326 lines
13 KiB
Python
Executable File
326 lines
13 KiB
Python
Executable File
#!/usr/bin/env python3
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# This file is Copyright (c) 2020 Jędrzej Boczar <jboczar@antmicro.com>
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# License: BSD
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import os
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import re
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import sys
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import argparse
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import subprocess
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from collections import defaultdict, namedtuple
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import yaml
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from litedram.common import Settings
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from .benchmark import LiteDRAMBenchmarkSoC
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# constructs python regex named group
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def ng(name, regex):
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return r'(?P<{}>{})'.format(name, regex)
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def center(text, width, fillc=' '):
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added = width - len(text)
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left = added // 2
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right = added - left
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return fillc * left + text + fillc * right
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def human_readable(value):
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binary_prefixes = ['', 'k', 'M', 'G', 'T']
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mult = 1.0
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for prefix in binary_prefixes:
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if value * mult < 1024:
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break
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mult /= 1024
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return mult, prefix
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# Benchmark configuration --------------------------------------------------------------------------
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class BenchmarkConfiguration(Settings):
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def __init__(self, sdram_module, sdram_data_width, bist_length, bist_random):
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self.set_attributes(locals())
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self._settings = {k: v for k, v in locals().items() if k != 'self'}
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def as_args(self):
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args = []
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for attr, value in self._settings.items():
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arg_string = '--%s' % attr.replace('_', '-')
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if isinstance(value, bool):
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if value:
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args.append(arg_string)
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else:
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args.extend([arg_string, str(value)])
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return args
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def __eq__(self, other):
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if not isinstance(other, BenchmarkConfiguration):
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return NotImplemented
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return all((getattr(self, setting) == getattr(other, setting)
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for setting in self._settings.keys()))
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@classmethod
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def load_yaml(cls, yaml_file):
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with open(yaml_file) as f:
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description = yaml.safe_load(f)
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configurations = {name: cls(**desc) for name, desc in description.items()}
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return configurations
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# Benchmark results --------------------------------------------------------------------------------
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class BenchmarkResult:
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def __init__(self, config, output):
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self.config = config
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self.parse_output(output)
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# instantiate the benchmarked soc to check its configuration
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self.benchmark_soc = LiteDRAMBenchmarkSoC(**self.config._settings)
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def parse_output(self, output):
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bist_pattern = r'{stage}\s+{var}:\s+{value}'
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def find(stage, var):
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pattern = bist_pattern.format(
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stage=stage,
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var=var,
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value=ng('value', '[0-9]+'),
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)
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result = re.search(pattern, output)
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assert result is not None, 'Could not find pattern in output: %s, %s' % (pattern, output)
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return int(result.group('value'))
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self.generator_ticks = find('BIST-GENERATOR', 'ticks')
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self.checker_errors = find('BIST-CHECKER', 'errors')
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self.checker_ticks = find('BIST-CHECKER', 'ticks')
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def cmd_count(self):
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data_width = self.benchmark_soc.sdram.controller.interface.data_width
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return self.config.bist_length / (data_width // 8)
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def clk_period(self):
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clk_freq = self.benchmark_soc.sdrphy.module.clk_freq
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return 1 / clk_freq
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def write_bandwidth(self):
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return (8 * self.config.bist_length) / (self.generator_ticks * self.clk_period())
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def read_bandwidth(self):
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return (8 * self.config.bist_length) / (self.checker_ticks * self.clk_period())
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def write_efficiency(self):
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return self.cmd_count() / self.generator_ticks
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def read_efficiency(self):
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return self.cmd_count() / self.checker_ticks
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# Results summary ----------------------------------------------------------------------------------
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class ResultsSummary:
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# value_scaling is a function: value -> (multiplier, prefix)
