Files
composable_kernel/dispatcher/examples/gemm/python/11_json_import.py
Vidyasagar Ananthan 86591de476 [rocm-libraries] ROCm/rocm-libraries#5260 (commit a1834d2)
[CK] [CK_Tile] Add FMHA scaffolding to CK kernel dispatcher
 (#5260)
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## Motivation

The CK Tile dispatcher currently supports GEMM and Grouped Convolution
but has no support for Fused Multi-Head Attention (FMHA). The
example/ck_tile/01_fmha folder contains a comprehensive FMHA
implementation with forward, backward, split-KV, paged-KV, append-KV,
and batch-prefill kernels across multiple GPU architectures — but there
is no unified dispatch layer for it. This PR ports the FMHA stack into
the dispatcher, following the same architectural patterns established by
GEMM and Grouped Convolution, enabling runtime kernel selection, JIT
compilation from Python, and a declarative C++ example flow. Autotuning
heuristics to follow.

## Technical Details

This PR adds FMHA scaffolding to the CK dispatcher framework, mirroring
GEMM's layered architecture. Seven new C++ runtime headers provide type
definitions (coexisting with upstream headers via __has_include,
requiring zero modifications to example/ck_tile/01_fmha/), a problem
builder with 18+ setters, Signature + Algorithm kernel key matching, a
virtual kernel instance, a DECL_FMHA_KERNEL_SET macro with wildcard
support and named tile/wave/warp setters, arch-aware registry with JSON
export, and a dispatcher with seqtune-aware selection, configurable
timing, and multi-stage execution plans for split-KV (two-stage) and
backward (three-stage). The codegen pipeline is driven by a
fmha_arch_specs.json capturing per-arch tile tables and pipeline
constraints for five architectures (gfx90a/942/950/1100/1201), migrated
from hardcoded logic in 01_fmha/codegen/, with supporting modules for
C++ symbol mappings, validation rules, and named receipt profiles
(ck_default, flash, pytorch, aiter, fp32, fp8). Python integration
(fmha_utils.py) mirrors the C++ layer with JIT compilation, parallel
multi-kernel builds, HIP memory management via ctypes, tolerance-based
validation, and a NumPy CPU reference with GQA support. Twenty-seven C++
and thirty-two Python examples cover the full feature surface — forward,
split-KV, masks, bias, dropout, GQA, backward, append-KV, batch prefill,
fp8, logits soft cap, sink tokens, and parameter sweeps — all
JIT-compiled on the fly.

## Test Plan

Seven test files cover the runtime types, codegen, and end-to-end
correctness. C++ unit tests validate the problem builder, dispatcher
planning (single-stage for forward/paged-KV/append-KV; multi-stage for
split-KV and backward), registry operations, and the kernel-set
declaration macro. Python unit tests verify codegen emission, profile
filtering, and 15 validation rules for masks, hdim constraints, and
pipeline requirements. GPU execution validation in 01_basic_fmha
--validate reports zero errors across 65,536 elements with max absolute
error of 7.29e-05. A gold-standard parity suite (test_fmha_parity.py)
runs 14 configurations through both the upstream tile_example_fmha_fwd
and the dispatcher, comparing exit codes to confirm behavioral parity —
all 14 match.

## Test Result

The C++ smoke test builds and passes all 9 compiled examples, and a
Python JIT sweep (29_sweep_seqlen.py) passes 7/7 configurations reaching
up to 375 TFLOPS at seqlen 2048.

## Submission Checklist

- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
2026-05-17 07:30:33 +00:00

308 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,
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()
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())