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#!/usr/bin/env python3
"""analyze-kconfig - Generate a model for KConfig selections
analyze-kconfig builds a model determining system attributes
(e.g. ROM or RAM usage) based on KConfig configuration variables.
Only boolean variables are supported at the moment.
"""
import argparse
import json
import kconfiglib
import logging
from dfatool.loader import KConfigAttributes
from dfatool.model import KConfigModel
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
)
parser.add_argument(
"--export-tree",
type=str,
help="Export decision tree model to file",
metavar="FILE",
)
parser.add_argument(
"--config",
type=str,
help="Show model results for symbols in .config file",
metavar="FILE",
)
parser.add_argument(
"--attribute", choices=["rom", "ram"], default="rom", help="Model attribute"
)
parser.add_argument(
"--max-stddev",
type=float,
help="Maximum acceptable model standard deviation for DecisionTree Leaves",
default=10,
)
parser.add_argument(
"--log-level",
default=logging.INFO,
type=lambda level: getattr(logging, level.upper()),
help="Set log level",
)
parser.add_argument("kconfig_path", type=str, help="Path to Kconfig file")
parser.add_argument("experiment_root", type=str, help="Path to experiment results")
args = parser.parse_args()
if isinstance(args.log_level, int):
logging.basicConfig(level=args.log_level)
else:
print(f"Invalid log level. Setting log level to INFO.", file=sys.stderr)
data = KConfigAttributes(args.kconfig_path, args.experiment_root)
model = KConfigModel(data, args.attribute)
if args.max_stddev:
model.max_stddev = args.max_stddev
model.build_tree()
if args.export_tree:
with open(args.export_tree, "w") as f:
json.dump(model.to_json(), f)
if args.config:
kconf = kconfiglib.Kconfig(args.kconfig_path)
kconf.load_config(args.config)
print(f"Model result for .config: {model.value_for_config(kconf)}")
for symbol in model.symbols:
kconf2 = kconfiglib.Kconfig(args.kconfig_path)
kconf2.load_config(args.config)
kconf_sym = kconf2.syms[symbol]
if kconf_sym.tri_value == 0 and 2 in kconf_sym.assignable:
kconf_sym.set_value(2)
elif kconf_sym.tri_value == 2 and 0 in kconf_sym.assignable:
kconf_sym.set_value(0)
else:
continue
# specific to multipass:
# Do not suggest changes which affect the application
skip = False
num_changes = 0
changed_symbols = list()
for i, csymbol in enumerate(model.symbols):
if kconf.syms[csymbol].tri_value != kconf2.syms[csymbol].tri_value:
num_changes += 1
changed_symbols.append(csymbol)
if (
csymbol.startswith("app_")
and kconf.syms[csymbol].tri_value
!= kconf2.syms[csymbol].tri_value
):
skip = True
break
if skip:
continue
model_diff = model.value_for_config(kconf2) - model.value_for_config(kconf)
if kconf_sym.choice:
print(
f"Setting {kconf_sym.choice.name} to {kconf_sym.name} changes {num_changes:2d} symbols, model change: {model_diff:+5.0f}"
)
else:
print(
f"Setting {symbol} to {kconf_sym.str_value} changes {num_changes:2d} symbols, model change: {model_diff:+5.0f}"
)
for changed_symbol in changed_symbols:
print(
f" {changed_symbol:30s} -> {kconf2.syms[changed_symbol].str_value}"
)
if __name__ == "__main__":
main()
|
