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[CK] [CK_Tile] Add FMHA scaffolding to CK kernel dispatcher (#5260) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## 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.
262 lines
8.4 KiB
Python
262 lines
8.4 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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# SPDX-License-Identifier: MIT
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"""Generate FMHA fallback kernel + dispatch header for the Python ctypes library.
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Mirrors generate_conv_dispatch_header.py: generates a single FMHA forward
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kernel and creates a dispatch header that can be force-included into
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fmha_ctypes_lib.cpp.
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Usage:
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python3 generate_fmha_fallback.py --output-dir /path/to/output --gpu-target gfx950
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"""
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import argparse
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import json
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import subprocess
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import sys
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from pathlib import Path
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# Default kernel config for fallback — a single fwd fp16 kernel with
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# known-good tile (128x128x32, qr_async) for basic smoke-test capability.
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# Source: tile dims from fmha_fwd.py FmhaFwdTileSize for hdim=128 fp16.
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DEFAULT_CONFIG = {
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"arch": "gfx950",
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"signature": {
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"family": "fwd",
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"data_type": "fp16",
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"mode": "batch",
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"vlayout": "r",
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"hdim_q": 128,
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"hdim_v": 128,
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"mask": "no",
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"bias": "no",
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"lse": False,
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"dropout": False,
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"qscale": "no",
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"rope": "none",
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"logits": False,
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"paged_kv": False,
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"fp8_static_quant": False,
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"skip_min_seqlen_q": False,
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"sink": False,
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"dbias": False,
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"store_randval": False,
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"deterministic": False,
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"kv_memory_layout": "vectorized",
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"kv_lookup_table": "sglang",
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"page_size": 1,
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},
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"algorithm": {
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"pipeline": "qr_async",
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"tile": [128, 128, 32, 128, 32, 128],
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"wave": [4, 1, 1, 4, 1, 1, 1, 1, 1],
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"warp": [32, 32, 16, 32, 32, 16, 16, 16, 16],
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"padding": [True, True, True, True],
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"block_per_cu": 1,
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"num_wave_groups": 1,
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"max_splits_log2": 0,
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"max_seq_len_q": 0,
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},
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}
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def generate_dispatch_header(output_dir: Path, wrapper_dir: Path) -> Path:
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"""Generate fmha_python_dispatch.hpp from the wrapper headers."""
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wrappers = sorted(wrapper_dir.glob("dispatcher_wrapper_fmha_*.hpp"))
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if not wrappers:
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raise RuntimeError(f"No FMHA dispatcher wrappers found in {wrapper_dir}")
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kernel_names = []
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make_calls = []
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for w in wrappers:
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stem = w.stem.replace("dispatcher_wrapper_", "")
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kernel_names.append(stem)
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make_calls.append(
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f" registry.register_kernel("
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f"ck_tile::dispatcher::generated::make_{stem}(arch));"
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)
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lines = [
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"// Auto-generated FMHA dispatch header for Python ctypes library",
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"#pragma once",
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"",
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]
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for w in wrappers:
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lines.append(f'#include "dispatcher_wrappers/{w.name}"')
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lines += [
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"",
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'#include "ck_tile/dispatcher/fmha_registry.hpp"',
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'#include "ck_tile/dispatcher/fmha_dispatcher.hpp"',
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"",
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"namespace generated {",
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"",
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"inline void register_fmha_python_kernels("
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"ck_tile::dispatcher::FmhaRegistry& registry, const std::string& arch) {",
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" (void)arch;",
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]
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lines += make_calls
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lines += [
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"}",
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"",
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"} // namespace generated",
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"",
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"#ifndef REGISTER_GENERATED_KERNELS",
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"#define REGISTER_GENERATED_KERNELS(registry, arch) "
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"::generated::register_fmha_python_kernels(registry, arch)",
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"#endif",
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"",
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"// Stable C ABI for dlopen/dlsym-based kernel registration.",
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'// Plugins call dlsym(handle, "ck_fmha_register_kernels") to get this.',
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'extern "C" __attribute__((visibility("default")))',
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"int ck_fmha_register_kernels(",
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" ck_tile::dispatcher::FmhaRegistry& registry, const char* arch) {",
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" ::generated::register_fmha_python_kernels(registry, arch ? std::string(arch) : std::string());",
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f" return {len(kernel_names)};",
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"}",
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"",
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"// Kernel inventory for Python introspection",
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f"static const int FMHA_KERNEL_COUNT = {len(kernel_names)};",
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"static const char* FMHA_KERNEL_NAMES[] = {"
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+ ", ".join(f'"{n}"' for n in kernel_names)
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+ "};",
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"",
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]
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header_path = output_dir / "fmha_python_dispatch.hpp"
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header_path.write_text("\n".join(lines) + "\n")
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return header_path
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def compile_kernels(output_dir: Path, gpu_target: str, include_dirs: str) -> Path:
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"""Compile kernel .cpp files into a static library."""
