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#!/usr/bin/env python3
import json
import kconfiglib
import logging
import os
import numpy as np
numeric_level = getattr(logging, "DEBUG", None)
if not isinstance(numeric_level, int):
print(f"Invalid log level: {loglevel}", file=sys.stderr)
sys.exit(1)
logging.basicConfig(level=numeric_level)
kconfig_path = "/tmp/multipass/Kconfig"
configs_base = "/tmp/multipass-model"
experiments = list()
for direntry in os.listdir(configs_base):
if "Multipass-" in direntry:
config_path = f"{configs_base}/{direntry}/.config"
attr_path = f"{configs_base}/{direntry}/attributes.json"
if os.path.exists(attr_path):
experiments.append((config_path, attr_path))
kconf = kconfiglib.Kconfig(kconfig_path)
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)))))
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)
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