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Merge pull request #123 from antmicro/jboc/benchmark 

Add plotting of benchmark results
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enjoy-digital 2020-02-03 10:35:09 +01:00 committed by GitHub
parent 5a90a8b0dd 811c73254b
commit d8f3feb971
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1 changed files with 190 additions and 62 deletions
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test/run_benchmarks.py
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@ -5,9 +5,12 @@
import os
import re
import yaml
import sys
import argparse
import subprocess
from collections import defaultdict, namedtuple
import yaml
from litedram.common import Settings
@ -18,21 +21,27 @@ from .benchmark import LiteDRAMBenchmarkSoC
def ng(name, regex):
return r'(?P<{}>{})'.format(name, regex)
def center(text, width, fillc=' '):
added = width - len(text)
left = added // 2
right = added - left
return fillc * left + text + fillc * right
def human_readable(value):
binary_prefixes = ['', 'k', 'M', 'G', 'T']
mult = 1.0
for prefix in binary_prefixes:
if value < 1024:
if value * mult < 1024:
break
value /= 1024
return value, prefix
mult /= 1024
return mult, prefix
# Benchmark configuration --------------------------------------------------------------------------
class BenchmarkConfiguration(Settings):
def __init__(self, sdram_module, sdram_data_width, bist_length, bist_random):
self.set_attributes(locals())
self._settings = {k: v for k, v in locals().items() if v != self}
self._settings = {k: v for k, v in locals().items() if k != 'self'}
def as_args(self):
args = []
@ -45,6 +54,12 @@ class BenchmarkConfiguration(Settings):
args.extend([arg_string, str(value)])
return args
def __eq__(self, other):
if not isinstance(other, BenchmarkConfiguration):
return NotImplemented
return all((getattr(self, setting) == getattr(other, setting)
for setting in self._settings.keys()))
@classmethod
def load_yaml(cls, yaml_file):
with open(yaml_file) as f:
@ -101,61 +116,166 @@ class BenchmarkResult:
# Results summary ----------------------------------------------------------------------------------
class ResultsSummary:
# value_scaling is a function: value -> (multiplier, prefix)
Fmt = namedtuple('MetricFormatting', ['name', 'unit', 'value_scaling'])
metric_formats = {
'write_bandwidth': Fmt('Write bandwidth', 'bps', lambda value: human_readable(value)),
'read_bandwidth': Fmt('Read bandwidth', 'bps', lambda value: human_readable(value)),
'write_efficiency': Fmt('Write efficiency', '', lambda value: (100, '%')),
'read_efficiency': Fmt('Read efficiency', '', lambda value: (100, '%')),
}
def __init__(self, results):
self.results = results
# convert results, which map config->metrics to a mapping metric->(config->result)
self.write_bandwidth = self.collect('write_bandwidth')
self.read_bandwidth = self.collect('read_bandwidth')
self.write_efficiency = self.collect('write_efficiency')
self.read_efficiency = self.collect('read_efficiency')
def create_name(self, config):
return '{}:{}:{}:{}'.format(
config.sdram_module, config.sdram_data_width,
config.bist_length, config.bist_random)
def collect(self, attribute):
by_case = {}
def by_metric(self, metric):
"""Returns pairs of value of the given metric and the configuration used for benchmark"""
for result in self.results:
value = getattr(result, attribute)()
by_case[self.create_name(result.config)] = value
return by_case
def value_string(self, metric, value):
if metric in ['write_bandwidth', 'read_bandwidth']:
return '{:6.3f} {}bps'.format(*human_readable(value))
elif ['write_efficiency', 'read_efficiency']:
return '{:5.1f} %'.format(100 * value)
else:
raise ValueError()
value = getattr(result, metric)()
yield value, result.config
def print(self):
print('\n---====== Summary ======---')
for metric in ['write_bandwidth', 'read_bandwidth', 'write_efficiency', 'read_efficiency']:
print(metric)
for case, value in getattr(self, metric).items():
print(' {:30} {}'.format(case, self.value_string(metric, value)))
legend = '(module, datawidth, length, random, result)'
fmt = ' {module:15} {dwidth:2} {length:4} {random:1} {result}'
def plot(self):
raise NotImplementedError()
# store formatted lines per metric
metric_lines = defaultdict(list)
for metric, (_, unit, formatter) in self.metric_formats.items():
