Restore bench_util.py

bench/bench_util.py

Index: bench/bench_util.py
diff --git a/bench/bench_util.py b/bench/bench_util.py
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+++ b/bench/bench_util.py
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+'''
+Created on May 19, 2011
+
+@author: bungeman
+'''
+
+import os
+import re
+import math
+
+# bench representation algorithm constant names
+ALGORITHM_AVERAGE = 'avg'
+ALGORITHM_MEDIAN = 'med'
+ALGORITHM_MINIMUM = 'min'
+ALGORITHM_25TH_PERCENTILE = '25th'
+
+# Regular expressions used throughout.
+PER_SETTING_RE = '([^\s=]+)(?:=(\S+))?'
+SETTINGS_RE = 'skia bench:((?:\s+' + PER_SETTING_RE + ')*)'
+BENCH_RE = 'running bench (?:\[\d+ \d+\] )?\s*(\S+)'
+TIME_RE = '(?:(\w*)msecs = )?\s*((?:\d+\.\d+)(?:,\s*\d+\.\d+)*)'
+# non-per-tile benches have configs that don't end with ']' or '>'
+CONFIG_RE = '(\S+[^\]>]):\s+((?:' + TIME_RE + '\s+)+)'
+# per-tile bench lines are in the following format. Note that there are
+# non-averaged bench numbers in separate lines, which we ignore now due to
+# their inaccuracy.
+TILE_RE = ('  tile_(\S+): tile \[\d+,\d+\] out of \[\d+,\d+\] <averaged>:'
+           ' ((?:' + TIME_RE + '\s+)+)')
+# for extracting tile layout
+TILE_LAYOUT_RE = ' out of \[(\d+),(\d+)\] <averaged>: '
+
+PER_SETTING_RE_COMPILED = re.compile(PER_SETTING_RE)
+SETTINGS_RE_COMPILED = re.compile(SETTINGS_RE)
+BENCH_RE_COMPILED = re.compile(BENCH_RE)
+TIME_RE_COMPILED = re.compile(TIME_RE)
+CONFIG_RE_COMPILED = re.compile(CONFIG_RE)
+TILE_RE_COMPILED = re.compile(TILE_RE)
+TILE_LAYOUT_RE_COMPILED = re.compile(TILE_LAYOUT_RE)
+
+class BenchDataPoint:
+    """A single data point produced by bench.
+    """
+    def __init__(self, bench, config, time_type, time, settings,
+                 tile_layout='', per_tile_values=[], per_iter_time=[]):
+        # string name of the benchmark to measure
+        self.bench = bench
+        # string name of the configurations to run
+        self.config = config
+        # type of the timer in string: '' (walltime), 'c' (cpu) or 'g' (gpu)
+        self.time_type = time_type
+        # float number of the bench time value
+        self.time = time
+        # dictionary of the run settings
+        self.settings = settings
+        # how tiles cover the whole picture: '5x3' means 5 columns and 3 rows
+        self.tile_layout = tile_layout
+        # list of float for per_tile bench values, if applicable
+        self.per_tile_values = per_tile_values
+        # list of float for per-iteration bench time, if applicable
+        self.per_iter_time = per_iter_time
+
+    def __repr__(self):
+        return "BenchDataPoint(%s, %s, %s, %s, %s)" % (
+                   str(self.bench),
+                   str(self.config),
+                   str(self.time_type),
+                   str(self.time),
+                   str(self.settings),
+               )
+
+class _ExtremeType(object):
+    """Instances of this class compare greater or less than other objects."""
+    def __init__(self, cmpr, rep):
+        object.__init__(self)
+        self._cmpr = cmpr
+        self._rep = rep
+
+    def __cmp__(self, other):
+        if isinstance(other, self.__class__) and other._cmpr == self._cmpr:
+            return 0
+        return self._cmpr
+
+    def __repr__(self):
+        return self._rep
+
+Max = _ExtremeType(1, "Max")
+Min = _ExtremeType(-1, "Min")
+
+class _ListAlgorithm(object):
+    """Algorithm for selecting the representation value from a given list.
+    representation is one of the ALGORITHM_XXX representation types."""
+    def __init__(self, data, representation=None):
+        if not representation:
+            representation = ALGORITHM_AVERAGE  # default algorithm
+        self._data = data
+        self._len = len(data)
+        if representation == ALGORITHM_AVERAGE:
+            self._rep = sum(self._data) / self._len
+        else:
+            self._data.sort()
+            if representation == ALGORITHM_MINIMUM:
+                self._rep = self._data[0]
+            else:
+                # for percentiles, we use the value below which x% of values are
+                # found, which allows for better detection of quantum behaviors.
