extract_tensorflow_logs.py

import os
import re
import sys
import glob
import json
import argparse
import pprint

import numpy as np

pp = pprint.PrettyPrinter(indent=1)
os.chdir(sys.path[0])

parser = argparse.ArgumentParser(description="flags for cnn benchmark")
parser.add_argument("--log_dir", type=str, default="../ngc/tensorflow", required=True)
parser.add_argument("--output_dir", type=str, default="./result", required=False)
parser.add_argument('--warmup_batches', type=int, default=20)
parser.add_argument('--train_batches', type=int, default=120)
parser.add_argument('--batch_size_per_device', type=int, default=128)

args = parser.parse_args()


class AutoVivification(dict):
    """Implementation of perl's autovivification feature."""

    def __getitem__(self, item):
        try:
            return dict.__getitem__(self, item)
        except KeyError:
            value = self[item] = type(self)()
        return value


def extract_info_from_file(log_file, result_dict, speed_dict):
    # extract info from file name
    fname = os.path.basename(log_file)
    run_case = log_file.split("/")[-2]  # eg: 1n1g
    model = fname.split("_")[0]
    batch_size = int(fname.split("_")[1].strip("b"))
    
    pricition = fname.split("_")[2]
    test_iter = int(fname.split("_")[3].strip(".log"))

    total_batch_size = 0
    node_num = int(run_case[0])
    if len(run_case) == 4:
        card_num = int(run_case[-2])
    elif len(run_case) == 5:
        card_num = int(run_case[-3:-1])

    tmp_dict = {
        'average_speed': 0,
        'batch_size_per_device': batch_size,
    }

    from_iter = 0 if args.warmup_batches < 20 else args.warmup_batches-20
    to_iter = args.train_batches-20
    avg_speed_list = []
    # extract info from file content
    with open(log_file) as f:
        lines = f.readlines()
        for line in lines:
            if " imgs_per_sec " in line:
                p1 = re.compile(r' imgs_per_sec \: \d+.\d+ ', re.S)
                s = re.findall(p1, line)
                speed = round(float(s[0].split(" : ")[1].strip()), 2)
                avg_speed_list.append(speed)

    # compute avg throughoutput
    avg_speed = round(np.mean(avg_speed_list[from_iter:to_iter]), 2)
    tmp_dict['average_speed'] = avg_speed

    result_dict[model][run_case]['average_speed'] = tmp_dict['average_speed']
    result_dict[model][run_case]['batch_size_per_device'] = tmp_dict['batch_size_per_device']
    speed_dict[model][run_case][test_iter] = avg_speed

    print(log_file, speed_dict[model][run_case])


def compute_speedup(result_dict, speed_dict):
    model_list = [key for key in result_dict]  # eg.['vgg16', 'rn50']
    for m in model_list:
        run_case = [key for key in result_dict[m]]  # eg.['4n8g', '2n8g', '1n8g', '1n4g', '1n1g']
        for d in run_case:
            speed_up = 1.0
            if result_dict[m]['1n1g']['average_speed']:
                result_dict[m][d]['average_speed'] = compute_average(speed_dict[m][d])
                result_dict[m][d]['median_speed'] = compute_median(speed_dict[m][d])
                speed_up = result_dict[m][d]['median_speed'] / compute_median(speed_dict[m]['1n1g'])
            result_dict[m][d]['speedup'] = round(speed_up, 2)


def compute_median(iter_dict):
    speed_list = [i for i in iter_dict.values()]
    return round(np.median(speed_list), 2)


def compute_average(iter_dict):
    i = 0
    total_speed = 0
    for iter in iter_dict:
        i += 1
        total_speed += iter_dict[iter]
    return round(total_speed / i, 2)


def extract_result():
    result_dict = AutoVivification()
    speed_dict = AutoVivification()
    logs_list = glob.glob(os.path.join(args.log_dir, "*/*.log"))
    for l in logs_list:
        extract_info_from_file(l, result_dict, speed_dict)

    # compute speedup
    compute_speedup(result_dict, speed_dict)

    # print result
    pp.pprint(result_dict)

    # write to file as JSON format
    os.makedirs(args.output_dir, exist_ok=True)
    framwork = args.log_dir.split('/')[-1]
    result_file_name = os.path.join(args.output_dir, framwork + "_result.json")
    print("Saving result to {}".format(result_file_name))
    with open(result_file_name, 'w') as f:
        json.dump(result_dict, f)


if __name__ == "__main__":
    assert args.warmup_batches >=20 and args.train_batches > args.warmup_batches
    extract_result()