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# Copyright 2023 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.
"""Tests for grad_utils."""
import tensorflow as tf, tf_keras
from official.modeling import grad_utils
from official.modeling import performance
class GradUtilsTest(tf.test.TestCase):
def test_minimize(self):
optimizer = tf_keras.optimizers.SGD(0.1)
with tf.GradientTape() as tape:
model = tf_keras.layers.Dense(2)
outputs = model(tf.zeros((2, 2), tf.float32))
loss = tf.reduce_mean(outputs)
grad_utils.minimize_using_explicit_allreduce(tape, optimizer, loss,
model.trainable_variables)
def test_minimize_fp16(self):
optimizer = performance.configure_optimizer(
tf_keras.optimizers.SGD(0.1), use_float16=True)
performance.set_mixed_precision_policy(tf.float16)
with tf.GradientTape() as tape:
model = tf_keras.layers.Dense(2)
outputs = model(tf.zeros((2, 2), tf.float16))
loss = tf.reduce_mean(outputs)
grad_utils.minimize_using_explicit_allreduce(tape, optimizer, loss,
model.trainable_variables)
# Test other fp16 settings.
def _clip_by_global_norm(grads_and_vars):
grads, tvars = list(zip(*grads_and_vars))
(grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)
return zip(grads, tvars)
with tf.GradientTape() as tape:
model = tf_keras.layers.Dense(2)
outputs = model(tf.zeros((2, 2), tf.float16))
loss = tf.reduce_mean(outputs)
optimizer = performance.configure_optimizer(
tf_keras.optimizers.SGD(0.1), use_float16=True, loss_scale=128)
grad_utils.minimize_using_explicit_allreduce(
tape,
optimizer,
loss,
model.trainable_variables,
pre_allreduce_callbacks=[_clip_by_global_norm],
post_allreduce_callbacks=[_clip_by_global_norm])
def test_set_mixed_precision_policy(self):
performance.set_mixed_precision_policy(tf.float16)
performance.set_mixed_precision_policy(tf.bfloat16)
performance.set_mixed_precision_policy(tf.float32)
with self.assertRaises(ValueError):
performance.set_mixed_precision_policy(tf.int32)
if __name__ == '__main__':
tf.test.main()
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