composable_kernel/dispatcher

CK Tile Dispatcher

A unified kernel dispatch system for AMD GPUs with C++ and Python frontends, supporting GEMM and Grouped Convolution operations.

Validated Platform: AMD Instinct MI300 series (gfx942)

---

Table of Contents

1. Quick Start
2. Docker Setup
3. Prerequisites
4. Step-by-Step Build Guide
5. Running Examples
6. External Integration
7. Core Concepts
8. Operation Support Matrix
9. Troubleshooting
10. File Structure

---

Quick Start

Complete setup from scratch (5 minutes):

# From the composable_kernel root directory
cd dispatcher

# Step 1: Create build directory
mkdir -p build && cd build

# Step 2: Configure CMake
cmake .. \
  -DCMAKE_PREFIX_PATH=/opt/rocm \
  -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
  -DCMAKE_BUILD_TYPE=Release \
  -DGPU_TARGETS="gfx942" \
  -DBUILD_DISPATCHER_EXAMPLES=ON

# Step 3: Generate kernels and build (CMake handles this automatically)
make -j$(nproc)

# Step 4: Run C++ examples
./examples/gemm_01_basic

# Step 5: Build Python libraries (required for Python examples)
make python_libs

# Step 6: Run Python examples (from dispatcher directory)
cd ..
python3 examples/gemm/python/01_basic_gemm.py

---

Docker Setup (Recommended)

For a reproducible build environment, use the official ROCm Docker image:

Step 1: Pull and Run Container

# Pull the CK Docker image
docker pull rocm/composable_kernel:ck_ub24.04_rocm7.0.1

# Run container with GPU access
docker run \
  -it \
  --privileged \
  --device=/dev/kfd \
  --device=/dev/dri \
  --group-add video \
  --group-add render \
  -w /root/workspace \
  -v $(pwd):/root/workspace \
  rocm/composable_kernel:ck_ub24.04_rocm7.0.1 \
  /bin/bash

Note: Omit --device flags if building without GPU access.

Step 2: Clone and Build

# Inside the container
git clone https://github.com/ROCm/composable_kernel.git
cd composable_kernel
git checkout builder-dispatch-tile-gemm

# Set up Python environment
python3 -m venv .venv
source .venv/bin/activate
pip install numpy

# Build dispatcher
cd dispatcher
mkdir -p build && cd build
cmake .. \
  -DCMAKE_PREFIX_PATH=/opt/rocm \
  -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
  -DCMAKE_BUILD_TYPE=Release \
  -DGPU_TARGETS="gfx942" \
  -DBUILD_DISPATCHER_EXAMPLES=ON

make -j$(nproc)

One-Liner Build (inside container)

git clone https://github.com/ROCm/composable_kernel.git && \
cd composable_kernel && git checkout builder-dispatch-tile-gemm && \
python3 -m venv .venv && source .venv/bin/activate && pip install numpy && \
cd dispatcher && mkdir -p build && cd build && \
cmake .. -DCMAKE_PREFIX_PATH=/opt/rocm -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
  -DCMAKE_BUILD_TYPE=Release -DGPU_TARGETS="gfx942" -DBUILD_DISPATCHER_EXAMPLES=ON && \
make -j$(nproc)

---

Prerequisites

Required Software

Software	Minimum Version	Check Command
ROCm	6.4+	rocminfo
CMake	3.16+	cmake --version
Python	3.8+	python3 --version
NumPy	1.20+	pip show numpy
hipcc	(from ROCm)	/opt/rocm/bin/hipcc --version

Note: Newer GPU targets (gfx950, gfx1201) require ROCm 6.3+. For ROCm 6.4+, you can also use amdclang++ instead of hipcc.

Check Your GPU Architecture

# Find your GPU architecture
rocminfo | grep -i "gfx"
# Example output: "gfx942"

Supported architectures:

• gfx942 - MI300X, MI300A, MI308, MI325 (Instinct MI300 series)
• gfx90a - MI200 series (MI250, MI250X)
• gfx950 - MI350 series
• gfx1101 - RDNA3 series
• gfx1201 - RDNA4 series

Install Python Dependencies

Core Dependencies (Required)

NumPy is required for Python examples and kernel generation. We recommend using a virtual environment:

Option 1: Using standard venv

# Create virtual environment
python3 -m venv .venv

# Activate virtual environment
source .venv/bin/activate  # Linux/macOS
# .venv\Scripts\activate   # Windows

# Install core dependencies
pip install -r python/requirements.txt

Option 2: Using uv (faster alternative)

# Install uv if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create and activate virtual environment
uv venv .venv
source .venv/bin/activate  # Linux/macOS
# .venv\Scripts\activate   # Windows

# Install core dependencies
uv pip install -r python/requirements.txt

Option 3: System-wide install (not recommended)

pip install -r python/requirements.txt

Note: Always activate your virtual environment before running CMake or Python examples.

