Custom training with tf.distribute.Strategy

This tutorial demonstrates how to use tf.distribute.Strategy—a TensorFlow API that provides an abstraction for distributing your training across multiple processing units (GPUs, multiple machines, or TPUs)—with custom training loops. In this example, you will train a simple convolutional neural network on the Fashion MNIST dataset containing 70,000 images of size 28 x 28.

Custom training loops provide flexibility and a greater control on training. They also make it easier to debug the model and the training loop.

# Import TensorFlow
import tensorflow as tf

# Helper libraries
import numpy as np
import os

print(tf.__version__)

2022-12-14 03:58:36.707326: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-14 03:58:36.707416: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-14 03:58:36.707426: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2.11.0

Download the Fashion MNIST dataset

fashion_mnist = tf.keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

# Add a dimension to the array -> new shape == (28, 28, 1)
# This is done because the first layer in our model is a convolutional
# layer and it requires a 4D input (batch_size, height, width, channels).
# batch_size dimension will be added later on.
train_images = train_images[..., None]
test_images = test_images[..., None]

# Scale the images to the [0, 1] range.
train_images = train_images / np.float32(255)
test_images = test_images / np.float32(255)

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
29515/29515 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz
26421880/26421880 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz
5148/5148 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz
4422102/4422102 [==============================] - 0s 0us/step

Create a strategy to distribute the variables and the graph

How does tf.distribute.MirroredStrategy strategy work?

All the variables and the model graph are replicated across the replicas.
Input is evenly distributed across the replicas.
Each replica calculates the loss and gradients for the input it received.
The gradients are synced across all the replicas by summing them.
After the sync, the same update is made to the copies of the variables on each replica.

# If the list of devices is not specified in
# `tf.distribute.MirroredStrategy` constructor, they will be auto-detected.
strategy = tf.distribute.MirroredStrategy()

INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3')

print('Number of devices: {}'.format(strategy.num_replicas_in_sync))

Number of devices: 4

Setup input pipeline

BUFFER_SIZE = len(train_images)

BATCH_SIZE_PER_REPLICA = 64
GLOBAL_BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync

EPOCHS = 10

Create the datasets and distribute them:

train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels)).shuffle(BUFFER_SIZE).batch(GLOBAL_BATCH_SIZE) 
test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(GLOBAL_BATCH_SIZE) 

train_dist_dataset = strategy.experimental_distribute_dataset(train_dataset)
test_dist_dataset = strategy.experimental_distribute_dataset(test_dataset)

2022-12-14 03:58:43.807083: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:784] AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy because of the following reason: Found an unshardable source dataset: name: "TensorSliceDataset/_2"
op: "TensorSliceDataset"
input: "Placeholder/_0"
input: "Placeholder/_1"
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2022-12-14 03:58:43.864909: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:784] AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy because of the following reason: Found an unshardable source dataset: name: "TensorSliceDataset/_2"
op: "TensorSliceDataset"
input: "Placeholder/_0"
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Create the model

Create a model using tf.keras.Sequential. You can also use the Model Subclassing API or the functional API to do this.

def create_model():
  model = tf.keras.Sequential([
      tf.keras.layers.Conv2D(32, 3, activation='relu'),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Conv2D(64, 3, activation='relu'),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(64, activation='relu'),
      tf.keras.layers.Dense(10)
    ])

  return model

# Create a checkpoint directory to store the checkpoints.
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")

Define the loss function

Normally, on a single machine with a single GPU/CPU, the loss function is divided by the number of examples in the batch of input.

So, how should the loss be calculated when using a tf.distribute.Strategy?

For an example, let's say you have 4 GPUs and a batch size of 64. One batch of input is distributed across the replicas (4 GPUs), and each replica gets an input of size 16.
The model on each replica does a forward pass with its respective input and calculates the loss. Now, instead of dividing the loss by the number of examples in its respective input (BATCH_SIZE_PER_REPLICA = 16), the loss should be divided by the GLOBAL_BATCH_SIZE (64).

Why do this?

This needs to be done because after the gradients are calculated on each replica, they are synced across the replicas by summing them.

How to do this in TensorFlow?