for value, config in self.by_metric(metric):
mult, prefix = formatter(value)
result = '{:5.1f} {}{}'.format(value * mult, prefix, unit)
line = fmt.format(module=config.sdram_module,
dwidth=config.sdram_data_width,
length=config.bist_length,
random=int(config.bist_random),
result=result)
metric_lines[metric].append(line)
# find length of the longest line
max_length = max((len(l) for lines in metric_lines.values() for l in lines))
max_length = max(max_length, len(legend) + 2)
width = max_length + 2
# print the formatted summary
def header(text):
mid = center(text, width - 6, '=')
return center(mid, width, '-')
print(header(' Summary '))
print(center(legend, width))
for metric, lines in metric_lines.items():
print(center(self.metric_formats[metric].name, width))
for line in lines:
print(line)
print(header(''))
def plot(self, output_dir, backend='Agg', theme='default', save_format='png', **savefig_kwargs):
"""Create plots with benchmark results summary
Default backend is Agg, which is non-GUI backed and only allows
to save figures as files. If a GUI backed is passed, plt.show()
will be called at the end.
"""
# import locally here to be able to run benchmarks without installing matplotlib
import matplotlib
matplotlib.use(backend)
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import FuncFormatter, PercentFormatter
plt.style.use(theme)
def bandwidth_formatter_func(value, pos):
mult, prefix = human_readable(value)
return '{:.1f}{}bps'.format(value * mult, prefix)
tick_formatters = {
'write_bandwidth': FuncFormatter(bandwidth_formatter_func),
'read_bandwidth': FuncFormatter(bandwidth_formatter_func),
'write_efficiency': PercentFormatter(1.0),
'read_efficiency': PercentFormatter(1.0),
}
def config_tick_name(config):
return '{}\n{}, {}, {}'.format(config.sdram_module, config.sdram_data_width,
config.bist_length, int(config.bist_random))
for metric, (name, unit, _) in self.metric_formats.items():
fig = plt.figure()
axis = plt.gca()
values, configs = zip(*self.by_metric(metric))
ticks = np.arange(len(configs))
axis.barh(ticks, values, align='center')
axis.set_yticks(ticks)
axis.set_yticklabels([config_tick_name(c) for c in configs])
axis.invert_yaxis()
axis.xaxis.set_major_formatter(tick_formatters[metric])
axis.xaxis.set_tick_params(rotation=30)
axis.grid(True)
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.set_axisbelow(True)
# force xmax to 100%
if metric in ['write_efficiency', 'read_efficiency']:
axis.set_xlim(right=1.0)
title = self.metric_formats[metric].name
axis.set_title(title, fontsize=12)
plt.tight_layout()
filename = '{}.{}'.format(metric, save_format)
fig.savefig(os.path.join(output_dir, filename), **savefig_kwargs)
if backend != 'Agg':
plt.show()
# Run ----------------------------------------------------------------------------------------------
def run_benchmark(args):
def run_benchmark(cmd_args):
# run as separate process, because else we cannot capture all output from verilator
benchmark_script = os.path.join(os.path.dirname(__file__), 'benchmark.py')
command = ['python3', benchmark_script, *args]
command = ['python3', benchmark_script, *cmd_args]
proc = subprocess.run(command, capture_output=True, text=True, check=True)
return proc.stdout
def main():
def run_benchmarks(configurations):
benchmarks = []
for name, config in configurations.items():
cmd_args = config.as_args()
print('{}: {}'.format(name, ' '.join(cmd_args)))
output = run_benchmark(cmd_args)
# return raw outputs, not BenchmarkResult so that we can store them in a file
benchmarks.append((config, output))
# exit if checker had any read error
result = BenchmarkResult(config, output)
if result.checker_errors != 0:
print('Error during benchmark "{}": checker_errors = {}'.format(
name, result.checker_errors), file=sys.stderr)
sys.exit(1)
return benchmarks
def main(argv=None):
parser = argparse.ArgumentParser(
description='Run LiteDRAM benchmarks and collect the results')
parser.add_argument('--yaml', required=True, help='Load benchmark configurations from YAML file')
parser.add_argument('--names', nargs='*', help='Limit benchmarks to given names')
parser.add_argument('--regex', help='Limit benchmarks to names matching the regex')
parser.add_argument('--not-regex', help='Limit benchmarks to names not matching the regex')
args = parser.parse_args()
description='Run LiteDRAM benchmarks and collect the results.')
