# Copyright 2020 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file 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 tensorflow as tf

import smdistributed.dataparallel.tensorflow as dist

tf.random.set_seed(42)

dist.init()

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[dist.local_rank()], "GPU")

(mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data(
    path="mnist-%d.npz" % dist.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()
# LR for 8 node run : 0.000125
# LR for single node run : 0.001
opt = tf.optimizers.Adam(0.000125 * dist.size())

checkpoint_dir = "./checkpoints"
checkpoint = tf.train.Checkpoint(model=mnist_model, optimizer=opt)


@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)

    tape = dist.DistributedGradientTape(tape)

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

    if first_batch:
        dist.broadcast_variables(mnist_model.variables, root_rank=0)
        dist.broadcast_variables(opt.variables(), root_rank=0)

    loss_value = dist.oob_allreduce(loss_value)  # Average the loss across workers
    return loss_value


for batch, (images, labels) in enumerate(dataset.take(10000 // dist.size())):
    loss_value = training_step(images, labels, batch == 0)

    if batch % 50 == 0 and dist.rank() == 0:
        print("Step #%d\tLoss: %.6f" % (batch, loss_value))

if dist.rank() == 0:
    checkpoint.save(checkpoint_dir)