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The 32x32x16 tiers (prefill_d{64,128}, decode_d{64,128}_m{32,64,128}) keep
the cheap in-register `ds_bpermute_b32` cross-lane swap that fixes the
QK-C / PV-A per-thread alias for the union'd `sp_compute` / `p`.
The 16x16x32 m16 tiers (decode_d{64,128}_m16) cannot use the swap -- the
MFMA puts the paired-lane bit at a different position and the
sub=0/sub=1 4-fp8 chunks no longer map onto each other. We add a
layout-agnostic LDS roundtrip as the `else` branch, gated by the same
`PVWarpTile` constexpr:
- Hoist two distribution-bound windows over the existing `p_lds`
region (one bound to the QK-C output distribution, one to the PV-A
input distribution). Done once per kernel invocation.
- In `fmha_alu1`, after the cvt_pk_fp8_f32 packing chain, view the
union's bytes as a `static_distributed_tensor<fp8>` in the QK-C
distribution, `store_tile` it through `p_lds` in canonical (M, N)
order, `s_barrier`, then `load_tile` back with the PV-A
distribution and copy into `sp(idx).p`.
A/B'd a uniform LDS-roundtrip (no fast-path) vs the split: pure LDS
regressed decode_m128 by ~1.5x end-to-end (CK FP8 dropped from
~0.39x of Triton FP8 to ~0.16x), driven by the extra block-wide
barrier on the 4-warp decode path. Keeping the swap for 32x32x16
preserves the previously-tuned perf.
Dispatcher (`unified_attention.cpp`) now FP8-enables every UA variant
including decode_d{64,128}_m16. Four new instance .cpp files
(`unified_attention_d{64,128}_fp8_{mask,nmask}_decode_t.cpp`)
instantiate the m16 FP8 kernels.
Pytest (`test_unified_attention_ck_correctness.py`):
- 245 BF16/FP16: pass (no regression from the pipeline edit).
- 160 FP8: pass (was 112 before m16 enablement).
- 80 skipped: block_size<32 or query_len>kv_len -- pre-existing.
Single-shape m16 dispatches verified on gfx950:
b=128 sq=1 hq=hk=8 d=128 fp8 PASS (CK 0.109 ms / Triton 0.043 ms)
b=128 sq=1 hq=hk=8 d=64 fp8 PASS (CK 0.077 ms / Triton 0.039 ms)
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.