GitLab

* [Dev] Add RetNet Linear Attention example

* [Dev] Add WgmmaSync rewriter for pipelined WGMMA operations and add MHA WGMMA pipelined example (FA3-like scheduling)

This commit introduces a new transformation pass `RewriteWgmmaSync` to optimize warp group matrix multiply accumulate (WGMMA) operations in the TileLang compiler:

- Implemented `WgmmaSyncRewriter` in `src/transform/wgmma_sync_rewriter.cc`
- Added pass registration for `RewriteWgmmaSync`
- Updated `tilelang/engine/phase.py` to include the new transformation pass
- Updated `tilelang/transform/__init__.py` to expose the new pass

The rewriter intelligently manages synchronization and dependencies between WGMMA operations, improving pipeline efficiency for complex matrix multiplication kernels.

* [Bugfix] Fix bug in ThreadTagChecker for warp specialization

Improve thread tag validation in warp specialized rewriter to prevent unintended transformations:
- Add more precise checks for threadIdx.y and threadIdx.z
- Validate thread extent to ensure only single-extent thread bindings are allowed
- Prevent warp specialization for multi-extent thread bindings in y and z dimensions

* lint

* [CI] Add TMA descriptor attribute to transformed module in test case

* [Dev] Refactor DeepSeek MLA Decode Example with Non-Split and Split Flash Attention Implementations

- Add new `flash_attn` macro for non-split flash attention implementation
- Add swizzled layout for tile in shared memory
- Use threadblock swizzle to imporve L2 cache hit rate

* [Dev] Add DeepSeek MLA Decode Example with Documentation and Performance Benchmarks

- Add detailed README.md explaining MLA (Multi-Head Latent Attention) implementation
- Include performance benchmark images for batch sizes 64 and 128
- Add layout visualization images for QK and PV operations
- Implement torch reference implementations in torch_refs.py
- Update example_mla_decode.py with command-line argument support and flexible configuration
- Add performance benchmarking and comparison with other implementations