Allows to customize feedforward layer in transformer_scaffold.

PiperOrigin-RevId: 312366167

official/nlp/modeling/layers/transformer_scaffold.py

@@ -23,7 +23,6 @@ import gin
 import tensorflow as tf
 
 from official.nlp.modeling.layers import attention
-from official.nlp.modeling.layers import dense_einsum
 
 
 @tf.keras.utils.register_keras_serializable(package="Text")
@@ -32,18 +31,25 @@ class TransformerScaffold(tf.keras.layers.Layer):
   """Transformer scaffold layer.
 
   This layer implements the Transformer from "Attention Is All You Need".
-  (https://arxiv.org/abs/1706.03762), with a customizable attention layer
-  option. Users can pass a class to `attention_cls` and associated config to
-  `attention_cfg`, in which case the scaffold will instantiate the class with
-  the config, or pass a class instance to `attention_cls`.
+  (https://arxiv.org/abs/1706.03762), with a customizable attention layer and
+  feedforward layer option. Users can pass a class to
+  `attention_cls`/`feedforward_cls` and associated config to
+  `attention_cfg`/`feedforward_cfg`, in which case the scaffold will
+  instantiate the class with the config, or pass a class instance to
+  `attention_cls`/`feedforward_cls`.
 
   Arguments:
     num_attention_heads: Number of attention heads.
     intermediate_size: Size of the intermediate layer.
     intermediate_activation: Activation for the intermediate layer.
-    attention_cls: A class to instantate, or a layer instance.
+    attention_cls: A class to instantiate attention layer, or a layer instance.
     attention_cfg: The config with which to instantiate `attention_cls`. Ignored
       if attention_cls is a layer instance.
+    feedforward_cls: A class to instantiate feedforward layer, or a layer
+      instance. If None, will use the standard feedforward layer as described
+      in "Attention Is All You Need" paper.
+    feedforward_cfg: The config with which to instantiate `feedforward_cls`.
+      Ignored if feedforward_cls is a layer instance or is None.
     dropout_rate: Dropout probability for the post-attention and output dropout.
     attention_dropout_rate: Dropout probability for within the attention layer.
     kernel_initializer: Initializer for dense layer kernels.
@@ -61,6 +67,8 @@ class TransformerScaffold(tf.keras.layers.Layer):
                intermediate_activation,
                attention_cls=attention.MultiHeadAttention,
                attention_cfg=None,
+               feedforward_cls=None,
+               feedforward_cfg=None,
                dropout_rate=0.0,
                attention_dropout_rate=0.0,
                kernel_initializer="glorot_uniform",
@@ -75,6 +83,8 @@ class TransformerScaffold(tf.keras.layers.Layer):
 
     self._attention_cfg = attention_cfg
     self._attention_cls = attention_cls
+    self._feedforward_cls = feedforward_cls
+    self._feedforward_cfg = feedforward_cfg
     self._num_heads = num_attention_heads
     self._intermediate_size = intermediate_size
     self._intermediate_activation = intermediate_activation
@@ -112,26 +122,49 @@ class TransformerScaffold(tf.keras.layers.Layer):
           "heads (%d)" % (hidden_size, self._num_heads))
     self._attention_head_size = int(hidden_size // self._num_heads)
 
-    if isinstance(self._attention_cls, tf.keras.layers.Layer):
-      self._attention_layer = self._attention_cls
+    common_kwargs = dict(
+        kernel_initializer=self._kernel_initializer,
+        bias_initializer=self._bias_initializer,
+        kernel_regularizer=self._kernel_regularizer,
+        bias_regularizer=self._bias_regularizer,
+        activity_regularizer=self._activity_regularizer,
+        kernel_constraint=self._kernel_constraint,
+        bias_constraint=self._bias_constraint)
+
+    def get_layer_instance(instance_or_cls, config, default_config):
+      if isinstance(instance_or_cls, tf.keras.layers.Layer):
+        return instance_or_cls
       else:
-      if self._attention_cfg is None:
-        attention_cfg = {
+        if config is None:
+          return instance_or_cls(**default_config)
+        else:
+          return instance_or_cls(**config)
+
+    default_attention_cfg = {
         "num_heads": self._num_heads,
         "key_size": self._attention_head_size,
         "dropout_rate": self._attention_dropout_rate,
-            "kernel_initializer": self._kernel_initializer,
-            "bias_initializer": self._bias_initializer,
-            "kernel_regularizer": self._kernel_regularizer,
-            "bias_regularizer": self._bias_regularizer,
-            "activity_regularizer": self._activity_regularizer,
-            "kernel_constraint": self._kernel_constraint,
-            "bias_constraint": self._bias_constraint,
         "name": "self_attention"
     }
+    default_attention_cfg.update(common_kwargs)
+    self._attention_layer = get_layer_instance(
+        self._attention_cls,
+        config=self._attention_cfg,
+        default_config=default_attention_cfg)
+
+    if self._feedforward_cls is not None:
+      default_feedforward_cfg = {
+          "intermediate_size": self._intermediate_size,
+          "intermediate_activation": self._intermediate_activation,
+          "name": "feedforward",
+      }
+      default_feedforward_cfg.update(common_kwargs)
+      self._feedforward_block = get_layer_instance(
+          self._feedforward_cls,
+          config=self._feedforward_cfg,
+          default_config=default_feedforward_cfg)
     else:
-        attention_cfg = self._attention_cfg
-      self._attention_layer = self._attention_cls(**attention_cfg)
+      self._feedforward_block = None
 
