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| # 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. | |
| # ============================================================================== | |
| """Keras-based one-hot embedding layer.""" | |
| # pylint: disable=g-classes-have-attributes | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| # from __future__ import google_type_annotations | |
| from __future__ import print_function | |
| import tensorflow as tf | |
| class OnDeviceEmbedding(tf.keras.layers.Layer): | |
| """Performs an embedding lookup suitable for accelerator devices. | |
| This layer uses either tf.gather or tf.one_hot to translate integer indices to | |
| float embeddings. | |
| Arguments: | |
| vocab_size: Number of elements in the vocabulary. | |
| embedding_width: Output size of the embedding layer. | |
| initializer: The initializer to use for the embedding weights. Defaults to | |
| "glorot_uniform". | |
| use_one_hot: Whether to use tf.one_hot over tf.gather for the embedding | |
| lookup. Defaults to False (that is, using tf.gather). Setting this option | |
| to True may improve performance, especially on small vocabulary sizes, but | |
| will generally require more memory. | |
| """ | |
| def __init__(self, | |
| vocab_size, | |
| embedding_width, | |
| initializer="glorot_uniform", | |
| use_one_hot=False, | |
| **kwargs): | |
| super(OnDeviceEmbedding, self).__init__(**kwargs) | |
| self._vocab_size = vocab_size | |
| self._embedding_width = embedding_width | |
| self._initializer = initializer | |
| self._use_one_hot = use_one_hot | |
| def get_config(self): | |
| config = { | |
| "vocab_size": self._vocab_size, | |
| "embedding_width": self._embedding_width, | |
| "initializer": self._initializer, | |
| "use_one_hot": self._use_one_hot, | |
| } | |
| base_config = super(OnDeviceEmbedding, self).get_config() | |
| return dict(list(base_config.items()) + list(config.items())) | |
| def build(self, input_shape): | |
| self.embeddings = self.add_weight( | |
| "embeddings", | |
| shape=[self._vocab_size, self._embedding_width], | |
| initializer=self._initializer, | |
| dtype=tf.float32) | |
| super(OnDeviceEmbedding, self).build(input_shape) | |
| def call(self, inputs): | |
| flat_inputs = tf.reshape(inputs, [-1]) | |
| if self._use_one_hot: | |
| one_hot_data = tf.one_hot( | |
| flat_inputs, depth=self._vocab_size, dtype=self.embeddings.dtype) | |
| embeddings = tf.matmul(one_hot_data, self.embeddings) | |
| else: | |
| embeddings = tf.gather(self.embeddings, flat_inputs) | |
| embeddings = tf.reshape( | |
| embeddings, | |
| # Work around b/142213824: prefer concat to shape over a Python list. | |
| tf.concat([tf.shape(inputs), [self._embedding_width]], axis=0)) | |
| embeddings.set_shape(inputs.shape.as_list() + [self._embedding_width]) | |
| return embeddings | |