ML Heuristics Dependencies (Optional)

For ML-based kernel selection (examples 09-11), install additional dependencies:

# Activate your virtual environment first
source .venv/bin/activate

# Install ML dependencies (LightGBM, pandas, pyarrow, scikit-learn)
pip install -r requirements-ml.txt

Why separate? ML dependencies are large (especially pyarrow) and not needed for basic dispatcher usage. Install only if you need:

• ML-based kernel selection (examples/gemm/python/09_ml_heuristic.py)
• Model training (heuristics/train.py)
• Model evaluation (heuristics/evaluate.py)
• Automated benchmark analysis

Core dependencies: ~50 MB (NumPy only) With ML dependencies: ~500 MB (includes LightGBM, pandas, pyarrow, scikit-learn)

Supported Data Types

CK Tile supports a wide range of data types for GEMM operations:

A dtype	B dtype	Acc dtype	Warp Tile Sizes	Notes
fp32	fp32	fp32	16x16x4, 16x16x16	Full precision
fp16	fp16	fp32	32x32x8, 32x32x16, 16x16x16, 16x16x32	Standard half
bf16	bf16	fp32	32x32x8, 32x32x16, 16x16x16, 16x16x32	Brain float 16
fp8	fp8	fp32	32x32x16, 32x32x32, 16x16x32, 16x16x64	FP8 E4M3
fp8	bf8	fp32	32x32x16, 16x16x32	Mixed FP8/BF8
bf8	fp8	fp32	32x32x16, 16x16x128	Mixed BF8/FP8
bf8	bf8	fp32	32x32x16, 32x32x32, 16x16x32	BF8 E5M2
int8	int8	int32	32x32x16, 16x16x32, 16x16x16	Integer GEMM
pk_fp4	pk_fp4	fp32	16x16x128	Packed 4-bit float

Notes:

• Accumulator is always fp32 except for int8 which uses int32
• FP8 types: fp8 = E4M3, bf8 = E5M2
• pk_fp4 = Packed 4-bit float (2 values per byte)
• Some dtypes require specific GPU architectures (e.g., FP8 requires MI300+)

---

Step-by-Step Build Guide

Step 1: Navigate to Dispatcher Directory

# From composable_kernel root
cd dispatcher

# Verify you're in the right place
ls CMakeLists.txt  # Should exist

Step 2: Create Build Directory

mkdir -p build
cd build

Step 3: Configure CMake

Basic configuration (library only):

cmake .. \
  -DCMAKE_PREFIX_PATH=/opt/rocm \
  -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
  -DCMAKE_BUILD_TYPE=Release \
  -DGPU_TARGETS="gfx942"

Full configuration (with examples and tests):

cmake .. \
  -DCMAKE_PREFIX_PATH=/opt/rocm \
  -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
  -DCMAKE_BUILD_TYPE=Release \
  -DGPU_TARGETS="gfx942" \
  -DBUILD_DISPATCHER_EXAMPLES=ON \
  -DBUILD_DISPATCHER_TESTS=ON

Expected output:

-- Found hip: /opt/rocm (found suitable version "6.x.x")
-- Generating GEMM kernels...
-- Built: gemm_01 through gemm_06, dispatcher_gemm_lib.so
-- Configuring done

Step 4: Build

# Build all targets (generates kernels automatically, then compiles)
make -j$(nproc)

# Or build specific targets
make gemm_01_basic          # Single GEMM example
make dispatcher_gemm_lib    # GEMM shared library for Python

# Build ONLY Python libraries (faster if you don't need C++ examples)
make python_libs -j$(nproc)

Kernel Generation Targets

Kernels are generated automatically during make, but you can also control generation explicitly:

# Generate all kernels only (no compilation)
make generate_all_kernels

# Generate GEMM kernels only
make generate_gemm_kernels

# Force regenerate (even if kernels exist)
make regenerate_all_kernels
make regenerate_gemm_kernels

# Generate for specific GPU architecture
make generate_kernels_gfx942    # MI300X
make generate_kernels_gfx90a    # MI200
make generate_kernels_gfx1100   # RDNA3

