Internal change

PiperOrigin-RevId: 328269066

official/nlp/keras_nlp/layers/transformer_encoder_block.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.
+# ==============================================================================
+"""Keras-based TransformerEncoder block layer."""
+
+# Import libraries
+import tensorflow as tf
+
+
+@tf.keras.utils.register_keras_serializable(package="Text")
+class TransformerEncoderBlock(tf.keras.layers.Layer):
+  """TransformerEncoderBlock layer.
+
+  This layer implements the Transformer Encoder from
+  "Attention Is All You Need". (https://arxiv.org/abs/1706.03762),
+  which combines a `tf.keras.layers.MultiHeadAttention` layer with a
+  two-layer feedforward network.
+
+  References:
+    [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
+    [BERT: Pre-training of Deep Bidirectional Transformers for Language
+     Understanding](https://arxiv.org/abs/1810.04805)
+  """
+
+  def __init__(self,
+               num_attention_heads,
+               inner_dim,
+               inner_activation,
+               output_range=None,
+               kernel_initializer="glorot_uniform",
+               bias_initializer="zeros",
+               kernel_regularizer=None,
+               bias_regularizer=None,
+               activity_regularizer=None,
+               kernel_constraint=None,
+               bias_constraint=None,
+               use_bias=True,
+               norm_first=False,
+               norm_epsilon=1e-12,
+               output_dropout=0.0,
+               attention_dropout=0.0,
+               inner_dropout=0.0,
+               attention_initializer=None,
+               **kwargs):
+    """Initializes `TransformerEncoderBlock`.
+
+    Arguments:
+      num_attention_heads: Number of attention heads.
+      inner_dim: The output dimension of the first Dense layer in a two-layer
+        feedforward network.
+      inner_activation: The activation for the first Dense layer in a two-layer
+        feedforward network.
+      output_range: the sequence output range, [0, output_range) for slicing the
+        target sequence. `None` means the target sequence is not sliced.
+      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.
+      use_bias: Whether to enable use_bias in attention layer. If set False,
+        use_bias in attention layer is disabled.
+      norm_first: Whether to normalize inputs to attention and intermediate
+        dense layers. If set False, output of attention and intermediate dense
+        layers is normalized.
+      norm_epsilon: Epsilon value to initialize normalization layers.
+      output_dropout: Dropout probability for the post-attention and output
+        dropout.
+      attention_dropout: Dropout probability for within the attention layer.
+      inner_dropout: Dropout probability for the first Dense layer in a
+        two-layer feedforward network.
+      attention_initializer: Initializer for kernels of attention layers. If set
+        `None`, attention layers use kernel_initializer as initializer for
+        kernel.
+      **kwargs: keyword arguments/
+    """
+    super(TransformerEncoderBlock, self).__init__(**kwargs)
+
+    self._num_heads = num_attention_heads
+    self._inner_dim = inner_dim
+    self._inner_activation = inner_activation
+    self._attention_dropout = attention_dropout
+    self._output_dropout = output_dropout
+    self._output_range = output_range
+    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._activity_regularizer = tf.keras.regularizers.get(activity_regularizer)
+    self._kernel_constraint = tf.keras.constraints.get(kernel_constraint)
+    self._bias_constraint = tf.keras.constraints.get(bias_constraint)
+    self._use_bias = use_bias
+    self._norm_first = norm_first
+    self._norm_epsilon = norm_epsilon
+    self._inner_dropout = inner_dropout
+    if attention_initializer:
+      self._attention_initializer = tf.keras.initializers.get(
+          attention_initializer)
+    else:
+      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 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))
+    if hidden_size % self._num_heads != 0:
+      raise ValueError(
+          "The input size (%d) is not a multiple of the number of attention "
+          "heads (%d)" % (hidden_size, self._num_heads))
+    self._attention_head_size = int(hidden_size // self._num_heads)
+    common_kwargs = dict(
+        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)
+    self._attention_layer = tf.keras.layers.MultiHeadAttention(
+        num_heads=self._num_heads,
+        key_dim=self._attention_head_size,
+        dropout=self._attention_dropout,
+        use_bias=self._use_bias,
+        kernel_initializer=self._attention_initializer,
+        name="self_attention",
+        **common_kwargs)
+    self._attention_dropout = tf.keras.layers.Dropout(rate=self._output_dropout)
+    # Use float32 in layernorm for numeric stability.
+    # It is probably safe in mixed_float16, but we haven't validated this yet.