parser.add_argument('--yaml', required=True, help='Load benchmark configurations from YAML file')
parser.add_argument('--names', nargs='*', help='Limit benchmarks to given names')
parser.add_argument('--regex', help='Limit benchmarks to names matching the regex')
parser.add_argument('--not-regex', help='Limit benchmarks to names not matching the regex')
parser.add_argument('--plot', action='store_true', help='Generate plots with results summary')
parser.add_argument('--plot-format', default='png', help='Specify plots file format (default=png)')
parser.add_argument('--plot-backend', default='Agg', help='Optionally specify matplotlib GUI backend')
parser.add_argument('--plot-transparent', action='store_true', help='Use transparent background when saving plots')
parser.add_argument('--plot-output-dir', default='plots', help='Specify where to save the plots')
parser.add_argument('--plot-theme', default='default', help='Use different matplotlib theme')
parser.add_argument('--output-cache', help='Cache benchmark outputs to given file if it exists, else load them from the file without running benchmarks. This allows to run the script multiple times to produce different outputs from the same run')
args = parser.parse_args(argv)
# load and filter configurations
configurations = BenchmarkConfiguration.load_yaml(args.yaml)
@ -169,29 +289,37 @@ def main():
for f in filters:
configurations = dict(filter(f, configurations.items()))
# run the benchmarks
results = []
for name, config in configurations.items():
args = config.as_args()
print('{}: {}'.format(name, ' '.join(args)))
cache_exists = args.output_cache and os.path.isfile(args.output_cache)
result = BenchmarkResult(config, run_benchmark(args))
results.append(result)
# load outputs from cache if it exsits
if args.output_cache and cache_exists:
import pickle
with open(args.output_cache, 'rb') as f:
cached_benchmarks = pickle.load(f)
# take only those that match configurations
benchmarks = [(c, o) for c, o in cached_benchmarks if c in configurations.values()]
else: # run all the benchmarks normally
benchmarks = run_benchmarks(configurations)
print("""\
write_bandwidth = {:6.3f} {}bps
read_bandwidth = {:6.3f} {}bps
write_efficiency = {:6.2f} %
read_efficiency = {:6.2f} %
""".rstrip().format(
*human_readable(result.write_bandwidth()),
*human_readable(result.read_bandwidth()),
result.write_efficiency() * 100,
result.read_efficiency() * 100,
))
# store outputs in cache
if args.output_cache and not cache_exists:
import pickle
with open(args.output_cache, 'wb') as f:
pickle.dump(benchmarks, f, pickle.HIGHEST_PROTOCOL)
# display the summary
results = [BenchmarkResult(config, output) for config, output in benchmarks]
summary = ResultsSummary(results)
summary.print()
if args.plot:
if not os.path.isdir(args.plot_output_dir):
os.makedirs(args.plot_output_dir)
summary.plot(args.plot_output_dir,
backend=args.plot_backend,
theme=args.plot_theme,
save_format=args.plot_format,
transparent=args.plot_transparent)
if __name__ == "__main__":
main()