GitLab

* [Example] Update kernel compilation in examples to use @tilelang.jit

- Refactored multiple examples to eliminate the use of `tilelang.compile` for kernel creation, directly invoking the functions instead.
- Added `@tilelang.jit` decorators with appropriate output indices to enhance performance and maintainability.
- Improved code clarity by simplifying the kernel invocation process across various examples, ensuring consistency in how kernels are defined and executed.

* format

* Update example_tilelang_sparse_gqa_decode_varlen_indice.py

* Update example_dequant_gemm_fine_grained.py

* Update example_gemm_autotune.py

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

* [Refactor] Enhance TMA barrier validation and support for additional architectures (#463)

* Updated the TMA barrier validation in `inject_tma_barrier.cc` to check for non-empty `barrier_id_to_range_` before raising an error for missing `create_list_of_mbarrier`.
* Refactored architecture checks in `phase.py` to utilize a new constant `SUPPORTED_TMA_ARCHS`, allowing for easier updates and improved readability in the target architecture validation logic.

* [CI] Add BlocksparseGemm, Dynamic, and Cast examples to CI.

* Lint

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

* [Dev] Add FP8 Quantization Examples and Absolute Maximum Reduction Operation Support

* Added `example_per_token_cast_to_fp8.py` in examples/cast, providing token-wise FP8 quantization implementation.
* Added `example_triton_cast_to_fp8.py` in examples/cast, providing Triton-based FP8 quantization implementation.
* Added support for absolute maximum (absmax) reduction operation in reduce.cc and reduce.h.
* Implemented `reduce_absmax` function in reduce.py, allowing absolute maximum reduction on input buffers.
* Updated tilelang.language module to include the new `reduce_absmax` function.

These changes enhance FP8 quantization capabilities and extend reduction operation support.

* [Enhancement] Update per_token_cast_to_fp8 for improved FP8 quantization

* Modified the `per_token_cast_to_fp8` function to support variable block sizes and improved memory layout annotations.
* Adjusted the handling of absolute maximum values and scaling factors for better performance and accuracy.
* Updated the main execution block to allow for larger matrix dimensions and refined the profiler setup for benchmarking.

These changes enhance the flexibility and efficiency of the FP8 quantization process.

* lint

* [Dev] Update per_token_cast_fp8.py