Merge pull request #7 from tensorflow/master

updated

official/nlp/modeling/layers/talking_heads_attention.py

+# Copyright 2020 The TensorFlow Authors. 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.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License 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.
+# ==============================================================================
+"""Talking Head Attention layer."""
+# pylint: disable=g-classes-have-attributes
+import math
+import gin
+import tensorflow as tf
+
+from official.nlp.modeling.layers import dense_einsum
+from official.nlp.modeling.layers import masked_softmax
+
+
+@tf.keras.utils.register_keras_serializable(package="Text")
+@gin.configurable
+class TalkingHeadsAttention(tf.keras.layers.Layer):
+  """Implements Talking-Heads Attention.
+
+  https://arxiv.org/abs/2003.02436
+
+  Arguments:
+    num_heads: Number of attention heads.
+    head_size: Size of each attention head.
+    dropout: Dropout probability.
+    kernel_initializer: Initializer for dense layer kernels.
+    bias_initializer: Initializer for dense layer biases.
+    kernel_regularizer: Regularizer for dense layer kernels.
+    bias_regularizer: Regularizer for dense layer biases.
+    activity_regularizer: Regularizer for dense layer activity.
+    kernel_constraint: Constraint for dense layer kernels.
+    bias_constraint: Constraint for dense layer kernels.
+  """
+
+  def __init__(self,
+               num_heads,
+               head_size,
+               dropout_rate=0.0,
+               kernel_initializer="glorot_uniform",
+               bias_initializer="zeros",
+               kernel_regularizer=None,
+               bias_regularizer=None,
+               activity_regularizer=None,
+               kernel_constraint=None,
+               bias_constraint=None,
+               **kwargs):
+    super(TalkingHeadsAttention, self).__init__(**kwargs)
+    self._num_heads = num_heads
+    self._head_size = head_size
+    self._dropout_rate = dropout_rate
+    self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
+    self._bias_initializer = tf.keras.initializers.get(bias_initializer)
+    self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
+    self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
+    self._kernel_constraint = tf.keras.constraints.get(kernel_constraint)
+    self._bias_constraint = tf.keras.constraints.get(bias_constraint)
+
+    self._query_dense = dense_einsum.DenseEinsum(
+        output_shape=(self._num_heads, self._head_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="query")
+
+    self._key_dense = dense_einsum.DenseEinsum(
+        output_shape=(self._num_heads, self._head_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="key")
+
+    self._value_dense = dense_einsum.DenseEinsum(
+        output_shape=(self._num_heads, self._head_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="value")
+
+    self._masked_softmax = masked_softmax.MaskedSoftmax(mask_expansion_axes=[1])
+
+    self._dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
+
+  def build(self, input_shape):
+    super(TalkingHeadsAttention, self).build(input_shape)
+    self._pre_softmax_weight = self.add_weight(
+        "pre_softmax_weight",
+        shape=(self._num_heads, self._num_heads),
+        initializer=self._kernel_initializer,
+        regularizer=self._kernel_regularizer,
+        constraint=self._kernel_constraint,
+        dtype=self.dtype,
+        trainable=True)
+    self._post_softmax_weight = self.add_weight(
+        "post_softmax_weight",
+        shape=(self._num_heads, self._num_heads),
+        initializer=self._kernel_initializer,
+        regularizer=self._kernel_regularizer,
+        constraint=self._kernel_constraint,
+        dtype=self.dtype,
+        trainable=True)
+
+  def get_config(self):
+    config = {
+        "num_heads":
+            self._num_heads,
+        "head_size":
+            self._head_size,
+        "dropout_rate":
+            self._dropout_rate,
+        "kernel_initializer":
+            tf.keras.initializers.serialize(self._kernel_initializer),
+        "bias_initializer":
+            tf.keras.initializers.serialize(self._bias_initializer),
+        "kernel_regularizer":
+            tf.keras.regularizers.serialize(self._kernel_regularizer),
+        "bias_regularizer":
+            tf.keras.regularizers.serialize(self._bias_regularizer),
+        "activity_regularizer":
+            tf.keras.regularizers.serialize(self._activity_regularizer),
+        "kernel_constraint":
+            tf.keras.constraints.serialize(self._kernel_constraint),
+        "bias_constraint":
+            tf.keras.constraints.serialize(self._bias_constraint)
+    }
+    base_config = super(TalkingHeadsAttention, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))
+
+  def call(self, inputs):
+    from_tensor = inputs[0]
+    to_tensor = inputs[1]
+    attention_mask = inputs[2] if len(inputs) == 3 else None
+
+    # Scalar dimensions referenced here:
+    #   B = batch size (number of sequences)
+    #   F = `from_tensor` sequence length
+    #   T = `to_tensor` sequence length
+    #   N = L = `num_attention_heads`
+    #   H = `size_per_head`
+    # `query_tensor` = [B, F, N ,H]
+    query_tensor = self._query_dense(from_tensor)
+
+    # `key_tensor` = [B, T, N, H]
+    key_tensor = self._key_dense(to_tensor)
+
+    # `value_tensor` = [B, T, N, H]
+    value_tensor = self._value_dense(to_tensor)
+
+    # Take the dot product between "query" and "key" to get the raw
+    # attention scores.
+    attention_scores = tf.einsum("BTNH,BFNH->BNFT", key_tensor, query_tensor)
+    attention_scores = tf.multiply(attention_scores,
+                                   1.0 / math.sqrt(float(self._head_size)))
+
+    # Apply talking heads before softmax.
+    attention_scores = tf.einsum("BNFT,NL->BLFT", attention_scores,
+                                 self._pre_softmax_weight)
+
+    # Normalize the attention scores to probabilities.
+    # `attention_probs` = [B, N, F, T]
+    attention_probs = self._masked_softmax([attention_scores, attention_mask])
+
+    # Apply talking heads after softmax.
+    attention_probs = tf.einsum("BNFT,NL->BLFT", attention_probs,
+                                self._post_softmax_weight)
+
+    # This is actually dropping out entire tokens to attend to, which might
+    # seem a bit unusual, but is taken from the original Transformer paper.
+    attention_probs = self._dropout(attention_probs)
+
+    # `context_layer` = [B, F, N, H]
+    return tf.einsum("BNFT,BTNH->BFNH", attention_probs, value_tensor)

