#KerasNLP Update TransformerEncoderBlock to support Q, KV as two input streams.

PiperOrigin-RevId: 350170448

official/modeling/progressive/utils.py

-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 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.

official/modeling/tf_utils.py

-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 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.

official/nlp/keras_nlp/layers/transformer_encoder_block.py

@@ -85,7 +85,7 @@ class TransformerEncoderBlock(tf.keras.layers.Layer):
         kernel.
       **kwargs: keyword arguments/
     """
-    super(TransformerEncoderBlock, self).__init__(**kwargs)
+    super().__init__(**kwargs)
 
     self._num_heads = num_attention_heads
     self._inner_dim = inner_dim
@@ -111,23 +111,18 @@ class TransformerEncoderBlock(tf.keras.layers.Layer):
       self._attention_initializer = self._kernel_initializer
 
   def build(self, input_shape):
-    input_tensor = input_shape[0] if len(input_shape) == 2 else input_shape
-    input_tensor_shape = tf.TensorShape(input_tensor)
+    if isinstance(input_shape, tf.TensorShape):
+      input_tensor_shape = input_shape
+    elif isinstance(input_shape, (list, tuple)):
+      input_tensor_shape = tf.TensorShape(input_shape[0])
+    else:
+      raise ValueError(
+          "The type of input shape argument is not supported, got: %s" %
+          type(input_shape))
     if len(input_tensor_shape.as_list()) != 3:
       raise ValueError("TransformerEncoderBlock expects a three-dimensional "
                        "input of shape [batch, sequence, width].")
-    batch_size, sequence_length, hidden_size = input_tensor_shape
-
-    if len(input_shape) == 2:
-      mask_tensor_shape = tf.TensorShape(input_shape[1])
-      expected_mask_tensor_shape = tf.TensorShape(
-          [batch_size, sequence_length, sequence_length])
-      if not expected_mask_tensor_shape.is_compatible_with(mask_tensor_shape):
-        raise ValueError("When passing a mask tensor to "
-                         "TransformerEncoderBlock, the mask tensor must be of "
-                         "shape [batch, sequence_length, sequence_length] "
-                         "(here %s). Got a mask tensor of shape %s." %
-                         (expected_mask_tensor_shape, mask_tensor_shape))
+    hidden_size = input_tensor_shape[-1]
     if hidden_size % self._num_heads != 0:
       raise ValueError(
           "The input size (%d) is not a multiple of the number of attention "
@@ -234,15 +229,38 @@ class TransformerEncoderBlock(tf.keras.layers.Layer):
     return dict(list(base_config.items()) + list(config.items()))
 
   def call(self, inputs):
-    if isinstance(inputs, (list, tuple)) and len(inputs) == 2:
+    """Transformer self-attention encoder block call.
+
+    Args:
+      inputs: a single tensor or a list of tensors.
+        `input tensor` as the single sequence of embeddings.
+        [`input tensor`, `attention mask`] to have the additional attention
+          mask.
+        [`query tensor`, `key value tensor`, `attention mask`] to have separate
+          input streams for the query, and key/value to the multi-head
+          attention.
+
+    Returns:
+      An ouput tensor with the same dimensions as input/query tensor.
+    """
+    if isinstance(inputs, (list, tuple)):
+      if len(inputs) == 2:
         input_tensor, attention_mask = inputs
+        key_value = None
+      elif len(inputs) == 3:
+        input_tensor, key_value, attention_mask = inputs
+      else:
+        raise ValueError("Unexpected inputs to %s with length at %d" %
+                         (self.__class__, len(inputs)))
     else:
-      input_tensor, attention_mask = (inputs, None)
+      input_tensor, key_value, attention_mask = (inputs, None, None)
 
     if self._output_range:
       if self._norm_first:
         source_tensor = input_tensor[:, 0:self._output_range, :]
         input_tensor = self._attention_layer_norm(input_tensor)
+        if key_value is not None:
+          key_value = self._attention_layer_norm(key_value)
       target_tensor = input_tensor[:, 0:self._output_range, :]
       if attention_mask is not None:
         attention_mask = attention_mask[:, 0:self._output_range, :]
@@ -250,10 +268,14 @@ class TransformerEncoderBlock(tf.keras.layers.Layer):
       if self._norm_first:
         source_tensor = input_tensor
         input_tensor = self._attention_layer_norm(input_tensor)
+        if key_value is not None:
+          key_value = self._attention_layer_norm(key_value)
       target_tensor = input_tensor
 
