GitLab

* [Feature] Enhance fill operation to support various buffer types

- Added support for `BufferLoad` in the `fill` function to handle different buffer types.
- Updated `Fill` class to process region descriptors and buffer regions, improving flexibility in buffer handling.
- Introduced checks for static bounds in region definitions to ensure safety during operations.
- Refactored loop induction variable handling in `FillNode` to accommodate sliced regions.

* lint fix

* [Refactor] Improve Python compatibility for ParamSpec and Self

- Added compatibility handling for ParamSpec and Self to support Python versions below 3.10 and 3.11 respectively.
- Updated type annotations across multiple files to ensure consistent usage of typing features.

* [Update] Require Python 3.9 and enhance type annotations

- Updated the minimum required Python version from 3.8 to 3.9 in `pyproject.toml`.
- Removed references to Python 3.8 in classifiers.
- Changed type annotations from `int | None` to `Optional[int]` in multiple example files for better clarity and compatibility.
- Improved import statements to use `collections.abc` for `Iterable` and `contextlib` for `AbstractContextManager` in relevant files.

* [Refactor] Update import statements to enhance type annotations

- Replaced imports from `typing` with `collections.abc` for `Iterable` and `Mapping` in relevant files to improve compatibility and clarity.
- Updated the caching decorator from `functools.lru_cache` to `functools.cache` for better performance in the C++ compiler retrieval function.
- Adjusted import statements in the language proxy file to maintain consistency in type annotations.

* disable rocm rs nt test.

* lint fix

* [Feature] Add vectorized float16 and float32 conversion support in CUDA codegen

* Implemented handling for conversions between float16 and float32 types, specifically for vectorized operations using __half22float2 and __float22half2_rn.
* Enhanced the existing code to support both directions of conversion based on the lane count.
* Improved overall type handling in the VisitExpr_ method for better compatibility with TileLang.

* [Feature] Add float32 to float8 conversion support in CUDA codegen

* Implemented handling for conversion from float32 to float8 (E4M3/E5M2) in the VisitExpr_ method.
* Added vectorized conversion support using __nv_cvt_float2_to_fp8x2 for float2 to fp8x2 transformations.
* Enhanced type handling for better compatibility with TileLang, particularly for float8 types.

* lint

* fix a bug

* [Enhancement] Support lanes=4 cases and add unit test for vectorized cast

* lint

* [Feature] Refactor bf16 convertion operations and remove legacy compile flags

* lint

* revert split+sum template for MHA backward

* lint

* Update example_mha_bwd.py

* Update example_mha_bwd_wgmma_pipelined.py

* Refactor attention sink examples to support bf16 and user-defined softmax scale

* fix typos

* Adding compile flags for fast math optimizations and enabling BF16 support in both GQA and MHA backward implementations.

* Update backward configuration for GQA and MHA examples to align with flash attention

* Refactor GQA backward implementation to improve atomic add performance

* Allow for slightly larger numerical error for bf16

* upd readme to show bf16 benchmark results

* lint

* fix ci and lint

* fix comments and lint

* refactor atomic add

---------
Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>

* [Example] Add a new example to support attention sink for MHA

- Introduced a new example script for multi-head attention (MHA) with sliding window attention and sink tokens.
- Added a reference attention function to validate the implementation against PyTorch.
- Included argument parsing for command-line execution of the example.

* [Example] Replace MHA sink forward example with updated implementation

- Removed the old example script for multi-head attention (MHA) with sliding window attention and sink tokens.
- Introduced a new example script that modifies the attention mechanism to enhance performance and maintainability.
- Updated argument parsing and reference functions to align with the new implementation.

* Enhance MHA sink example with sliding window support

- Added a `window_size` parameter to the `flashattn` function to enable sliding window attention.
- Implemented assertions to ensure `window_size` is compatible with `block_N`.
- Updated the main function to include a `tune` option for performance tuning.
- Introduced a new test file to validate both full attention and sliding window scenarios.
- Adjusted FLOPS calculation to account for the sliding window configuration.

* lint

* [Fix] Add checkinf process to fix the bug of swa

* Migrate to BSHD layout to align with triton baselines

* lint

* fix typo

* Refactor MHA sink example to use seq_q and seq_kv parameters to accommodate the new sequence length parameters.

* Add GQA sink example for optimized attention mechanism & lint fix

* fix several typos and bugs

* lint

* fix speed issues of swa

* Add flash attention example with backward pass for BHSD layout and corresponding test cases

* Add backward pass implementation for flash attention with sinks and corresponding test case

* fix lint and typo

* Optimze the calculation of `dsinks`

* Add support for swa backward and update examples

* fix previous typos

* Add example for GQA sink backward pass and update tests for both MHA and GQA sinks

* fix lint

* fix previous typos

* typo