tensorflow2_synthetic_benchmark.py

# Copyright 2019 Uber Technologies, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import argparse
import os
import numpy as np
import timeit
import pickle

import tensorflow as tf
import horovod.tensorflow as hvd
from tensorflow.keras import applications

# Path to save the results
RESULTS_PATH = '/home/results/'

# Benchmark settings
parser = argparse.ArgumentParser(description='TensorFlow Synthetic Benchmark',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--fp16-allreduce', action='store_true', default=False,
                    help='use fp16 compression during allreduce')

parser.add_argument('--model', type=str, default='ResNet50',
                    help='model to benchmark')
parser.add_argument('--batch-size', type=int, default=32,
                    help='input batch size')

parser.add_argument('--num-warmup-batches', type=int, default=10,
                    help='number of warm-up batches that don\'t count towards benchmark')
parser.add_argument('--num-batches-per-iter', type=int, default=10,
                    help='number of batches per benchmark iteration')
parser.add_argument('--num-iters', type=int, default=10,
                    help='number of benchmark iterations')

parser.add_argument('--no-cuda', action='store_true', default=False,
                    help='disables CUDA training')


args = parser.parse_args()
args.cuda = not args.no_cuda

# Horovod: initialize Horovod.
hvd.init()

# Horovod: pin GPU to be used to process local rank (one GPU per process)
if args.cuda:
    gpus = tf.config.experimental.list_physical_devices('GPU')
    for gpu in gpus:
        tf.config.experimental.set_memory_growth(gpu, True)
    if gpus:
        tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
else:
    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

# Set up standard model.
model = getattr(applications, args.model)(weights=None)
opt = tf.optimizers.SGD(0.01)

if args.model == 'InceptionV3':
    data = tf.random.uniform([args.batch_size, 299, 299, 3])
else: # ResNets
    data = tf.random.uniform([args.batch_size, 224, 224, 3])

target = tf.random.uniform([args.batch_size, 1], minval=0, maxval=999, dtype=tf.int64)

@tf.function
def benchmark_step(first_batch):
    
    # Horovod: (optional) compression algorithm.
    compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none

    # Horovod: use DistributedGradientTape
    with tf.GradientTape() as tape:
        probs = model(data, training=True)
        loss = tf.losses.sparse_categorical_crossentropy(target, probs)

    # Horovod: add Horovod Distributed GradientTape.
    tape = hvd.DistributedGradientTape(tape, compression=compression)

    gradients = tape.gradient(loss, model.trainable_variables)
    opt.apply_gradients(zip(gradients, model.trainable_variables))

    # Horovod: broadcast initial variable states from rank 0 to all other processes.
    # This is necessary to ensure consistent initialization of all workers when
    # training is started with random weights or restored from a checkpoint.
    #
    # Note: broadcast should be done after the first gradient step to ensure optimizer
    # initialization.
    if first_batch:
        hvd.broadcast_variables(model.variables, root_rank=0)
        hvd.broadcast_variables(opt.variables(), root_rank=0)


def log(s, nl=True):
    if hvd.rank() != 0:
        return
    print(s, end='\n' if nl else '')


log('Model: %s' % args.model)
log('Batch size: %d' % args.batch_size)
device = 'GPU' if args.cuda else 'CPU'
log('Number of %ss: %d' % (device, hvd.size()))


with tf.device(device):
    # Warm-up
    log('Running warmup...')
    benchmark_step(first_batch=True)
    timeit.timeit(lambda: benchmark_step(first_batch=False),
                  number=args.num_warmup_batches)

    # Benchmark
    log('Running benchmark...')
    img_secs = []
    for x in range(args.num_iters):
        time = timeit.timeit(lambda: benchmark_step(first_batch=False),
                             number=args.num_batches_per_iter)
        img_sec = args.batch_size * args.num_batches_per_iter / time
        log('Iter #%d: %.1f img/sec per %s' % (x, img_sec, device))
        img_secs.append(img_sec)

    # Results
    img_sec_mean = np.mean(img_secs)
    img_sec_conf = 1.96 * np.std(img_secs)
    log('Img/sec per %s: %.1f +-%.1f' % (device, img_sec_mean, img_sec_conf))
    log('Total img/sec on %d %s(s): %.1f +-%.1f' %
        (hvd.size(), device, hvd.size() * img_sec_mean, hvd.size() * img_sec_conf))

    timeData = {'model': args.model,
                'batch_size': args.batch_size,
		'num_GPUs': hvd.size(),
                'image_sec_mean': img_sec_mean,
                'image_sec_conf': img_sec_conf}

# Save the results
i = 1 
while os.path.isfile(RESULTS_PATH + 'history_' + args.model + '_GPU' + str(hvd.size()) \
		     + '_' + str(i) + '.pkl'):
    i += 1

with open(RESULTS_PATH + 'history_' + args.model + '_GPU' + str(hvd.size()) + '_' + str(i) + '.pkl', 'wb') as f:
    pickle.dump(timeData, f, pickle.HIGHEST_PROTOCOL)