diff options
| -rwxr-xr-x | bin/analyze-config.py | 107 |
1 files changed, 72 insertions, 35 deletions
diff --git a/bin/analyze-config.py b/bin/analyze-config.py index fd8b982..c961467 100755 --- a/bin/analyze-config.py +++ b/bin/analyze-config.py @@ -21,6 +21,18 @@ from dfatool.model import AnalyticModel, ModelAttribute from dfatool.utils import NpEncoder +def make_config_vector(kconf, params, symbols, choices): + config_vector = [None for i in params] + for i, param in enumerate(params): + if param in choices: + choice = kconf.choices[choices.index(param)] + if choice.selection: + config_vector[i] = choice.selection.name + else: + config_vector[i] = kconf.syms[param].str_value + return tuple(config_vector) + + def main(): parser = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__ @@ -32,6 +44,11 @@ def main(): default="warning", help="Set log level", ) + parser.add_argument( + "--with-choice-node", + action="store_true", + help="Add special decisiontree nodes for Kconfig choice symbols", + ) parser.add_argument("kconfig_file") parser.add_argument("data_dir") @@ -68,6 +85,13 @@ def main(): ) ) + if args.with_choice_node: + choices = list(map(lambda choice: choice.name, kconf.choices)) + else: + choices = list() + + params = sorted(symbols + choices) + by_name = { "multipass": { "isa": "state", @@ -86,7 +110,8 @@ def main(): with open(attr_path, "r") as f: attr = json.load(f) - config_vector = tuple(map(lambda sym: kconf.syms[sym].str_value, symbols)) + config_vector = make_config_vector(kconf, params, symbols, choices) + config_vectors.add(config_vector) by_name["multipass"]["rom_usage"].append(attr["total"]["ROM"]) by_name["multipass"]["ram_usage"].append(attr["total"]["RAM"]) @@ -103,7 +128,7 @@ def main(): "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) + model = AnalyticModel(by_name, params, compute_stats=False) def get_min(this_symbols, this_data, data_index=1, threshold=100, level=0): @@ -115,47 +140,59 @@ def main(): mean_stds = list() for i, param in enumerate(this_symbols): - enabled = list(filter(lambda vrr: vrr[0][i] == "y", this_data)) - disabled = list(filter(lambda vrr: vrr[0][i] == "n", 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] + unique_values = list(set(map(lambda vrr: vrr[0][i], this_data))) + + child_values = list() + for value in unique_values: + child_values.append( + list(filter(lambda vrr: vrr[0][i] == value, this_data)) + ) + + if len(list(filter(len, child_values))) < 2: + # this param only has a single value. there's no point in splitting. + mean_stds.append(np.inf) + continue + + children = list() + for child in child_values: + children.append(np.std(list(map(lambda x: x[1], child)))) if np.any(np.isnan(children)): mean_stds.append(np.inf) else: mean_stds.append(np.mean(children)) + if np.all(np.isinf(mean_stds)): + # all children have the same configuration. We shouldn't get here due to the threshold check above... + logging.warning("Waht") + return StaticFunction(np.mean(rom_sizes)) + symbol_index = np.argmin(mean_stds) symbol = this_symbols[symbol_index] - enabled = list(filter(lambda vrr: vrr[0][symbol_index] == "y", this_data)) - disabled = list(filter(lambda vrr: vrr[0][symbol_index] == "n", this_data)) + new_symbols = this_symbols[:symbol_index] + this_symbols[symbol_index + 1 :] + + unique_values = list(set(map(lambda vrr: vrr[0][symbol_index], 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["y"] = get_min(new_symbols, enabled, data_index, threshold, level + 1) - if len(disabled): - child["n"] = get_min( - new_symbols, disabled, data_index, threshold, level + 1 + for value in unique_values: + children = filter(lambda vrr: vrr[0][symbol_index] == value, this_data) + children = list( + map( + lambda x: (x[0][:symbol_index] + x[0][symbol_index + 1 :], *x[1:]), + children, + ) ) + if len(children): + print( + f"Level {level} split on {symbol} == {value} has {len(children)} children" + ) + child[value] = get_min( + new_symbols, children, data_index, threshold, level + 1 + ) + + assert len(child.values()) >= 2 return SplitFunction(np.mean(rom_sizes), symbol_index, child) @@ -165,25 +202,25 @@ def main(): "rom_usage", by_name["multipass"]["rom_usage"], by_name["multipass"]["param"], - symbols, + params, ) model.attr_by_name["multipass"]["ram_usage"] = ModelAttribute( "multipass", "rom_usage", by_name["multipass"]["ram_usage"], by_name["multipass"]["param"], - symbols, + params, ) model.attr_by_name["multipass"]["rom_usage"].model_function = get_min( - symbols, data, 1, 100 + params, data, 1, 100 ) model.attr_by_name["multipass"]["ram_usage"].model_function = get_min( - symbols, data, 2, 20 + params, data, 2, 20 ) with open("kconfigmodel.json", "w") as f: - json_model = model.to_json(with_param_name=True, param_names=symbols) + json_model = model.to_json(with_param_name=True, param_names=params) json.dump(json_model, f, sort_keys=True, cls=NpEncoder) |