122 lines
4.9 KiB
Python
Executable file
122 lines
4.9 KiB
Python
Executable file
#!/usr/bin/env python3
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import sys
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import json
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import traceback
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import numpy as np
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from collections import OrderedDict
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from subprocess import Popen
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def int_list(x):
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return list(map(int, x.split(',')))
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parser = argparse.ArgumentParser(description='Benchmark',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--executable', default='./runner', help='path to runner')
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parser.add_argument('-o', '--output', metavar='OUT', required=True, help="path to benchmark report")
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parser.add_argument('-n', '--ngpus', metavar='N1,[N2,...]', type=int_list,
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required=True, help='numbers of gpus separated by comma')
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parser.add_argument('-b', '--batch-sizes', metavar='B1,[B2,...]', type=int_list,
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required=True, help='batch sizes separated by comma')
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parser.add_argument('-i', '--benchmark-iters', metavar='I',
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type=int, default=100, help='iterations')
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parser.add_argument('-e', '--epochs', metavar='E',
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type=int, default=1, help='number of epochs')
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parser.add_argument('-w', '--warmup', metavar='N',
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type=int, default=0, help='warmup epochs')
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parser.add_argument('--timeout', metavar='T',
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type=str, default='inf', help='timeout for each run')
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parser.add_argument('--mode', metavar='MODE', choices=('train_val', 'train', 'val'), default='train_val',
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help="benchmark mode")
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args, other_args = parser.parse_known_args()
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latency_percentiles = [50, 90, 95, 99, 100]
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harmonic_mean_metrics = ['train.ips', 'val.ips']
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res = OrderedDict()
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res['model'] = ''
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res['ngpus'] = args.ngpus
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res['bs'] = args.batch_sizes
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res['metric_keys'] = []
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if args.mode == 'train' or args.mode == 'train_val':
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res['metric_keys'].append('train.ips')
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if args.mode == 'val' or args.mode == 'train_val':
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res['metric_keys'].append('val.ips')
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res['metric_keys'].append('val.latency_avg')
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if args.mode == 'val':
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for percentile in latency_percentiles:
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res['metric_keys'].append('val.latency_{}'.format(percentile))
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res['metrics'] = OrderedDict()
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for n in args.ngpus:
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res['metrics'][str(n)] = OrderedDict()
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for bs in args.batch_sizes:
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res['metrics'][str(n)][str(bs)] = OrderedDict()
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log_file = args.output + '-{},{}'.format(n, bs)
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Popen(['timeout', args.timeout, args.executable, '-n', str(n), '-b', str(bs),
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'--benchmark-iters', str(args.benchmark_iters),
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'-e', str(args.epochs), '--dllogger-log', log_file,
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'--mode', args.mode, '--no-metrics'] + other_args,
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stdout=sys.stderr).wait()
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try:
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with open(log_file, 'r') as f:
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lines = f.read().splitlines()
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log_data = [json.loads(line[5:]) for line in lines]
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epochs_report = list(filter(lambda x: len(x['step']) == 1, log_data))
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if len(epochs_report) != args.epochs:
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raise ValueError('Wrong number epochs in report')
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epochs_report = epochs_report[args.warmup:]
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for metric in res['metric_keys']:
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data = list(map(lambda x: x['data'][metric], epochs_report))
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if metric in harmonic_mean_metrics:
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avg = len(data) / sum(map(lambda x: 1 / x, data))
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else:
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avg = np.mean(data)
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res['metrics'][str(n)][str(bs)][metric] = avg
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except Exception as e:
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traceback.print_exc()
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for metric in res['metric_keys']:
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res['metrics'][str(n)][str(bs)][metric] = float('nan')
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column_len = 11
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for m in res['metric_keys']:
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print(m, file=sys.stderr)
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print(' ' * column_len, end='|', file=sys.stderr)
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for bs in args.batch_sizes:
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print(str(bs).center(column_len), end='|', file=sys.stderr)
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print(file=sys.stderr)
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print('-' * (len(args.batch_sizes) + 1) * (column_len + 1), file=sys.stderr)
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for n in args.ngpus:
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print(str(n).center(column_len), end='|', file=sys.stderr)
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for bs in args.batch_sizes:
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print('{:.5g}'.format(res['metrics'][str(n)][str(bs)][m]).center(column_len), end='|', file=sys.stderr)
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print(file=sys.stderr)
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print(file=sys.stderr)
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with open(args.output, 'w') as f:
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json.dump(res, f, indent=4)
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