+    if key_value is None:
+      key_value = input_tensor
     attention_output = self._attention_layer(
-        query=target_tensor, value=input_tensor, attention_mask=attention_mask)
+        query=target_tensor, value=key_value, attention_mask=attention_mask)
     attention_output = self._attention_dropout(attention_output)
     if self._norm_first:
       attention_output = source_tensor + attention_output

official/nlp/keras_nlp/layers/transformer_encoder_block_test.py

@@ -55,18 +55,6 @@ class TransformerEncoderBlockLayerTest(keras_parameterized.TestCase):
     # 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())
 
-  def test_layer_creation_with_incorrect_mask_fails(self, transformer_cls):
-    test_layer = transformer_cls(
-        num_attention_heads=10, inner_dim=2048, inner_activation='relu')
-    sequence_length = 21
-    width = 80
-    # 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 - 3))
-    with self.assertRaisesRegex(ValueError, 'When passing a mask tensor.*'):
-      _ = test_layer([data_tensor, mask_tensor])
-
   def test_layer_invocation(self, transformer_cls):
     test_layer = transformer_cls(
         num_attention_heads=10, inner_dim=2048, inner_activation='relu')
@@ -249,6 +237,20 @@ class TransformerEncoderBlockLayerTest(keras_parameterized.TestCase):
 
     self.assertAllEqual([1, input_length, width], output_data.shape)
 
+  def test_separate_qkv(self, transformer_cls):
+    test_layer = transformer_cls(
+        num_attention_heads=2,
+        inner_dim=128,
+        inner_activation='relu',
+        kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02))
+    # Forward path.
+    q_tensor = tf.zeros([2, 4, 16], dtype=tf.float32)
+    kv_tensor = tf.zeros([2, 8, 16], dtype=tf.float32)
+    dummy_mask = tf.zeros([2, 4, 8], dtype=tf.float32)
+    inputs = [q_tensor, kv_tensor, dummy_mask]
+    output = test_layer(inputs)
+    self.assertEqual(output.shape, q_tensor.shape)
+
 
 @keras_parameterized.run_all_keras_modes
 class TransformerArgumentTest(keras_parameterized.TestCase):

official/nlp/modeling/layers/transformer.py

@@ -77,7 +77,7 @@ class Transformer(keras_nlp.layers.TransformerEncoderBlock):
                intermediate_dropout=0.0,
                attention_initializer=None,
                **kwargs):
-    super(Transformer, self).__init__(
+    super().__init__(
         num_attention_heads=num_attention_heads,
         inner_dim=intermediate_size,
         inner_activation=intermediate_activation,
@@ -105,7 +105,7 @@ class CompiledTransformer(Transformer):
 
   @tf_function_if_eager(experimental_compile=True)
   def call(self, inputs):
-    return super(CompiledTransformer, self).call(inputs)
+    return super().call(inputs)
 
 
 @tf.keras.utils.register_keras_serializable(package="Text")

orbit/__init__.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/controller.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/controller_test.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/runner.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/standard_runner.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/standard_runner_test.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/utils/__init__.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/utils/common.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/utils/common_test.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/utils/epoch_helper.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/utils/loop_fns.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/utils/summary_manager.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/utils/tpu_summaries.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.

orbit/utils/tpu_summaries_test.py

-# Copyright 2020 The Orbit Authors. All Rights Reserved.
+# Copyright 2021 The Orbit 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.