and update execution backend options - Remove TorchCPPKernelAdapter and related code from JIT modules - Update execution backend options in jit/__init__.py, kernel.py, and adapter/__init__.py - Remove "torch_cpp" from supported execution backend literals - Simplify backend validation and remove unused torch_cpp-related code 。 * lint fix * Add block sparse attention implementations for TileLang and Triton - Implement block sparse attention kernels for TileLang and Triton - Add example scripts for block sparse attention with top-k and threshold-based masking - Include utility functions for generating sparse attention masks - Demonstrate causal attention with block-level sparsity - Add test cases to validate sparse attention implementations against PyTorch reference
-
Lei Wang authored
* [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
-
Lei Wang authored
* Remove Torch CPP backend and update execution backend options - Remove TorchCPPKernelAdapter and related code from JIT modules - Update execution backend options in jit/__init__.py, kernel.py, and adapter/__init__.py - Remove "torch_cpp" from supported execution backend literals - Simplify backend validation and remove unused torch_cpp-related code 。 * lint fix
-
- 21 Feb, 2025 2 commits
-
-
Lei Wang authored
* [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
-
Lei Wang authored
Create Dockerfiles for CUDA versions 11.8, 12.1, 12.3, 12.4, and 12.5, using the latest PyTorch NGC base images. Update docker/README.md to provide clearer instructions for building and running the Docker container with a specific CUDA version.
-
- 20 Feb, 2025 1 commit
-
-
Lei Wang authored
* [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
-
- 19 Feb, 2025 3 commits
-
-
Lei Wang authored
* 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
-
Lei Wang authored
-
Lei Wang authored
* 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
-
- 18 Feb, 2025 1 commit
-
-
Lei Wang authored
* 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
-
- 15 Feb, 2025 1 commit
-
-
Lei Wang authored
* 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
-
- 14 Feb, 2025 1 commit
-
-
Lei Wang authored
* 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
-
- 13 Feb, 2025 3 commits
-
-
Lei Wang authored
* 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
-
Lei Wang authored
* 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
-
Wenhao Xie authored
* [CI] Clean up target repository before publishing documentation. * [Doc] Convert docs from rst format to Markdown format.
-
- 12 Feb, 2025 2 commits
- 11 Feb, 2025 4 commits
-
-
Yu Cheng authored
* [CI][Test] Add test cases for tilelang transform MultiVersionBuffer and WarpSpecialized * Relax the mismatch ratio restrictions in the flash_linear_attention and mha tests * [Dev] Add mha backward example
-
Lei Wang authored
* [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 * Add BF16 support to matrix multiplication and introduce corresponding test cases * Add a blank line for improved readability in BF16 GEMM test * Update acknowledgements in README to include supervision by Zhi Yang at Peking University * enhance acknowledgement * Replace tutorial on memory layout optimization with new tutorial on writing high-performance kernels with thread primitives * Update subproject commit for TVM dependency * Update subproject commit for TVM dependency * Add int4_t type and functions for packing char values in CUDA common header * Add plot_layout example and implement GetForwardVars method in layout classes * Refactor code for improved readability by adjusting line breaks and formatting in layout and test files * Fix formatting by removing unnecessary line break in layout.h * Refactor make_int4 function for improved readability by adjusting parameter formatting * Add legend to plot_layout for improved clarity of thread and local IDs * Remove unnecessary dependencies from requirements files for cleaner setup * Remove flash_mha.py and add .gitkeep to deepseek_mla directory * Add build requirements and update installation scripts for improved setup * Introduce carver * Refactor imports and improve code formatting for consistency * Add unit tests for carver recommendation hints * lint fix * Enhance ElementwiseTemplate and BaseTemplate with detailed docstrings for improved code documentation and clarity * Refactor import statements and clean up whitespace in template files for improved readability * Add README.md for Carver framework with usage examples and architecture support * Refactor import statement in matmul_analysis.py for consistency * Refactor TileDict and TensorCorePolicy methods for improved clarity and functionality * Add tests for general matrix multiplication emit configurations * Refactor formatting in test_tilelang_carver_generate_hints.py for improved readability * Add FlashAttentionTemplate and related functionality for hint recommendations * Refactor whitespace in FlashAttentionTemplate and test_tilelang_carver_recommend_hints for improved readability * Update README.md to include FlashAttentionTemplate in the carver section
-
Lei Wang authored
