wehub-resource-sync
58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
# Copyright (c) 2019 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 import base
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class TestImperativePartialBackward(unittest.TestCase):
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def test_partial_backward(self):
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with base.dygraph.guard():
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x = np.random.randn(2, 4, 5).astype("float32")
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x = paddle.to_tensor(x)
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linear1 = paddle.nn.Linear(5, 10)
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linear2 = paddle.nn.Linear(5, 10)
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y = linear1(x[:, :2])
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z = linear2(x[:, 2:])
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loss = paddle.mean(y)
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loss.backward()
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for param in linear1.parameters():
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self.assertIsNotNone(param._grad_ivar())
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for param in linear2.parameters():
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self.assertIsNone(param._grad_ivar())
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optimizer = paddle.optimizer.Adam(
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parameters=(linear1.parameters() + linear2.parameters())
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)
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_, params_grads = optimizer.minimize(loss)
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self.assertListEqual(
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sorted([p.name for p in linear1.parameters()]),
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sorted([p_g[0].name for p_g in params_grads]),
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)
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linear1.clear_gradients()
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linear2.clear_gradients()
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if __name__ == '__main__':
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unittest.main()
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