GitLab

* Remove Torch CPP backend and update execution backend options

- Remove TorchCPPKernelAdapter and related code from JIT modules
- Update execution backend options in jit/__init__.py, kernel.py, and adapter/__init__.py
- Remove "torch_cpp" from supported execution backend literals
- Simplify backend validation and remove unused torch_cpp-related code
。

* lint fix

* Add block sparse attention implementations for TileLang and Triton

- Implement block sparse attention kernels for TileLang and Triton
- Add example scripts for block sparse attention with top-k and threshold-based masking
- Include utility functions for generating sparse attention masks
- Demonstrate causal attention with block-level sparsity
- Add test cases to validate sparse attention implementations against PyTorch reference

* Bump version to 0.1.1

* Refactor block sparse attention examples for improved code quality

- Apply consistent code formatting and style in TileLang and Triton block sparse attention implementations
- Add ruff linter ignore comment for specific line in Triton implementation
- Improve readability by adjusting indentation and line breaks
- Standardize sparse mask generation and test function implementations
- Minor optimizations in test case configurations

* lint

* Remove Torch CPP backend and update execution backend options

- Remove TorchCPPKernelAdapter and related code from JIT modules
- Update execution backend options in jit/__init__.py, kernel.py, and adapter/__init__.py
- Remove "torch_cpp" from supported execution backend literals
- Simplify backend validation and remove unused torch_cpp-related code
。

* lint fix

* Add block sparse attention implementations for TileLang and Triton

- Implement block sparse attention kernels for TileLang and Triton
- Add example scripts for block sparse attention with top-k and threshold-based masking
- Include utility functions for generating sparse attention masks
- Demonstrate causal attention with block-level sparsity
- Add test cases to validate sparse attention implementations against PyTorch reference