* [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 * Add BF16 support to matrix multiplication and introduce corresponding test cases * Add a blank line for improved readability in BF16 GEMM test * Update acknowledgements in README to include supervision by Zhi Yang at Peking University * enhance acknowledgement * Replace tutorial on memory layout optimization with new tutorial on writing high-performance kernels with thread primitives * Update subproject commit for TVM dependency * Update subproject commit for TVM dependency * Add int4_t type and functions for packing char values in CUDA common header * Add plot_layout example and implement GetForwardVars method in layout classes * Refactor code for improved readability by adjusting line breaks and formatting in layout and test files * Fix formatting by removing unnecessary line break in layout.h * Refactor make_int4 function for improved readability by adjusting parameter formatting * Add legend to plot_layout for improved clarity of thread and local IDs * Remove unnecessary dependencies from requirements files for cleaner setup * Remove flash_mha.py and add .gitkeep to deepseek_mla directory * Add build requirements and update installation scripts for improved setup * Introduce carver * Refactor imports and improve code formatting for consistency * Add unit tests for carver recommendation hints * lint fix * Enhance ElementwiseTemplate and BaseTemplate with detailed docstrings for improved code documentation and clarity * Refactor import statements and clean up whitespace in template files for improved readability * Add README.md for Carver framework with usage examples and architecture support * Refactor import statement in matmul_analysis.py for consistency * Refactor TileDict and TensorCorePolicy methods for improved clarity and functionality * Add tests for general matrix multiplication emit configurations * Refactor formatting in test_tilelang_carver_generate_hints.py for improved readability * Add FlashAttentionTemplate and related functionality for hint recommendations * Refactor whitespace in FlashAttentionTemplate and test_tilelang_carver_recommend_hints for improved readability
-
Yu Cheng authored
* [CI][Test] Add test cases for tilelang transform MultiVersionBuffer and WarpSpecialized * Relax the mismatch ratio restrictions in the flash_linear_attention and mha tests
-
- 10 Feb, 2025 3 commits
-
-
Lei Wang authored
* [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 * Add BF16 support to matrix multiplication and introduce corresponding test cases * Add a blank line for improved readability in BF16 GEMM test * Update acknowledgements in README to include supervision by Zhi Yang at Peking University * enhance acknowledgement * Replace tutorial on memory layout optimization with new tutorial on writing high-performance kernels with thread primitives * Update subproject commit for TVM dependency * Update subproject commit for TVM dependency * Add int4_t type and functions for packing char values in CUDA common header * Add plot_layout example and implement GetForwardVars method in layout classes * Refactor code for improved readability by adjusting line breaks and formatting in layout and test files * Fix formatting by removing unnecessary line break in layout.h * Refactor make_int4 function for improved readability by adjusting parameter formatting * Add legend to plot_layout for improved clarity of thread and local IDs * Remove unnecessary dependencies from requirements files for cleaner setup * Remove flash_mha.py and add .gitkeep to deepseek_mla directory * Add build requirements and update installation scripts for improved setup * Introduce carver * Refactor imports and improve code formatting for consistency * Add unit tests for carver recommendation hints * lint fix * Enhance ElementwiseTemplate and BaseTemplate with detailed docstrings for improved code documentation and clarity * Refactor import statements and clean up whitespace in template files for improved readability * Add README.md for Carver framework with usage examples and architecture support * Refactor import statement in matmul_analysis.py for consistency
-
Lei Wang authored
* [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 * Add BF16 support to matrix multiplication and introduce corresponding test cases * Add a blank line for improved readability in BF16 GEMM test * Update acknowledgements in README to include supervision by Zhi Yang at Peking University * enhance acknowledgement * Replace tutorial on memory layout optimization with new tutorial on writing high-performance kernels with thread primitives * Update subproject commit for TVM dependency * Update subproject commit for TVM dependency * Add int4_t type and functions for packing char values in CUDA common header * Add plot_layout example and implement GetForwardVars method in layout classes * Refactor code for improved readability by adjusting line breaks and formatting in layout and test files * Fix formatting by removing unnecessary line break in layout.h * Refactor make_int4 function for improved readability by adjusting parameter formatting * Add legend to plot_layout for improved clarity of thread and local IDs * Remove unnecessary dependencies from requirements files for cleaner setup * Remove flash_mha.py and add .gitkeep to deepseek_mla directory * Add build requirements and update installation scripts for improved setup * Introduce carver * Refactor imports and improve code formatting for consistency * Add unit tests for carver recommendation hints * lint fix * Enhance ElementwiseTemplate and BaseTemplate with detailed docstrings for improved code documentation and clarity * Refactor import statements and clean up whitespace in template files for improved readability * Add README.md for Carver framework with usage examples and architecture support
-
Lei Wang authored
