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Paddle/python/paddle/fluid/tests/unittests/test_collective_optimizer.py

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3.0 KiB

# Copyright (c) 2020 PaddlePaddle 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.
# 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 print_function
import unittest
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.collective import CollectiveOptimizer, DistributedStrategy
class CollectiveOptimizerTest(unittest.TestCase):
def test_ds_as_None(self):
optimizer = fluid.optimizer.AdamOptimizer()
dist_optimizer = CollectiveOptimizer(optimizer, strategy=None)
def test_recompute_checkpoints(self):
optimizer = fluid.optimizer.AdamOptimizer()
dist_strategy = DistributedStrategy()
dist_strategy.forward_recompute = True
dist_strategy.recompute_checkpoints = "NoneListTest"
self.assertRaises(ValueError, CollectiveOptimizer, optimizer,
dist_strategy)
dist_strategy.recompute_checkpoints = []
dist_optimizer = CollectiveOptimizer(optimizer, dist_strategy)
self.assertRaises(ValueError, dist_optimizer.minimize, None)
def test_recompute_strategy(self):
optimizer = fluid.optimizer.AdamOptimizer()
optimizer = fluid.optimizer.RecomputeOptimizer(optimizer)
dist_strategy = DistributedStrategy()
dist_strategy.forward_recompute = True
dist_strategy.recompute_checkpoints = ["Test"]
dist_optimizer = CollectiveOptimizer(optimizer, strategy=dist_strategy)
self.assertRaises(ValueError, dist_optimizer.minimize, None)
def test_amp_strategy(self):
optimizer = fluid.optimizer.AdamOptimizer()
optimizer = fluid.contrib.mixed_precision.decorate(
optimizer, init_loss_scaling=1.0, use_dynamic_loss_scaling=True)
dist_strategy = DistributedStrategy()
dist_strategy.use_amp = True
dist_optimizer = CollectiveOptimizer(optimizer, strategy=dist_strategy)
self.assertRaises(ValueError, dist_optimizer.minimize, None)
if __name__ == '__main__':
unittest.main()