Files
composable_kernel/dispatcher/examples/gemm/python/11_json_import.py
Vidyasagar Ananthan 920acd2c12 [rocm-libraries] ROCm/rocm-libraries#5168 (commit 8b5afcb)
[CK] [CK_Tile] Add GroupConv to Kernel Dispatcher

## Motivation

This PR adds CK Tile group convolution (forward, backward-data,
backward-weight) support to the kernel dispatcher, matching and unifying
with the existing dispatcher GEMM infrastructure in architecture and
usability. The dispatcher provides a unified kernel dispatch system with
both C++ and Python frontends, and until now only supported GEMM
operations. This PR enables framework integrators to use the same
declarative kernel workflow for convolutions as they do for GEMM:
declare kernels, build a registry JIT, select kernels within the
registry at runtime, and dispatch to GPU. Future PRs will include
runtime kernel selection heuristics for autotuning of kernel parameters
based on (problem, hardware arch).

## Technical Details

Grouped convolution support has been added to the CK Tile Dispatcher
with generated_conv_backend.hpp enabling dispatcher.run(in, wei, out,
problem) for all 6 conv variants (fwd/bwdd/bwdw x 2D/3D), runtime
heuristic kernel selection, and GroupedConvKernelKey with full
ConvConfigBase fields. Python side adds parallel JIT via
registry.build(max_workers) and heuristic registry.select(). Includes 7
C++ and 6 Python examples covering all directions with CPU reference
validation, and shared infrastructure improvements (BaseRegistry CRTP,
structured exceptions). As a sanity check, JIT compile times for a
single kernel remains the same and for multiple kernels there is better
parallelism:
Kernels | 1 worker | 8 workers
1 | 7.7 s | 7.7 s
2 | 15.9 s | 8.2 s
4 | 33.4 s | 9.7 s
6 | 52.3 s | 10.2 s

## Test Plan

145 ephemeral unit tests have been added to test basic functionality.
All 30 examples/integration tests run end-to-end on gfx950 (MI350): 7
C++ conv, 7 C++ GEMM, 6 Python conv, 10 Python GEMM. CPU reference
validation for forward, backward-data, and backward-weight (2D) in both
C++ and Python examples pass.

## Test Result

30 examples pass. Peak performance: 132 TFLOPS (Batch-32 forward 56x56),
53 TFLOPS (pointwise 1x1). CPU reference accuracy: max_abs_diff < 0.002
for all directions (fp16 vs fp32 reference).

## Submission Checklist

- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
2026-04-09 17:39:35 +00:00

