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[CK Tile] Rule-based configuration generation in CK Dispatcher codegen (#8157) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Motivation The CK Tile Dispatcher code generation for CK Tile Profiler relies on flat JSON files to list the generated configurations. This approach has the following problems - The JSON files are verbose - The JSON files get easily out of sync with the CK Builder .config files from which they were generated from. - The JSON file based configuration make it hard to list explicitly the rules that govern the instance generation. ## Technical Details Replaced the JSON files with a rule based configuration. To preserve the existing functionality, the `profiler` and the `tests` instance sets are generated directly from the CK Builder config files. The JSON config files are removed from source control, and the "on-the-fly" generation guarantees that the Dispatcher codegen uses up to date configurations. This is PR introduces six different rule sets for the CK Tile Dispatcher code generation 1. `profiler`: matches with the old JSON set of profiler configurations. 2. `tests`: matches with the old JSON set of tests configurations. 3. `full`: full configuration set created from a rule-based config selection 4. `full-tests`: a subset of `full` for generating configurations for convolution integration tests. 5. `tiny`: a subset of `full-tests` to produce the minimal set of configurations to test the Dispatcher codegen. 6. `default`: the default rules, which corresponds to the existing heuristic rules for configuration selection. This ensures that ML based kernel selection doesn't get broken. The main use of the `full` rule set is to define a reasonable solution space for the possible implicit GEMM configurations. We start from the configurations that allowed by the device architecture. The `full` rule set defines the relevant tile sizes for each convolution direction. From the tile size we have a curated mapping to the number of waves over the different GEMM axes, i.e., we describe how many waves each GEMM dimensions corresponds to. The GEMM-K wave tile dimension can be computed from the other parameters and does not need to be listed explicitly. An orthogonal axis to the tiling strategy is the vectorization strategy. This mainly defined by the data type and hardware as in general, we want to use the maximum possible load widths. The maximum sizes for each convolution direction variant are defined by the implicit GEMM matrix dimensions. For cases where have a low number of channels per convolution group, we need smaller vector load sizes. These are captured by the `VecStrategy` enumeration in the codegen rules. The problem with the rule based configuration selection is that we "over generate" configurations. The old JSON configurations compose approximately 25% of all configuration that the `full` rule set creates. The additional configurations are valid, but they many not provide any performance benefits. Hence, we keep the `profiler` and `tests` rule set for now to avoid building an excessive amount configurations by default. The `full` rule set can be taken into use by specifying CMake configuration flag `-D DISPATCHER_RULE_SET=full`. By default, the `tests` rule set is used, i.e., we don't change the existing bahaviour. ## Test Plan Added a new stage in the CI/CD pipeline that ensures the Dispatcher codegen rules are up to date. Otherwise the functionality is covered by the existing CI/CD tests. There are no functional changes to the convolution kernels. Only how the different instances are generated. ## Test Result If the CK Tile conv instances build without errors, the Dispatcher codegen is generating valid code. If all tests in CI/CD pipeline are passing, the Dispatcher codegen generates valid instances. ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
173 lines
6.3 KiB
Python
Executable File
173 lines
6.3 KiB
Python
Executable File
#!/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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#
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# Generates dispatcher-based kernels for the CK Profiler (all directions).
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#
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# This script:
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# 1. Generates GEMM + depthwise configs from tile_math rules via get_default_configs()
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# 2. Calls UnifiedGroupedConvCodegen.generate_all() to emit C++ kernel headers
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# 3. Generates include_all_grouped_conv_<variant>_kernels.hpp
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# 4. Generates chunked register_*_chunk_N.cpp files + register_all_grouped_conv_kernels.cpp
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#
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# Usage:
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# python3 generate_profiler_kernels.py \
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# --variant {fwd,bwd_data,bwd_weight} \
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# --output-dir <generated-kernel-output-dir> \
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# --arch gfx950 \
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# [--mode tests|profiler]
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import argparse
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import sys
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from pathlib import Path
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from registration_codegen import generate_chunked_registration
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VARIANT_CONFIG = {
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"fwd": {
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"glob_pattern": "grouped_conv_fwd_*.hpp",
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"include_all_header": "include_all_grouped_conv_fwd_kernels.hpp",
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"description": "forward",
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"op_enum": "GroupedConvOp::Forward",
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"run_fn_maker": "backends::make_conv_fwd_run_fn",
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"is_supported_fn_maker": "backends::make_conv_fwd_is_supported_fn",
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"register_fn_name": "register_all_grouped_conv_fwd_kernels",
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},
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"bwd_data": {
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"glob_pattern": "grouped_conv_bwd_data_*.hpp",
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"include_all_header": "include_all_grouped_conv_bwd_data_kernels.hpp",
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"description": "backward data",
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"op_enum": "GroupedConvOp::BackwardData",
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"run_fn_maker": "backends::make_conv_bwd_data_run_fn",
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"is_supported_fn_maker": "backends::make_conv_bwd_data_is_supported_fn",
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"register_fn_name": "register_all_grouped_conv_bwd_data_kernels",
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},
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"bwd_weight": {
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"glob_pattern": "grouped_conv_bwd_weight_*.hpp",
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"include_all_header": "include_all_grouped_conv_bwd_weight_kernels.hpp",
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"description": "backward weight",
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"op_enum": "GroupedConvOp::BackwardWeight",
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"run_fn_maker": "backends::make_conv_bwd_weight_run_fn",
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"is_supported_fn_maker": "backends::make_conv_bwd_weight_is_supported_fn",
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"register_fn_name": "register_all_grouped_conv_bwd_weight_kernels",
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},
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}
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VARIANT_MAP = {
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"fwd": "FORWARD",
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"bwd_data": "BACKWARD_DATA",
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"bwd_weight": "BACKWARD_WEIGHT",
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}
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def _ensure_codegen_importable():
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"""Ensure the codegen directory is on sys.path."""
