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Lei Wang
8e1845d279
* [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 * Refactor Native Sparse Attention Example with Enhanced Triton Kernel - Update parallel_nsa_fwd_kernel to support more flexible sparse attention computation - Add support for block counts and offsets in the Triton kernel - Modify kernel grid and computation logic for improved performance - Update example script to use naive_nsa_simple reference implementation - Improve type hints and kernel configuration * Add Native Sparse Attention Examples with Tilelang and Triton Implementations - Introduce new example scripts for native sparse attention: * example_tilelang_nsa_fwd.py: Forward pass implementation using TileLang * example_tilelang_nsa_decode.py: Decoding-specific sparse attention implementation * example_triton_nsa_fwd.py: Triton-based sparse attention forward pass - Update reference.py with naive implementations for sparse attention - Support different sparse attention scenarios including forward pass and inference - Add comprehensive testing and validation against reference implementations * lint fix * Add Variable-Length Native Sparse Attention Examples for TileLang and Triton - Introduce new example scripts for variable-length native sparse attention: * example_tilelang_nsa_fwd_varlen.py: TileLang implementation with variable sequence lengths * example_triton_nsa_fwd_varlen.py: Triton implementation with variable sequence lengths - Update reference.py to support variable-length sparse attention scenarios - Enhance existing sparse attention implementations to handle variable-length inputs - Add comprehensive testing and validation for variable-length sparse attention * Refactor Native Sparse Attention Examples: Code Style and Formatting Improvements - Standardize function and parameter formatting across NSA example files - Improve code readability by adjusting indentation and line breaks - Enhance type hints and parameter alignment - Remove unnecessary whitespaces and optimize imports - Maintain consistent code style across TileLang and Triton implementations