Step 5: Verify Build

# Check executables were built
ls examples/gemm_*

# Check shared libraries were built
ls examples/libdispatcher_gemm_lib.so

CMake Options Reference

Flag	Default	Description
CMAKE_BUILD_TYPE	Debug	Use Release for performance!
GPU_TARGETS	None	Target GPU: "gfx942", "gfx90a", etc.
BUILD_DISPATCHER_EXAMPLES	OFF	Build C++ examples and Python libs
BUILD_DISPATCHER_TESTS	OFF	Build unit tests
CMAKE_PREFIX_PATH	-	ROCm installation path
CMAKE_CXX_COMPILER	-	Path to hipcc compiler

WARNING: Important: Always use -DCMAKE_BUILD_TYPE=Release for benchmarking. Debug builds are slower. WARNING: Important: Note that the current system provides single GPU target support for architecture-based kernel filtering, please do not use multiple GPU targets at a time (if necessary, please compile into different build directories).

---

Running Examples

C++ Examples

After building, executables are in build/examples/:

cd build/examples

# GEMM Examples
./gemm_01_basic              # Basic GEMM with autofill/autocorrect
./gemm_02_multi_size         # Wildcard expansion
./gemm_03_benchmark_validation  # Benchmarking + validation
./gemm_04_heuristics         # Heuristic kernel selection
./gemm_05_json_export        # Registry JSON export
./gemm_06_multi_registry     # Multiple registries

# Grouped Convolution Examples
./grouped_conv_01_basic         # Declaration patterns + GPU execution
./grouped_conv_02_all_dirs      # Forward/BwdData/BwdWeight with GPU
./grouped_conv_03_bench_val     # Benchmark + CPU reference validation
./grouped_conv_04_registry_json # Heuristic selection + JSON export
./grouped_conv_05_bwd_data      # Backward data + CPU validation
./grouped_conv_06_bwd_weight    # Backward weight + CPU validation
./grouped_conv_07_benchmark     # Multi-tile ResNet benchmark

Python Examples

Run from the dispatcher directory:

cd /path/to/composable_kernel/dispatcher

# GEMM Examples
python3 examples/gemm/python/01_basic_gemm.py     # Basic multi-kernel GEMM
python3 examples/gemm/python/04_validation.py     # CPU reference validation
python3 examples/gemm/python/07_stress_test.py    # Stress test
python3 examples/gemm/python/08_heuristics.py     # Heuristic selection

# Grouped Convolution Examples
python3 examples/grouped_conv/python/01_basic_grouped_conv.py  # Config patterns + registry + GPU
python3 examples/grouped_conv/python/02_forward.py             # Forward 2D/3D + CPU ref
python3 examples/grouped_conv/python/03_bwd_data.py            # Backward data + CPU ref
python3 examples/grouped_conv/python/04_bwd_weight.py          # Backward weight + CPU ref
python3 examples/grouped_conv/python/05_benchmark.py           # Multi-problem benchmark
python3 examples/grouped_conv/python/06_registry_json.py       # Heuristic selection + JSON

Example Output

Expected C++ output (gemm_01_basic):

======================================================================
Example 01: Basic GEMM with Declarative Kernel Definition
======================================================================

Step 1: Declared Kernels
------------------------
Kernel Set: fp16_gemm_kernels
  Architecture: gfx942
  Configurations: 1
    - gemm_fp16_rcr_compv4_cshuffle_intrawave_128x128x32

Step 2: Create Registry and Dispatcher
--------------------------------------
  Registered 1 kernels

Step 3: Define Problem
----------------------
  M=1024, N=1024, K=1024

Step 4: GPU Execution
---------------------
  *** GPU EXECUTION ***
  Time:   <varies> ms
  TFLOPS: <varies>

Note: Timing values vary by GPU model and system configuration.

