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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 softmax layer with optional masking.""" | |
| # 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 MaskedSoftmax(tf.keras.layers.Layer): | |
| """Performs a softmax with optional masking on a tensor. | |
| Arguments: | |
| mask_expansion_axes: Any axes that should be padded on the mask tensor. | |
| normalization_axes: On which axes the softmax should perform. | |
| """ | |
| def __init__(self, | |
| mask_expansion_axes=None, | |
| normalization_axes=None, | |
| **kwargs): | |
| self._mask_expansion_axes = mask_expansion_axes | |
| if normalization_axes is None: | |
| self._normalization_axes = (-1,) | |
| else: | |
| self._normalization_axes = normalization_axes | |
| super(MaskedSoftmax, self).__init__(**kwargs) | |
| def call(self, scores, mask=None): | |
| if mask is not None: | |
| for _ in range(len(scores.shape) - len(mask.shape)): | |
| mask = tf.expand_dims(mask, axis=self._mask_expansion_axes) | |
| # Since attention_mask is 1.0 for positions we want to attend and 0.0 for | |
| # masked positions, this operation will create a tensor which is 0.0 for | |
| # positions we want to attend and -10000.0 for masked positions. | |
| adder = (1.0 - tf.cast(mask, scores.dtype)) * -10000.0 | |
| # Since we are adding it to the raw scores before the softmax, this is | |
| # effectively the same as removing these entirely. | |
| scores += adder | |
| if len(self._normalization_axes) == 1: | |
| return tf.nn.softmax(scores, axis=self._normalization_axes[0]) | |
| else: | |
| return tf.math.exp(scores - tf.math.reduce_logsumexp( | |
| scores, axis=self._normalization_axes, keepdims=True)) | |
| def get_config(self): | |
| config = { | |
| 'mask_expansion_axes': self._mask_expansion_axes, | |
| 'normalization_axes': self._normalization_axes | |
| } | |
| base_config = super(MaskedSoftmax, self).get_config() | |
| return dict(list(base_config.items()) + list(config.items())) | |