311 lines
11 KiB
Python

#!/usr/bin/env python3
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
# SPDX-License-Identifier: MIT
"""
Example 11: JSON-based Kernel Configuration Import
Demonstrates loading kernel configurations from JSON files, similar to tile_engine.
This enables easy customization of kernel sets without modifying code.
Key Features:
- Load tile configs from JSON (compatible with tile_engine format)
- Generate kernel sets from configuration
- Use arch_filter validation on loaded configs
- Export to C++ DECL_KERNEL_SET format
Usage:
python3 11_json_import.py
python3 11_json_import.py --config my_kernels.json
python3 11_json_import.py --export-cpp
"""
import sys
import argparse
import json
from pathlib import Path
# Add codegen to path for kernel_config_loader
script_dir = Path(__file__).parent.resolve()
sys.path.insert(0, str(script_dir.parent.parent.parent / "codegen"))
sys.path.insert(0, str(script_dir.parent.parent.parent / "python"))
from kernel_config_loader import ( # noqa: E402
load_kernel_configs,
KernelConfig,
generate_cpp_kernel_set_declaration,
)
from ctypes_utils import ( # noqa: E402
KernelConfig as DispatcherKernelConfig,
setup_gemm_dispatcher,
cleanup_gemm,
reset_for_example,
validate_kernel_config,
detect_gpu_arch,
)
# Sample JSON configuration (embedded for demonstration)
SAMPLE_JSON_CONFIG = {
"_comment": "Sample kernel configuration for GEMM",
"kernel_set_name": "inference_kernels",
"datatype": {"a": "fp16", "b": "fp16", "c": "fp16", "acc": "fp32"},
"layout": "rcr",
"tile_config": {
"tile_m": {"values": [128, 256]},
"tile_n": {"values": [128, 256]},
"tile_k": {"values": [32]},
"warp_m": {"values": [2]},
"warp_n": {"values": [2]},
"warp_k": {"values": [1]},
"warp_tile_m": {"values": [32]},
"warp_tile_n": {"values": [32]},
"warp_tile_k": {"values": [16]},
},
"trait_config": {
"pipeline": {"values": ["compv4"]},
"scheduler": {"values": ["intrawave"]},
"epilogue": {"values": ["cshuffle"]},
"pad_m": {"values": [False]},
"pad_n": {"values": [False]},
"pad_k": {"values": [False]},
},
"gpu_targets": ["gfx942"],
}
def print_section(title: str):
"""Print a section header"""
print(f"\n{'=' * 70}")
print(f" {title}")
print(f"{'=' * 70}\n")
def convert_to_dispatcher_config(
config: KernelConfig, arch: str = "gfx942"
) -> DispatcherKernelConfig:
"""Convert kernel_config_loader.KernelConfig to dispatcher KernelConfig"""
return DispatcherKernelConfig(
dtype_a=config.dtype_a,
dtype_b=config.dtype_b,
dtype_c=config.dtype_c,
dtype_acc=config.dtype_acc,
tile_m=config.tile.tile_m,
tile_n=config.tile.tile_n,
tile_k=config.tile.tile_k,
wave_m=config.tile.warp_m,
wave_n=config.tile.warp_n,
wave_k=config.tile.warp_k,
warp_m=config.tile.warp_tile_m,
warp_n=config.tile.warp_tile_n,
warp_k=config.tile.warp_tile_k,
pipeline=config.trait.pipeline,
scheduler=config.trait.scheduler,
epilogue=config.trait.epilogue,
pad_m=config.trait.pad_m,
pad_n=config.trait.pad_n,
pad_k=config.trait.pad_k,
gfx_arch=arch,
variant=config.variant,
)
def main():
parser = argparse.ArgumentParser(
description="JSON Kernel Configuration Import Example",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
python3 11_json_import.py # Use embedded sample config
python3 11_json_import.py --config my.json # Load from file
python3 11_json_import.py --export-cpp # Generate C++ declarations
python3 11_json_import.py --validate # Validate configs against arch
""",
)
parser.add_argument(
"--config",
type=str,
help="Path to JSON configuration file (uses embedded sample if not provided)",
)
parser.add_argument(
"--export-cpp",
action="store_true",
help="Export kernel set as C++ DECL_KERNEL_SET",
)
parser.add_argument(
"--validate",
action="store_true",
help="Validate all configurations against arch filter",
)
parser.add_argument(
"--arch",
default=detect_gpu_arch(),
help="Target GPU architecture (auto-detected from rocminfo, override with --arch gfxNNN)",
)
args = parser.parse_args()
reset_for_example()
print_section("Example 11: JSON Kernel Configuration Import")
# =========================================================================
# Step 1: Load configuration from JSON
# =========================================================================
print("Step 1: Load Kernel Configuration from JSON")
print("-" * 50)
if args.config:
config_path = Path(args.config)
if not config_path.exists():
print(f" ERROR: Config file not found: {config_path}")
return 1
print(f" Loading from: {config_path}")
config_set = load_kernel_configs(config_path)
else:
# Use embedded sample config
print(" Using embedded sample configuration")
# Write to temp file and load
temp_path = Path("/tmp/sample_gemm_config.json")
with open(temp_path, "w") as f:
json.dump(SAMPLE_JSON_CONFIG, f, indent=2)
