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a3714e82cf
Eight files outside the UA scope had drifted onto this branch over time
via earlier commits whose subject lines explicitly carried no "CK-UA:"
prefix — they are independent fmha bug fixes and codegen additions that
do not touch any code path the unified_attention example, kernel or
pipeline actually compiles or includes.
Reverted to the merge-base content of:
include/ck_tile/ops/fmha/block/block_masking.hpp (-39 lines)
added by 6729989b9 "Fix FMHA split-KV for paged-KV with
page_block_size < kN0"
include/ck_tile/ops/fmha/kernel/fmha_fwd_kernel.hpp (-2 lines)
added by ec2db01e4 "Fix fmha_fwd early-exit bug: seqlen_q <=
min_seqlen_q should be <"
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
example/ck_tile/01_fmha/codegen/ops/fmha_pagedkv_prefill.py
example/ck_tile/01_fmha/codegen/ops/fmha_batch_prefill.py
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
example/ck_tile/01_fmha/fmha_fwd_runner.hpp
example/ck_tile/01_fmha/mask.hpp
added by 63821af1f / cb6fb2802 / c5600bc8a / e5272603c / 10564b0c4
/ cd7ba6e2e / 07ba03bcb — split-KV decode tiles, codegen tweaks,
and a sliding-window mask fix, all in the 01_fmha example program
(a separate build target; the UA example lives in
42_unified_attention and pulls in zero 01_fmha sources).
These commits are still reachable from the branch's reflog and from
their original commit hashes; they should each be cherry-picked onto
their own branches and sent upstream as standalone fmha bug-fix PRs —
they look like clean fixes that upstream would welcome, but they don't
belong in the UA PR's scope.
Verified empirically: clean JIT rebuild of module_unified_attention
followed by both regression shapes pass at full perf
b=128/sk=16384/d=128/bf16 : 1.5152 ms, 5672 GB/s, PASS
b=1/sk=1M/d=128/bf16 nb=70k : 0.7677 ms, 5594 GB/s, PASS
matching the pre-revert numbers to within run-to-run noise.
Branch's shared-CK touch surface after this revert: tile_scatter_gather.hpp
(+152 from our async_load_raw_long method), load_tile.hpp (+21 from the
sister dispatcher), warp_gemm[.|_dispatcher.]hpp (+13 for the FP8 e4m3
small-tile registration), and the new amd_global_load_lds_raw.hpp file.
Down from 14 shared files to 4.
Co-authored-by: Cursor <cursoragent@cursor.com>
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.