tf2_mnist_gradient_tape.py

import argparse

import tensorflow as tf
from kungfu.python import current_cluster_size, current_rank
from kungfu.tensorflow.optimizers import (PairAveragingOptimizer,
                                          SynchronousAveragingOptimizer,
                                          SynchronousSGDOptimizer)

parser = argparse.ArgumentParser(description='KungFu mnist example.')
parser.add_argument('--kf-optimizer',
                    type=str,
                    default='sync-sgd',
                    help='available options: sync-sgd, async-sgd, sma')
args = parser.parse_args()

(mnist_images, mnist_labels), _ = \
    tf.keras.datasets.mnist.load_data(path='mnist-%d.npz' % current_rank())

dataset = tf.data.Dataset.from_tensor_slices(
    (tf.cast(mnist_images[..., tf.newaxis] / 255.0,
             tf.float32), tf.cast(mnist_labels, tf.int64)))
dataset = dataset.repeat().shuffle(10000).batch(128)

mnist_model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, [3, 3], activation='relu'),
    tf.keras.layers.Conv2D(64, [3, 3], activation='relu'),
    tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
    tf.keras.layers.Dropout(0.25),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(10, activation='softmax')
])
loss = tf.losses.SparseCategoricalCrossentropy()

# KungFu: adjust learning rate based on number of GPUs.
# opt = tf.keras.optimizers.SGD(0.001 * current_cluster_size())
opt = tf.compat.v1.train.AdamOptimizer(0.001 * current_cluster_size())

# KungFu: wrap tf.compat.v1.train.Optimizer.
if args.kf_optimizer == 'sync-sgd':
    opt = SynchronousSGDOptimizer(opt)
elif args.kf_optimizer == 'async-sgd':
    opt = PairAveragingOptimizer(opt)
elif args.kf_optimizer == 'sma':
    opt = SynchronousAveragingOptimizer(opt)
else:
    raise RuntimeError('Unknown KungFu optimizer')


@tf.function
def training_step(images, labels, first_batch):
    with tf.GradientTape() as tape:
        probs = mnist_model(images, training=True)
        loss_value = loss(labels, probs)

    grads = tape.gradient(loss_value, mnist_model.trainable_variables)
    opt.apply_gradients(zip(grads, mnist_model.trainable_variables))

    # KungFu: broadcast is done after the first gradient step to ensure optimizer initialization.
    if first_batch:
        from kungfu.tensorflow.initializer import broadcast_variables
        broadcast_variables(mnist_model.variables)
        broadcast_variables(opt.variables())

    return loss_value


# KungFu: adjust number of steps based on number of GPUs.
for batch, (images, labels) in enumerate(
        dataset.take(10000 // current_cluster_size())):
    loss_value = training_step(images, labels, batch == 0)

    if batch % 10 == 0 and current_rank() == 0:
        print('Step #%d\tLoss: %.6f' % (batch, loss_value))