     self._attention_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
     # Use float32 in layernorm for numeric stability.
@@ -140,27 +173,22 @@ class TransformerScaffold(tf.keras.layers.Layer):
         tf.keras.layers.LayerNormalization(
             name="self_attention_layer_norm", axis=-1, epsilon=1e-12,
             dtype=tf.float32))
-    self._intermediate_dense = dense_einsum.DenseEinsum(
+
+    if self._feedforward_block is None:
+      self._intermediate_dense = tf.keras.layers.experimental.EinsumDense(
+          "...x,xy->...y",
           output_shape=self._intermediate_size,
+          bias_axes="y",
           activation=self._intermediate_activation,
-        kernel_initializer=self._kernel_initializer,
-        bias_initializer=self._bias_initializer,
-        kernel_regularizer=self._kernel_regularizer,
-        bias_regularizer=self._bias_regularizer,
-        activity_regularizer=self._activity_regularizer,
-        kernel_constraint=self._kernel_constraint,
-        bias_constraint=self._bias_constraint,
-        name="intermediate")
-    self._output_dense = dense_einsum.DenseEinsum(
+          name="intermediate",
+          **common_kwargs)
+      self._output_dense = tf.keras.layers.experimental.EinsumDense(
+          "...x,xy->...y",
           output_shape=hidden_size,
-        kernel_initializer=self._kernel_initializer,
-        bias_initializer=self._bias_initializer,
-        kernel_regularizer=self._kernel_regularizer,
-        bias_regularizer=self._bias_regularizer,
-        activity_regularizer=self._activity_regularizer,
-        kernel_constraint=self._kernel_constraint,
-        bias_constraint=self._bias_constraint,
-        name="output")
+          bias_axes="y",
+          name="output",
+          **common_kwargs)
+
     self._output_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
     # Use float32 in layernorm for numeric stability.
     self._output_layer_norm = tf.keras.layers.LayerNormalization(
@@ -172,6 +200,8 @@ class TransformerScaffold(tf.keras.layers.Layer):
     config = {
         "attention_cls":
             self._attention_layer,
+        "feedforward_cls":
+            self._feedforward_block,
         "num_attention_heads":
             self._num_heads,
         "intermediate_size":
@@ -212,8 +242,11 @@ class TransformerScaffold(tf.keras.layers.Layer):
     attention_output = self._attention_dropout(attention_output)
     attention_output = self._attention_layer_norm(input_tensor +
                                                   attention_output)
+    if self._feedforward_block is None:
       intermediate_output = self._intermediate_dense(attention_output)
       layer_output = self._output_dense(intermediate_output)
+    else:
+      layer_output = self._feedforward_block(attention_output)
     layer_output = self._output_dropout(layer_output)
     # During mixed precision training, attention_output is from layer norm and
     # is always fp32 for now. Cast layer_output to fp32 for the subsequent add.

official/nlp/modeling/layers/transformer_scaffold_test.py

@@ -32,7 +32,7 @@ from official.nlp.modeling.layers import transformer_scaffold
 # at any point, the list passed to the config object will be filled with a
 # boolean 'True'. We register this class as a Keras serializable so we can
 # test serialization below.
-@tf.keras.utils.register_keras_serializable(package='TestOnly')
+@tf.keras.utils.register_keras_serializable(package='TestOnlyAttention')
 class ValidatedAttentionLayer(attention.MultiHeadAttention):
 
   def __init__(self, call_list, **kwargs):
@@ -50,6 +50,38 @@ class ValidatedAttentionLayer(attention.MultiHeadAttention):
     return config
 