Loss reduction and scaling is done automatically in Keras Model.compile and Model.fit
If you're writing a custom training loop, as in this tutorial, you should sum the per example losses and divide the sum by the GLOBAL_BATCH_SIZE: scale_loss = tf.reduce_sum(loss) * (1. / GLOBAL_BATCH_SIZE) or you can use tf.nn.compute_average_loss which takes the per example loss, optional sample weights, and GLOBAL_BATCH_SIZE as arguments and returns the scaled loss.
If you're writing a custom training loop for a model with a non-empty list of Model.losses (e.g., weight regularizers), you should sum them up and divide the sum by the number of replicas. You can do this by using the tf.nn.scale_regularization_loss function.
Be careful about batches that are shorter than the GLOBAL_BATCH_SIZE, if your training data allows them: Dividing the prediction loss by GLOBAL_BATCH_SIZE (instead of using tf.reduce_mean over the actual batch size) avoids overweighting examples from short batches. However, this does not apply to regularization losses.
If using tf.keras.losses classes (as in the example below), the loss reduction needs to be explicitly specified to be one of NONE or SUM. AUTO and SUM_OVER_BATCH_SIZE are disallowed when used with tf.distribute.Strategy. AUTO is disallowed because the user should explicitly think about what reduction they want to make sure it is correct in the distributed case. SUM_OVER_BATCH_SIZE is disallowed because currently it would only divide by per replica batch size, and leave the dividing by number of replicas to the user, which might be easy to miss. So, instead, you need to do the reduction yourself explicitly.
If labels is multi-dimensional, then average the per_example_loss across the number of elements in each sample. For example, if the shape of predictions is (batch_size, H, W, n_classes) and labels is (batch_size, H, W), you will need to update per_example_loss like: per_example_loss /= tf.cast(tf.reduce_prod(tf.shape(labels)[1:]), tf.float32)

Caution: Verify the shape of your loss. Loss functions in tf.losses/tf.keras.losses typically return the average over the last dimension of the input. The loss classes wrap these functions. Passing reduction=Reduction.NONE when creating an instance of a loss class means "no additional reduction". For categorical losses with an example input shape of [batch, W, H, n_classes] the n_classes dimension is reduced. For pointwise losses like losses.mean_squared_error or losses.binary_crossentropy include a dummy axis so that [batch, W, H, 1] is reduced to [batch, W, H]. Without the dummy axis [batch, W, H] will be incorrectly reduced to [batch, W].

with strategy.scope():
  # Set reduction to `NONE` so you can do the reduction afterwards and divide by
  # global batch size.
  loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
      from_logits=True,
      reduction=tf.keras.losses.Reduction.NONE)
  def compute_loss(labels, predictions, model_losses):
    per_example_loss = loss_object(labels, predictions)
    loss = tf.nn.compute_average_loss(per_example_loss,
                                      global_batch_size=GLOBAL_BATCH_SIZE)
    if model_losses:
      loss += tf.nn.scale_regularization_loss(tf.add_n(model_losses))
    return loss

Define the metrics to track loss and accuracy

These metrics track the test loss and training and test accuracy. You can use .result() to get the accumulated statistics at any time.

with strategy.scope():
  test_loss = tf.keras.metrics.Mean(name='test_loss')

  train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
      name='train_accuracy')
  test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
      name='test_accuracy')

INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).

Training loop

# A model, an optimizer, and a checkpoint must be created under `strategy.scope`.
with strategy.scope():
  model = create_model()

  optimizer = tf.keras.optimizers.Adam()

  checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)

def train_step(inputs):
  images, labels = inputs

  with tf.GradientTape() as tape:
    predictions = model(images, training=True)
    loss = compute_loss(labels, predictions, model.losses)

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

  train_accuracy.update_state(labels, predictions)
  return loss 

def test_step(inputs):
  images, labels = inputs

  predictions = model(images, training=False)
  t_loss = loss_object(labels, predictions)

  test_loss.update_state(t_loss)
  test_accuracy.update_state(labels, predictions)

# `run` replicates the provided computation and runs it
# with the distributed input.
@tf.function
def distributed_train_step(dataset_inputs):
  per_replica_losses = strategy.run(train_step, args=(dataset_inputs,))
  return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses,
                         axis=None)

@tf.function
def distributed_test_step(dataset_inputs):
  return strategy.run(test_step, args=(dataset_inputs,))

for epoch in range(EPOCHS):
  # TRAIN LOOP
  total_loss = 0.0
  num_batches = 0
  for x in train_dist_dataset:
    total_loss += distributed_train_step(x)
    num_batches += 1
  train_loss = total_loss / num_batches

  # TEST LOOP
  for x in test_dist_dataset:
    distributed_test_step(x)

  if epoch % 2 == 0:
    checkpoint.save(checkpoint_prefix)

  template = ("Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, "
              "Test Accuracy: {}")
  print(template.format(epoch + 1, train_loss,
                         train_accuracy.result() * 100, test_loss.result(),
                         test_accuracy.result() * 100))

  test_loss.reset_states()
  train_accuracy.reset_states()
  test_accuracy.reset_states()