+                if representation == ALGORITHM_MEDIAN:
+                    x = int(round(0.5 * self._len + 0.5))
+                elif representation == ALGORITHM_25TH_PERCENTILE:
+                    x = int(round(0.25 * self._len + 0.5))
+                else:
+                    raise Exception("invalid representation algorithm %s!" %
+                                    representation)
+                self._rep = self._data[x - 1]
+
+    def compute(self):
+        return self._rep
+
+def _ParseAndStoreTimes(config_re_compiled, is_per_tile, line, bench,
+                        value_dic, layout_dic):
+    """Parses given bench time line with regex and adds data to value_dic.
+
+    config_re_compiled: precompiled regular expression for parsing the config
+        line.
+    is_per_tile: boolean indicating whether this is a per-tile bench.
+        If so, we add tile layout into layout_dic as well.
+    line: input string line to parse.
+    bench: name of bench for the time values.
+    value_dic: dictionary to store bench values. See bench_dic in parse() below.
+    layout_dic: dictionary to store tile layouts. See parse() for descriptions.
+    """
+
+    for config in config_re_compiled.finditer(line):
+        current_config = config.group(1)
+        tile_layout = ''
+        if is_per_tile:  # per-tile bench, add name prefix
+            current_config = 'tile_' + current_config
+            layouts = TILE_LAYOUT_RE_COMPILED.search(line)
+            if layouts and len(layouts.groups()) == 2:
+              tile_layout = '%sx%s' % layouts.groups()
+        times = config.group(2)
+        for new_time in TIME_RE_COMPILED.finditer(times):
+            current_time_type = new_time.group(1)
+            iters = [float(i) for i in
+                     new_time.group(2).strip().split(',')]
+            value_dic.setdefault(bench, {}).setdefault(
+                current_config, {}).setdefault(current_time_type, []).append(
+                    iters)
+            layout_dic.setdefault(bench, {}).setdefault(
+                current_config, {}).setdefault(current_time_type, tile_layout)
+
+def parse_skp_bench_data(directory, revision, rep, default_settings=None):
+    """Parses all the skp bench data in the given directory.
+
+    Args:
+      directory: string of path to input data directory.
+      revision: git hash revision that matches the data to process.
+      rep: bench representation algorithm, see bench_util.py.
+      default_settings: dictionary of other run settings. See writer.option() in
+          bench/benchmain.cpp.
+
+    Returns:
+      A list of BenchDataPoint objects.
+    """
+    revision_data_points = []
+    file_list = os.listdir(directory)
+    file_list.sort()
+    for bench_file in file_list:
+        scalar_type = None
+        # Scalar type, if any, is in the bench filename after 'scalar_'.
+        if (bench_file.startswith('bench_' + revision + '_data_')):
+            if bench_file.find('scalar_') > 0:
+                components = bench_file.split('_')
+                scalar_type = components[components.index('scalar') + 1]
+        else:  # Skips non skp bench files.
+            continue
+
+        with open('/'.join([directory, bench_file]), 'r') as file_handle:
+          settings = dict(default_settings or {})
+          settings['scalar'] = scalar_type
+          revision_data_points.extend(parse(settings, file_handle, rep))
+
+    return revision_data_points
+
+# TODO(bensong): switch to reading JSON output when available. This way we don't
+# need the RE complexities.
+def parse(settings, lines, representation=None):
+    """Parses bench output into a useful data structure.
+
+    ({str:str}, __iter__ -> str) -> [BenchDataPoint]
+    representation is one of the ALGORITHM_XXX types."""
+
+    benches = []
+    current_bench = None
+    # [bench][config][time_type] -> [[per-iter values]] where per-tile config
+    # has per-iter value list for each tile [[<tile1_iter1>,<tile1_iter2>,...],
+    # [<tile2_iter1>,<tile2_iter2>,...],...], while non-per-tile config only
+    # contains one list of iterations [[iter1, iter2, ...]].
