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[CK Tile] Rule-based configuration generation in CK Dispatcher codegen (#8157) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Motivation The CK Tile Dispatcher code generation for CK Tile Profiler relies on flat JSON files to list the generated configurations. This approach has the following problems - The JSON files are verbose - The JSON files get easily out of sync with the CK Builder .config files from which they were generated from. - The JSON file based configuration make it hard to list explicitly the rules that govern the instance generation. ## Technical Details Replaced the JSON files with a rule based configuration. To preserve the existing functionality, the `profiler` and the `tests` instance sets are generated directly from the CK Builder config files. The JSON config files are removed from source control, and the "on-the-fly" generation guarantees that the Dispatcher codegen uses up to date configurations. This is PR introduces six different rule sets for the CK Tile Dispatcher code generation 1. `profiler`: matches with the old JSON set of profiler configurations. 2. `tests`: matches with the old JSON set of tests configurations. 3. `full`: full configuration set created from a rule-based config selection 4. `full-tests`: a subset of `full` for generating configurations for convolution integration tests. 5. `tiny`: a subset of `full-tests` to produce the minimal set of configurations to test the Dispatcher codegen. 6. `default`: the default rules, which corresponds to the existing heuristic rules for configuration selection. This ensures that ML based kernel selection doesn't get broken. The main use of the `full` rule set is to define a reasonable solution space for the possible implicit GEMM configurations. We start from the configurations that allowed by the device architecture. The `full` rule set defines the relevant tile sizes for each convolution direction. From the tile size we have a curated mapping to the number of waves over the different GEMM axes, i.e., we describe how many waves each GEMM dimensions corresponds to. The GEMM-K wave tile dimension can be computed from the other parameters and does not need to be listed explicitly. An orthogonal axis to the tiling strategy is the vectorization strategy. This mainly defined by the data type and hardware as in general, we want to use the maximum possible load widths. The maximum sizes for each convolution direction variant are defined by the implicit GEMM matrix dimensions. For cases where have a low number of channels per convolution group, we need smaller vector load sizes. These are captured by the `VecStrategy` enumeration in the codegen rules. The problem with the rule based configuration selection is that we "over generate" configurations. The old JSON configurations compose approximately 25% of all configuration that the `full` rule set creates. The additional configurations are valid, but they many not provide any performance benefits. Hence, we keep the `profiler` and `tests` rule set for now to avoid building an excessive amount configurations by default. The `full` rule set can be taken into use by specifying CMake configuration flag `-D DISPATCHER_RULE_SET=full`. By default, the `tests` rule set is used, i.e., we don't change the existing bahaviour. ## Test Plan Added a new stage in the CI/CD pipeline that ensures the Dispatcher codegen rules are up to date. Otherwise the functionality is covered by the existing CI/CD tests. There are no functional changes to the convolution kernels. Only how the different instances are generated. ## Test Result If the CK Tile conv instances build without errors, the Dispatcher codegen is generating valid code. If all tests in CI/CD pipeline are passing, the Dispatcher codegen generates valid instances. ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
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.