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[CK] Fix FMHA sink dispatch when init_sink_value is set (#7530) ## Summary - Fix `traits.has_sink` in `fmha_fwd_runner.hpp` to also check `init_sink_value != 0`, so the GPU kernel dispatches with sink support when `-init_sink=1` is passed. - Gate `run_sink_mask_tests` (StreamLLM) and `run_sink_init_tests` (GPT-OSS) behind opt-in flags `-m` and `-g` in `smoke_test_fwd.sh`. These tests require sink=true kernel instances which are excluded by the `BUILD_TESTING` CMake filter (`*_nsink*`), causing unconditional "not supported yet" failures (48 tests in CI). The opt-in flag approach was borrowed from PR #6057. ## Why gate tests instead of compiling sink=true kernels? The `BUILD_TESTING` filter in `CMakeLists.txt` uses `*_nsink*` glob patterns for the `fwd` and `fwd_splitkv` APIs, excluding sink=true kernel instances from compilation. We chose opt-in flags over widening the filter because: - **Compile time**: Enabling sink=true kernels doubles the kernel variants for `fwd` and `fwd_splitkv` APIs. The filter exists specifically to reduce CI build times. - **Incremental enablement**: Sink support (StreamLLM / GPT-OSS) is still maturing. Gating lets teams opt in explicitly (`smoke_test_fwd.sh -g`) while keeping the default CI path fast. - **Precedent**: splitkv (`-s`) and appendkv (`-a`) tests already follow this opt-in pattern. ## Test plan - [ ] Run `smoke_test_fwd.sh -g` with sink=true kernels compiled and verify sink-enabled kernels are dispatched - [ ] Verify `smoke_test_fwd.sh` still passes without `-m` / `-g` flags - [ ] Confirm CI no longer fails on sink tests (they are now opt-in)
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.