---

Benchmark Parameters

The dispatcher supports fine-grained control over benchmarking, matching CK Tile's stream_config:

Available Parameters

Parameter	Type	Default	Description
warmup	int	5	Warmup iterations (discarded from timing)
repeat	int	20	Benchmark iterations (averaged)
flush_cache	bool	false	Flush GPU L2 cache between iterations
rotating_count	int	1	Rotating buffer count (for cache simulation)
timer	string	"gpu"	Timer type: "gpu" (HIP events) or "cpu"
init	string	"random"	Matrix initialization: "random", "linear", "constant"
split_k	int	1	Split-K parallelism factor

Python Usage

from ctypes_utils import DispatcherLib

# Basic usage (default benchmark settings)
lib = DispatcherLib.load()

# Advanced benchmark settings via command line
python3 examples/gemm/python/10_advanced_benchmark.py \
    --warmup 10 \
    --repeat 100 \
    --flush-cache

C++ Usage

// Basic timing
ck_tile::stream_config cfg{nullptr, true};

// Advanced benchmark settings
ck_tile::stream_config cfg{
    nullptr,          // stream_id (nullptr = default stream)
    true,             // time_kernel
    1,                // log_level
    10,               // cold_niters (warmup)
    100,              // nrepeat
    true,             // is_gpu_timer
    true,             // flush_cache
    4                 // rotating_count
};

float avg_time = kernel.run(args, cfg);

Command Line (Python Examples)

# Basic run
python3 examples/gemm/python/10_advanced_benchmark.py

# With benchmark parameters
python3 examples/gemm/python/10_advanced_benchmark.py \
    --warmup 10 \
    --repeat 100 \
    --flush-cache \
    --rotating-count 4 \
    --timer gpu

When to Use Each Parameter

Use Case	Recommended Settings
Quick test	warmup=1, repeat=3
Stable benchmark	warmup=10, repeat=100
Memory-bound analysis	flush_cache=True, rotating_count=4
Compute-bound analysis	flush_cache=False (default)
Debug timing	timer="cpu"
Production	timer="gpu" (default)

---

ML-Based Kernel Selection (Optional)

The dispatcher includes ML heuristics for automated kernel selection using trained LightGBM models.

Prerequisites: Install ML dependencies first:

pip install -r requirements-ml.txt  # ~500 MB (LightGBM, pandas, pyarrow, scikit-learn)

Documentation: See heuristics/README.md for:

• Training and evaluating models
• Feature engineering (72 features)
• Using pre-trained models
• Python API reference

Examples:

python3 examples/gemm/python/09_ml_heuristic.py      # ML-based kernel selection
python3 examples/gemm/python/10_rank_kernels.py      # Kernel ranking

Model Compression: Trained models are stored in compressed .lgbm.gz format to save space (~67% size reduction). Python tools automatically decompress models on first use. For C++ examples, decompress manually:

# If you have compressed models
cd heuristics/models/gemm_universal_fp16_gfx950
gunzip model_tflops.lgbm.gz

# Then use in C++ example
cd ../../../build
./gemm_09_ml_heuristic --model ../heuristics/models/gemm_universal_fp16_gfx950/model_tflops.lgbm

---

External Integration

Using Dispatcher in Your Own Project

Option 1: CMake Integration (Recommended)

Add to your CMakeLists.txt:

# Set path to composable_kernel
set(CK_ROOT "/path/to/composable_kernel")

# Add dispatcher subdirectory
add_subdirectory(${CK_ROOT}/dispatcher dispatcher_build)

# Link to your target
target_link_libraries(your_target PRIVATE ck_tile_dispatcher)
target_include_directories(your_target PRIVATE 
    ${CK_ROOT}/dispatcher/include
    ${CK_ROOT}/include
)

Option 2: Include as Pre-built Library

# Find the pre-built library
find_library(CK_DISPATCHER ck_tile_dispatcher 
    PATHS /path/to/composable_kernel/dispatcher/build)

# Include directories
set(CK_INCLUDE_DIRS
    /path/to/composable_kernel/include
    /path/to/composable_kernel/dispatcher/include
)

target_link_libraries(your_target PRIVATE ${CK_DISPATCHER})
target_include_directories(your_target PRIVATE ${CK_INCLUDE_DIRS})

Option 3: Python Integration

import sys
sys.path.insert(0, "/path/to/composable_kernel/dispatcher/examples/gemm/python")

# For GEMM
from ctypes_utils import DispatcherLib, Dispatcher, KernelConfig

Required Include Paths

When integrating, you need these include paths:

/path/to/composable_kernel/include              # CK Tile core headers
/path/to/composable_kernel/dispatcher/include   # Dispatcher headers
/path/to/composable_kernel/dispatcher/build/generated_kernels  # Generated kernels