+    bench_dic = {}
+    # [bench][config][time_type] -> tile_layout
+    layout_dic = {}
+
+    for line in lines:
+
+        # see if this line is a settings line
+        settingsMatch = SETTINGS_RE_COMPILED.search(line)
+        if (settingsMatch):
+            settings = dict(settings)
+            for settingMatch in PER_SETTING_RE_COMPILED.finditer(settingsMatch.group(1)):
+                if (settingMatch.group(2)):
+                    settings[settingMatch.group(1)] = settingMatch.group(2)
+                else:
+                    settings[settingMatch.group(1)] = True
+
+        # see if this line starts a new bench
+        new_bench = BENCH_RE_COMPILED.search(line)
+        if new_bench:
+            current_bench = new_bench.group(1)
+
+        # add configs on this line to the bench_dic
+        if current_bench:
+            if line.startswith('  tile_') :
+                _ParseAndStoreTimes(TILE_RE_COMPILED, True, line, current_bench,
+                                    bench_dic, layout_dic)
+            else:
+                _ParseAndStoreTimes(CONFIG_RE_COMPILED, False, line,
+                                    current_bench, bench_dic, layout_dic)
+
+    # append benches to list
+    for bench in bench_dic:
+        for config in bench_dic[bench]:
+            for time_type in bench_dic[bench][config]:
+                tile_layout = ''
+                per_tile_values = []  # empty for non-per-tile configs
+                per_iter_time = []  # empty for per-tile configs
+                bench_summary = None  # a single final bench value
+                if len(bench_dic[bench][config][time_type]) > 1:
+                    # per-tile config; compute representation for each tile
+                    per_tile_values = [
+                        _ListAlgorithm(iters, representation).compute()
+                            for iters in bench_dic[bench][config][time_type]]
+                    # use sum of each tile representation for total bench value
+                    bench_summary = sum(per_tile_values)
+                    # extract tile layout
+                    tile_layout = layout_dic[bench][config][time_type]
+                else:
+                    # get the list of per-iteration values
+                    per_iter_time = bench_dic[bench][config][time_type][0]
+                    bench_summary = _ListAlgorithm(
+                        per_iter_time, representation).compute()
+                benches.append(BenchDataPoint(
+                    bench,
+                    config,
+                    time_type,
+                    bench_summary,
+                    settings,
+                    tile_layout,
+                    per_tile_values,
+                    per_iter_time))
+
+    return benches
+
+class LinearRegression:
+    """Linear regression data based on a set of data points.
+
+    ([(Number,Number)])
+    There must be at least two points for this to make sense."""
+    def __init__(self, points):
+        n = len(points)
+        max_x = Min
+        min_x = Max
+
+        Sx = 0.0
+        Sy = 0.0
+        Sxx = 0.0
+        Sxy = 0.0
+        Syy = 0.0
+        for point in points:
+            x = point[0]
+            y = point[1]
+            max_x = max(max_x, x)
+            min_x = min(min_x, x)
+
+            Sx += x
+            Sy += y
+            Sxx += x*x
+            Sxy += x*y
+            Syy += y*y
+
+        denom = n*Sxx - Sx*Sx
+        if (denom != 0.0):
+            B = (n*Sxy - Sx*Sy) / denom
+        else:
+            B = 0.0
+        a = (1.0/n)*(Sy - B*Sx)
+
+        se2 = 0
+        sB2 = 0
+        sa2 = 0
+        if (n >= 3 and denom != 0.0):
+            se2 = (1.0/(n*(n-2)) * (n*Syy - Sy*Sy - B*B*denom))
+            sB2 = (n*se2) / denom
+            sa2 = sB2 * (1.0/n) * Sxx
+
+
+        self.slope = B
+        self.intercept = a
+        self.serror = math.sqrt(max(0, se2))
+        self.serror_slope = math.sqrt(max(0, sB2))
+        self.serror_intercept = math.sqrt(max(0, sa2))
+        self.max_x = max_x
+        self.min_x = min_x
+
+    def __repr__(self):
+        return "LinearRegression(%s, %s, %s, %s, %s)" % (
+                   str(self.slope),
+                   str(self.intercept),
+                   str(self.serror),
+                   str(self.serror_slope),
+                   str(self.serror_intercept),
+               )
+
+    def find_min_slope(self):
+        """Finds the minimal slope given one standard deviation."""
+        slope = self.slope
+        intercept = self.intercept
+        error = self.serror
+        regr_start = self.min_x
+        regr_end = self.max_x
+        regr_width = regr_end - regr_start
+
+        if slope < 0:
+            lower_left_y = slope*regr_start + intercept - error
+            upper_right_y = slope*regr_end + intercept + error
+            return min(0, (upper_right_y - lower_left_y) / regr_width)
+
+        elif slope > 0:
+            upper_left_y = slope*regr_start + intercept + error
+            lower_right_y = slope*regr_end + intercept - error
+            return max(0, (lower_right_y - upper_left_y) / regr_width)
+
+        return 0
+
+def CreateRevisionLink(revision_number):
+    """Returns HTML displaying the given revision number and linking to
+    that revision's change page at code.google.com, e.g.
+    http://code.google.com/p/skia/source/detail?r=2056
+    """
+    return '<a href="http://code.google.com/p/skia/source/detail?r=%s">%s</a>'%(
+        revision_number, revision_number)
+
+def main():
+    foo = [[0.0, 0.0], [0.0, 1.0], [0.0, 2.0], [0.0, 3.0]]
+    LinearRegression(foo)
+
+if __name__ == "__main__":
+    main()