wehub-resource-sync
74 lines
2.0 KiB
Python
74 lines
2.0 KiB
Python
# Copyright (c) 2022 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.sparse import nn
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class TestGradientAdd(unittest.TestCase):
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def sparse(self, sp_x):
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identity = sp_x
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out = nn.functional.relu(sp_x)
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values = out.values() + identity.values()
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out = paddle.sparse.sparse_coo_tensor(
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out.indices(),
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values,
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shape=out.shape,
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stop_gradient=out.stop_gradient,
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)
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return out
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def dense(self, x):
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identity = x
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out = paddle.nn.functional.relu(x)
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out = out + identity
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return out
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def test(self):
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x = paddle.randn((3, 3))
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sparse_x = x.to_sparse_coo(sparse_dim=2)
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x.stop_gradient = False
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sparse_x.stop_gradient = False
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dense_out = self.dense(x)
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loss = dense_out.mean()
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loss.backward(retain_graph=True)
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sparse_out = self.sparse(sparse_x)
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sparse_loss = sparse_out.values().mean()
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sparse_loss.backward(retain_graph=True)
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np.testing.assert_allclose(
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dense_out.numpy(), sparse_out.to_dense().numpy()
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)
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np.testing.assert_allclose(
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x.grad.numpy(), sparse_x.grad.to_dense().numpy()
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)
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loss.backward()
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sparse_loss.backward()
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np.testing.assert_allclose(
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x.grad.numpy(), sparse_x.grad.to_dense().numpy()
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)
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if __name__ == "__main__":
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unittest.main()
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