forked from tensorflow/models
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathself_attention_mask.py
39 lines (31 loc) · 1.36 KB
/
self_attention_mask.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
# Copyright 2021 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 layer that creates a self-attention mask."""
import tensorflow as tf
from official.nlp.keras_nlp import layers
@tf.keras.utils.register_keras_serializable(package='Text')
class SelfAttentionMask(layers.SelfAttentionMask):
"""Creates 3D attention mask from a 2D tensor mask.
**Warning: Please use the `keras_nlp.layers.SelfAttentionMask`.**
inputs[0]: from_tensor: 2D or 3D Tensor of shape
`(batch_size, from_seq_length, ...)`.
inputs[1]: to_mask: int32 Tensor of shape `(batch_size, to_seq_length)`.
Returns:
Float Tensor of shape `(batch_size, from_seq_length, to_seq_length)`.
"""
def call(self, inputs):
if isinstance(inputs, list):
return super().call(inputs[0], inputs[1])
else:
return super().call(inputs)