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d9df801be2
Extends the fp8 paged ps128 work to bf16/fp16 prefill_d128 and removes the paged addressing overhead that made paged ~50% slower than contiguous. - Constexpr ps128 instances for bf16/fp16 (d128 prefill, mask + nmask). The dispatch in unified_attention.cpp now routes page_blk_size==128 to a constexpr instance for every dtype, so the per-tile div/mod/mul-by-128 strength-reduces to shifts/masks instead of falling into the PageSize=0 runtime-divide catch-all (which left real 32-bit integer divides in the bf16/fp16 address chain). - Decouple the single-page SRD rebase (kRebaseKSrd/kRebaseVSrd) from the scalar-promote gate. The rebase only needs the tile to fit in one page (kPageSize % kPageBlockSize == 0); it was wrongly gated behind NRepeat>=2, which excluded the trivial single-issue (NRepeat==1) tile -- bf16/fp16 d128 @ ps128 -- forcing it onto the per-lane multi-page fallback. Now single-issue tiles fold the page base into the SRD and hoist the per-lane scatter offsets; the paged 'addr' phase disappears from the ATT trace. - bf16/fp16 prefill_d128 quarters the N tile to 32 (kBf16QuarterBlockN) to drain the 2-byte score/P live set under the 256-VGPR ceiling (fp8's 1-byte tile fits at the halved N=64). - Route the multi-page K fallback (ps16/ps32) through the LDS-resident block-table cache (kKFallbackLds, on by default): LDS-resolving the per-lane block-table reads off the critical path recovers ~+10.5% at ps16/ps32. kKNContigLoad / kKMultiPageDedup added as default-off experimental levers. bf16 paged causal canonical (b1 sq=sk=75600 hq=hk=5 d128 bs128): 9.97 ms, 1.19x over Triton (was 10.5 ms / 1.13x). fp8 paged unchanged (1.64x). Full correctness matrix 263/263 PASS incl. all split-KV/causal/GQA regression fixtures. 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.