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codegen_dir = str(Path(__file__).resolve().parent.parent / "codegen")
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if codegen_dir not in sys.path:
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sys.path.insert(0, codegen_dir)
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def collect_kernel_headers(output_dir, glob_pattern):
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"""Collect all generated .hpp kernel headers matching the variant pattern."""
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headers = sorted(Path(output_dir).glob(glob_pattern))
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return headers
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def generate_include_all_header(headers, output_dir, header_filename, description):
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"""Generate include_all_grouped_conv_<variant>_kernels.hpp."""
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lines = [
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"// Auto-generated \u2014 do not edit",
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f"// Includes all generated {description} kernel headers.",
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"#pragma once",
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"",
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]
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for h in headers:
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lines.append(f'#include "{h.name}"')
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lines.append("")
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path = Path(output_dir) / header_filename
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path.write_text("\n".join(lines))
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print(f"Generated {path} ({len(headers)} includes)")
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return path
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def main():
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parser = argparse.ArgumentParser(
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description="Generate dispatcher-based kernels for CK Profiler."
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)
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parser.add_argument("--variant", required=True, choices=list(VARIANT_CONFIG.keys()))
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parser.add_argument("--output-dir", required=True)
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parser.add_argument("--arch", default="gfx950")
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parser.add_argument("--rule-set", default="tests",
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choices=["profiler", "tests", "full", "full-tests", "tiny", "default"],
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help="Rule set: 'profiler'/'tests' (CK Builder "
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"profiler/tests instance sets generated in memory "
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"from the .conf configs), 'full' (full rule-derived "
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"per-(variant,ndim,datatype) set), 'full-tests' "
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"(~20% stratified subset of 'full'), 'tiny' "
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"(minimal >=10-config subset of 'full-tests'), or "
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"'default' (original hand-curated heuristics)")
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args = parser.parse_args()
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cfg = VARIANT_CONFIG[args.variant]
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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_ensure_codegen_importable()
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from unified_grouped_conv_codegen import (
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UnifiedGroupedConvCodegen,
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GroupedConvVariant,
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get_default_configs,
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)
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variant_enum = GroupedConvVariant[VARIANT_MAP[args.variant]]
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datatypes = ["fp16", "bf16", "fp32"]
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# --- Step 1: Generate configs from rules ---
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print(f"Generating configs from rules (variant={args.variant}, "
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f"arch={args.arch}, rule_set={args.rule_set})...")
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configs = get_default_configs(
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arch=args.arch,
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variants=[variant_enum],
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ndims=[2, 3],
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datatypes=datatypes,
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rule_set=args.rule_set,
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)
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print(f"Generated {len(configs)} configs from rules")
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if not configs:
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print("ERROR: No configs generated from rules", file=sys.stderr)
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sys.exit(1)
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# --- Step 2: Generate kernel headers ---
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codegen = UnifiedGroupedConvCodegen(
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output_dir=output_dir, gpu_target=args.arch, enable_arch_filter=False,
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)
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codegen.generate_all(configs, datatypes=datatypes)
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# --- Step 3: Collect headers and generate registration ---
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headers = collect_kernel_headers(output_dir, cfg["glob_pattern"])
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print(f"Found {len(headers)} generated kernel headers")
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if not headers:
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print("ERROR: No kernel headers generated", file=sys.stderr)
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sys.exit(1)
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generate_include_all_header(headers, output_dir, cfg["include_all_header"], cfg["description"])
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generate_chunked_registration(
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headers, output_dir,
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variant=args.variant,
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op_enum=cfg["op_enum"],
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run_fn_maker=cfg["run_fn_maker"],
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is_supported_fn_maker=cfg["is_supported_fn_maker"],
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register_fn_name=cfg["register_fn_name"],
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)
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print(f"\nDone. {len(headers)} kernels ready in {output_dir}")
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if __name__ == "__main__":
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main()
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