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Softmax codegen wins for the canonical fp8 prefill shape (b1, sq=sk=75600, hq=hk=5, d128, non-causal), matching the hand-tuned ASM softmax instruction mix: * Packed score shift (UA_FA4_PACKED_SHIFT, default on): each thread holds one rowmax, so the shift addend (-scale_s*max) is uniform across the thread's score elements. Broadcast it and emit v_pk_fma_f32 (2 f32/instr) instead of 64 scalar v_fma_f32 -> 237 v_pk_fma in the ISA. Bit-identical. +4.5%. * Packed alu1 o_acc partial rescale (UA_FA4_PACKED_ALU1_RESCALE, default on): pack the 6-register *= o_acc_scale with v_pk_mul_f32, halving the asm-volatile scheduling boundaries. Bit-identical. +4% (barrier-wait drops 2504->1544 cyc). Combined +8% (1649 -> 1783 TF/s standalone; ~1774 TF/s contiguous prod) over the prior baseline; full regression matrix (bf16/fp8 prefill/decode/splitkv/long/ps16 + fixtures) PASS. Also consolidates the fastest fp8 prefill config as the compile-time default: kv128 tile (UA_PREFILL_D128_BLOCKSIZE=128) + cooperative K/V load + single-sp + wide 32x32x64 MMA, all 0-spill. Gated-off experiments kept with measured verdicts: UA_FA4_EXP2_APPROX (Schraudolph 2^x, null -- exp is hidden by MFMA overlap) and UA_FA4_PACKED_ROWSUM (-13%, serial chain worse than the tree reduce). 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.