official/nlp/modeling/layers/transformer.py

@@ -23,6 +23,7 @@ import tensorflow as tf
 
 from official.nlp.modeling.layers import attention
 from official.nlp.modeling.layers import dense_einsum
+from official.nlp.modeling.layers.util import tf_function_if_eager
 
 
 @tf.keras.utils.register_keras_serializable(package="Text")
@@ -142,10 +143,8 @@ class Transformer(tf.keras.layers.Layer):
         kernel_constraint=self._kernel_constraint,
         bias_constraint=self._bias_constraint,
         name="intermediate")
-    # Use float32 in intermediate gelu activation for numeric stability.
-    # TODO(b/149117297): investigate gelu numeric stability.
     self._intermediate_activation_layer = tf.keras.layers.Activation(
-        self._intermediate_activation, dtype=tf.float32)
+        self._intermediate_activation)
     self._output_dense = dense_einsum.DenseEinsum(
         output_shape=hidden_size,
         kernel_initializer=self._kernel_initializer,
@@ -221,3 +220,10 @@ class Transformer(tf.keras.layers.Layer):
     layer_output = self._output_layer_norm(layer_output + attention_output)
 
     return layer_output
+
+
+class CompiledTransformer(Transformer):
+
+  @tf_function_if_eager(experimental_compile=True)
+  def call(self, inputs):
+    return super(CompiledTransformer, self).call(inputs)

official/nlp/modeling/networks/albert_transformer_encoder.py

@@ -122,7 +122,8 @@ class AlbertTransformerEncoder(network.Network):
     self._position_embedding_layer = layers.PositionEmbedding(
         initializer=initializer,
         use_dynamic_slicing=True,
-        max_sequence_length=max_sequence_length)
+        max_sequence_length=max_sequence_length,
+        name='position_embedding')
     position_embeddings = self._position_embedding_layer(word_embeddings)
 
     type_embeddings = (