+    self._attention_layer_norm = (
+        tf.keras.layers.LayerNormalization(
+            name="self_attention_layer_norm",
+            axis=-1,
+            epsilon=self._norm_epsilon,
+            dtype=tf.float32))
+    self._inner_dense = tf.keras.layers.experimental.EinsumDense(
+        "abc,cd->abd",
+        output_shape=(None, self._inner_dim),
+        bias_axes="d",
+        kernel_initializer=self._kernel_initializer,
+        name="intermediate",
+        **common_kwargs)
+    policy = tf.keras.mixed_precision.experimental.global_policy()
+    if policy.name == "mixed_bfloat16":
+      # bfloat16 causes BERT with the LAMB optimizer to not converge
+      # as well, so we use float32.
+      # TODO(b/154538392): Investigate this.
+      policy = tf.float32
+    self._inner_activation_layer = tf.keras.layers.Activation(
+        self._inner_activation, dtype=policy)
+    self._inner_dropout_layer = tf.keras.layers.Dropout(
+        rate=self._inner_dropout)
+    self._output_dense = tf.keras.layers.experimental.EinsumDense(
+        "abc,cd->abd",
+        output_shape=(None, hidden_size),
+        bias_axes="d",
+        name="output",
+        kernel_initializer=self._kernel_initializer,
+        **common_kwargs)
+    self._output_dropout = tf.keras.layers.Dropout(rate=self._output_dropout)
+    # Use float32 in layernorm for numeric stability.
+    self._output_layer_norm = tf.keras.layers.LayerNormalization(
+        name="output_layer_norm",
+        axis=-1,
+        epsilon=self._norm_epsilon,
+        dtype=tf.float32)
+
+    super(TransformerEncoderBlock, self).build(input_shape)
+
+  def get_config(self):
+    config = {
+        "num_attention_heads":
+            self._num_heads,
+        "inner_dim":
+            self._inner_dim,
+        "inner_activation":
+            self._inner_activation,
+        "output_dropout":
+            self._output_dropout,
+        "attention_dropout":
+            self._attention_dropout,
+        "output_range":
+            self._output_range,
+        "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),
+        "use_bias":
+            self._use_bias,
+        "norm_first":
+            self._norm_first,
+        "norm_epsilon":
+            self._norm_epsilon,
+        "inner_dropout":
+            self._inner_dropout,
+        "attention_initializer":
+            tf.keras.initializers.serialize(self._attention_initializer)
+    }
+    base_config = super(TransformerEncoderBlock, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))
+
+  def call(self, inputs):
+    if isinstance(inputs, (list, tuple)) and len(inputs) == 2:
+      input_tensor, attention_mask = inputs
+    else:
+      input_tensor, attention_mask = (inputs, None)
+
+    if self._output_range:
+      target_tensor = input_tensor[:, 0:self._output_range, :]
+      attention_mask = attention_mask[:, 0:self._output_range, :]
+    else:
+      if self._norm_first:
+        source_tensor = input_tensor
+        input_tensor = self._attention_layer_norm(input_tensor)
+      target_tensor = input_tensor
+
+    attention_output = self._attention_layer(
+        query=target_tensor, value=input_tensor, attention_mask=attention_mask)
+    attention_output = self._attention_dropout(attention_output)
+    if self._norm_first:
+      attention_output = source_tensor + attention_output
+    else:
+      attention_output = self._attention_layer_norm(target_tensor +
+                                                    attention_output)
+    if self._norm_first:
+      source_attention_output = attention_output
+      attention_output = self._output_layer_norm(attention_output)
+    inner_output = self._inner_dense(attention_output)
+    inner_output = self._inner_activation_layer(inner_output)
+    inner_output = self._inner_dropout_layer(inner_output)
+    layer_output = self._output_dense(inner_output)
+    layer_output = self._output_dropout(layer_output)
+
+    if self._norm_first:
+      return source_attention_output + layer_output
+
+    # During mixed precision training, layer norm output is always fp32 for now.
+    # Casts fp32 for the subsequent add.
+    layer_output = tf.cast(layer_output, tf.float32)
+    return self._output_layer_norm(layer_output + attention_output)

official/nlp/keras_nlp/layers/transformer_encoder_block_test.py

+# Copyright 2019 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.
+# ==============================================================================
+"""Tests for Keras-based transformer block layer."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Import libraries
+from absl.testing import parameterized
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.python.keras import keras_parameterized  # pylint: disable=g-direct-tensorflow-import
+from official.nlp.keras_nlp.layers.transformer_encoder_block import TransformerEncoderBlock
+
+
+@keras_parameterized.run_all_keras_modes
+@parameterized.named_parameters(
+    ('base', TransformerEncoderBlock))
+class TransformerEncoderBlockLayerTest(keras_parameterized.TestCase):
+
+  def tearDown(self):
+    super(TransformerEncoderBlockLayerTest, self).tearDown()
+    tf.keras.mixed_precision.experimental.set_policy('float32')
+
+  def test_layer_creation(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))
+    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())
+
+  def test_layer_creation_with_mask(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))
+    output_tensor = test_layer([data_tensor, mask_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())
+
+  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')
+    sequence_length = 21
+    width = 80
+    # 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)
+
+    # Create a model from the test layer.
