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5d1def74a6
Pipeline cleanup (-fav4):
* Delete the 8-wave compute/memory ping-pong baseline (the ~200-line
monolithic `core_loop` lambda + its 2-warp-group dispatch). It was
reachable only under -DUA_FA4_PIPELINE=0 and never beat FA4 on any
measured prefill shape, so it was dead under the default build.
* Drop the UA_FA4_PIPELINE toggle entirely. kFA4 is now derived purely
from NumWarpGroups==2 + the 32x32x16 within-wave FP8 P-relayout
invariant, with a static_assert pinning that every 2-WG instance is
FA4-capable (fails the build loudly instead of running an empty loop).
* Remove the now-orphaned ADD_SBARRIER_FOR_PHASE0/PHASE2 knobs (they
only gated barriers inside the deleted core_loop). MOVE_FMHA_MASK_*
stay (still consumed by the FA4 core-loop scheduler).
* The non-FA4 pre-stage + fmha_post_process epilogue are retained: they
are shared by the single-warp-group (NumWarpGroups==1) serial decode
path, where kFA4 is false.
Behaviour-preserving for the default build: FA4 prefill perf is bit-for-
bit unchanged (b16 sq=sk=10000 fp8 CK=5.76ms before/after) and the full
decode regression (d{64,128} x {bf16,fp8} x split-KV {2,64}) still PASSes.
Add opt-in prefill fallback knob (unified_attention.cpp):
* AITER_UA_PREFILL_FALLBACK=1 routes prefill-sized shapes to the 4-warp
single-warp-group *serial* decode_*_m128 instances instead of FA4.
Reuses already-compiled instances (no extra binary). OFF by default:
the serial path has no matrix/softmax overlap and measured ~0.66-0.70x
Triton vs FA4's ~0.73-0.80x on gfx950 fp8 GQA-12/2 (i.e. SLOWER than
FA4). Kept as a diagnostic / robustness A-B knob only.
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.