* [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.
- Added a new entry in the README for the introduction of `T.gemm_sp` supporting 2:4 sparse tensor core.
- Removed several outdated documentation files related to convolution, flash attention, and other tutorials to streamline the documentation structure.
* [Add] Introduce deepseek_mla documentation for high-performance FlashMLA with TileLang
- Added a comprehensive guide on writing high-performance kernels using TileLang, focusing on the Multi-Head Latent Attention (MLA) mechanism.
- Included benchmark results comparing FlashMLA, TileLang, Torch, Triton, and FlashInfer, highlighting TileLang's efficiency and ease of use.
- Detailed implementation strategies, including layout inference, threadblock swizzling, shared memory swizzling, and warp specialization.
- Provided examples and explanations of optimization techniques to enhance performance in GPU kernel programming.
* doc update
* [Add] Enhance AMD FlashMLA implementation and documentation
- Refactored variable names in `benchmark_mla_decode_amd_tilelang.py` for clarity, changing `Q_shared` and `Q_pe_shared` to `Q_local` and `Q_pe_local` to reflect their usage in register allocation.
- Added a new `README.md` detailing the high-performance FlashMLA implementation on AMD MI300X accelerators, including architectural considerations, optimization strategies, and performance evaluation.
- Introduced a performance comparison figure to illustrate the efficiency of the TileLang implementation against other frameworks.
* lint fix
* [Add] Expand deepseek_mla documentation for AMD MI300X optimization strategies
- Introduced a new section detailing architectural differences and optimization strategies for implementing FlashMLA on AMD MI300X accelerators.
- Highlighted key considerations such as instruction set variations, shared memory constraints, tile size flexibility, and memory bank conflict swizzling.
- Included performance evaluation results demonstrating TileLang's efficiency compared to other frameworks.
- Discussed future optimization opportunities for memory bank conflict mitigation and dimension parallelization.
* [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.
* [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
* 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
* 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
Lei Wang