libcxx/utils/compare-benchmarks

#!/usr/bin/env python3

import argparse
import functools
import pathlib
import re
import statistics
import sys
import tempfile

import numpy
import pandas
import plotly.express
import tabulate

def parse_lnt(lines, aggregate=statistics.median):
    """
    Parse lines in LNT format and return a list of dictionnaries of the form:

        [
            {
                'benchmark': <benchmark1>,
                <metric1>: float,
                <metric2>: float,
                ...
            },
            {
                'benchmark': <benchmark2>,
                <metric1>: float,
                <metric2>: float,
                ...
            },
            ...
        ]

    If a metric has multiple values associated to it, they are aggregated into a single
    value using the provided aggregation function.
    """
    results = {}
    for line in lines:
        line = line.strip()
        if not line:
            continue

        (identifier, value) = line.split(' ')
        (benchmark, metric) = identifier.split('.')
        if benchmark not in results:
            results[benchmark] = {'benchmark': benchmark}

        entry = results[benchmark]
        if metric not in entry:
            entry[metric] = []
        entry[metric].append(float(value))

    for (bm, entry) in results.items():
        for metric in entry:
            if isinstance(entry[metric], list):
                entry[metric] = aggregate(entry[metric])

    return list(results.values())

def plain_text_comparison(data, metric, baseline_name=None, candidate_name=None):
    """
    Create a tabulated comparison of the baseline and the candidate for the given metric.
    """
    # Compute additional info in new columns. In text mode, we can assume that we are
    # comparing exactly two data sets (suffixed _0 and _1).
    data['difference'] = data[f'{metric}_1'] - data[f'{metric}_0']
    data['percent'] = 100 * (data['difference'] / data[f'{metric}_0'])

    data = data.replace(numpy.nan, None).sort_values(by='benchmark') # avoid NaNs in tabulate output
    headers = ['Benchmark', baseline_name, candidate_name, 'Difference', '% Difference']
    fmt = (None, '.2f', '.2f', '.2f', '.2f')
    table = data[['benchmark', f'{metric}_0', f'{metric}_1', 'difference', 'percent']].set_index('benchmark')
    return tabulate.tabulate(table, headers=headers, floatfmt=fmt, numalign='right')

def create_chart(data, metric, subtitle=None, series_names=None):
    """
    Create a bar chart comparing the given metric across the provided series.
    """
    data = data.sort_values(by='benchmark').rename(columns={f'{metric}_{i}': series_names[i] for i in range(len(series_names))})
    title = ' vs '.join(series_names)
    figure = plotly.express.bar(data, title=title, subtitle=subtitle, x='benchmark', y=series_names, barmode='group')
    figure.update_layout(xaxis_title='', yaxis_title='', legend_title='')
    return figure

def main(argv):
    parser = argparse.ArgumentParser(
        prog='compare-benchmarks',
        description='Compare the results of multiple sets of benchmarks in LNT format.',
        epilog='This script depends on the modules listed in `libcxx/utils/requirements.txt`.')
    parser.add_argument('files', type=argparse.FileType('r'), nargs='+',
        help='Path to LNT format files containing the benchmark results to compare. In the text format, '
             'exactly two files must be compared.')
    parser.add_argument('--output', '-o', type=pathlib.Path, required=False,
        help='Path of a file where to output the resulting comparison. If the output format is `text`, '
             'default to stdout. If the output format is `chart`, default to a temporary file which is '
             'opened automatically once generated, but not removed after creation.')
    parser.add_argument('--metric', type=str, default='execution_time',
        help='The metric to compare. LNT data may contain multiple metrics (e.g. code size, execution time, etc) -- '
             'this option allows selecting which metric is being analyzed. The default is `execution_time`.')
    parser.add_argument('--filter', type=str, required=False,
        help='An optional regular expression used to filter the benchmarks included in the comparison. '
             'Only benchmarks whose names match the regular expression will be included.')
    parser.add_argument('--format', type=str, choices=['text', 'chart'], default='text',
        help='Select the output format. `text` generates a plain-text comparison in tabular form, and `chart` '
             'generates a self-contained HTML graph that can be opened in a browser. The default is `text`.')
    parser.add_argument('--open', action='store_true',
        help='Whether to automatically open the generated HTML file when finished. This option only makes sense '
             'when the output format is `chart`.')
    parser.add_argument('--series-names', type=str, required=False,
        help='Optional comma-delimited list of names to use for the various series. By default, we use '
             'Baseline and Candidate for two input files, and CandidateN for subsequent inputs.')
    parser.add_argument('--subtitle', type=str, required=False,
        help='Optional subtitle to use for the chart. This can be used to help identify the contents of the chart. '
             'This option cannot be used with the plain text output.')
    args = parser.parse_args(argv)

    if args.format == 'text':
        if len(args.files) != 2:
            parser.error('--format=text requires exactly two input files to compare')
        if args.subtitle is not None:
            parser.error('Passing --subtitle makes no sense with --format=text')
        if args.open:
            parser.error('Passing --open makes no sense with --format=text')

    if args.series_names is None:
        args.series_names = ['Baseline']
        if len(args.files) == 2:
            args.series_names += ['Candidate']
        elif len(args.files) > 2:
            args.series_names.extend(f'Candidate{n}' for n in range(1, len(args.files)))
    else:
        args.series_names = args.series_names.split(',')
        if len(args.series_names) != len(args.files):
            parser.error(f'Passed incorrect number of series names: got {len(args.series_names)} series names but {len(args.files)} inputs to compare')

    # Parse the raw LNT data and store each input in a dataframe
    lnt_inputs = [parse_lnt(file.readlines()) for file in args.files]
    inputs = [pandas.DataFrame(lnt).rename(columns={args.metric: f'{args.metric}_{i}'}) for (i, lnt) in enumerate(lnt_inputs)]

    # Join the inputs into a single dataframe
    data = functools.reduce(lambda a, b: a.merge(b, how='outer', on='benchmark'), inputs)

    if args.filter is not None:
        keeplist = [b for b in data['benchmark'] if re.search(args.filter, b) is not None]
        data = data[data['benchmark'].isin(keeplist)]

    if args.format == 'chart':
        figure = create_chart(data, args.metric, subtitle=args.subtitle, series_names=args.series_names)
        do_open = args.output is None or args.open
        output = args.output or tempfile.NamedTemporaryFile(suffix='.html').name
        plotly.io.write_html(figure, file=output, auto_open=do_open)
    else:
        diff = plain_text_comparison(data, args.metric, baseline_name=args.series_names[0],
                                                        candidate_name=args.series_names[1])
        diff += '\n'
        if args.output is not None:
            with open(args.output, 'w') as out:
                out.write(diff)
        else:
            sys.stdout.write(diff)

if __name__ == '__main__':
    main(sys.argv[1:])