Required Compile Flags

# Minimum flags for hipcc
-std=c++17
-D__HIP_PLATFORM_AMD__=1
--offload-arch=gfx942  # Your target GPU

# Recommended flags
-O3
-mllvm -enable-noalias-to-md-conversion=0
-Wno-undefined-func-template
-Wno-float-equal
-Wall 
-Werror

Python Path Setup

For Python scripts outside the dispatcher directory:

# Option 1: Environment variable
export PYTHONPATH="/path/to/composable_kernel/dispatcher/examples/gemm/python:$PYTHONPATH"

# Option 2: In your Python script
import sys
sys.path.insert(0, "/path/to/composable_kernel/dispatcher/examples/gemm/python")

Library Search Paths

The Python utilities search for the shared library in these locations:

# For GEMM (ctypes_utils.py)
SEARCH_PATHS = [
    "build/examples/libdispatcher_gemm_lib.so",
    "../build/examples/libdispatcher_gemm_lib.so",
    "../../build/examples/libdispatcher_gemm_lib.so",
]

If using from a different location, set the library path explicitly:

# GEMM
from ctypes_utils import DispatcherLib
lib = DispatcherLib.load("/absolute/path/to/libdispatcher_gemm_lib.so")

---

Core Concepts

Data Flow

KernelConfig -> Registry -> Dispatcher -> GPU Execution

1. KernelConfig: Defines kernel parameters (tile sizes, data types, layouts)
2. Registry: Stores multiple kernel configurations
3. Dispatcher: Selects best kernel for a given problem and executes it

GEMM Layouts

Layout	A	B	C	Use Case
RCR	Row	Col	Row	Most common (PyTorch default)
RRR	Row	Row	Row	Both inputs row-major
CRR	Col	Row	Row	A transposed
CCR	Col	Col	Row	Both inputs column-major

Split-K Support

Split-K divides the K dimension across multiple thread blocks, useful for large K dimensions.

Usage (C++):

// GEMM with 4-way K split
auto problem = ProblemBuilder()
    .m(1024).n(1024).k(8192)
    .split_k(4)
    .build();

---

Operation Support Matrix

This matrix shows all CK Tile operations with per-data-type, per-layout, and per-GPU support status. It uses a three-state convention: ✅ = supported by both CK Tile and the dispatcher, ❌ = supported by CK Tile but not yet in the dispatcher, blank = not supported by CK Tile itself.

					Data Types					Layouts				GPU Targets
Op	CK Tile Kernel	fp16	fp8	bf16	bf8	int8	fp4	fp6	rcr	rrr	ccr	crr	90a	942	950	1201
GEMM	gemm_multi_d [5] engine: dispatcher/ example: 19_gemm_multi_d/	✅	✅	✅	✅	✅	✅		✅	✅	✅	✅	✅	✅	✅	✅
GEMM	gemm_preshuffle [1][2] engine: dispatcher/	✅	✅	✅	✅	✅			✅				✅	✅	✅	❌
GEMM	gemm_universal [3][4][7][8] engine: dispatcher/ example: 03_gemm/	✅	✅	✅	✅	✅	✅		✅	✅	✅	✅	✅	✅	✅	✅
GEMM	batched_contraction example: 41_batched_contraction/	❌							❌	❌	❌	❌	❌	❌	❌	❌
GEMM	batched_gemm example: 16_batched_gemm/	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
GEMM	block_scale_gemm example: 38_block_scale_gemm/	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
GEMM	flatmm example: 18_flatmm/	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
GEMM	gemm_multi_abd example: 22_gemm_multi_abd/	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
GEMM	gemm_quant		❌	❌	❌	❌	❌		❌	❌	❌	❌	❌	❌	❌	❌
GEMM	grouped_gemm example: 17_grouped_gemm/	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
GEMM	grouped_gemm_quant		❌	❌	❌	❌	❌		❌	❌	❌	❌	❌	❌	❌	❌
GEMM	streamk_gemm example: 40_streamk_gemm/	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Reduce	multi_reduce2d example: 05_reduce/	❌		❌									❌	❌	❌	❌
Reduce	reduce2d example: 05_reduce/	❌		❌									❌	❌	❌	❌
Attention	fmha example: 01_fmha/	❌	❌	❌	❌	❌							❌	❌	❌	❌
Attention	sparse_attn example: 50_sparse_attn/	❌		❌		❌							❌	❌	❌	❌
Activation	softmax	❌		❌									❌	❌	❌	❌
Activation	topk_softmax example: 09_topk_softmax/	❌	❌	❌									❌	❌	❌	❌
Conv	grouped_conv [6] example: 20_grouped_convolution/	❌	❌	❌	❌								❌	❌	❌	❌
Data Move	batched_transpose example: 35_batched_transpose/	❌	❌	❌									❌	❌	❌	❌
Data Move	image_to_column example: 04_img2col/	❌											❌	❌	❌	❌
Data Move	permute example: 06_permute/	❌	❌	❌		❌							❌	❌	❌	❌
Elementwise	elementwise example: 21_elementwise/	❌	❌	❌	❌	❌	❌						❌	❌	❌	❌
MoE	fused_moe example: 15_fused_moe/	❌	❌	❌	❌	❌							❌	❌	❌	❌
Norm	add_rmsnorm2d_rdquant example: 11_add_rmsnorm2d_rdquant/	❌	❌	❌	❌	❌							❌	❌	❌	❌
Norm	layernorm2d example: 02_layernorm2d/	❌	❌	❌	❌	❌							❌	❌	❌	❌
Norm	norm_reduce	❌		❌									❌	❌	❌	❌
Norm	rmsnorm2d example: 10_rmsnorm2d/	❌	❌	❌	❌	❌							❌	❌	❌	❌
Pooling	pooling example: 36_pooling/	❌											❌	❌	❌	❌
Quant	smoothquant example: 12_smoothquant/	❌	❌	❌	❌	❌							❌	❌	❌	❌

