composable_kernel/dispatcher/examples

CK Tile Dispatcher Examples

Comprehensive examples for GEMM and Grouped Convolution operations with GPU execution.

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Quick Start

Step 1: Build

cd /path/to/composable_kernel/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

# Build everything (C++ examples + Python libraries)
make -j$(nproc)

# Or build ONLY Python libraries (faster)
make python_libs -j$(nproc)

Step 2: Run C++ Examples

cd build/examples

# GEMM
./gemm_01_basic
./gemm_02_multi_size
./gemm_03_benchmark_validation
./gemm_04_heuristics
./gemm_05_json_export
./gemm_06_multi_registry

Step 3: Run Python Examples

cd /path/to/composable_kernel/dispatcher

# GEMM
python3 examples/gemm/python/01_basic_gemm.py
python3 examples/gemm/python/04_validation.py
python3 examples/gemm/python/07_stress_test.py
python3 examples/gemm/python/08_heuristics.py

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Directory Structure

examples/
|---- gemm/
|   |---- cpp/           # 6 C++ GEMM examples
|   +---- python/        # 11 Python GEMM examples
|
+---- README.md

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GEMM Examples

C++ Examples

#	Example	Description
01	gemm_01_basic	Basic GEMM with declarative API, autofill, autocorrect
02	gemm_02_multi_size	Wildcard expansion for multiple configurations
03	gemm_03_benchmark_validation	Performance benchmarking with CPU/GPU validation
04	gemm_04_heuristics	Heuristic-based kernel selection
05	gemm_05_json_export	Registry JSON export for external tools
06	gemm_06_multi_registry	Multiple registries with named kernel sets

Details: gemm/cpp/README.md

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Python Examples

#	Example	Description
01	01_basic_gemm.py	Basic GEMM with multi-kernel support
02	02_batch_gemm.py	Batched GEMM operations
03	03_benchmark.py	Performance benchmarking
04	04_validation.py	CPU reference validation
05	05_numpy_integration.py	NumPy array integration
06	06_json_export.py	Registry JSON export
07	07_stress_test.py	Multi-kernel stress testing (48 configs)
08	08_heuristics.py	Heuristic-based kernel selection (24 configs)
09	09_multi_registry.py	Multiple registries
10	10_advanced_benchmark.py	Advanced benchmark with full control
11	11_json_import.py	Import kernels from JSON

Details: gemm/python/README.md

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Key Features

Declarative Kernel API

Both C++ and Python examples use a declarative approach:

C++ (DECL_KERNEL_SET macro):

DECL_KERNEL_SET(my_kernels,
    .add(
        Signature().dtype("fp16").layout("rcr"),
        Algorithm().tile(256, 256, 32).wave(2, 2, 1).warp(32, 32, 16)
                   .pipeline("compv4").scheduler("intrawave"),
        "gfx942"
    )
);

Python (KernelConfig):

config = KernelConfig(
    tile_m=256, tile_n=256, tile_k=32,
    wave_m=2, wave_n=2, wave_k=1,
    warp_tile_m=32, warp_tile_n=32, warp_tile_k=16,
    pipeline="compv4", scheduler="intrawave"
)

Autofill and Autocorrect

The build system automatically:

• Autofills missing parameters with sensible defaults
• Autocorrects invalid parameters based on architecture constraints
• Expands wildcards (*, -1, ANY_INT) to all valid configurations

Architecture Filtering

Kernel configurations are validated against GPU architecture constraints:

• Tile divisibility requirements
• Warp tile constraints
• Pipeline compatibility

Invalid configurations are automatically pruned during code generation.

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Validation Examples

C++ Validation

./gemm_03_benchmark_validation --verify 1    # GEMM with CPU reference
./gemm_03_benchmark_validation --verify 2    # GEMM with GPU reference

Python Validation

python3 examples/gemm/python/04_validation.py
python3 examples/gemm/python/07_stress_test.py   # Multi-kernel validation

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Troubleshooting

Python: Library not found

# Run from dispatcher directory
cd /path/to/composable_kernel/dispatcher
python3 examples/gemm/python/01_basic_gemm.py

C++: Executables not found

# Build with examples enabled
cmake .. -DBUILD_DISPATCHER_EXAMPLES=ON
make -j$(nproc)

# Run from build/examples
cd build/examples
./gemm_01_basic

GPU not detected

rocminfo | grep "Name:"
# Should show: gfx942, gfx90a, etc.

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Grouped Convolution

Grouped convolution support has been re-introduced with a unified infrastructure shared with GEMM.

Infrastructure

The grouped convolution code generation, utilities, and build scripts are available:

Component	Location
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

Building Grouped Conv Kernels

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

# Compile a grouped conv example
python3 scripts/compile_grouped_conv_examples.py my_grouped_conv_example.cpp

See the main README for more details.