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* Let fmha_fwd_v3() compatible with fmha_fwd()
* Decouple get_fwd_blobs() and FmhaFwdKernel
* Decouple compatibility checks from get_fwd_blobs()
* Extract product feature checks out from get_fwd_blobs()
* Remove duplicated code in factories and redundant checks
* Remove FmhaFwdKernel<>::GetName()
* Let FmhaFwdApiPool support pipelines with different mask_impl
* Add tile setting for fmha fwd v3 pipeline
* Add fwd v3 instances to tile_example_fmha_fwd manually
* Remove unused function import
* Undo irrelevant changes
* Remove fwd v3 instances from tile_example_fmha_fwd
* Finish fmha fwd v3 kernel instance codegen
* Fix formatting
* Remove unused F_idx attribute
* Add is_generic_attention_mask<> traits
* Add constraints to the fmha fwd v3 pipeline
* Unify traits & problem used for fmha fwd v3
* Unify kernel launch code for fmha fwd v2 & v3
* Unify kernel template selection logic
* Use same kernel codegen template for both v2 & v3
* Rename api() property as render() method
* Allow specifying filter for fmha fwd api pool
* Allow specifying function name when rendering api pool items
* Separate fmha fwd v3 kernel dispatching logic from v2
* Remove lambda assignment
* Add simple v2/v3 dispatch logic
* Stop generating empty if-clauses
Skip iterating over dictionaries that have no traits, and avoid assigning i_* to them.
* Use "".join() to concatenate fmha fwd api string content
* Add more feature checks for fmha fwd v3 pipeline
* Check features before dispatch to fmha_fwd_v3()
* Add more feature checks for fmha_fwd_v3()
* Add missing filter call
* Use Tuple to reserve the dtype orders
* Fix wrong pipeline matching logic
* Add fmha fwd v3 group mode instances
* Add functor_transform<>
* Add type constraints to make_tile_window()
* Remove fmha fwd v3 example
* Fix wrong product(aiter mha_fwd()) config
* Fix wrong fmha fwd v2/v3 selection logic
* Fix formatting
* Add comment to warning v3 kernel users
* Fix wrong codegen logics
* Remove unnecessary param
* Fix format
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
[ROCm/composable_kernel commit: 05292b3604]
CK Tile Example Suite
This directory contains a comprehensive suite of examples demonstrating the CK Tile programming model for high-performance GPU kernels. Each example illustrates a key deep learning or HPC operation, implemented using tile-based parallelism, modular pipelines, and data movement policy.
What is CK Tile?
CK Tile is a composable GPU programming API that expresses kernels as a composition of "tiles"—rectangular blocks of computation and data movement. The pipeline & policy orchestrates data movement (global <-> LDS <-> registers), computation, and synchronization, enabling high efficiency and flexibility.
Example Index
| Example | Operation | Description |
|---|---|---|
| 01_fmha | Fused Multi-Head Attention | Tile-based FMHA with masking, quantization, and epilogue fusion |
| 02_layernorm2d | LayerNorm2D | Blockwise layer normalization with fusion and quantization |
| 03_gemm | GEMM | Matrix multiplication with tilewise parallelism |
| 04_img2col | im2col | Image-to-column transformation for GEMM-based convolution |
| 05_reduce | Reduction | Tilewise sum, max, mean reductions |
| 06_permute | Permute | Generic tensor permutation (up to rank-8) |
| 09_topk_softmax | TopK-Softmax | Rowwise softmax and top-k selection for MoE gating |
| 10_rmsnorm2d | RMSNorm2D | Root mean square normalization for LLMs |
| 11_add_rmsnorm2d_rdquant | Add + RMSNorm2D + RDQuant | Fused add, RMSNorm, and rowwise dynamic quantization |
| 12_smoothquant | SmoothQuant | Per-channel scaling and quantization for int8 inference |
| 13_moe_sorting | MoE Sorting | Token-to-expert rearrangement for MoE dispatch |
| 14_moe_smoothquant | MoE-SmoothQuant | Expert-dependent quantization fused with top-k selection |
| 15_fused_moe | Fused MoE | End-to-end fused MoE block: sorting, group-GEMM, activation, weighting |
| 16_batched_gemm | Batched GEMM | Parallel computation of multiple GEMMs |
| 17_grouped_gemm | Grouped GEMM | Multiple independent GEMMs with different shapes |
| 18_flatmm | FLATMM | Flattened matrix multiplication for packed layouts |
| 19_gemm_multi_d | Multi-D GEMM | GEMM with multiple side inputs (bias, residual, etc.) |
| 35_batched_transpose | Batched Transpose | NCHW <-> NHWC and other layout conversions |
| 36_copy | Copy | Minimal example for tile-based memory movement |
| 37_transpose | Block Transpose | High-performance tiled transpose for large tensors |
Technical Highlights
- Tile Distribution: See
include/ck_tile/tile_program/tile_distribution/for mapping tiles to thread blocks. - Block Tile Pipelines: See
include/ck_tile/tile_program/block_tile_pipeline/for memory/computation pipelines. - Policies and Utilities: Many examples use custom policies for tile/block size and memory access.
How to Build & Run
mkdir build && cd build
sh ../script/cmake-ck-dev.sh ../ <arch>
make -j
Each example produces its own executable in build/bin/.
Learning and Extending
- Start Simple: Try 03_gemm or 36_copy to learn tile basics.
- Explore Fusion: See 11_add_rmsnorm2d_rdquant, 15_fused_moe, or 14_moe_smoothquant for advanced fusion.
- Experiment: Modify tile sizes, layouts, or pipelines to explore performance and flexibility.