Notes:

• [1] gemm_preshuffle: Supports only rcr layout. Uses fixed preshufflev2 pipeline, Auto scheduler, and cshuffle epilogue.
• [2] gemm_preshuffle: int8 preshuffle support is limited to gfx942 and gfx950 (entries in preshuffle_warp_tile_combos).
• [3] gemm_universal: fp4 (pk_fp4) support is only available on gfx950.
• [4] gemm_universal: fp32 GEMM is supported by the dispatcher (fp32_fp32_fp32 warp tile combos exist) but is omitted from matrix columns for consistency with the tile engine matrix format.
• [5] gemm_multi_d: Codegen supports MultiDAdd and MultiDMultiply element-wise ops. Preselected kernel sets also test Relu, Gelu, FastGelu.
• [6] grouped_conv: arch_filter.py defines conv operator types (CONV_FWD, CONV_BWD_DATA, CONV_BWD_WEIGHT, CONV3D_*) but dispatcher infrastructure is incomplete (ctypes bindings are stubs, conv_utils.hpp does not exist).
• [7] (all dispatcher ops): gfx908, gfx1100, and gfx1200 also have warp_tile_combos in arch_specs.json but are not shown in the matrix's 4 GPU columns.
• [8] (all dispatcher ops): int4, fp32, fp64 are valid dispatcher data types (defined in kernel_key.hpp DataType enum) but have no dedicated matrix columns.

Dispatcher GEMM Configuration Detail

Per-Variant Configuration

GEMM Variant	Pipelines	Schedulers	Epilogues	Element-wise Ops	Output Dtype
gemm_universal	mem, compv3, compv4	intrawave, interwave	cshuffle, default	PassThrough	Same as input (fp8/bf8 -> fp16)
gemm_preshuffle	preshufflev2	Auto	cshuffle	PassThrough	Same as input (fp8/bf8 -> fp16)
gemm_multi_d	mem, compv3, compv4	intrawave, interwave	cshuffle, default	MultiDAdd, MultiDMultiply	Same as input (fp8/bf8 -> fp16)