 
+# Test class implements a simple feedforward layer. If this layer is called
+# at any point, the list passed to the config object will be filled with a
+# boolean 'True'. We register this class as a Keras serializable so we can
+# test serialization below.
+@tf.keras.utils.register_keras_serializable(package='TestOnlyFeedforward')
+class ValidatedFeedforwardLayer(tf.keras.layers.Layer):
+
+  def __init__(self, call_list, activation, **kwargs):
+    super(ValidatedFeedforwardLayer, self).__init__(**kwargs)
+    self.list = call_list
+    self.activation = activation
+
+  def build(self, input_shape):
+    hidden_size = input_shape.as_list()[-1]
+    self._feedforward_dense = tf.keras.layers.experimental.EinsumDense(
+        '...x,xy->...y',
+        output_shape=hidden_size,
+        bias_axes='y',
+        activation=self.activation,
+        name='feedforward')
+
+  def call(self, inputs):
+    self.list.append(True)
+    return self._feedforward_dense(inputs)
+
+  def get_config(self):
+    config = super(ValidatedFeedforwardLayer, self).get_config()
+    config['call_list'] = []
+    config['activation'] = self.activation
+    return config
+
+
 # This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It
 # guarantees forward compatibility of this code for the V2 switchover.
 @keras_parameterized.run_all_keras_modes
@@ -87,6 +119,44 @@ class TransformerLayerTest(keras_parameterized.TestCase):
     self.assertNotEmpty(call_list)
     self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
 
+  def test_layer_creation_with_feedforward_cls(self):
+    sequence_length = 21
+    width = 80
+
+    call_list = []
+    attention_layer_cfg = {
+        'num_heads': 10,
+        'key_size': 8,
+        'call_list': call_list,
+    }
+    feedforward_call_list = []
+    feedforward_layer_cfg = {
+        'activation': 'relu',
+        'call_list': feedforward_call_list,
+    }
+    test_layer = transformer_scaffold.TransformerScaffold(
+        attention_cls=ValidatedAttentionLayer,
+        attention_cfg=attention_layer_cfg,
+        feedforward_cls=ValidatedFeedforwardLayer,
+        feedforward_cfg=feedforward_layer_cfg,
+        num_attention_heads=10,
+        intermediate_size=None,
+        intermediate_activation=None)
+
+    # Create a 3-dimensional input (the first dimension is implicit).
+    data_tensor = tf.keras.Input(shape=(sequence_length, width))
+    output_tensor = test_layer(data_tensor)
+    # The default output of a transformer layer should be the same as the input.
+    self.assertEqual(data_tensor.shape.as_list(), output_tensor.shape.as_list())
+
+    # If call_list[0] exists and is True, the passed layer class was
+    # instantiated from the given config properly.
+    self.assertNotEmpty(call_list)
+    self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
+    self.assertNotEmpty(feedforward_call_list)
+    self.assertTrue(feedforward_call_list[0],
+                    "The passed layer class wasn't instantiated.")
+
   def test_layer_creation_with_mask(self):
     sequence_length = 21
     width = 80
@@ -175,6 +245,57 @@ class TransformerLayerTest(keras_parameterized.TestCase):
     self.assertNotEmpty(call_list)
     self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
 
