chore: import upstream snapshot with attribution

test/legacy_test/test_sparse_model.py

@@ -0,0 +1,73 @@
+# Copyright (c) 2022 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.
+
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle.sparse import nn
+
+
+class TestGradientAdd(unittest.TestCase):
+    def sparse(self, sp_x):
+        identity = sp_x
+        out = nn.functional.relu(sp_x)
+        values = out.values() + identity.values()
+        out = paddle.sparse.sparse_coo_tensor(
+            out.indices(),
+            values,
+            shape=out.shape,
+            stop_gradient=out.stop_gradient,
+        )
+        return out
+
+    def dense(self, x):
+        identity = x
+        out = paddle.nn.functional.relu(x)
+        out = out + identity
+        return out
+
+    def test(self):
+        x = paddle.randn((3, 3))
+        sparse_x = x.to_sparse_coo(sparse_dim=2)
+
+        x.stop_gradient = False
+        sparse_x.stop_gradient = False
+
+        dense_out = self.dense(x)
+        loss = dense_out.mean()
+        loss.backward(retain_graph=True)
+
+        sparse_out = self.sparse(sparse_x)
+        sparse_loss = sparse_out.values().mean()
+        sparse_loss.backward(retain_graph=True)
+
+        np.testing.assert_allclose(
+            dense_out.numpy(), sparse_out.to_dense().numpy()
+        )
+        np.testing.assert_allclose(
+            x.grad.numpy(), sparse_x.grad.to_dense().numpy()
+        )
+
+        loss.backward()
+        sparse_loss.backward()
+
+        np.testing.assert_allclose(
+            x.grad.numpy(), sparse_x.grad.to_dense().numpy()
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()