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Fmt = namedtuple('MetricFormatting', ['name', 'unit', 'value_scaling'])
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metric_formats = {
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'write_bandwidth': Fmt('Write bandwidth', 'bps', lambda value: human_readable(value)),
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'read_bandwidth': Fmt('Read bandwidth', 'bps', lambda value: human_readable(value)),
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'write_efficiency': Fmt('Write efficiency', '', lambda value: (100, '%')),
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'read_efficiency': Fmt('Read efficiency', '', lambda value: (100, '%')),
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}
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def __init__(self, results):
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self.results = results
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def by_metric(self, metric):
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"""Returns pairs of value of the given metric and the configuration used for benchmark"""
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for result in self.results:
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value = getattr(result, metric)()
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yield value, result.config
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def print(self):
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legend = '(module, datawidth, length, random, result)'
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fmt = ' {module:15} {dwidth:2} {length:4} {random:1} {result}'
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# store formatted lines per metric
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metric_lines = defaultdict(list)
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for metric, (_, unit, formatter) in self.metric_formats.items():
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for value, config in self.by_metric(metric):
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mult, prefix = formatter(value)
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result = '{:5.1f} {}{}'.format(value * mult, prefix, unit)
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line = fmt.format(module=config.sdram_module,
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dwidth=config.sdram_data_width,
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length=config.bist_length,
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random=int(config.bist_random),
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result=result)
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metric_lines[metric].append(line)
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# find length of the longest line
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max_length = max((len(l) for lines in metric_lines.values() for l in lines))
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max_length = max(max_length, len(legend) + 2)
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width = max_length + 2
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# print the formatted summary
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def header(text):
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mid = center(text, width - 6, '=')
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return center(mid, width, '-')
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print(header(' Summary '))
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print(center(legend, width))
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for metric, lines in metric_lines.items():
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print(center(self.metric_formats[metric].name, width))
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for line in lines:
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print(line)
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print(header(''))
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def plot(self, output_dir, backend='Agg', theme='default', save_format='png', **savefig_kwargs):
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"""Create plots with benchmark results summary
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Default backend is Agg, which is non-GUI backed and only allows
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to save figures as files. If a GUI backed is passed, plt.show()
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will be called at the end.
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"""
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# import locally here to be able to run benchmarks without installing matplotlib
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import matplotlib
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matplotlib.use(backend)
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.ticker import FuncFormatter, PercentFormatter
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plt.style.use(theme)
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def bandwidth_formatter_func(value, pos):
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mult, prefix = human_readable(value)
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return '{:.1f}{}bps'.format(value * mult, prefix)
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tick_formatters = {
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'write_bandwidth': FuncFormatter(bandwidth_formatter_func),
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'read_bandwidth': FuncFormatter(bandwidth_formatter_func),
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'write_efficiency': PercentFormatter(1.0),
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'read_efficiency': PercentFormatter(1.0),
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}
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def config_tick_name(config):
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return '{}\n{}, {}, {}'.format(config.sdram_module, config.sdram_data_width,
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config.bist_length, int(config.bist_random))
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for metric, (name, unit, _) in self.metric_formats.items():
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fig = plt.figure()
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axis = plt.gca()
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values, configs = zip(*self.by_metric(metric))
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ticks = np.arange(len(configs))
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axis.barh(ticks, values, align='center')
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axis.set_yticks(ticks)
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axis.set_yticklabels([config_tick_name(c) for c in configs])
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axis.invert_yaxis()
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axis.xaxis.set_major_formatter(tick_formatters[metric])
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axis.xaxis.set_tick_params(rotation=30)
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axis.grid(True)
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axis.spines['top'].set_visible(False)
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axis.spines['right'].set_visible(False)
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axis.set_axisbelow(True)
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# force xmax to 100%
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if metric in ['write_efficiency', 'read_efficiency']:
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axis.set_xlim(right=1.0)
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title = self.metric_formats[metric].name
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axis.set_title(title, fontsize=12)
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plt.tight_layout()
