diff options
Diffstat (limited to 'bin')
| -rwxr-xr-x | bin/analyze-config.py | 199 |
1 files changed, 199 insertions, 0 deletions
diff --git a/bin/analyze-config.py b/bin/analyze-config.py new file mode 100755 index 0000000..197faf2 --- /dev/null +++ b/bin/analyze-config.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python3 +""" +analyze-config - generate NFP model from system config benchmarks + +analyze-connfig generates an NFP model from benchmarks with various system +configs (.config entries generated from a common Kconfig definition). +""" + +import argparse +import json +import kconfiglib +import logging +import os + +import numpy as np + +from dfatool.model import AnalyticModel + + +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) + + class DTreeLeaf: + def __init__(self, value, stddev): + self.value = value + self.stddev = stddev + + def __repr__(self): + return f"<DTreeLeaf({self.value}, {self.stddev})>" + + def to_json(self): + return {"value": self.value, "stddev": self.stddev} + + class DTreeNode: + def __init__(self, symbol): + self.symbol = symbol + self.false_child = None + self.true_child = None + + def set_false_child(self, child_node): + self.false_child = child_node + + def set_true_child(self, child_node): + self.true_child = child_node + + def __repr__(self): + return f"<DTreeNode({self.false_child}, {self.true_child})>" + + def to_json(self): + ret = {"symbol": self.symbol} + if self.false_child: + ret["false"] = self.false_child.to_json() + else: + ret["false"] = None + if self.true_child: + ret["true"] = self.true_child.to_json() + else: + ret["true"] = None + return ret + + 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 DTreeLeaf(np.mean(rom_sizes), 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)) + + node = DTreeNode(symbol) + + 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): + node.set_true_child(get_min(new_symbols, enabled, level + 1)) + if len(disabled): + node.set_false_child(get_min(new_symbols, disabled, level + 1)) + + return node + + model = get_min(symbols, data, 0) + + output = {"model": model.to_json(), "symbols": symbols} + + with open("kconfigmodel.json", "w") as f: + json.dump(output, f) + + +if __name__ == "__main__": + main() |