* [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 * Add BF16 support to matrix multiplication and introduce corresponding test cases * Add a blank line for improved readability in BF16 GEMM test * Update acknowledgements in README to include supervision by Zhi Yang at Peking University * enhance acknowledgement * Replace tutorial on memory layout optimization with new tutorial on writing high-performance kernels with thread primitives * Update subproject commit for TVM dependency * Update subproject commit for TVM dependency * Add int4_t type and functions for packing char values in CUDA common header * Add plot_layout example and implement GetForwardVars method in layout classes * Refactor code for improved readability by adjusting line breaks and formatting in layout and test files * Fix formatting by removing unnecessary line break in layout.h * Refactor make_int4 function for improved readability by adjusting parameter formatting * Add legend to plot_layout for improved clarity of thread and local IDs * Remove unnecessary dependencies from requirements files for cleaner setup * Remove flash_mha.py and add .gitkeep to deepseek_mla directory * Add build requirements and update installation scripts for improved setup
-
- 09 Feb, 2025 1 commit
-
-
Lei Wang authored
* [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 * Add BF16 support to matrix multiplication and introduce corresponding test cases * Add a blank line for improved readability in BF16 GEMM test * Update acknowledgements in README to include supervision by Zhi Yang at Peking University * enhance acknowledgement * Replace tutorial on memory layout optimization with new tutorial on writing high-performance kernels with thread primitives * Update subproject commit for TVM dependency * Update subproject commit for TVM dependency * Add int4_t type and functions for packing char values in CUDA common header * Add plot_layout example and implement GetForwardVars method in layout classes * Refactor code for improved readability by adjusting line breaks and formatting in layout and test files * Fix formatting by removing unnecessary line break in layout.h * Refactor make_int4 function for improved readability by adjusting parameter formatting
-
- 08 Feb, 2025 1 commit
-
-
Yu Cheng authored
* [Dev] Add FlashDecoding example * [CI][Test] Add test cases for tilelang kernel convolution * [CI][Test] Add test cases for tilelang kernel FlashAttention * Reduce the number of stages to ensure the shared memory allocation is valid * Temporarily remove the dim128 case * lint * update einops in requirements-dev.txt * update einops in requirements-test.txt * remove einops in requirements-dev.txt * [CI][Test] Add test cases for tilelang transform ClusterPlanning * [CI][Test] Add test cases for tilelang transform LowerHopperIntrin * [CI][Test] Add test cases for tilelang transform InjectFenceProxy
-
- 06 Feb, 2025 2 commits
-
-
Lei Wang authored
* [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 * Add BF16 support to matrix multiplication and introduce corresponding test cases * Add a blank line for improved readability in BF16 GEMM test * Update acknowledgements in README to include supervision by Zhi Yang at Peking University
-
Lei Wang authored
* [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
-
- 03 Feb, 2025 1 commit
-
-
Lei Wang authored
* [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
-
- 02 Feb, 2025 1 commit
-
-
Lei Wang authored
* implement jit test case * [Dev] implement auto tune test case for matrix multiplication * Implement test for legalize memory access and vectorized loop * lint fix * introduce run_once * Refactor callback function names for consistency and improve code readability * enhance documentations * lint fix * lint fix * lint fix * lint fix * fix formatting issues in rt_mod_hip.cc * add random seed initialization for deterministic testing * Add documentation images and comprehensive GEMM tutorial for TileLang * Update MATMUL documentation title to highlight Tile Library
-
- 27 Jan, 2025 1 commit
-
-
Yu Cheng authored
* [Dev] Add FlashDecoding example * [CI][Test] Add test cases for tilelang kernel convolution * [CI][Test] Add test cases for tilelang kernel FlashAttention * Reduce the number of stages to ensure the shared memory allocation is valid * Temporarily remove the dim128 case * lint * update einops in requirements-dev.txt * update einops in requirements-test.txt * remove einops in requirements-dev.txt * [CI][Test] Add test cases for tilelang transform ClusterPlanning * [CI][Test] Add test cases for tilelang transform LowerHopperIntrin
-
- 26 Jan, 2025 3 commits
-
-
Lei Wang authored
* implement jit test case * [Dev] implement auto tune test case for matrix multiplication * Implement test for legalize memory access and vectorized loop * lint fix * introduce run_once * Refactor callback function names for consistency and improve code readability * enhance documentations * lint fix * lint fix * lint fix * lint fix * fix formatting issues in rt_mod_hip.cc * add random seed initialization for deterministic testing
-
Yu Cheng authored
* [Dev] Add FlashDecoding example * [CI][Test] Add test cases for tilelang kernel convolution * [CI][Test] Add test cases for tilelang kernel FlashAttention * Reduce the number of stages to ensure the shared memory allocation is valid * Temporarily remove the dim128 case * lint * update einops in requirements-dev.txt * update einops in requirements-test.txt * remove einops in requirements-dev.txt * [CI][Test] Add test cases for tilelang transform ClusterPlanning
-
Wenhao Xie authored
-
- 25 Jan, 2025 3 commits
-
-
Cunxiao Ni authored
* [CI][Test] Add test cases for element_add * [Doc] fix typo * Parallelization * format * remove useless condition * format
-
Yu Cheng authored
* [Dev] Add FlashDecoding example * [CI][Test] Add test cases for tilelang kernel convolution * [CI][Test] Add test cases for tilelang kernel FlashAttention * Reduce the number of stages to ensure the shared memory allocation is valid * Temporarily remove the dim128 case * lint * update einops in requirements-dev.txt * update einops in requirements-test.txt * remove einops in requirements-dev.txt
-
Lei Wang authored
* implement jit test case * [Dev] implement auto tune test case for matrix multiplication * Implement test for legalize memory access and vectorized loop * lint fix
-
