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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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
import numpy as np
import tensorflow as tf
from official.nlp.xlnet import xlnet_modeling
class PositionalEmbeddingLayerTest(tf.test.TestCase):
def test_positional_embedding(self):
"""A low-dimensional example is tested.
With len(pos_seq)=2 and d_model=4:
pos_seq = [[1.], [0.]]
inv_freq = [1., 0.01]
pos_seq x inv_freq = [[1, 0.01], [0., 0.]]
pos_emb = [[sin(1.), sin(0.01), cos(1.), cos(0.01)],
[sin(0.), sin(0.), cos(0.), cos(0.)]]
= [[0.84147096, 0.00999983, 0.54030228, 0.99994999],
[0., 0., 1., 1.]]
"""
target = np.array([[[0.84147096, 0.00999983, 0.54030228, 0.99994999]],
[[0., 0., 1., 1.]]])
d_model = 4
pos_seq = tf.range(1, -1, -1.0) # [1., 0.]
pos_emb_layer = xlnet_modeling.PositionalEmbedding(d_model)
pos_emb = pos_emb_layer(pos_seq, batch_size=None).numpy().astype(float)
logging.info(pos_emb)
self.assertAllClose(pos_emb, target)
if __name__ == "__main__":
tf.test.main()
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