composable_kernel/tile_engine/ops/gemm

CK Tile Engine GEMM Operations

Overview

The CK Tile Engine GEMM module provides a comprehensive system for generating, building, and benchmarking GEMM (General Matrix Multiplication) kernels with various configurations. It supports multiple data types, layouts, and optimization strategies. The system has evolved from a monolithic build approach (where all kernels compile into a single executable) to a more flexible individual kernel compilation system, providing better build parallelism and targeted testing capabilities.

Table of Contents

1. Build System Architecture
2. Build Instructions
3. Running Benchmarks
4. Configuration System
5. Scripts and Tools
6. Command Line Options
7. Understanding Kernel Names
8. Troubleshooting
9. Performance Tips

Build System Architecture

Individual Kernel Compilation (New Approach)

The new tile engine benchmark system compiles each kernel configuration into a separate executable. This provides:

• Better build parallelism
• Faster incremental builds
• More targeted testing
• Easier debugging of specific configurations

Each benchmark executable follows the naming pattern:

benchmark_gemm_<dtype>_<layout>_<config>_<tile_sizes>

Monolithic Build (Legacy Approach)

The original system compiles all kernels into a single executable (benchmark_gemm_[Datatype]_[Layout]), which can then be filtered at runtime using command-line arguments.

Build Instructions

Prerequisites

• ROCm installation
• CMake 3.16 or higher
• C++17 compatible compiler

Basic Build

# In the root of composable kernel, create build directory
mkdir build && cd build

# Configure with specific datatypes and layouts
# Replace [Arch] with your GPU architecture (e.g., gfx90a, gfx942)
# Replace [Datatype1;Datatype2;...] with datatypes (fp8, bf8, int8, fp16, bf16, fp32, fp64)
# Replace [Layout1;Layout2;...] with layouts (rcr, rrr, crr, ccr)
../script/cmake-ck-dev.sh ../ [Arch] -DGEMM_DATATYPE="[Datatype1;Datatype2]" -DGEMM_LAYOUT="[Layout1;Layout2]"

# Build specific benchmarks
make benchmark_gemm_[Datatype1]_[Layout1] -j

Configuration Options

The build system supports several configuration options:

Using Custom Config Files

# Method 1: CMake variable (config file must be in configs/ directory)
cmake -DGEMM_CONFIG_FILE=my_custom_config.json ...

# Method 2: Environment variable (takes precedence over CMake variable)
export GEMM_CONFIG_FILE=my_custom_config.json
cmake ...

Config File Priority Order

1. Environment variable GEMM_CONFIG_FILE (highest priority)
2. CMake variable GEMM_CONFIG_FILE
3. Default config (default_config.json for all layouts)

Note: All custom config files must be placed in the tile_engine/ops/gemm/configs/ directory.

Example Build Commands

# Build for gfx942 with fp8 and fp16 datatypes, rcr layout
mkdir build && cd build
../script/cmake-ck-dev.sh ../ gfx942 -DGEMM_DATATYPE="fp8;fp16" -DGEMM_LAYOUT="rcr;ccr;rrr;crr"
make benchmark_gemm_universal_fp8_rcr -j
make benchmark_gemm_universal_fp16_rcr -j

Building Individual Kernels

# Build a specific kernel configuration
make benchmark_gemm_universal_fp8_rcr_compv4_default_intrawave_False_False_False_False_256x256x32_1x4x1_32x32x32

# Build all fp16 benchmarks in parallel
make -j$(nproc) $(make help | grep benchmark_gemm_fp16 | awk '{print $2}')

Rebuilding After Configuration Changes

If you modify the configuration file, you must rebuild:

rm -rf tile_engine/ && make benchmark_gemm_universal_[Datatype]_[Layout] -j

Running Benchmarks

Individual Kernel Execution

cd /path/to/build/directory
./bin/benchmark_gemm_universal_fp16_rcr_compv3_default_intrawave_False_False_False_False_256x128x32_4x1x1_32x32x16 \
    -m=512 -n=512 -k=512 -verify=1

Monolithic Executable (Legacy)

# Run specific pipeline/scheduler/epilogue combination
./bin/benchmark_gemm_universal_[Datatype]_[Layout] -pipeline=compv3 -scheduler=intrawave -epilogue=default

Automated Testing

Use the provided test script to run multiple benchmarks:

cd /path/to/composable_kernel/tile_engine/ops/gemm
./test_benchmark.sh [build_directory]

Configuration System

Configuration Files

The system uses JSON configuration files to specify kernel parameters:

