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# Copyright 2020 The HuggingFace Team. 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.
import unittest
import numpy as np
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers.activations_tf import get_tf_activation
@require_tf
class TestTFActivations(unittest.TestCase):
def test_gelu_10(self):
x = tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0])
gelu = get_tf_activation("gelu")
gelu10 = get_tf_activation("gelu_10")
y_gelu = gelu(x)
y_gelu_10 = gelu10(x)
clipped_mask = tf.where(y_gelu_10 < 10.0, 1.0, 0.0)
self.assertEqual(tf.math.reduce_max(y_gelu_10).numpy().item(), 10.0)
self.assertTrue(np.allclose(y_gelu * clipped_mask, y_gelu_10 * clipped_mask))
def test_get_activation(self):
get_tf_activation("gelu")
get_tf_activation("gelu_10")
get_tf_activation("gelu_fast")
get_tf_activation("gelu_new")
get_tf_activation("glu")
get_tf_activation("mish")
get_tf_activation("quick_gelu")
get_tf_activation("relu")
get_tf_activation("sigmoid")
get_tf_activation("silu")
get_tf_activation("swish")
get_tf_activation("tanh")
with self.assertRaises(KeyError):
get_tf_activation("bogus")
with self.assertRaises(KeyError):
get_tf_activation(None)
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