#!/usr/bin/env python3 # Copyright 2016 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS-IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json from typing import Tuple, List, Dict, Union, Callable, Any, Sequence, Set, Iterable import yaml from collections import defaultdict def extract_results(bench_results: List[Dict[str, Dict[Any, Any]]], fixed_benchmark_params: Dict[str, Union[str, Tuple[str, ...]]], column_dimension: str, row_dimension: str, result_dimension: str) -> Tuple[Dict[str, Dict[str, Dict[str, Any]]], Set[Tuple[List[Tuple[str, str]], ...]], Set[Tuple[Tuple[List[Tuple[str, str]], ...], str]]]: table_data = defaultdict(lambda: dict()) # type: Dict[str, Dict[str, Dict[str, Any]]] remaining_dimensions_by_row_column = dict() used_bench_results = set() # type: Set[Tuple[List[Tuple[str, str]], ...]] used_bench_result_values = set() # type: Set[Tuple[Tuple[List[Tuple[str, str]], ...], str]] for bench_result in bench_results: try: params = {dimension_name: make_immutable(dimension_value) for dimension_name, dimension_value in bench_result['benchmark'].items()} original_params = dict(params) results = bench_result['results'] matches = True if result_dimension not in results: # result_dimension not found in this result, skip matches = False for param_name, param_value in fixed_benchmark_params.items(): if (isinstance(param_value, tuple) and params.get(param_name) in param_value) or (params.get(param_name) == param_value): pass else: # fixed_benchmark_params not satisfied by this result, skip matches = False if matches: # fixed_benchmark_params were satisfied by these params (and were removed) assert row_dimension in params.keys(), '%s not in %s' % (row_dimension, params.keys()) assert column_dimension in params.keys(), '%s not in %s' % (column_dimension, params.keys()) assert result_dimension in results, '%s not in %s' % (result_dimension, results) used_bench_results.add(tuple(sorted(original_params.items()))) used_bench_result_values.add((tuple(sorted(original_params.items())), result_dimension)) row_value = params[row_dimension] column_value = params[column_dimension] remaining_dimensions = params.copy() remaining_dimensions.pop(row_dimension) remaining_dimensions.pop(column_dimension) if column_value in table_data[row_value]: previous_remaining_dimensions = remaining_dimensions_by_row_column[(row_value, column_value)] raise Exception( 'Found multiple benchmark results with the same fixed benchmark params, benchmark param for row and benchmark param for column, so a result can\'t be uniquely determined. ' + 'Consider adding additional values in fixed_benchmark_params. Remaining dimensions:\n%s\nvs\n%s' % ( remaining_dimensions, previous_remaining_dimensions)) table_data[row_value][column_value] = results[result_dimension] remaining_dimensions_by_row_column[(row_value, column_value)] = remaining_dimensions except Exception as e: raise Exception('While processing %s' % bench_result) from e return table_data, used_bench_results, used_bench_result_values # Takes a 2-dimensional array (list of lists) and prints a markdown table with that content. def print_markdown_table(table_data: List[List[str]]) -> None: max_content_length_by_column = [max([len(str(row[column_index])) for row in table_data]) for column_index in range(len(table_data[0]))] for row_index in range(len(table_data)): row = table_data[row_index] cell_strings = [] for column_index in range(len(row)): value = str(row[column_index]) # E.g. if max_content_length_by_column=20, table_cell_format='%20s' table_cell_format = '%%%ss' % max_content_length_by_column[column_index] cell_strings += [table_cell_format % value] print('| ' + ' | '.join(cell_strings) + ' |') if row_index == 0: # Print the separator line, e.g. |---|-----|---| print('|-' + '-|-'.join(['-' * max_content_length_by_column[column_index] for column_index in range(len(row))]) + '-|') # A sequence of length 2, with the lower and upper bound of the interval. # TODO: use a class instead. Interval = Sequence[float] def compute_min_max(table_data, row_headers: List[str], column_headers: List[str]) -> Interval: values_by_row = {row_header: [table_data[row_header][column_header] for column_header in column_headers if column_header in table_data[row_header]] for row_header in row_headers} # We compute min and max and pass it to the value pretty-printer, so that it can determine a unit that works well for all values in