Warp Tile Combinations per GPU

GPU	fp16	bf16	fp8	bf8	int8	pk_fp4
gfx1100	16x16x16	16x16x16	--	--	16x16x16	--
gfx1200	16x16x16	16x16x16	16x16x16	16x16x16	16x16x16	--
gfx1201	16x16x16	16x16x16	16x16x16	16x16x16	16x16x16	--
gfx908	32x32x8, 16x16x16, 32x32x16, 16x16x32	32x32x8, 16x16x16, 32x32x16, 16x16x32	--	--	32x32x16, 16x16x32	--
gfx90a	32x32x8, 16x16x16, 32x32x16, 16x16x32, 4x64x16, 64x4x16	32x32x8, 16x16x16, 32x32x16, 16x16x32, 4x64x16, 64x4x16	32x32x16, 32x32x32	32x32x16, 32x32x32	32x32x16, 16x16x32	--
gfx942	32x32x8, 16x16x16, 32x32x16, 16x16x32, 4x64x16, 64x4x16	32x32x8, 16x16x16, 32x32x16, 16x16x32, 4x64x16, 64x4x16	32x32x16, 32x32x32, 16x16x32, 16x16x64	32x32x16, 32x32x32, 16x16x32, 16x16x64	32x32x16, 16x16x32	--
gfx950	32x32x8, 16x16x16, 32x32x16, 16x16x32, 4x64x16, 64x4x16	32x32x8, 16x16x16, 32x32x16, 16x16x32, 4x64x16, 64x4x16	32x32x16, 32x32x32, 16x16x32, 16x16x64, 16x16x128, 32x32x64	32x32x16, 32x32x32, 16x16x32, 16x16x64, 16x16x128, 32x32x64	32x32x16, 16x16x32	16x16x128

Preshuffle Warp Tile Combinations

GPU	fp16	bf16	fp8	bf8	int8
gfx90a	32x32x8, 16x16x16, 32x32x16, 16x16x32, 64x4x16	32x32x8, 16x16x16, 32x32x16, 16x16x32, 64x4x16	32x32x16, 32x32x32	32x32x16, 32x32x32	--
gfx942	32x32x8, 16x16x16, 32x32x16, 16x16x32, 64x4x16	32x32x8, 16x16x16, 32x32x16, 16x16x32, 64x4x16	32x32x16, 32x32x32, 16x16x32, 16x16x64	32x32x16, 32x32x32, 16x16x64, 16x16x32	16x16x32, 32x32x16
gfx950	32x32x8, 16x16x16, 32x32x16, 16x16x32, 64x4x16	32x32x8, 16x16x16, 32x32x16, 16x16x32, 64x4x16	32x32x16, 32x32x32, 16x16x32, 16x16x64, 16x16x128, 32x32x64	32x32x16, 32x32x32, 16x16x64, 16x16x32, 16x16x128, 32x32x64	--

Legend:

• CK Tile Kernel column: First line is the kernel name. Lines prefixed with "engine:" show the dispatcher directory. Lines prefixed with "example:" show the CK Tile example directory under example/ck_tile/.
• Green cell (✅): CK Tile implementation exists and the dispatcher supports it.
• Red cell (❌): CK Tile implementation exists but the dispatcher does not support it.
• Grey cell (blank): No CK Tile implementation exists for this combination.

Layout codes: Each 3-character layout code specifies the memory layout for tensors A, B, and C:

• r = row-major, c = column-major
• Example: rcr means A is row-major, B is column-major, C is row-major
• gemm_multi_d uses 4-character codes internally (e.g., rcrr) where the 4th character is the D tensor layout (always r). The matrix shows only the 3-character A/B/C portion.

Data type mapping per config label:

Config Label	A (source)	B (source)	Acc	C (output)
fp16	fp16	fp16	fp32	fp16
bf16	bf16	bf16	fp32	bf16
int8	int8	int8	int32	int32
fp8	fp8	fp8	fp32	fp16
bf8	bf8	bf8	fp32	fp16
fp6	fp6	fp6	fp32	fp32
fp4	fp16 or bf16	fp4	fp32	fp16 or bf16

Troubleshooting

Build Issues

Problem	Solution
hipcc not found	Set -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc
hip not found	Set -DCMAKE_PREFIX_PATH=/opt/rocm
Very slow performance	Use -DCMAKE_BUILD_TYPE=Release
gfx942 not supported	Check ROCm version (need 6.0+)
Kernel generation fails	Ensure Python 3.8+ with NumPy installed in active venv
Build errors	First verify CK builds without dispatcher (see main CK README)

Runtime Issues

Problem	Solution
Library not found	Build with -DBUILD_DISPATCHER_EXAMPLES=ON
No kernel found	Check GPU arch matches build target
Python ModuleNotFoundError	Add paths to PYTHONPATH (see above)
Wrong results	Verify layout matches your data

Debug Commands

# Check ROCm installation
rocminfo | head -20

# Check GPU architecture
rocminfo | grep "Name:"

# Verify library exists
ls -la build/examples/libdispatcher_*.so

# Run with verbose output
./build/examples/gemm_01_basic 2>&1

# Python: Check library loading
python3 -c "
import ctypes
lib = ctypes.CDLL('/path/to/libdispatcher_gemm_lib.so')
print('Library loaded successfully')
"

