Tensorflow 2.0 Keras is training 4x slower than 2.0 Estimator

Question

We recently switched to Keras for TF 2.0, but when we compared it to the DNNClassifier Estimator on 2.0, we experienced around 4x slower speeds with Keras. But I cannot for the life of me figure out why this is happening. The rest of the code for both are identical, using an input_fn() that returns the same tf.data.Dataset, and using identical feature_columns. Been struggling with this problem for days now. Any help would be greatly greatly appreciated. Thank you

Estimator code:

estimator = tf.estimator.DNNClassifier(
        feature_columns = feature_columns,
        hidden_units = [64,64],
        activation_fn = tf.nn.relu,
        optimizer = 'Adagrad',
        dropout = 0.4,
        n_classes = len(vocab),
        model_dir = model_dir,
        batch_norm = false)

estimator.train(input_fn=train_input_fn, steps=400)

Keras code:

feature_layer = tf.keras.layers.DenseFeatures(feature_columns);

model = tf.keras.Sequential([
        feature_layer,
        layers.Dense(64, input_shape = (len(vocab),), activation = tf.nn.relu),
        layers.Dropout(0.4),
        layers.Dense(64, activation = tf.nn.relu),
        layers.Dropout(0.4),
        layers.Dense(len(vocab), activation = 'softmax')]);

model.compile(
        loss = 'sparse_categorical_crossentropy',
        optimizer = 'Adagrad'
        distribute = None)

model.fit(x = train_input_fn(),
          epochs = 1,
          steps_per_epoch = 400,
          shuffle = True)

UPDATE: To test further, I wrote a custom subclassed Model (See: Get Started For Experts), which runs faster than Keras but slower than Estimators. If Estimator trains in 100 secs, the custom model takes approx ~180secs, and Keras approx ~350secs. An interesting note is that Estimator runs slower with Adam() than Adagrad() while Keras seems to run faster. With Adam() Keras takes less than twice as long as DNNClassifier. Assuming I didn't mess up the custom code, I'm beginning to think that DNNClassifier just has a lot of backend optimization / efficiencies that make it run faster than Keras.

Custom code:

class MyModel(Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.features = layers.DenseFeatures(feature_columns, trainable=False)
    self.dense = layers.Dense(64, activation = 'relu')
    self.dropout = layers.Dropout(0.4)
    self.dense2 = layers.Dense(64, activation = 'relu')
    self.dropout2 = layers.Dropout(0.4)
    self.softmax = layers.Dense(len(vocab_of_codes), activation = 'softmax')

  def call(self, x):
    x = self.features(x)
    x = self.dense(x)
    x = self.dropout(x)
    x = self.dense2(x)
    x = self.dropout2(x)
    return self.softmax(x)

model = MyModel()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adagrad()

@tf.function
def train_step(features, label):
  with tf.GradientTape() as tape:
    predictions = model(features)
    loss = loss_object(label, predictions)
  gradients = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))

itera = iter(train_input_fn())
for i in range(400):
  features, labels = next(itera)
  train_step(features, labels)

UPDATE: It possibly seems to be the dataset. When I print a row of the dataset within the train_input_fn(), in estimators, it prints out the non-eager Tensor definition. In Keras, it prints out the eager values. Going through the Keras backend code, when it receives a tf.data.dataset as input, it handles it eagerly (and ONLY eagerly), which is why it was crashing whenever I used tf.function on the train_input_fn(). Basically, my guess is DNNClassifier is training faster than Keras because it runs more dataset code in graph mode. Will post any updates/finds.

Answer 1

I believe it is slower because it is not being executed on the graph. In order to execute on the graph in TF2 you'll need a function decorated with the tf.function decorator. Check out this section for ideas on how to restructure your code.