config_set = load_kernel_configs(temp_path)
print(f"\n Kernel Set Name: {config_set.name}")
print(
f" Data Types: A={config_set.dtype_a}, B={config_set.dtype_b}, C={config_set.dtype_c}"
)
print(f" Layout: {config_set.layout}")
print(f" GPU Targets: {config_set.gpu_targets}")
print(f" Total Configurations: {config_set.config_count()}")
# =========================================================================
# Step 2: Display configuration details
# =========================================================================
print("\nStep 2: Configuration Details")
print("-" * 50)
print("\n Tile Configurations:")
print(f" tile_m: {config_set.tile_m_values}")
print(f" tile_n: {config_set.tile_n_values}")
print(f" tile_k: {config_set.tile_k_values}")
print(
f" warp (wave): {config_set.warp_m_values}x{config_set.warp_n_values}x{config_set.warp_k_values}"
)
print(
f" warp_tile: {config_set.warp_tile_m_values}x{config_set.warp_tile_n_values}x{config_set.warp_tile_k_values}"
)
print("\n Trait Configurations:")
print(f" pipeline: {config_set.pipeline_values}")
print(f" scheduler: {config_set.scheduler_values}")
print(f" epilogue: {config_set.epilogue_values}")
print(
f" padding: m={config_set.pad_m_values}, n={config_set.pad_n_values}, k={config_set.pad_k_values}"
)
# =========================================================================
# Step 3: Generate and display kernel names
# =========================================================================
print("\nStep 3: Generated Kernel Names")
print("-" * 50)
configs = list(config_set.generate_configs())
for i, config in enumerate(configs[:5]):
print(f" {i + 1}. {config.kernel_name()}")
if len(configs) > 5:
print(f" ... and {len(configs) - 5} more configurations")
# =========================================================================
# Step 4: Validate against arch filter (optional)
# =========================================================================
if args.validate:
print("\nStep 4: Architecture Validation")
print("-" * 50)
valid_count = 0
invalid_count = 0
for config in configs:
disp_config = convert_to_dispatcher_config(config, args.arch)
result = validate_kernel_config(disp_config)
if result.is_valid:
valid_count += 1
else:
invalid_count += 1
if invalid_count <= 3: # Show first 3 invalid
print(f"\n FAIL Invalid: {config.kernel_name()}")
for error in result.errors:
print(f" Error: {error}")
print("\n Validation Summary:")
print(f" OK Valid: {valid_count}")
print(f" FAIL Invalid: {invalid_count}")
print(f" Total: {len(configs)}")
# =========================================================================
# Step 5: Export to C++ (optional)
# =========================================================================
if args.export_cpp:
print("\nStep 5: C++ Export")
print("-" * 50)
print("\n // Generated DECL_KERNEL_SET from JSON config:")
print(" // " + "=" * 56)
cpp_code = generate_cpp_kernel_set_declaration(config_set)
for line in cpp_code.split("\n"):
print(f" {line}")
# =========================================================================
# Step 6: Use first config with dispatcher (demo)
# =========================================================================
print("\nStep 6: Dispatcher Integration Demo")
print("-" * 50)
if configs:
first_config = configs[0]
disp_config = convert_to_dispatcher_config(first_config, args.arch)
print(
f"\n Using first config: {first_config.tile.tile_m}x{first_config.tile.tile_n}x{first_config.tile.tile_k}"
)
setup = setup_gemm_dispatcher(
disp_config, registry_name="json_import", verbose=False
)
if setup.success:
print(" OK Dispatcher setup successful")
print(
f" Kernel header: {setup.kernel_header.name if setup.kernel_header else 'N/A'}"
)
else:
print(f" WARNING Dispatcher setup: {setup.error}")
print(" (This is expected if kernels aren't generated)")
# =========================================================================
# Summary
# =========================================================================
print_section("Summary")
print(" JSON configuration allows easy kernel set customization:")
print(" - Define tile sizes and ranges")
print(" - Specify trait combinations (pipeline, scheduler, etc.)")
print(" - Target multiple GPU architectures")
print(" - Export to C++ DECL_KERNEL_SET for static compilation")
print()
print(" JSON Format (tile_engine compatible):")
print(' {"tile_config": {"tile_m": {"values": [128, 256]}, ...},')
print(' "trait_config": {"pipeline": {"values": ["compv4"]}, ...}}')
print()
print(" Usage:")
print(" config_set = load_kernel_configs('my_kernels.json')")
print(" for config in config_set.generate_configs():")
print(" # Use config for codegen or dispatcher setup")
cleanup_gemm()
return 0
if __name__ == "__main__":
sys.exit(main())