wehub-resource-sync
76 lines
2.9 KiB
Python
76 lines
2.9 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle
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from paddle.incubate.distributed.fleet.collective import (
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CollectiveOptimizer,
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DistributedStrategy,
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)
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class CollectiveOptimizerTest(unittest.TestCase):
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def test_ds_as_None(self):
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optimizer = paddle.optimizer.Adam()
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dist_optimizer = CollectiveOptimizer(optimizer, strategy=None)
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def test_recompute_checkpoints(self):
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optimizer = paddle.optimizer.Adam()
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dist_strategy = DistributedStrategy()
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dist_strategy.forward_recompute = True
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dist_strategy.recompute_checkpoints = "NoneListTest"
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self.assertRaises(
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ValueError, CollectiveOptimizer, optimizer, dist_strategy
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)
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dist_strategy.recompute_checkpoints = []
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dist_optimizer = CollectiveOptimizer(optimizer, dist_strategy)
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self.assertRaises(ValueError, dist_optimizer.minimize, None)
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def test_recompute_strategy(self):
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optimizer = paddle.optimizer.Adam()
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optimizer = paddle.incubate.optimizer.RecomputeOptimizer(optimizer)
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dist_strategy = DistributedStrategy()
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dist_strategy.forward_recompute = True
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dist_strategy.recompute_checkpoints = ["Test"]
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dist_optimizer = CollectiveOptimizer(optimizer, strategy=dist_strategy)
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self.assertRaises(ValueError, dist_optimizer.minimize, None)
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def test_amp_strategy(self):
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optimizer = paddle.optimizer.Adam()
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optimizer = paddle.static.amp.decorate(
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optimizer, init_loss_scaling=1.0, use_dynamic_loss_scaling=True
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)
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dist_strategy = DistributedStrategy()
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dist_strategy.use_amp = True
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dist_optimizer = CollectiveOptimizer(optimizer, strategy=dist_strategy)
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self.assertRaises(ValueError, dist_optimizer.minimize, None)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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