wehub-resource-sync
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
# Copyright (c) 2021 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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import unittest
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import numpy as np
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import paddle
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from paddle.distributed import fleet
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class TestDistTraining(unittest.TestCase):
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def setUp(self):
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strategy = fleet.DistributedStrategy()
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self.model_parallel_size = 2
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strategy.hybrid_configs = {
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"dp_degree": 1,
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"mp_degree": self.model_parallel_size,
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"pp_degree": 1,
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}
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fleet.init(is_collective=True, strategy=strategy)
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def test_cuda_rng_tracker(self):
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seed_1 = 2021
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seed_2 = 1024
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size = [20, 15]
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paddle.seed(seed_1)
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target_11 = paddle.randn(size, "float32")
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target_12 = paddle.randn(size, "float32")
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paddle.seed(seed_2)
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target_21 = paddle.randn(size, "float32")
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target_22 = paddle.randn(size, "float32")
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paddle.seed(seed_1)
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fleet.meta_parallel.get_rng_state_tracker().add("test", seed_2)
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result_11 = paddle.randn(size, "float32")
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with fleet.meta_parallel.get_rng_state_tracker().rng_state("test"):
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result_21 = paddle.randn(size, "float32")
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result_12 = paddle.randn(size, "float32")
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with fleet.meta_parallel.get_rng_state_tracker().rng_state("test"):
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result_22 = paddle.randn(size, "float32")
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np.testing.assert_allclose(result_11.numpy(), target_11.numpy())
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np.testing.assert_allclose(result_12.numpy(), target_12.numpy())
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np.testing.assert_allclose(result_21.numpy(), target_21.numpy())
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np.testing.assert_allclose(result_22.numpy(), target_22.numpy())
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if __name__ == '__main__':
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unittest.main()
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