"src/include/direct_convolution.cuh" did not exist on "d51b81588ff6102dbde9c9d91810c1bb8f709cfc"
  1. 02 Mar, 2025 1 commit
    • Lei Wang's avatar
      [Kernel] Implement different SEQ Q/KV examples with block sparse (#133) · 159af5df
      Lei Wang authored
      * Change default log level from WARNING to INFO in TileLang initialization
      
      * Refactor Flash Attention Variable-Length MHA Example with Cython Backend Support
      
      - Update `example_mha_fwd_varlen.py` to use Cython backend for kernel compilation
      - Remove unused imports and simplify function signature
      - Modify `flashattn` function to handle max sequence length as a separate argument
      - Update kernel call to include max sequence length parameter
      - Improve code readability and remove commented-out code
      - Add print statement to confirm successful assertion
      
      * Refactor code formatting in TileLang lowering and example files
      
      - Improve line breaks and code formatting in `lower.py`, `wrapper.py`, and `tensor.py`
      - Simplify line breaks and reduce unnecessary whitespace
      - Enhance code readability by adjusting indentation and line breaks
      - Update example MHA forward pass script with cleaner tensor initialization
      
      * Update TileLang kernel test with import path changes for MMA layout and macro generator
      
      - Modify import statements in test_tilelang_kernel_dequantize_gemm.py
      - Replace bitblas imports with tilelang.intrinsics imports for MMA-related utilities
      - Update main function to use tilelang.testing.main()
      
      * Add Block Sparse Attention Examples for TileLang and Triton
      
      - Implement block sparse attention kernels for both TileLang and Triton
      - Add utility functions for generating sparse attention masks using top-k and threshold methods
      - Support causal and variable-length attention scenarios
      - Include test cases for different sequence length configurations
      - Demonstrate block-level sparse attention with configurable parameters
      
      * Refactor Block Sparse Attention Examples with Code Style Improvements
      
      - Improve code formatting in block_sparse_attn_tilelang.py and block_sparse_attn_triton.py
      - Enhance readability by adjusting line breaks and indentation
      - Simplify kernel and function calls with better formatting
      - Add whitespace and line break improvements for better code clarity
      159af5df