• configs/default_config.json - Default configurations for various datatypes
• configs/user_provided_config.json - User-customizable configurations

Configuration Structure

{
    "tile_config": {
        "tile_m": {"values": [256, 128]},
        "tile_n": {"values": [256, 128]},
        "tile_k": {"values": [64, 32]},
        "warp_m": {"values": [2, 4]},
        "warp_n": {"values": [2, 1]},
        "warp_k": {"values": [1]},
        "warp_tile_m": {"values": [32, 16]},
        "warp_tile_n": {"values": [32, 16]},
        "warp_tile_k": {"values": [16, 32]}
    },
    "trait_config": {
        "pipeline": {"values": ["compv3", "compv4", "mem"]},
        "scheduler": {"values": ["intrawave", "interwave"]},
        "epilogue": {"values": ["default", "cshuffle"]},
        "pad_m": {"values": [false]},
        "pad_n": {"values": [false]},
        "pad_k": {"values": [false]},
        "persistent": {"values": [false]}
    }
}

Scripts and Tools

Python Scripts

gemm_universal_instance_builder.py

Purpose: Main kernel instance generation script that creates C++ kernel implementations based on configuration files.

Key Features:

• Generates individual kernel header files for separate compilation
• Supports multiple data types (fp16, fp8, bf16, fp32, fp64)
• Validates tile configurations for correctness
• Creates CMake integration files

Usage:

python gemm_universal_instance_builder.py \
    --working_path ./generated \
    --datatype fp16 \
    --layout rcr \
    --config_json configs/user_provided_config.json \
    --gen_all_individual

gemm_instance_builder_parallel.py

Purpose: Parallel version of the instance builder for faster generation of multiple kernel configurations.

Features:

• Multi-threaded kernel generation
• Improved performance for large configuration spaces

validation_utils.py

Purpose: Provides comprehensive validation functions for kernel configurations.

Key Functions:

• is_tile_config_valid() - Validates tile dimensions and alignments
• is_trait_combination_valid() - Checks if pipeline/epilogue/scheduler combinations are supported
• validate_warp_tile_combination() - GPU-specific warp tile validation
• validate_lds_capacity() - Ensures configurations fit in LDS memory

Validation Checks:

• Dimension alignment (tile dimensions must be divisible by warp dimensions)
• LDS capacity constraints
• GPU-specific warp tile support
• Unsupported trait combinations

test_validation.py

Purpose: Test suite for the validation logic to ensure correctness.

Usage:

python test_validation.py

Tests:

• Warp tile combination validation
• Trait combination validation
• Full tile configuration validation

gemm_universal_benchmark.py

Purpose: Python script for running and analyzing GEMM benchmarks.

Features:

• Automated benchmark execution
• Performance data collection
• Result analysis and reporting

json_config.py

Purpose: Configuration file parsing and management.

Features:

• JSON configuration loading
• Default configuration handling
• Configuration validation

codegen_utils.py

Purpose: Utility functions for code generation.

Features:

• Template processing
• Code formatting utilities
• File generation helpers

Shell Scripts

test_benchmark.sh

Purpose: Automated benchmark testing script that finds and runs all built benchmark executables.

Features:

• Automatic build directory detection
• Batch execution of multiple benchmarks
• CSV result collection
• Colored output for easy reading
• Example command generation

Usage:

# Auto-detect build directory
./test_benchmark.sh

# Specify build directory
./test_benchmark.sh /path/to/build/directory

What it does:

1. Finds all benchmark executables in the build directory
2. Runs each with multiple problem sizes (512, 1024, 2048)
3. Performs GPU verification
4. Saves results to timestamped CSV file
5. Provides summary statistics

Command Line Options

All benchmark executables support the following options:

Matrix Dimensions

• -m=<value> - M dimension (default: 3840)
• -n=<value> - N dimension (default: 4096)
• -k=<value> - K dimension (default: 2048)

Strides

• -stride_a=<value> - Stride for matrix A (default: 0, auto-calculated)
• -stride_b=<value> - Stride for matrix B (default: 0, auto-calculated)
• -stride_c=<value> - Stride for matrix C (default: 0, auto-calculated)

Verification

• -verify=<0|1|2> - Verification mode
  • 0: No verification (default)
  • 1: CPU verification
  • 2: GPU verification

Performance Testing

• -warmup=<value> - Warmup iterations (default: 50)
• -repeat=<value> - Benchmark iterations (default: 100)
• -timer=<true|false> - Use GPU timer (default: true)
• -flush_cache=<true|false> - Flush cache between runs (default: true)
• -rotating_count=<value> - Cache rotation count (default: 1000)