INFO:tensorflow:batch_all_reduce: 8 all-reduces with algorithm = nccl, num_packs = 1
INFO:tensorflow:batch_all_reduce: 8 all-reduces with algorithm = nccl, num_packs = 1
INFO:tensorflow:batch_all_reduce: 8 all-reduces with algorithm = nccl, num_packs = 1
Epoch 1, Loss: 0.6628355383872986, Accuracy: 76.33332824707031, Test Loss: 0.4561820924282074, Test Accuracy: 82.81999969482422
Epoch 2, Loss: 0.39365142583847046, Accuracy: 85.75666809082031, Test Loss: 0.4020243287086487, Test Accuracy: 85.23999786376953
Epoch 3, Loss: 0.3401503264904022, Accuracy: 87.65333557128906, Test Loss: 0.35901474952697754, Test Accuracy: 86.7699966430664
Epoch 4, Loss: 0.31314024329185486, Accuracy: 88.69166564941406, Test Loss: 0.33639273047447205, Test Accuracy: 87.66999816894531
Epoch 5, Loss: 0.2936164140701294, Accuracy: 89.21833038330078, Test Loss: 0.3192780017852783, Test Accuracy: 88.05999755859375
Epoch 6, Loss: 0.2765035033226013, Accuracy: 89.8933334350586, Test Loss: 0.29890647530555725, Test Accuracy: 89.3499984741211
Epoch 7, Loss: 0.26154735684394836, Accuracy: 90.46833801269531, Test Loss: 0.28919824957847595, Test Accuracy: 89.5
Epoch 8, Loss: 0.24878241121768951, Accuracy: 90.86833953857422, Test Loss: 0.2841128408908844, Test Accuracy: 89.76000213623047
Epoch 9, Loss: 0.2387547492980957, Accuracy: 91.1816635131836, Test Loss: 0.27830588817596436, Test Accuracy: 89.70000457763672
Epoch 10, Loss: 0.22312773764133453, Accuracy: 91.86499786376953, Test Loss: 0.27480950951576233, Test Accuracy: 90.26000213623047

Things to note in the example above:

Iterate over the train_dist_dataset and test_dist_dataset using a for x in ... construct.
The scaled loss is the return value of the distributed_train_step. This value is aggregated across replicas using the tf.distribute.Strategy.reduce call and then across batches by summing the return value of the tf.distribute.Strategy.reduce calls.
tf.keras.Metrics should be updated inside train_step and test_step that gets executed by tf.distribute.Strategy.run.
tf.distribute.Strategy.run returns results from each local replica in the strategy, and there are multiple ways to consume this result. You can do tf.distribute.Strategy.reduce to get an aggregated value. You can also do tf.distribute.Strategy.experimental_local_results to get the list of values contained in the result, one per local replica.

Restore the latest checkpoint and test

A model checkpointed with a tf.distribute.Strategy can be restored with or without a strategy.

eval_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
      name='eval_accuracy')

new_model = create_model()
new_optimizer = tf.keras.optimizers.Adam()

test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(GLOBAL_BATCH_SIZE)

@tf.function
def eval_step(images, labels):
  predictions = new_model(images, training=False)
  eval_accuracy(labels, predictions)

checkpoint = tf.train.Checkpoint(optimizer=new_optimizer, model=new_model)
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))

for images, labels in test_dataset:
  eval_step(images, labels)

print('Accuracy after restoring the saved model without strategy: {}'.format(
    eval_accuracy.result() * 100))

Accuracy after restoring the saved model without strategy: 89.70000457763672

Alternate ways of iterating over a dataset

Using iterators

If you want to iterate over a given number of steps and not through the entire dataset, you can create an iterator using the iter call and explicitly call next on the iterator. You can choose to iterate over the dataset both inside and outside the tf.function. Here is a small snippet demonstrating iteration of the dataset outside the tf.function using an iterator.

for _ in range(EPOCHS):
  total_loss = 0.0
  num_batches = 0
  train_iter = iter(train_dist_dataset)

  for _ in range(10):
    total_loss += distributed_train_step(next(train_iter))
    num_batches += 1
  average_train_loss = total_loss / num_batches

  template = ("Epoch {}, Loss: {}, Accuracy: {}")
  print(template.format(epoch + 1, average_train_loss, train_accuracy.result() * 100))
  train_accuracy.reset_states()

