Implement the Vision TransformerScaffold which is a subclass from the NLP TransformerScaffold.

PiperOrigin-RevId: 480969429

Author: tensorflower-gardener

official/projects/vit/modeling/transformer_scaffold.py

@@ -0,0 +1,161 @@
+# Copyright 2022 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 Scaffold TransformerEncoder block for vision models.
+
+This implementation is subclassed from NLP TransformerScaffold to support
+customized `attention_layer` and `feedforward_layer`. In addition, this
+implementation has a few features to better support vision use cases:
+1. `stochastic_depth_drop_rate` to supress model overfitting.
+2. `return_attention_scores`, optionally returns the attention output.
+3. `ffn_has_residual_connection`, clearly define whether feedforward network has
+   residual connection or not to avoid ambiguity.
+"""
+from typing import List, Optional, Tuple, Union
+
+import gin
+import tensorflow as tf
+
+from official.nlp import modeling
+from official.vision.modeling.layers.nn_layers import StochasticDepth
+
+
+@tf.keras.utils.register_keras_serializable(package="Vision")
+@gin.configurable
+class TransformerScaffold(modeling.layers.TransformerScaffold):
+  """TransformerScaffold layer for vision applications.
+
+  This layer is a subclass of NLP TransformerScaffold:
+
+  Attributes:
+    stochastic_depth_drop_rate: Drop rate for the residual connections.
+    return_attention_scores: Optionally return the attention output.
+    ffn_has_residual_connection: Whether the feedforward network has internal
+      residual connection and layer norm. If False, the residual connection and
+      the layer norm op are called inside TransformerScaffold.
+  """
+
+  def __init__(self,
+               *args,
+               stochastic_depth_drop_rate: float = 0.0,
+               return_attention_scores: bool = False,
+               ffn_has_residual_connection: bool = False,
+               **kwargs):
+    """Initializes TransformerEncoderBlock."""
+    super().__init__(*args, **kwargs)
+    self._stochastic_depth_drop_rate = stochastic_depth_drop_rate
+    self._return_attention_scores = return_attention_scores
+    self._ffn_has_residual_connection = ffn_has_residual_connection
+
+  def build(self, input_shape: Union[tf.TensorShape, List[int]]):
+    if self._stochastic_depth_drop_rate:
+      self._stochastic_depth = StochasticDepth(self._stochastic_depth_drop_rate)
+    else:
+      self._stochastic_depth = lambda x, *args, **kwargs: tf.identity(x)
+
+    super().build(input_shape)
+
+  def get_config(self):
+    config = {"stochastic_depth_drop_rate": self._stochastic_depth_drop_rate,
+              "return_attention_scores": self._return_attention_scores,
+              "ffn_has_residual_connection": self._ffn_has_residual_connection}
+    base_config = super().get_config()
+    base_config.update(config)
+    return base_config
+
+  def call(
+      self,
+      inputs: tf.Tensor,
+      training: Optional[bool] = None
+  ) -> Union[tf.Tensor, Tuple[tf.Tensor, tf.Tensor]]:
+    """Transformer self-attention encoder block call."""
+    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, key_value, attention_mask = (inputs, None, None)
+
+    if key_value is None:
+      key_value = input_tensor
+
+    if self._norm_first:
+      source_tensor = input_tensor
+      input_tensor = self._attention_layer_norm(input_tensor, training=training)
+
+    attention_layer_output = self._attention_layer(
+        query=input_tensor,
+        value=key_value,
+        attention_mask=attention_mask,
+        training=training,
+        return_attention_scores=self._return_attention_scores)
+    if isinstance(attention_layer_output, tuple):
+      # `attention_layer_output` contains two tensors when
+      # `return_attention_scores` is True.
+      attention_output, attention_scores = attention_layer_output
+    else:
+      attention_output = attention_layer_output
+    attention_output = self._attention_dropout(attention_output,
+                                               training=training)
+
+    if self._norm_first:
+      source_attention_output = source_tensor + self._stochastic_depth(
+          attention_output, training=training)
+      attention_output = self._output_layer_norm(source_attention_output,
+                                                 training=training)
+    else:
+      attention_output = self._attention_layer_norm(
+          input_tensor +
+          self._stochastic_depth(attention_output, training=training),
+          training=training)
+
+    if self._feedforward_block is None:
+      intermediate_output = self._intermediate_dense(attention_output)
+      intermediate_output = self._intermediate_activation_layer(
+          intermediate_output)
+      layer_output = self._output_dense(intermediate_output, training=training)
+      layer_output = self._output_dropout(layer_output, training=training)
+    else:
+      layer_output = self._feedforward_block(attention_output,
+                                             training=training)
+
+    # 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)
+
+    if self._norm_first:
+      if self._ffn_has_residual_connection:
+        raise ValueError(
+            "In the case of `norm_first`, the residual connection should be"
+            "done in the TransformerScaffold call function, not FFN's"
+            "call function.")
+      output = source_attention_output + self._stochastic_depth(
+          layer_output, training=training)
+    else:
+      if self._ffn_has_residual_connection:
+        output = self._stochastic_depth(layer_output, training=training)
+      else:
+        output = self._output_layer_norm(
+            attention_output + self._stochastic_depth(
+                layer_output, training=training))
+
+    if self._return_attention_scores:
+      return output, attention_scores
+    else:
+      return output