chore: import upstream snapshot with attribution

test/dygraph_to_static/test_gradient_aggregation.py

@@ -0,0 +1,65 @@
+# 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
+from dygraph_to_static_utils import (
+    Dy2StTestBase,
+)
+
+import paddle
+
+SEED = 2020
+np.random.seed(SEED)
+
+
+class SimpleNet(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.linear1 = paddle.nn.Linear(10, 3)
+        self.linear2 = paddle.nn.Linear(3, 1)
+
+    def forward(self, x):
+        out1 = self.linear1(x)
+        out2 = self.linear2(out1)
+        return [out1, out2]  # 梯度为0
+        # return [out1]        # 梯度正常
+        # return [out2, out1] # 梯度正常
+
+
+class TestGradientAggregationInDy2Static(Dy2StTestBase):
+    def test_to_static(self):
+        def simplenet_grad(inp, to_static=False):
+            net = SimpleNet()
+            if to_static:
+                net = paddle.jit.to_static(net)
+            loss = net(inp)
+            loss[0].backward()
+            return net.linear1.weight.grad
+
+        inp = paddle.to_tensor(
+            np.random.randn(
+                10,
+            )
+        ).astype("float32")
+        np.testing.assert_allclose(
+            simplenet_grad(inp, True).numpy(),
+            simplenet_grad(inp, False).numpy(),
+            rtol=1e-05,
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()