Epoch 10, Loss: 0.20553798973560333, Accuracy: 92.5
Epoch 10, Loss: 0.19978073239326477, Accuracy: 92.8125
Epoch 10, Loss: 0.2043170928955078, Accuracy: 92.5
Epoch 10, Loss: 0.20364265143871307, Accuracy: 92.4609375
Epoch 10, Loss: 0.20627591013908386, Accuracy: 92.4609375
Epoch 10, Loss: 0.21071460843086243, Accuracy: 92.1484375
Epoch 10, Loss: 0.2239733189344406, Accuracy: 92.1484375
Epoch 10, Loss: 0.21312539279460907, Accuracy: 92.6953125
Epoch 10, Loss: 0.21038644015789032, Accuracy: 92.03125
Epoch 10, Loss: 0.21483521163463593, Accuracy: 92.0703125

Iterating inside a `tf.function`

You can also iterate over the entire input train_dist_dataset inside a tf.function using the for x in ... construct or by creating iterators like you did above. The example below demonstrates wrapping one epoch of training with a @tf.function decorator and iterating over train_dist_dataset inside the function.

@tf.function
def distributed_train_epoch(dataset):
  total_loss = 0.0
  num_batches = 0
  for x in dataset:
    per_replica_losses = strategy.run(train_step, args=(x,))
    total_loss += strategy.reduce(
      tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)
    num_batches += 1
  return total_loss / tf.cast(num_batches, dtype=tf.float32)

for epoch in range(EPOCHS):
  train_loss = distributed_train_epoch(train_dist_dataset)

  template = ("Epoch {}, Loss: {}, Accuracy: {}")
  print(template.format(epoch + 1, train_loss, train_accuracy.result() * 100))

  train_accuracy.reset_states()

/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/data/ops/dataset_ops.py:461: UserWarning: To make it possible to preserve tf.data options across serialization boundaries, their implementation has moved to be part of the TensorFlow graph. As a consequence, the options value is in general no longer known at graph construction time. Invoking this method in graph mode retains the legacy behavior of the original implementation, but note that the returned value might not reflect the actual value of the options.
  warnings.warn("To make it possible to preserve tf.data options across "
INFO:tensorflow:batch_all_reduce: 8 all-reduces with algorithm = nccl, num_packs = 1
Epoch 1, Loss: 0.21304954588413239, Accuracy: 92.1866683959961
Epoch 2, Loss: 0.20434944331645966, Accuracy: 92.57833099365234
Epoch 3, Loss: 0.193551167845726, Accuracy: 92.84666442871094
Epoch 4, Loss: 0.18342040479183197, Accuracy: 93.31666564941406
Epoch 5, Loss: 0.17504367232322693, Accuracy: 93.53333282470703
Epoch 6, Loss: 0.16769950091838837, Accuracy: 93.788330078125
Epoch 7, Loss: 0.16176080703735352, Accuracy: 94.05999755859375
Epoch 8, Loss: 0.15285256505012512, Accuracy: 94.41000366210938
Epoch 9, Loss: 0.14692439138889313, Accuracy: 94.70333099365234
Epoch 10, Loss: 0.1429813951253891, Accuracy: 94.81666564941406

Tracking training loss across replicas

Because of the loss scaling computation that is carried out, it's not recommended to use tf.keras.metrics.Mean to track the training loss across different replicas.

For example, if you run a training job with the following characteristics:

Two replicas
Two samples are processed on each replica
Resulting loss values: [2, 3] and [4, 5] on each replica
Global batch size = 4

With loss scaling, you calculate the per-sample value of loss on each replica by adding the loss values, and then dividing by the global batch size. In this case: (2 + 3) / 4 = 1.25 and (4 + 5) / 4 = 2.25.

If you use tf.keras.metrics.Mean to track loss across the two replicas, the result is different. In this example, you end up with a total of 3.50 and count of 2, which results in total/count = 1.75 when result() is called on the metric. Loss calculated with tf.keras.Metrics is scaled by an additional factor that is equal to the number of replicas in sync.

Guide and examples

Here are some examples for using distribution strategy with custom training loops:

Distributed training guide
DenseNet example using MirroredStrategy.
BERT example trained using MirroredStrategy and TPUStrategy. This example is particularly helpful for understanding how to load from a checkpoint and generate periodic checkpoints during distributed training etc.
NCF example trained using MirroredStrategy that can be enabled using the keras_use_ctl flag.
NMT example trained using MirroredStrategy.

You can find more examples listed under Examples and tutorials in the Distribution strategy guide.

Next steps

Try out the new tf.distribute.Strategy API on your models.
Visit the Better performance with tf.function and TensorFlow Profiler guides to learn more about tools to optimize the performance of your TensorFlow models.
Check out the Distributed training in TensorFlow guide, which provides an overview of the available distribution strategies.