Clean Rebuild

If you encounter issues, try a clean rebuild:

cd dispatcher
rm -rf build
mkdir build && cd build
cmake .. [your options]
make -j$(nproc)

---

File Structure

dispatcher/
|---- README.md                    # This file
|---- CMakeLists.txt              # Build configuration
|
|---- include/ck_tile/dispatcher/  # C++ headers
|   |---- dispatcher.hpp           # Main dispatcher include
|   |---- registry.hpp             # GEMM kernel registry
|   |---- kernel_key.hpp           # Kernel configuration
|   |---- grouped_conv_config.hpp  # Grouped conv configuration
|   |---- grouped_conv_problem.hpp # Grouped conv problem (with builder)
|   |---- grouped_conv_kernel_decl.hpp  # Grouped conv kernel declarations
|   |---- grouped_conv_registry.hpp     # Grouped conv registry (thread-safe)
|   +---- grouped_conv_utils.hpp        # Grouped conv utilities
|
|---- src/                        # C++ implementation
|
|---- codegen/                    # Kernel generation
|   |---- codegen_common.py       # Shared: TileConfig, TraitConfigBase, type mappings
|   |---- unified_gemm_codegen.py # GEMM kernel generator
|   |---- unified_grouped_conv_codegen.py  # Grouped conv kernel generator
|   +---- arch_specs.json         # GPU specifications
|
|---- python/                     # Python utilities
|   |---- dispatcher_common.py    # Shared: paths, validation, Colors, phased output
|   |---- ctypes_utils.py         # GEMM ctypes utilities
|   +---- grouped_conv_utils.py   # Grouped conv utilities
|
|---- scripts/                    # Build scripts
|   |---- compile_gemm_examples.py           # GEMM build script
|   +---- compile_grouped_conv_examples.py   # Grouped conv build script
|
|---- bindings/ctypes/            # Python ctypes interface
|   |---- gemm_ctypes_lib.cpp     # GEMM Python library
|   +---- conv_ctypes_lib.cpp     # Grouped conv Python library
|
|---- examples/                   # Examples
|   |---- gemm/
|   |   |---- cpp/                # C++ GEMM examples (01-07)
|   |   +---- python/             # Python GEMM examples (01-11)
|   +---- grouped_conv/
|       |---- cpp/                # C++ Grouped Conv examples (01-07)
|       +---- python/             # Python Grouped Conv examples (01-06)
|
+---- tests/                      # Unit tests (C++ and Python)

---

Example Documentation

Directory	README
GEMM C++	examples/gemm/cpp/README.md
GEMM Python	examples/gemm/python/README.md
Codegen	codegen/README.md
Python Utils	python/README.md
C++ Headers	include/ck_tile/dispatcher/README.md

---

Grouped Convolution Support

Grouped convolution is fully supported alongside GEMM, with shared infrastructure to eliminate duplication.

Python

# Generate grouped conv kernels
python3 codegen/unified_grouped_conv_codegen.py \
    --output-dir build/generated_kernels \
    --datatype fp16 --variant forward --ndim-spatial 2

# Build grouped conv examples
python3 scripts/compile_grouped_conv_examples.py examples/grouped_conv/cpp/01_basic_grouped_conv.cpp

Key Files

Component	File
C++ Headers	include/ck_tile/dispatcher/grouped_conv_*.hpp
Python Codegen	codegen/unified_grouped_conv_codegen.py
Python Utils	python/grouped_conv_utils.py
Build Script	scripts/compile_grouped_conv_examples.py
Shared Codegen	codegen/codegen_common.py
Shared Utils	python/dispatcher_common.py

Variants

• Forward (grouped_conv_fwd) - Standard grouped convolution
• Backward Data (grouped_conv_bwd_data) - Gradient w.r.t. input
• Backward Weight (grouped_conv_bwd_weight) - Gradient w.r.t. weights

Shared Infrastructure

GEMM and grouped convolution share common code to avoid duplication:

• codegen/codegen_common.py - TileConfig, TraitConfigBase, type mappings, parallel generation, arch-aware expansion
• python/dispatcher_common.py - Path helpers, validation, auto-correction, Colors, phased output

---

License

MIT License - Copyright (c) 2025, Advanced Micro Devices, Inc.