litedram/test/run_benchmarks.py

326 lines
13 KiB
Python
Executable File

#!/usr/bin/env python3
# This file is Copyright (c) 2020 Jędrzej Boczar <jboczar@antmicro.com>
# License: BSD
import os
import re
import sys
import argparse
import subprocess
from collections import defaultdict, namedtuple
import yaml
from litedram.common import Settings
from .benchmark import LiteDRAMBenchmarkSoC
# constructs python regex named group
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 * mult < 1024:
break
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 k != 'self'}
def as_args(self):
args = []
for attr, value in self._settings.items():
arg_string = '--%s' % attr.replace('_', '-')
if isinstance(value, bool):
if value:
args.append(arg_string)
else:
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:
description = yaml.safe_load(f)
configurations = {name: cls(**desc) for name, desc in description.items()}
return configurations
# Benchmark results --------------------------------------------------------------------------------
class BenchmarkResult:
def __init__(self, config, output):
self.config = config
self.parse_output(output)
# instantiate the benchmarked soc to check its configuration
self.benchmark_soc = LiteDRAMBenchmarkSoC(**self.config._settings)
def parse_output(self, output):
bist_pattern = r'{stage}\s+{var}:\s+{value}'
def find(stage, var):
pattern = bist_pattern.format(
stage=stage,
var=var,
value=ng('value', '[0-9]+'),
)
result = re.search(pattern, output)
assert result is not None, 'Could not find pattern in output: %s, %s' % (pattern, output)
return int(result.group('value'))
self.generator_ticks = find('BIST-GENERATOR', 'ticks')
self.checker_errors = find('BIST-CHECKER', 'errors')
self.checker_ticks = find('BIST-CHECKER', 'ticks')
def cmd_count(self):
data_width = self.benchmark_soc.sdram.controller.interface.data_width
return self.config.bist_length / (data_width // 8)
def clk_period(self):
clk_freq = self.benchmark_soc.sdrphy.module.clk_freq
return 1 / clk_freq
def write_bandwidth(self):
return (8 * self.config.bist_length) / (self.generator_ticks * self.clk_period())
def read_bandwidth(self):
return (8 * self.config.bist_length) / (self.checker_ticks * self.clk_period())
def write_efficiency(self):
return self.cmd_count() / self.generator_ticks
def read_efficiency(self):
return self.cmd_count() / self.checker_ticks
# 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
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, metric)()
yield value, result.config
def print(self):
legend = '(module, datawidth, length, random, result)'
fmt = ' {module:15} {dwidth:2} {length:4} {random:1} {result}'
# 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(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, *cmd_args]
proc = subprocess.run(command, capture_output=True, text=True, check=True)
return proc.stdout
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')
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)
filters = []
if args.regex:
filters.append(lambda name_value: re.search(args.regex, name_value[0]))
if args.not_regex:
filters.append(lambda name_value: not re.search(args.not_regex, name_value[0]))
if args.names:
filters.append(lambda name_value: name_value[0] in args.names)
for f in filters:
configurations = dict(filter(f, configurations.items()))
cache_exists = args.output_cache and os.path.isfile(args.output_cache)
# 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)
# 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()