* [Add] Create Dockerfile for ROCm environment setup
* Introduced a new Dockerfile for setting up a ROCm environment with PyTorch.
* Configured the working directory and installed necessary packages including Miniconda, Python, and development tools.
* Cloned the tilelang repository and executed the ROCm installation script.
* Set environment variables for compatibility and performance optimization.
* [Remove] Delete Dockerfile for ROCm environment setup
* Removed the Dockerfile used for setting up a ROCm environment with PyTorch.
* Updated README to reflect changes in Docker image naming conventions for AMD GPU support.
* 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.
* Add kernel source printing in example_tilelang_nsa_fwd.py and implement IfThenElse node replacement in inject_pipeline.cc
- Added a print statement to output the kernel source in `example_tilelang_nsa_fwd.py` for debugging purposes.
- Introduced a new function `replace_if_then_else` in `inject_pipeline.cc` to transform IfThenElse nodes while preserving attributes, enhancing the handling of conditional statements in the pipeline.
* Refactor condition handling in inject_pipeline.cc
- Change the data structure for mapping conditions to statements from a Map to an Array for improved performance and simplicity.
- Update condition comparison logic to use StructuralEqual for better accuracy.
- Enhance logging to provide detailed insights into condition changes and statement processing.
- Adjust final statement construction to utilize the new data structure, ensuring correct handling of conditions and statements.
* Improve logging and formatting in inject_pipeline.cc
- Enhance logging statements for better clarity on condition changes and statement processing.
- Adjust formatting for improved readability, including line breaks and consistent spacing.
- Ensure accurate condition comparison and handling in the pipeline logic.
* Refactor logging and clean up inject_pipeline.cc
- Remove excessive logging statements to streamline the code and improve performance.
- Simplify condition handling by eliminating unnecessary log outputs related to condition changes and statement processing.
- Maintain the core functionality while enhancing code readability and maintainability.
* Update Dockerfiles to specify exact version of libstdcxx-ng
- Change installation command in multiple Dockerfiles to use `libstdcxx-ng=12` instead of `libstdcxx-ng-12` for consistency and to avoid potential issues with package resolution.
- Ensure all Dockerfiles from cu118 to cu126 reflect this change for uniformity across builds.
Update Dockerfiles for CUDA 118, 120, 121, 123, 124, 125, and 126 to install libstdcxx-ng-12 from conda-forge, ensuring consistent standard library support across different CUDA versions
Create Dockerfiles for CUDA versions 11.8, 12.1, 12.3, 12.4, and 12.5, using the latest PyTorch NGC base images. Update docker/README.md to provide clearer instructions for building and running the Docker container with a specific CUDA version.
Jiaxing Ding