Initialization

• -init=<0|1|2> - Tensor initialization method
  • 0: Random values [-1, 1] (default)
  • 1: Linear sequence (i % 17)
  • 2: Constant value (1.0)

Output Options

• -log=<true|false> - Enable verbose logging (default: false)
• -metric=<0|1|2> - Performance metric
  • 0: Latency in ms (default)
  • 1: TFLOPS
  • 2: Bandwidth in GB/s
• -json_output=<true|false> - JSON format output (default: false)
• -csv_filename=<filename> - Save results to CSV
• -csv_format=<simple|comprehensive> - CSV format (default: comprehensive)

Advanced Options

• -split_k=<value> - Split-K factor (default: 1)
• -structured_sparsity=<true|false> - Enable structured sparsity (default: false)
• -pipeline=<compv3|compv4|mem> - Pipeline type (default: compv3)
• -scheduler=<intrawave|interwave> - Scheduler type (default: intrawave)
• -epilogue=<cshuffle|default> - Epilogue type (default: cshuffle)
• -pad_m=<true|false> - Pad M dimension (default: false)
• -pad_n=<true|false> - Pad N dimension (default: false)
• -pad_k=<true|false> - Pad K dimension (default: false)
• -persistent=<true|false> - Use persistent kernel (default: false)

Understanding Kernel Names

The kernel naming convention encodes the configuration:

benchmark_gemm_universal_fp16_rcr_compv3_default_intrawave_False_False_False_False_256x128x32_4x1x1_32x32x16
               		 ^^^^  ^^^ ^^^^^^ ^^^^^^^ ^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^ ^^^^^^^ ^^^^^^^^^
               		 |     |   |      |       |         |                       |         |       |
               		 |     |   |      |       |         Padding & flags         |         |       Warp tile
               		 |     |   |      |       Scheduler                         |         Thread tile
               		 |     |   |      Epilogue                                  Block tile
               		 |     |   Pipeline
               		 |     Layout (Row-Column-Row)
               		 Data type

Components:

• Data type: fp16, fp32, bf16, fp8, bf8, int8
• Layout: rcr (Row-Column-Row), rrr, crr, ccr
• Pipeline: mem, compv3, compv4
• Epilogue: default, cshuffle
• Scheduler: intrawave, interwave
• Flags: pad_m, pad_n, pad_k, persistent (4 boolean flags)
• Tile sizes: BlockTile x ThreadTile x WarpTile

Troubleshooting

Common Issues

1. Kernel not found

   • Ensure the specific benchmark executable is built
   • Check the build directory bin/ folder

2. Verification failures

   • Try GPU verification (-verify=2) which may be more accurate
   • Check data type compatibility
   • Verify stride calculations

3. Build failures

   • Check GPU architecture compatibility
   • Ensure ROCm is properly installed
   • Verify configuration file syntax

4. Performance variations

   • Increase warmup iterations
   • Disable CPU frequency scaling
   • Use GPU timer for accurate measurements

Debug Options

Enable verbose logging:

./bin/benchmark_gemm_... -log=true -verify=1

Test validation logic:

python test_validation.py

Performance Tips

1. Optimal Problem Sizes: Use sizes that are multiples of tile dimensions
2. Warmup: Use at least 50-100 warmup iterations
3. GPU Timer: Always use -timer=true for accurate measurements
4. Cache Management: Enable cache flushing for consistent results
5. Thread Affinity: Set CPU affinity to reduce variation

Integration Examples

Python Integration

import subprocess
import json

# Run benchmark with JSON output
result = subprocess.run([
    './bin/benchmark_gemm_universal_fp16_rcr_...', 
    '-m=1024', '-n=1024', '-k=1024',
    '-json_output=true'
], capture_output=True, text=True)

# Parse results
data = json.loads(result.stdout)
print(f"Performance: {data['tflops']} TFLOPS")

Batch Testing Script

#!/bin/bash
SIZES="512 1024 2048 4096"
for size in $SIZES; do
    echo "Testing ${size}x${size}x${size}"
    ./bin/benchmark_gemm_... -m=$size -n=$size -k=$size \
        -verify=2 -csv_filename=results.csv
done

Contributing

When adding new features or configurations:

1. Update validation logic in validation_utils.py
2. Add tests to test_validation.py
3. Update configuration examples
4. Document new command-line options

For more information about the Composable Kernel project, visit the main repository documentation.