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

* Add DeepSeek MLA decode example with Flash Attention implementation

* Add GEMM SplitK and StreamK example implementations

This commit introduces two new example scripts demonstrating advanced GEMM (matrix multiplication) techniques:
- `example_tilelang_gemm_splitk.py`: Implements a Split-K GEMM kernel using TileLang
- `example_tilelang_gemm_streamk.py`: Implements a Stream-K GEMM kernel using TileLang

Both examples showcase different parallel computation strategies for matrix multiplication, with comprehensive testing using PyTorch reference implementations.

* Refactor GEMM SplitK and StreamK example implementations

Clean up and improve code formatting for the SplitK and StreamK GEMM example scripts:
- Remove unused import (Profiler) in splitk example
- Simplify line breaks and improve code readability
- Standardize indentation and remove unnecessary whitespace
- Optimize atomic add and copy operations for better clarity

* Add block sparse attention benchmarks for multiple libraries

This commit introduces comprehensive block sparse attention benchmarks for different libraries:
- TileLang block sparse FMHA implementation
- Triton block sparse FMHA implementation
- PyTorch reference block sparse FMHA implementation
- FlashAttention dense FMHA reference implementation

The benchmarks include:
- Configurable benchmark parameters (batch size, heads, sequence length, etc.)
- Sparse mask generation using top-k and threshold methods
- Performance measurement for different sparse attention configurations
- Utility functions for mask generation and benchmarking

* Refactor block sparse attention benchmarks with code style improvements

- Add Ruff linter ignore comments to benchmark files
- Improve code formatting and line breaks
- Remove unused imports
- Standardize print statement formatting
- Enhance code readability across multiple library benchmarks

* lint fix

* Add CUDA atomic operations for BFLOAT16 and update function naming

- Implement AtomicAdd functions for BFLOAT16 and BFLOAT16x2 in CUDA common header
- Rename existing atomic add functions to use PascalCase (atomicAdd -> AtomicAdd)
- Add a new __pack_nv_bfloat162 function for packing BFLOAT16 values
- Update kernel and language customization to use new function names
- Add return type annotations in profiler module

* lint fix

* Add example for Group Query Attention (GQA) forward pass using Flash Attention in TileLang

This commit introduces a new example script `example_gqa_fwd_bshd.py` that demonstrates:
- Group Query Attention (GQA) implementation
- Flash Attention forward pass
- Performance benchmarking
- Configurable parameters for batch, heads, sequence length, and dimension
- Autotuning support
- Reference implementation comparison

* Refactor IR lowering pipeline into modular phases

This commit introduces a new module `phase.py` to modularize the IR lowering process by splitting the complex lowering pipeline into two distinct phases:
- `LowerAndLegalize`: Handles initial IR legalization and transformation
- `OptimizeForTarget`: Applies target-specific optimizations

The changes simplify the lowering logic in multiple files by extracting the transformation steps into reusable functions, improving code readability and maintainability.

* lintfix

* nas kernel

* Enhance Native Sparse Attention Examples with Code Improvements and Parameter Updates

- Updated example_tilelang_nsa.py and example_triton_nsa.py with code formatting and style improvements
- Increased default number of heads and selected blocks in TileLang NSA example
- Added Ruff linter ignore comments to reference.py
- Standardized function signatures and improved code readability across NSA implementations

* Add utility math functions for integer operations

- Implement `next_power_of_2()` to calculate the next power of 2 for an integer
- Add `cdiv()` function for ceiling division of integers

* Add utility math functions for integer operations

- Implement `next_power_of_2()` to calculate the next power of 2 for an integer
- Add `cdiv()` function for ceiling division of integers

* Refactor DeepSeek MLA Decode Example with Enhanced Flash Attention Implementation

- Update flash attention kernel to support positional embeddings (PE)
- Modify reference implementation to handle PE and group query attention
- Increase default batch size and adjust benchmarking parameters
- Improve kernel performance and readability
- Add einops and torch operations for more flexible tensor manipulation

* Update README.md with corrected Flash MLA Decoding example path

- Modify the example link for Flash MLA Decoding to point to the correct directory
- Ensure accurate navigation to the DeepSeek MLA decoding example

* Add DeepSeek MLA decode example with Flash Attention implementation

* Add GEMM SplitK and StreamK example implementations

This commit introduces two new example scripts demonstrating advanced GEMM (matrix multiplication) techniques:
- `example_tilelang_gemm_splitk.py`: Implements a Split-K GEMM kernel using TileLang
- `example_tilelang_gemm_streamk.py`: Implements a Stream-K GEMM kernel using TileLang

Both examples showcase different parallel computation strategies for matrix multiplication, with comprehensive testing using PyTorch reference implementations.

* Refactor GEMM SplitK and StreamK example implementations

Clean up and improve code formatting for the SplitK and StreamK GEMM example scripts:
- Remove unused import (Profiler) in splitk example
- Simplify line breaks and improve code readability
- Standardize indentation and remove unnecessary whitespace
- Optimize atomic add and copy operations for better clarity

* [Enhancement] Add VectorizeLoop function and update imports for compatibility

* [CI][Test] Improve test cases for vectorization and fix typos in parser comments

* lint fix

* Fix incorrect module reference for VectorizeLoop transformation

* Refactor vectorize_loop transformation by removing unused extent mutation logic

* [Enhancement] Add support for FP8 data types and global barriers in CUDA codegen

* Fix formatting in CUDA FP8 header file for consistency

* Refactor CI workflow to use 'tilelang_ci' virtual environment and update CUDA type printing for better clarity

* Update submodule 'tvm' to latest commit for improved functionality

* Refactor execution backend references from 'dl_pack' to 'dlpack' for consistency and clarity; add apply_simplify function to simplify PrimFunc or IRModule.

* Refactor CUDA code for improved readability; clean up formatting and remove unnecessary whitespace in multiple files.

* Refactor import statement in test_tilelang_kernel_dequantize_gemm.py to use 'tilelang.language' for consistency

* Add CUDA requirements to FP8 test cases and update references for clarity

* Add a blank line for improved readability in test_tilelang_kernel_fp8_gemm_mma.py

* Fix data type in reference result calculation for consistency in test_tilelang_kernel_gemm_mma_intrinsic.py

* Add CUDA requirements and FP8 test cases for matmul and gemv simulations

* Remove debug print statements and use tilelang's testing assertion for result validation in test_tilelang_kernel_gemm_mma_intrinsic.py

* Remove outdated comment regarding FP8 tests in test_tilelang_kernel_gemv_simt.py

* Add BF16 support to matrix multiplication and introduce corresponding test cases

* Add a blank line for improved readability in BF16 GEMM test

* Update acknowledgements in README to include supervision by Zhi Yang at Peking University

* enhance acknowledgement

* Replace tutorial on memory layout optimization with new tutorial on writing high-performance kernels with thread primitives

* Update subproject commit for TVM dependency

* Update subproject commit for TVM dependency

* Add int4_t type and functions for packing char values in CUDA common header

* Add plot_layout example and implement GetForwardVars method in layout classes

* Refactor code for improved readability by adjusting line breaks and formatting in layout and test files

* Fix formatting by removing unnecessary line break in layout.h

* Refactor make_int4 function for improved readability by adjusting parameter formatting

* Add legend to plot_layout for improved clarity of thread and local IDs

* Remove unnecessary dependencies from requirements files for cleaner setup

* Remove flash_mha.py and add .gitkeep to deepseek_mla directory

* Add build requirements and update installation scripts for improved setup