Merge pull request #8620 from tensorflow:revert-8619-revert-8617-xinliu

PiperOrigin-RevId: 314678046

official/nlp/modeling/layers/position_embedding.py

@@ -19,6 +19,8 @@ from __future__ import division
 # from __future__ import google_type_annotations
 from __future__ import print_function
 
+import math
+
 import tensorflow as tf
 
 from official.modeling import tf_utils
@@ -118,3 +120,86 @@ class PositionEmbedding(tf.keras.layers.Layer):
       position_embeddings = self._position_embeddings
 
     return tf.broadcast_to(position_embeddings, input_shape)
+
+
+@tf.keras.utils.register_keras_serializable(package="Text")
+class RelativePositionEmbedding(tf.keras.layers.Layer):
+  """Creates a positional embedding.
+
+  This layer calculates the position encoding as a mix of sine and cosine
+  functions with geometrically increasing wavelengths. Defined and formulized in
+   "Attention is All You Need", section 3.5.
+  (https://arxiv.org/abs/1706.03762).
+
+  Arguments:
+    hidden_size: Size of the hidden layer.
+    min_timescale: Minimum scale that will be applied at each position
+    max_timescale: Maximum scale that will be applied at each position.
+  """
+
+  def __init__(self,
+               hidden_size,
+               min_timescale=1.0,
+               max_timescale=1.0e4,
+               **kwargs):
+    # We need to have a default dtype of float32, since the inputs (which Keras
+    # usually uses to infer the dtype) will always be int32.
+    # We compute the positional encoding in float32 even if the model uses
+    # float16, as many of the ops used, like log and exp, are numerically
+    # unstable in float16.
+    if "dtype" not in kwargs:
+      kwargs["dtype"] = "float32"
+
+    super(RelativePositionEmbedding, self).__init__(**kwargs)
+    self._hidden_size = hidden_size
+    self._min_timescale = min_timescale
+    self._max_timescale = max_timescale
+
+  def get_config(self):
+    config = {
+        "hidden_size": self._hidden_size,
+        "min_timescale": self._min_timescale,
+        "max_timescale": self._max_timescale,
+        "length": self._length,
+    }
+    base_config = super(RelativePositionEmbedding, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))
+
+  def call(self, inputs, length=None):
+    """Implements call() for the layer.
+
+    Args:
+      inputs: An tensor whose second dimension will be used as `length`. If
+        `None`, the other `length` argument must be specified.
+      length: An optional integer specifying the number of positions. If both
+        `inputs` and `length` are spcified, `length` must be equal to the
+        second dimension of `inputs`.
+
+    Returns:
+      A tensor in shape of [length, hidden_size].
+    """
+    if inputs is None and length is None:
+      raise ValueError(
+          "If inputs is None, `length` must be set in "
+          "RelativePositionEmbedding().")
+    if inputs is not None:
+      input_shape = tf_utils.get_shape_list(inputs)
+      if length is not None and length != input_shape[1]:
+        raise ValueError(
+            "If inputs is not None, `length` must equal to input_shape[1]."
+        )
+      length = input_shape[1]
+    position = tf.cast(tf.range(length), tf.float32)
+    num_timescales = self._hidden_size // 2
+    min_timescale, max_timescale = self._min_timescale, self._max_timescale
+    log_timescale_increment = (
+        math.log(float(max_timescale) / float(min_timescale)) /
+        (tf.cast(num_timescales, tf.float32) - 1))
+    inv_timescales = min_timescale * tf.exp(
+        tf.cast(tf.range(num_timescales), tf.float32) *
+        -log_timescale_increment)
+    scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(inv_timescales,
+                                                               0)
+    position_embeddings = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)],
+                                    axis=1)
+    return position_embeddings

official/nlp/modeling/layers/position_embedding_test.py

@@ -98,6 +98,34 @@ class PositionEmbeddingLayerTest(keras_parameterized.TestCase):
 
     self.assertAllEqual([1, input_length, width], output_data.shape)
 
