GitLab

* Optimize CMake build process with dynamic job count calculation

- Modify build_csrc function to use 90% of available CPU cores
- Ensure at least one job is used during compilation
- Improve build performance by dynamically adjusting parallel job count

* Optimize build_csrc function with multiprocessing module

- Replace os.cpu_count() with multiprocessing.cpu_count()
- Maintain existing 90% CPU utilization logic
- Improve CPU core count calculation for build process

* Add dynamic shape support with out_idx in Cython JIT kernel compilation

- Implement `run_cython_dynamic_shape_with_out_idx` function in test_tilelang_jit_gemm_cython.py
- Update Cython wrapper to handle dynamic symbolic shapes during tensor allocation
- Add support for resolving dynamic shape dimensions using input tensor references
- Enhance flexibility of JIT kernel compilation with symbolic shape handling

* Enhance error reporting for dynamic symbolic shape resolution in Cython JIT kernel

- Add detailed error message when a dynamic symbolic dimension is not found in dynamic_symbolic_map
- Improve debugging by providing context about missing symbolic dimensions
- Maintain existing dynamic shape resolution logic

* Fix Copy operation handling for scalar and multi-dimensional tensors

- Add special handling for scalar tensor copy operations
- Enhance error reporting in MakeIndices method with more detailed diagnostic information
- Improve SIMT loop generation to support zero-dimensional tensors
- Add explicit check and handling for scalar tensor scenarios

* Refactor Copy operation code formatting and improve readability

- Improve code formatting in MakeIndices and MakeSIMTLoop methods
- Add line breaks to enhance readability of complex ICHECK statements
- Simplify code structure in scalar tensor handling
- Remove unnecessary whitespace and improve code alignment

* Simplify GEMM example with direct kernel compilation

- Update copyright header to Tile-AI Corporation
- Remove Profiler import and usage
- Replace tilelang.lower() with tilelang.compile()
- Simplify kernel execution workflow
- Update kernel source retrieval method

* Enhance block sparse attention implementation

- Update `blocksparse_flashattn` to use 2 stages for improved performance.
- Change `block_mask_dtype` from `int8` to `bool` for better memory efficiency.
- Modify condition checks in the kernel to utilize boolean values.
- Introduce a new example for top-k sparse attention and a benchmark for native sparse attention.
- Add support for asynchronous copy in PTX and improve pipeline planning with condition handling.

* Refactor and clean up code formatting across multiple files

- Added whitespace for improved readability in `example_blocksparse_gemm.py`, `example_tilelang_nsa_fwd.py`, and `benchmark_nsa_fwd.py`.
- Enhanced code structure and alignment in `inject_ptx_async_copy.cc` and `pipeline_planning.cc`.
- Updated comments and documentation for clarity in `__init__.py` and `phase.py`.
- Ensured consistent formatting and style across the codebase.

* Update native sparse attention example with scale parameter handling

- Add scale parameter processing in native_sparse_attention function
- Modify example script to include custom scale value
- Update function calls to pass scale parameter
- Enhance flexibility of sparse attention implementation

* Refactor Triton Native Sparse Attention Example

- Improve code formatting and readability in example_triton_nsa_bwd.py
- Standardize function and parameter alignment
- Remove unnecessary whitespaces and optimize imports
- Enhance code style consistency with previous commits

* [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

* [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