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import shutil
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hipcc = shutil.which("hipcc") or "/opt/rocm/bin/hipcc"
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kernel_cpps = sorted(output_dir.glob("fmha_*.cpp"))
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if not kernel_cpps:
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raise RuntimeError("No kernel .cpp files to compile")
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import re
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# Use the shared compile flags from fmha_utils
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sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "python"))
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from fmha_utils import fmha_compile_flags # noqa: E402
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base_flags = fmha_compile_flags(gpu_target, hipcc, family="bwd")
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inc_flags = []
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for d in re.split(r"[;:]", include_dirs):
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d = d.strip()
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if d:
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inc_flags.extend(["-I", d])
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objs = []
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for cpp in kernel_cpps:
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obj = cpp.with_suffix(".o")
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cmd = base_flags + inc_flags + [str(cpp), "-o", str(obj)]
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print(f" Compiling {cpp.name}...")
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r = subprocess.run(cmd, capture_output=True, text=True)
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if r.returncode != 0:
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print(f" FAILED: {r.stderr}", file=sys.stderr)
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raise RuntimeError(f"Failed to compile {cpp.name}")
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objs.append(str(obj))
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lib_path = output_dir / "libfmha_python_fallback.a"
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ar_cmd = ["ar", "rcs", str(lib_path)] + objs
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subprocess.check_call(ar_cmd)
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print(f" Static lib: {lib_path}")
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return lib_path
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def main():
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parser = argparse.ArgumentParser(
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description="Generate FMHA fallback kernel for Python library"
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)
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parser.add_argument("--output-dir", required=True, type=Path)
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parser.add_argument("--gpu-target", default="gfx950")
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parser.add_argument(
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"--config-json",
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default=None,
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help="Override default kernel config (JSON string)",
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)
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parser.add_argument(
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"--compile", action="store_true", help="Also compile the kernel .cpp into a .a"
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)
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parser.add_argument(
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"--include-dirs",
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default="",
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help="Semicolon-separated include directories for compilation",
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)
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args = parser.parse_args()
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output_dir = args.output_dir
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output_dir.mkdir(parents=True, exist_ok=True)
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codegen_dir = Path(__file__).parent
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codegen_script = codegen_dir / "codegen.py"
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# Accept either a single config dict or a list of configs
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if args.config_json:
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parsed = json.loads(args.config_json)
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if isinstance(parsed, list):
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# Multi-config: pass list directly to unified_fmha_codegen
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codegen_input = parsed
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else:
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# Single config: merge with defaults
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config = dict(DEFAULT_CONFIG)
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config["arch"] = args.gpu_target
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config["signature"] = dict(DEFAULT_CONFIG["signature"])
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config["algorithm"] = dict(DEFAULT_CONFIG["algorithm"])
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config.update(parsed)
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codegen_input = config
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else:
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config = dict(DEFAULT_CONFIG)
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config["arch"] = args.gpu_target
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config["signature"] = dict(DEFAULT_CONFIG["signature"])
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config["algorithm"] = dict(DEFAULT_CONFIG["algorithm"])
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codegen_input = config
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print(f"Generating FMHA fallback kernel for {args.gpu_target}...")
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print(f" Output: {output_dir}")
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cmd = [
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sys.executable,
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str(codegen_script),
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"--output-dir",
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str(output_dir),
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"--gpu-target",
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args.gpu_target,
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"--config-json",
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json.dumps(codegen_input),
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]
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result = subprocess.run(cmd, capture_output=True, text=True, cwd=str(codegen_dir))
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if result.returncode != 0:
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print(f" Codegen FAILED:\n{result.stderr}", file=sys.stderr)
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return 1
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wrapper_dir = output_dir / "dispatcher_wrappers"
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if not wrapper_dir.exists():
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print(" ERROR: No dispatcher_wrappers dir created", file=sys.stderr)
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return 1
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header_path = generate_dispatch_header(output_dir, wrapper_dir)
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print(f" Dispatch header: {header_path}")
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kernel_cpps = list(output_dir.glob("fmha_*.cpp"))
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print(f" Kernel TUs: {len(kernel_cpps)}")
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if args.compile and kernel_cpps:
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compile_kernels(output_dir, args.gpu_target, args.include_dirs)
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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