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#!/usr/bin/env python3
"""
analyze-config - generate NFP model from system config benchmarks
analyze-config generates an NFP model from benchmarks with various system
configs (.config entries generated from a common Kconfig definition). The
NFP model is not yet compatible with the type of models generated
by analyze-archive and analyze-timing
"""
import argparse
import json
import kconfiglib
import logging
import os
import numpy as np
from dfatool.functions import SplitFunction, StaticFunction
from dfatool.model import AnalyticModel
from dfatool.utils import NpEncoder
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
)
parser.add_argument(
"--log-level",
metavar="LEVEL",
choices=["debug", "info", "warning", "error"],
default="warning",
help="Set log level",
)
parser.add_argument("kconfig_file")
parser.add_argument("data_dir")
args = parser.parse_args()
if args.log_level:
numeric_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(numeric_level, int):
print(f"Invalid log level: {args.log_level}", file=sys.stderr)
sys.exit(1)
logging.basicConfig(level=numeric_level)
experiments = list()
for direntry in os.listdir(args.data_dir):
if "Multipass" in direntry:
config_path = f"{args.data_dir}/{direntry}/.config"
attr_path = f"{args.data_dir}/{direntry}/attributes.json"
if os.path.exists(attr_path):
experiments.append((config_path, attr_path))
kconf = kconfiglib.Kconfig(args.kconfig_file)
symbols = sorted(
map(
lambda sym: sym.name,
filter(
lambda sym: kconfiglib.TYPE_TO_STR[sym.type] == "bool",
kconf.syms.values(),
),
)
)
by_name = {
"multipass": {
"isa": "state",
"attributes": ["rom_usage", "ram_usage"],
"rom_usage": list(),
"ram_usage": list(),
"param": list(),
}
}
data = list()
config_vectors = set()
for config_path, attr_path in experiments:
kconf.load_config(config_path)
with open(attr_path, "r") as f:
attr = json.load(f)
config_vector = tuple(map(lambda sym: kconf.syms[sym].tri_value == 2, symbols))
config_vectors.add(config_vector)
by_name["multipass"]["rom_usage"].append(attr["total"]["ROM"])
by_name["multipass"]["ram_usage"].append(attr["total"]["RAM"])
by_name["multipass"]["param"].append(config_vector)
data.append((config_vector, attr["total"]["ROM"], attr["total"]["RAM"]))
print(
"Processing {:d} unique configurations of {:d} total".format(
len(config_vectors), len(experiments)
)
)
print(
"std of all data: {:5.0f} Bytes".format(np.std(list(map(lambda x: x[1], data))))
)
model = AnalyticModel(by_name, symbols, compute_stats=False)
def get_min(this_symbols, this_data, level):
rom_sizes = list(map(lambda x: x[1], this_data))
if np.std(rom_sizes) < 100 or len(this_symbols) == 0:
return StaticFunction(np.mean(rom_sizes))
# sf.value_error["std"] = np.std(rom_sizes)
mean_stds = list()
for i, param in enumerate(this_symbols):
enabled = list(filter(lambda vrr: vrr[0][i] == True, this_data))
disabled = list(filter(lambda vrr: vrr[0][i] == False, this_data))
enabled_std_rom = np.std(list(map(lambda x: x[1], enabled)))
disabled_std_rom = np.std(list(map(lambda x: x[1], disabled)))
children = [enabled_std_rom, disabled_std_rom]
if np.any(np.isnan(children)):
mean_stds.append(np.inf)
else:
mean_stds.append(np.mean(children))
symbol_index = np.argmin(mean_stds)
symbol = this_symbols[symbol_index]
enabled = list(filter(lambda vrr: vrr[0][symbol_index] == True, this_data))
disabled = list(filter(lambda vrr: vrr[0][symbol_index] == False, this_data))
child = dict()
new_symbols = this_symbols[:symbol_index] + this_symbols[symbol_index + 1 :]
enabled = list(
map(
lambda x: (x[0][:symbol_index] + x[0][symbol_index + 1 :], *x[1:]),
enabled,
)
)
disabled = list(
map(
lambda x: (x[0][:symbol_index] + x[0][symbol_index + 1 :], *x[1:]),
disabled,
)
)
print(
f"Level {level} split on {symbol} has {len(enabled)} children when enabled and {len(disabled)} children when disabled"
)
if len(enabled):
child[1] = get_min(new_symbols, enabled, level + 1)
if len(disabled):
child[0] = get_min(new_symbols, disabled, level + 1)
return SplitFunction(np.mean(rom_sizes), symbol_index, child)
model = get_min(symbols, data, 0)
output = {"model": model.to_json(), "symbols": symbols}
with open("kconfigmodel.json", "w") as f:
json.dump(output, f, cls=NpEncoder)
if __name__ == "__main__":
main()
|