the table. min_in_table = min([min([min(interval[0][0], interval[1][0]) for interval in values_by_row[row_header]]) for row_header in row_headers]) max_in_table = max([max([max(interval[0][1], interval[1][1]) for interval in values_by_row[row_header]]) for row_header in row_headers]) return (min_in_table, max_in_table) def pretty_print_percentage_difference(baseline_value: Interval, current_value: Interval): baseline_min = baseline_value[0] baseline_max = baseline_value[1] current_min = current_value[0] current_max = current_value[1] percentage_min = (current_min / baseline_max - 1) * 100 percentage_max = (current_max / baseline_min - 1) * 100 percentage_min_s = "%+.1f%%" % percentage_min percentage_max_s = "%+.1f%%" % percentage_max if percentage_min_s == percentage_max_s: return percentage_min_s else: return "%s - %s" % (percentage_min_s, percentage_max_s) DimensionPrettyPrinter = Callable[[Any], str] IntervalPrettyPrinter = Callable[[Interval, float, float], str] # Takes a table as a dict of dicts (where each table_data[row_key][column_key] is a confidence interval) and prints it as a markdown table using # the specified pretty print functions for column keys, row keys and values respectively. # column_header_pretty_printer and row_header_pretty_printer must be functions taking a single value and returning the pretty-printed version. # value_pretty_printer must be a function taking (value_confidence_interval, min_in_table, max_in_table). # baseline_table_data is an optional table (similar to table_data) that contains the "before" state. If present, the values in two tables will be compared. def print_confidence_intervals_table(table_name, table_data, baseline_table_data, column_header_pretty_printer: DimensionPrettyPrinter, row_header_pretty_printer: DimensionPrettyPrinter, value_pretty_printer: IntervalPrettyPrinter, row_sort_key: Callable[[Any], Any]): if table_data == {}: print('%s: (no data)' % table_name) return row_headers = sorted(list(table_data.keys()), key=row_sort_key) # We need to compute the union of the headers of all rows; some rows might be missing values for certain columns. column_headers = sorted(set().union(*[list(row_values.keys()) for row_values in table_data.values()])) if baseline_table_data: baseline_row_headers = sorted(list(baseline_table_data.keys()), key=row_sort_key) baseline_column_headers = sorted(set().union(*[list(row_values.keys()) for row_values in baseline_table_data.values()])) unmached_baseline_column_headers = set(baseline_row_headers) - set(row_headers) if unmached_baseline_column_headers: print('Found baseline column headers with no match in new results (they will be ignored): ', unmached_baseline_column_headers) unmached_baseline_row_headers = set(baseline_row_headers) - set(row_headers) if unmached_baseline_row_headers: print('Found baseline row headers with no match in new results (they will be ignored): ', unmached_baseline_row_headers) min_in_table, max_in_table = compute_min_max(table_data, row_headers, column_headers) if baseline_table_data: min_in_baseline_table, max_in_baseline_table = compute_min_max(table_data, row_headers, column_headers) min_in_table = min(min_in_table, min_in_baseline_table) max_in_table = max(max_in_table, max_in_baseline_table) table_content = [] table_content.append([table_name] + [column_header_pretty_printer(column_header) for column_header in column_headers]) for row_header in row_headers: row_content = [row_header_pretty_printer(row_header)] for column_header in column_headers: if column_header in table_data[row_header]: value = table_data[row_header][column_header] raw_confidence_interval, rounded_confidence_interval = value pretty_printed_value = value_pretty_printer(rounded_confidence_interval, min_in_table, max_in_table) if baseline_table_data and row_header in baseline_table_data and column_header in baseline_table_data[row_header]: baseline_value = baseline_table_data[row_header][column_header] raw_baseline_confidence_interval, rounded_baseline_confidence_interval = baseline_value pretty_printed_baseline_value = value_pretty_printer(rounded_baseline_confidence_interval, min_in_table, max_in_table) pretty_printed_percentage_difference = pretty_print_percentage_difference(raw_baseline_confidence_interval, raw_confidence_interval) row_content.append("%s -> %s (%s)" % (pretty_printed_baseline_value, pretty_printed_value, pretty_printed_percentage_difference)) else: row_content.append(pretty_printed_value) else: row_content.append("N/A") table_content.append(row_content) print_markdown_table(table_content) def format_string_pretty_printer(format_string: str) -> Callable[[str], str]: def pretty_print(s: str): return format_string % s return pretty_print def float_to_str(x: float) -> str: if x > 100: return str(int(x)) else: return '%.2g' % x def interval_pretty_printer(interval: Interval, unit: str, multiplier: float) -> str: interval = list(interval) # type: List[Any] interval[0] *= multiplier interval[1] *= multiplier # This prevents the format strings below from printing '.0' for numbers that already have 2 digits: # 23.0 -> 23 # 2.0 -> 2.0 (here we don't remove the '.0' because printing just '2' might suggest a lower precision) if int(interval[0]) == interval[0] and interval[0] >= 10: interval[0] = int(interval[0]) else: interval[0] = float_to_str(interval[0]) if int(interval[1]) == interval[1] and interval[1] >= 10: interval[1] = int(interval[1]) else: interval[1] = float_to_str(interval[1]) if interval[0] == interval[1]: return '%s %s' % (interval[0], unit) else: return '%s-%s %s' % (interval[0], interval[1], unit) # Finds the best unit to represent values in the range [min_value, max_value]. # The units must be specified as an ordered list [multiplier1, ..., multiplierN] def find_best_unit(units: List[float], min_value: float, max_value: float) -> float: assert min_value <= max_value if max_value <= units[0]: return units[0] for i in range(len(units) - 1): if min_value > units[i] and max_value < units[i + 1]: return units[i] if min_value > units[-1]: return units[-1] # There is no unit that works very well for all values, first let's try relaxing the min constraint for i in range(len(units) - 1): if min_value > units[i] * 0.2 and max_value < units[i + 1]: return units[i] if min_value > units[-1] * 0.2: return units[-1] # That didn't work either, just use a unit that works well for the min values then for i in reversed(range(len(units))): if min_value > units[i]: return units[i] assert min_value <= min(units) # Pick the smallest unit return units[0] def time_interval_pretty_printer(time_interval: Interval, min_in_table: float, max_in_table: float) -> str: sec = 1 milli = 0.001 micro = milli * milli units = [micro, milli, sec] unit_name_by_unit = {micro: 'μs', milli: 'ms', sec: 's'} unit = find_best_unit(units, min_in_table, max_in_table) unit_name = unit_name_by_unit[unit] return interval_pretty_printer(time_interval, unit=unit_name, multiplier=1 / unit) def file_size_interval_pretty_printer(file_size_interval: Interval, min_in_table: float, max_in_table: float) -> str: byte = 1 kb = 1024 mb = kb * kb units = [byte, kb, mb] unit_name_by_unit = {byte: 'bytes', kb: 'KB', mb: 'MB'} unit = find_best_unit(units, min_in_table, max_in_table) unit_name = unit_name_by_unit[unit] return interval_pretty_printer(file_size_interval, unit=unit_name, multiplier=1 / unit) def make_immutable(x): if isinstance(x, list): return tuple(make_immutable(elem) for elem in x) return x def dict_pretty_printer(dict_data: List[Dict[str, Union[str, Tuple[str]]]]) -> Callable[[Union[str, Tuple[str]]], str]: if isinstance(dict_data, list): dict_data = {make_immutable(mapping['from']): mapping['to'] for mapping in dict_data} def pretty_print(s: Union[str, Tuple[str]]) -> str: if s in dict_data: return dict_data[s] else: raise Exception('dict_pretty_printer(%s) can\'t handle the value %s' % (dict_data, s)) return pretty_print def determine_column_pretty_printer(pretty_printer_definition: Dict[str, Any]) -> DimensionPrettyPrinter: if 'format_string' in pretty_printer_definition: return format_string_pretty_printer(pretty_printer_definition['format_string']) if 'fixed_map' in pretty_printer_definition: return dict_pretty_printer(pretty_printer_definition['fixed_map']) raise Exception("Unrecognized pretty printer description: %s" % pretty_printer_definition) def determine_row_pretty_printer(pretty_printer_definition: Dict[str, Any]) -> DimensionPrettyPrinter: return determine_column_pretty_printer(pretty_printer_definition) def determine_row_sort_key(pretty_printer_definition: Dict[str, Any]) -> Callable[[Any], Any]: if 'fixed_map' in pretty_printer_definition: indexes = {x: i for i, x in enumerate(pretty_printer_definition['fixed_map'].keys())} return lambda s: indexes[s] return lambda x: x def determine_value_pretty_printer(unit: str) -> IntervalPrettyPrinter: if unit == "seconds": return time_interval_pretty_printer if unit == "bytes": return file_size_interval_pretty_printer raise Exception("Unrecognized unit: %s" % unit) def main(): parser = argparse.ArgumentParser(description='Runs all the benchmarks whose results are on the Fruit website.') parser.add_argument('--benchmark-results', help='The input file where benchmark results will be read from (1 per line, with each line in JSON format). You can use the run_benchmarks.py to run a benchmark and generate results in this format.') parser.add_argument('--baseline-benchmark-results', help='Optional. If specified, compares this file (considered the "before" state) with the one specified in --benchmark-results.') parser.add_argument('--benchmark-tables-definition', help='The YAML file that defines the benchmark tables (e.g. fruit_wiki_bench_tables.yaml).') args = parser.parse_args() if args.benchmark_results is None: raise Exception("You must specify a benchmark results file using --benchmark-results.") if args.benchmark_tables_definition is None: raise Exception("You must specify a benchmark tables definition file using --benchmark-tables-definition.") with open(args.benchmark_results, 'r') as f: bench_results = [json.loads(line) for line in f.readlines()] if args.baseline_benchmark_results: with open(args.baseline_benchmark_results, 'r') as f: baseline_bench_results = [json.loads(line) for line in f.readlines()] else: baseline_bench_results = None with open(args.benchmark_tables_definition, 'r') as f: used_bench_results = set() # Set of (Benchmark definition, Benchmark result name) pairs used_bench_result_values = set() config = yaml.full_load(f) for table_definition in config["tables"]: try: fixed_benchmark_params = {dimension_name: make_immutable(dimension_value) for dimension_name, dimension_value in table_definition['benchmark_filter'].items()} table_data, last_used_bench_results, last_used_bench_result_values = extract_results( bench_results, fixed_benchmark_params=fixed_benchmark_params, column_dimension=table_definition['columns']['dimension'], row_dimension=table_definition['rows']['dimension'], result_dimension=table_definition['results']['dimension']) used_bench_results = used_bench_results.union(last_used_bench_results) used_bench_result_values = used_bench_result_values.union(last_used_bench_result_values) if baseline_bench_results: baseline_table_data, _, _ = extract_results( baseline_bench_results, fixed_benchmark_params=fixed_benchmark_params, column_dimension=table_definition['columns']['dimension'], row_dimension=table_definition['rows']['dimension'], result_dimension=table_definition['results']['dimension']) else: baseline_table_data = None rows_pretty_printer_definition = table_definition['rows']['pretty_printer'] columns_pretty_printer_definition = table_definition['columns']['pretty_printer'] results_unit = table_definition['results']['unit'] print_confidence_intervals_table(table_definition['name'], table_data, baseline_table_data, column_header_pretty_printer=determine_column_pretty_printer(columns_pretty_printer_definition), row_header_pretty_printer=determine_row_pretty_printer(rows_pretty_printer_definition), value_pretty_printer=determine_value_pretty_printer(results_unit), row_sort_key=determine_row_sort_key(rows_pretty_printer_definition)) print() print() except Exception as e: print('While processing table:\n%s' % table_definition) print() raise e allowed_unused_benchmarks = set(config.get('allowed_unused_benchmarks', [])) allowed_unused_benchmark_results = set(config.get('allowed_unused_benchmark_results', [])) for bench_result in bench_results: params = {dimension_name: make_immutable(dimension_value) for dimension_name, dimension_value in bench_result['benchmark'].items()} benchmark_defn = tuple(sorted(params.items())) if benchmark_defn not in used_bench_results: if params['name'] not in allowed_unused_benchmarks: print('Warning: benchmark result did not match any tables: %s' % params) else: unused_result_dimensions = {result_dimension for result_dimension in bench_result['results'].keys() if (benchmark_defn, result_dimension) not in used_bench_result_values and result_dimension not in allowed_unused_benchmark_results} if unused_result_dimensions: print('Warning: unused result dimensions %s in benchmark result %s' % (unused_result_dimensions, params)) if __name__ == "__main__": main()