GitLab

* [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