+    model = tf.keras.Model(data_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))
+    _ = model.predict(input_data)
+
+  def test_layer_invocation_with_mask(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))
+    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])
+
+  def test_layer_output_range(self, transformer_cls):
+    test_layer = transformer_cls(
+        num_attention_heads=10, inner_dim=2048, inner_activation='relu')
+    sequence_length = 21
+    width = 80
+
+    batch_size = 6
+    input_data = 10 * np.random.random_sample(
+        (batch_size, sequence_length, width))
+    mask_data = np.random.randint(
+        2, size=(batch_size, sequence_length, sequence_length))
+    output_tensor = test_layer([input_data, mask_data])
+
+    # The layer only attends to the first token and outputs the first token
+    # embeeding.
+    new_layer = transformer_cls(
+        num_attention_heads=10,
+        inner_dim=2048,
+        inner_activation='relu',
+        output_range=1)
+    _ = new_layer([input_data, mask_data])
+    new_layer.set_weights(test_layer.get_weights())
+    new_output_tensor = new_layer([input_data, mask_data])
+    self.assertAllClose(
+        new_output_tensor, output_tensor[:, 0:1, :], atol=5e-5, rtol=0.003)
+
+  def test_layer_invocation_with_float16_dtype(self, transformer_cls):
+    tf.keras.mixed_precision.experimental.set_policy('mixed_float16')
+    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))
+    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])
+
+  def test_transform_with_initializer(self, transformer_cls):
+    test_layer = transformer_cls(
+        num_attention_heads=10,
+        inner_dim=2048,
+        inner_activation='relu',
+        kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02))
+    sequence_length = 21
+    width = 80
+    # Create a 3-dimensional input (the first dimension is implicit).
+    data_tensor = tf.keras.Input(shape=(sequence_length, width))
+    output = 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.shape.as_list())
+
+  def test_dynamic_layer_sequence(self, transformer_cls):
+    test_layer = transformer_cls(
+        num_attention_heads=10,
+        inner_dim=2048,
+        inner_activation='relu',
+        kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02))
+    # Create a 3-dimensional input (the first dimension is implicit).
+    width = 30
+    input_tensor = tf.keras.Input(shape=(None, width))
+    output_tensor = test_layer(input_tensor)
+    model = tf.keras.Model(input_tensor, output_tensor)
+
+    input_length = 17
+    input_data = np.ones((1, input_length, width))
+    output_data = model.predict(input_data)
+
+    self.assertAllEqual([1, input_length, width], output_data.shape)
+
+
+@keras_parameterized.run_all_keras_modes
+class TransformerArgumentTest(keras_parameterized.TestCase):
+
+  def test_use_bias_norm_first(self):
+    num_attention_heads = 2
+    hidden_size = 16
+    encoder_block = TransformerEncoderBlock(
+        num_attention_heads=num_attention_heads,
+        inner_dim=32,
+        inner_activation='relu',
+        output_dropout=0.1,
+        attention_dropout=0.1,
+        use_bias=False,
+        norm_first=True,
+        norm_epsilon=1e-6,
+        inner_dropout=0.1,
+        attention_initializer=tf.keras.initializers.RandomUniform(
+            minval=0., maxval=1.))
+    # Forward path.
+    dummy_tensor = tf.zeros([2, 4, 16], dtype=tf.float32)
+    dummy_mask = tf.zeros([2, 4, 4], dtype=tf.float32)
+    inputs = [dummy_tensor, dummy_mask]
+    output = encoder_block(inputs)
+    self.assertEqual(output.shape, (2, 4, hidden_size))
+
+  def test_get_config(self):
+    num_attention_heads = 2
+    encoder_block = TransformerEncoderBlock(
+        num_attention_heads=num_attention_heads,
+        inner_dim=32,
+        inner_activation='relu',
+        output_dropout=0.1,
+        attention_dropout=0.1,
+        use_bias=False,
+        norm_first=True,
+        norm_epsilon=1e-6,
+        inner_dropout=0.1,
+        attention_initializer=tf.keras.initializers.RandomUniform(
+            minval=0., maxval=1.))
+    encoder_block_config = encoder_block.get_config()
+    new_encoder_block = TransformerEncoderBlock.from_config(
+        encoder_block_config)
+    self.assertEqual(encoder_block_config, new_encoder_block.get_config())
+
+
+def _create_cache(batch_size, init_decode_length, num_heads, head_size):
+  return {
+      'key':
+          tf.zeros([batch_size, init_decode_length, num_heads, head_size],
+                   dtype=tf.float32),
+      'value':
+          tf.zeros([batch_size, init_decode_length, num_heads, head_size],
+                   dtype=tf.float32)
+  }
+
+
+if __name__ == '__main__':
+  tf.test.main()