+  def test_layer_invocation_with_feedforward_cls(self):
+    sequence_length = 21
+    width = 80
+
+    call_list = []
+    attention_layer_cfg = {
+        'num_heads': 10,
+        'key_size': 8,
+        'call_list': call_list,
+    }
+    feedforward_call_list = []
+    feedforward_layer_cfg = {
+        'activation': 'relu',
+        'call_list': feedforward_call_list,
+    }
+    feedforward_layer = ValidatedFeedforwardLayer(**feedforward_layer_cfg)
+    test_layer = transformer_scaffold.TransformerScaffold(
+        attention_cls=ValidatedAttentionLayer,
+        attention_cfg=attention_layer_cfg,
+        feedforward_cls=feedforward_layer,
+        num_attention_heads=10,
+        intermediate_size=None,
+        intermediate_activation=None)
+
+    # Create a 3-dimensional input (the first dimension is implicit).
+    data_tensor = tf.keras.Input(shape=(sequence_length, width))
+    # Create a 2-dimensional input (the first dimension is implicit).
+    mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))
+    output_tensor = test_layer([data_tensor, mask_tensor])
+
+    # Create a model from the test layer.
+    model = tf.keras.Model([data_tensor, mask_tensor], output_tensor)
+
+    # Invoke the model on test data. We can't validate the output data itself
+    # (the NN is too complex) but this will rule out structural runtime errors.
+    batch_size = 6
+    input_data = 10 * np.random.random_sample(
+        (batch_size, sequence_length, width))
+    # The attention mask should be of shape (batch, from_seq_len, to_seq_len),
+    # which here is (batch, sequence_length, sequence_length)
+    mask_data = np.random.randint(
+        2, size=(batch_size, sequence_length, sequence_length))
+    _ = model.predict([input_data, mask_data])
+    # If call_list[0] exists and is True, the passed layer class was
+    # instantiated from the given config properly.
+    self.assertNotEmpty(call_list)
+    self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
+    self.assertNotEmpty(feedforward_call_list)
+    self.assertTrue(feedforward_call_list[0],
+                    "The passed layer class wasn't instantiated.")
+
   def test_layer_invocation_with_mask(self):
     sequence_length = 21
     width = 80
@@ -346,6 +467,78 @@ class TransformerLayerTest(keras_parameterized.TestCase):
     self.assertTrue(new_call_list[0],
                     "The passed layer class wasn't instantiated.")
 
+  def test_layer_with_feedforward_cls_restoration_from_config(self):
+    sequence_length = 21
+    width = 80
+
+    call_list = []
+    attention_layer_cfg = {
+        'num_heads': 10,
+        'key_size': 8,
+        'call_list': call_list,
+        'name': 'test_layer',
+    }
+    feedforward_call_list = []
+    feedforward_layer_cfg = {
+        'activation': 'relu',
+        'call_list': feedforward_call_list,
+    }
+    test_layer = transformer_scaffold.TransformerScaffold(
+        attention_cls=ValidatedAttentionLayer,
+        attention_cfg=attention_layer_cfg,
+        feedforward_cls=ValidatedFeedforwardLayer,
+        feedforward_cfg=feedforward_layer_cfg,
+        num_attention_heads=10,
+        intermediate_size=None,
+        intermediate_activation=None)
+
+    # Create a 3-dimensional input (the first dimension is implicit).
+    data_tensor = tf.keras.Input(shape=(sequence_length, width))
+    # Create a 2-dimensional input (the first dimension is implicit).
+    mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))
+    output_tensor = test_layer([data_tensor, mask_tensor])
+
+    # Create a model from the test layer.
+    model = tf.keras.Model([data_tensor, mask_tensor], output_tensor)
+
+    # Invoke the model on test data. We can't validate the output data itself
+    # (the NN is too complex) but this will rule out structural runtime errors.
+    batch_size = 6
+    input_data = 10 * np.random.random_sample(
+        (batch_size, sequence_length, width))
+    # The attention mask should be of shape (batch, from_seq_len, to_seq_len),
+    # which here is (batch, sequence_length, sequence_length)
+    mask_data = np.random.randint(
+        2, size=(batch_size, sequence_length, sequence_length))
+    pre_serialization_output = model.predict([input_data, mask_data])
+
+    # Serialize the model config. Pass the serialized data through json to
+    # ensure that we can serialize this layer to disk.
+    serialized_data = json.dumps(model.get_config())
+    post_string_serialized_data = json.loads(serialized_data)
+
+    # Create a new model from the old config, and copy the weights. These models
+    # should have identical outputs.
+    new_model = tf.keras.Model.from_config(post_string_serialized_data)
+    new_model.set_weights(model.get_weights())
+    output = new_model.predict([input_data, mask_data])
+
+    self.assertAllClose(pre_serialization_output, output)
+
+    # If the layer was configured correctly, it should have a list attribute
+    # (since it should have the custom class and config passed to it).
+    new_model.summary()
+    new_call_list = new_model.get_layer(
+        name='transformer_scaffold')._attention_layer.list
+    self.assertNotEmpty(new_call_list)
+    self.assertTrue(new_call_list[0],
+                    "The passed layer class wasn't instantiated.")
+    new_feedforward_call_list = new_model.get_layer(
+        name='transformer_scaffold')._feedforward_block.list
+    self.assertNotEmpty(new_feedforward_call_list)
+    self.assertTrue(new_feedforward_call_list[0],
+                    "The passed layer class wasn't instantiated.")
+
 
 if __name__ == '__main__':
   tf.test.main()