+  def test_relative_tensor_input(self):
+    hidden_size = 8
+    test_layer = position_embedding.RelativePositionEmbedding(
+        hidden_size=hidden_size)
+
+    # create a 3-dimensional input for test_layer to infer length as 1.
+    input_tensor = tf.constant([[[0] * hidden_size]])
+    output_tensor = test_layer(input_tensor)
+
+    # expected output is the theoretical result of the input based on
+    # sine cosine relative position embedding formula.
+    expected_output_tensor = tf.constant([[0, 0, 0, 0, 1, 1, 1, 1]])
+    self.assertAllEqual(output_tensor, expected_output_tensor)
+
+  def test_relative_length_input(self):
+    hidden_size = 8
+
+    # When we do not have tensor as input, we explicitly specify length
+    # value when initializing test_layer.
+    test_layer = position_embedding.RelativePositionEmbedding(
+        hidden_size=hidden_size)
+    input_tensor = None
+    output_tensor = test_layer(input_tensor, length=1)
+
+    # expected output is the theoretical result of the input based on
+    # sine cosine relative position embedding formula.
+    expected_output_tensor = tf.constant([[0, 0, 0, 0, 1, 1, 1, 1]])
+    self.assertAllEqual(output_tensor, expected_output_tensor)
 
 if __name__ == "__main__":
   tf.test.main()

official/nlp/transformer/transformer.py

@@ -22,6 +22,7 @@ from __future__ import division
 from __future__ import print_function
 
 import tensorflow as tf
+from official.nlp.modeling.layers import position_embedding
 from official.nlp.transformer import attention_layer
 from official.nlp.transformer import beam_search
 from official.nlp.transformer import embedding_layer
@@ -89,6 +90,8 @@ class Transformer(tf.keras.Model):
         params["vocab_size"], params["hidden_size"])
     self.encoder_stack = EncoderStack(params)
     self.decoder_stack = DecoderStack(params)
+    self.position_embedding = position_embedding.RelativePositionEmbedding(
+        hidden_size=self.params["hidden_size"])
 
   def get_config(self):
     return {
@@ -170,9 +173,7 @@ class Transformer(tf.keras.Model):
       attention_bias = tf.cast(attention_bias, self.params["dtype"])
 
       with tf.name_scope("add_pos_encoding"):
-        length = tf.shape(embedded_inputs)[1]
-        pos_encoding = model_utils.get_position_encoding(
-            length, self.params["hidden_size"])
+        pos_encoding = self.position_embedding(inputs=embedded_inputs)
         pos_encoding = tf.cast(pos_encoding, self.params["dtype"])
         encoder_inputs = embedded_inputs + pos_encoding
 
@@ -209,8 +210,7 @@ class Transformer(tf.keras.Model):
                                 [[0, 0], [1, 0], [0, 0]])[:, :-1, :]
       with tf.name_scope("add_pos_encoding"):
         length = tf.shape(decoder_inputs)[1]
-        pos_encoding = model_utils.get_position_encoding(
-            length, self.params["hidden_size"])
+        pos_encoding = self.position_embedding(decoder_inputs)
         pos_encoding = tf.cast(pos_encoding, self.params["dtype"])
         decoder_inputs += pos_encoding
       if training:
@@ -232,9 +232,8 @@ class Transformer(tf.keras.Model):
 
   def _get_symbols_to_logits_fn(self, max_decode_length, training):
     """Returns a decoding function that calculates logits of the next tokens."""
-
-    timing_signal = model_utils.get_position_encoding(
-        max_decode_length + 1, self.params["hidden_size"])
+    timing_signal = self.position_embedding(
+        inputs=None, length=max_decode_length + 1)
     timing_signal = tf.cast(timing_signal, self.params["dtype"])
     decoder_self_attention_bias = model_utils.get_decoder_self_attention_bias(
         max_decode_length, dtype=self.params["dtype"])