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filename = '{}.{}'.format(metric, save_format)
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fig.savefig(os.path.join(output_dir, filename), **savefig_kwargs)
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if backend != 'Agg':
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plt.show()
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# Run ----------------------------------------------------------------------------------------------
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def run_benchmark(cmd_args):
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# run as separate process, because else we cannot capture all output from verilator
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benchmark_script = os.path.join(os.path.dirname(__file__), 'benchmark.py')
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command = ['python3', benchmark_script, *cmd_args]
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proc = subprocess.run(command, capture_output=True, text=True, check=True)
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return proc.stdout
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def run_benchmarks(configurations):
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benchmarks = []
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for name, config in configurations.items():
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cmd_args = config.as_args()
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print('{}: {}'.format(name, ' '.join(cmd_args)))
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output = run_benchmark(cmd_args)
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# return raw outputs, not BenchmarkResult so that we can store them in a file
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benchmarks.append((config, output))
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# exit if checker had any read error
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result = BenchmarkResult(config, output)
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if result.checker_errors != 0:
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print('Error during benchmark "{}": checker_errors = {}'.format(
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name, result.checker_errors), file=sys.stderr)
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sys.exit(1)
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return benchmarks
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def main(argv=None):
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parser = argparse.ArgumentParser(
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description='Run LiteDRAM benchmarks and collect the results.')
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parser.add_argument('--yaml', required=True, help='Load benchmark configurations from YAML file')
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parser.add_argument('--names', nargs='*', help='Limit benchmarks to given names')
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parser.add_argument('--regex', help='Limit benchmarks to names matching the regex')
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parser.add_argument('--not-regex', help='Limit benchmarks to names not matching the regex')
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parser.add_argument('--plot', action='store_true', help='Generate plots with results summary')
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parser.add_argument('--plot-format', default='png', help='Specify plots file format (default=png)')
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parser.add_argument('--plot-backend', default='Agg', help='Optionally specify matplotlib GUI backend')
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parser.add_argument('--plot-transparent', action='store_true', help='Use transparent background when saving plots')
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parser.add_argument('--plot-output-dir', default='plots', help='Specify where to save the plots')
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parser.add_argument('--plot-theme', default='default', help='Use different matplotlib theme')
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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')
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args = parser.parse_args(argv)
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# load and filter configurations
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configurations = BenchmarkConfiguration.load_yaml(args.yaml)
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filters = []
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if args.regex:
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filters.append(lambda name_value: re.search(args.regex, name_value[0]))
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if args.not_regex:
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filters.append(lambda name_value: not re.search(args.not_regex, name_value[0]))
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if args.names:
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filters.append(lambda name_value: name_value[0] in args.names)
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for f in filters:
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configurations = dict(filter(f, configurations.items()))
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cache_exists = args.output_cache and os.path.isfile(args.output_cache)
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# load outputs from cache if it exsits
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if args.output_cache and cache_exists:
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import pickle
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with open(args.output_cache, 'rb') as f:
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cached_benchmarks = pickle.load(f)
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# take only those that match configurations
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benchmarks = [(c, o) for c, o in cached_benchmarks if c in configurations.values()]
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else: # run all the benchmarks normally
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benchmarks = run_benchmarks(configurations)
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# store outputs in cache
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if args.output_cache and not cache_exists:
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import pickle
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with open(args.output_cache, 'wb') as f:
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pickle.dump(benchmarks, f, pickle.HIGHEST_PROTOCOL)
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# display the summary
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results = [BenchmarkResult(config, output) for config, output in benchmarks]
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summary = ResultsSummary(results)
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summary.print()
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if args.plot:
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if not os.path.isdir(args.plot_output_dir):
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os.makedirs(args.plot_output_dir)
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summary.plot(args.plot_output_dir,
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backend=args.plot_backend,
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theme=args.plot_theme,
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save_format=args.plot_format,
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transparent=args.plot_transparent)
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if __name__ == "__main__":
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main()
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