179 lines
7.2 KiB
Python
179 lines
7.2 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Additive attention layer that can be used in sequence DNN/CNN models.
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This file follows the terminology of https://arxiv.org/abs/1706.03762 Figure 2.
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Attention is formed by three tensors: Query, Key and Value.
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"""
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import tensorflow.compat.v2 as tf
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from keras.layers.attention.base_dense_attention import BaseDenseAttention
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.AdditiveAttention")
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class AdditiveAttention(BaseDenseAttention):
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"""Additive attention layer, a.k.a. Bahdanau-style attention.
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Inputs are `query` tensor of shape `[batch_size, Tq, dim]`, `value` tensor
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of shape `[batch_size, Tv, dim]` and `key` tensor of shape
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`[batch_size, Tv, dim]`. The calculation follows the steps:
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1. Reshape `query` and `key` into shapes `[batch_size, Tq, 1, dim]`
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and `[batch_size, 1, Tv, dim]` respectively.
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2. Calculate scores with shape `[batch_size, Tq, Tv]` as a non-linear
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sum: `scores = tf.reduce_sum(tf.tanh(query + key), axis=-1)`
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3. Use scores to calculate a distribution with shape
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`[batch_size, Tq, Tv]`: `distribution = tf.nn.softmax(scores)`.
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4. Use `distribution` to create a linear combination of `value` with
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shape `[batch_size, Tq, dim]`:
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`return tf.matmul(distribution, value)`.
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Args:
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use_scale: If `True`, will create a variable to scale the attention
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scores.
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dropout: Float between 0 and 1. Fraction of the units to drop for the
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attention scores. Defaults to 0.0.
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Call Args:
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inputs: List of the following tensors:
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* query: Query `Tensor` of shape `[batch_size, Tq, dim]`.
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* value: Value `Tensor` of shape `[batch_size, Tv, dim]`.
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* key: Optional key `Tensor` of shape `[batch_size, Tv, dim]`. If not
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given, will use `value` for both `key` and `value`, which is the
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most common case.
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mask: List of the following tensors:
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* query_mask: A boolean mask `Tensor` of shape `[batch_size, Tq]`.
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If given, the output will be zero at the positions where
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`mask==False`.
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* value_mask: A boolean mask `Tensor` of shape `[batch_size, Tv]`.
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If given, will apply the mask such that values at positions where
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`mask==False` do not contribute to the result.
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training: Python boolean indicating whether the layer should behave in
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training mode (adding dropout) or in inference mode (no dropout).
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return_attention_scores: bool, it `True`, returns the attention scores
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(after masking and softmax) as an additional output argument.
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use_causal_mask: Boolean. Set to `True` for decoder self-attention. Adds a
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mask such that position `i` cannot attend to positions `j > i`. This
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prevents the flow of information from the future towards the past.
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Defaults to `False`.`
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Output:
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Attention outputs of shape `[batch_size, Tq, dim]`.
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[Optional] Attention scores after masking and softmax with shape
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`[batch_size, Tq, Tv]`.
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The meaning of `query`, `value` and `key` depend on the application. In the
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case of text similarity, for example, `query` is the sequence embeddings of
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the first piece of text and `value` is the sequence embeddings of the second
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piece of text. `key` is usually the same tensor as `value`.
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Here is a code example for using `AdditiveAttention` in a CNN+Attention
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network:
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```python
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# Variable-length int sequences.
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query_input = tf.keras.Input(shape=(None,), dtype='int32')
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value_input = tf.keras.Input(shape=(None,), dtype='int32')
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# Embedding lookup.
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token_embedding = tf.keras.layers.Embedding(max_tokens, dimension)
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# Query embeddings of shape [batch_size, Tq, dimension].
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query_embeddings = token_embedding(query_input)
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# Value embeddings of shape [batch_size, Tv, dimension].
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value_embeddings = token_embedding(value_input)
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# CNN layer.
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cnn_layer = tf.keras.layers.Conv1D(
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filters=100,
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kernel_size=4,
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# Use 'same' padding so outputs have the same shape as inputs.
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padding='same')
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# Query encoding of shape [batch_size, Tq, filters].
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query_seq_encoding = cnn_layer(query_embeddings)
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# Value encoding of shape [batch_size, Tv, filters].
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value_seq_encoding = cnn_layer(value_embeddings)
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# Query-value attention of shape [batch_size, Tq, filters].
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query_value_attention_seq = tf.keras.layers.AdditiveAttention()(
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[query_seq_encoding, value_seq_encoding])
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# Reduce over the sequence axis to produce encodings of shape
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# [batch_size, filters].
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query_encoding = tf.keras.layers.GlobalAveragePooling1D()(
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query_seq_encoding)
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query_value_attention = tf.keras.layers.GlobalAveragePooling1D()(
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query_value_attention_seq)
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# Concatenate query and document encodings to produce a DNN input layer.
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input_layer = tf.keras.layers.Concatenate()(
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[query_encoding, query_value_attention])
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# Add DNN layers, and create Model.
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# ...
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```
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"""
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def __init__(self, use_scale=True, **kwargs):
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super().__init__(**kwargs)
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self.use_scale = use_scale
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def build(self, input_shape):
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v_shape = tf.TensorShape(input_shape[1])
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dim = v_shape[-1]
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dim = tf.compat.dimension_value(dim)
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if self.use_scale:
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self.scale = self.add_weight(
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name="scale",
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shape=[dim],
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initializer="glorot_uniform",
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dtype=self.dtype,
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trainable=True,
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)
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else:
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self.scale = None
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super().build(input_shape)
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def _calculate_scores(self, query, key):
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"""Calculates attention scores as a nonlinear sum of query and key.
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Args:
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query: Query tensor of shape `[batch_size, Tq, dim]`.
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key: Key tensor of shape `[batch_size, Tv, dim]`.
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Returns:
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Tensor of shape `[batch_size, Tq, Tv]`.
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"""
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# Reshape tensors to enable broadcasting.
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# Reshape into [batch_size, Tq, 1, dim].
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q_reshaped = tf.expand_dims(query, axis=-2)
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# Reshape into [batch_size, 1, Tv, dim].
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k_reshaped = tf.expand_dims(key, axis=-3)
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if self.use_scale:
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scale = self.scale
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else:
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scale = 1.0
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return tf.reduce_sum(scale * tf.tanh(q_reshaped + k_reshaped), axis=-1)
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def get_config(self):
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config = {"use_scale": self.use_scale}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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