# 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.
#
# Script adapted from: https://github.com/horovod/horovod/blob/master/examples/tensorflow2_keras_mnist.py
# ==============================================================================

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

import os
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("--learning-rate", "-lr", type=float, default=0.001)
parser.add_argument("--epochs", type=int, default=24)
parser.add_argument("--steps-per-epoch", type=int, default=500)
args = parser.parse_args()

# Horovod: initialize Horovod.
hvd.init()

# Horovod: pin GPU to be used to process local rank (one GPU per process)
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")

(mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data(
    path="mnist-%d.npz" % hvd.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"),
    ]
)

# Horovod: adjust learning rate based on number of GPUs.
scaled_lr = args.learning_rate * hvd.size()
opt = tf.optimizers.Adam(scaled_lr)

# Horovod: add Horovod DistributedOptimizer.
opt = hvd.DistributedOptimizer(opt)

# Horovod: Specify `experimental_run_tf_function=False` to ensure TensorFlow
# uses hvd.DistributedOptimizer() to compute gradients.
mnist_model.compile(
    loss=tf.losses.SparseCategoricalCrossentropy(),
    optimizer=opt,
    metrics=["accuracy"],
    experimental_run_tf_function=False,
)

callbacks = [
    # 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.
    hvd.callbacks.BroadcastGlobalVariablesCallback(0),
    # Horovod: average metrics among workers at the end of every epoch.
    #
    # Note: This callback must be in the list before the ReduceLROnPlateau,
    # TensorBoard or other metrics-based callbacks.
    hvd.callbacks.MetricAverageCallback(),
    # Horovod: using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final
    # accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during
    # the first three epochs. See https://arxiv.org/abs/1706.02677 for details.
    hvd.callbacks.LearningRateWarmupCallback(
        warmup_epochs=3, initial_lr=scaled_lr, verbose=1
    ),
]

# Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.
if hvd.rank() == 0:
    output_dir = "./outputs"
    os.makedirs(output_dir, exist_ok=True)
    callbacks.append(
        tf.keras.callbacks.ModelCheckpoint(
            os.path.join(output_dir, "checkpoint-{epoch}.h5")
        )
    )

# Horovod: write logs on worker 0.
verbose = 1 if hvd.rank() == 0 else 0

# Train the model.
# Horovod: adjust number of steps based on number of GPUs.
mnist_model.fit(
    dataset,
    steps_per_epoch=args.steps_per_epoch // hvd.size(),
    callbacks=callbacks,
    epochs=args.epochs,
    verbose=verbose,
)