grpc/tools/profiling/microbenchmarks/bm_diff.py

#!/usr/bin/env python2.7
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""" Computes the diff between two bm runs and outputs significant results """

import bm_json
import bm_constants
import bm_speedup

import json
import tabulate
import argparse
import collections

verbose = False

def _median(ary):
  ary = sorted(ary)
  n = len(ary)
  if n%2 == 0:
    return (ary[n/2] + ary[n/2+1]) / 2.0
  else:
    return ary[n/2]

def _args():
  argp = argparse.ArgumentParser(description='Perform diff on microbenchmarks')
  argp.add_argument('-t', '--track',
                    choices=sorted(bm_constants._INTERESTING),
                    nargs='+',
                    default=sorted(bm_constants._INTERESTING),
                    help='Which metrics to track')
  argp.add_argument('-b', '--benchmarks', nargs='+', choices=bm_constants._AVAILABLE_BENCHMARK_TESTS, default=bm_constants._AVAILABLE_BENCHMARK_TESTS)
  argp.add_argument('-l', '--loops', type=int, default=20)
  argp.add_argument('-n', '--new', type=str, help='New benchmark name')
  argp.add_argument('-o', '--old', type=str, help='Old benchmark name')
  argp.add_argument('-v', '--verbose', type=bool, help='print details of before/after')
  args = argp.parse_args()
  global verbose
  if args.verbose: verbose = True
  assert args.new
  assert args.old
  return args

def _maybe_print(str):
  if verbose: print str

class Benchmark:

  def __init__(self):
    self.samples = {
      True: collections.defaultdict(list),
      False: collections.defaultdict(list)
    }
    self.final = {}

  def add_sample(self, track, data, new):
    for f in track:
      if f in data:
        self.samples[new][f].append(float(data[f]))

  def process(self, track, new_name, old_name):
    for f in sorted(track):
      new = self.samples[True][f]
      old = self.samples[False][f]
      if not new or not old: continue
      mdn_diff = abs(_median(new) - _median(old))
      _maybe_print('%s: %s=%r %s=%r mdn_diff=%r' % 
          (f, new_name, new, old_name, old, mdn_diff))
      s = bm_speedup.speedup(new, old)
      if abs(s) > 3 and mdn_diff > 0.5:
        self.final[f] = '%+d%%' % s
    return self.final.keys()

  def skip(self):
    return not self.final

  def row(self, flds):
    return [self.final[f] if f in self.final else '' for f in flds]

def _read_json(filename):
  try:
    with open(filename) as f: return json.loads(f.read())
  except ValueError, e:
    return None

def diff(bms, loops, track, old, new):
  benchmarks = collections.defaultdict(Benchmark)

  for bm in bms:
    for loop in range(0, loops):
      js_new_ctr = _read_json('%s.counters.%s.%d.json' % (bm, new, loop))
      js_new_opt = _read_json('%s.opt.%s.%d.json' % (bm, new, loop))
      js_old_ctr = _read_json('%s.counters.%s.%d.json' % (bm, old, loop))
      js_old_opt = _read_json('%s.opt.%s.%d.json' % (bm, old, loop))

      if js_new_ctr:
        for row in bm_json.expand_json(js_new_ctr, js_new_opt):
          name = row['cpp_name']
          if name.endswith('_mean') or name.endswith('_stddev'): continue
          benchmarks[name].add_sample(track, row, True)
      if js_old_ctr:
        for row in bm_json.expand_json(js_old_ctr, js_old_opt):
          name = row['cpp_name']
          if name.endswith('_mean') or name.endswith('_stddev'): continue
          benchmarks[name].add_sample(track, row, False)

  really_interesting = set()
  for name, bm in benchmarks.items():
    _maybe_print(name)
    really_interesting.update(bm.process(track, new, old))
  fields = [f for f in track if f in really_interesting]

  headers = ['Benchmark'] + fields
  rows = []
  for name in sorted(benchmarks.keys()):
    if benchmarks[name].skip(): continue
    rows.append([name] + benchmarks[name].row(fields))
  if rows:
    return tabulate.tabulate(rows, headers=headers, floatfmt='+.2f')
  else:
    return None

if __name__ == '__main__':
  args = _args()
  print diff(args.benchmarks, args.loops, args.track, args.old, args.new)