chore: import upstream snapshot with attribution

test/dygraph_to_static/test_multi_forward.py

@@ -0,0 +1,78 @@
+#   Copyright (c) 2020 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
+
+from dygraph_to_static_utils import (
+    Dy2StTestBase,
+)
+
+import paddle
+
+
+class MyLayer(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.linear = paddle.nn.Linear(1, 1)
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+class TestBackward(Dy2StTestBase):
+    def test_order_0(self):
+        """
+        loss = 1 * w * 1 + 2 * w * 2
+        delta_w = 5
+        """
+        model = paddle.jit.to_static(
+            function=MyLayer(),
+            input_spec=[
+                paddle.static.InputSpec(
+                    shape=[None, None], dtype=paddle.float32
+                )
+            ],
+        )
+        model.clear_gradients()
+        inp = paddle.ones([1, 1])
+        out1 = model(inp * 1)
+        out2 = model(inp * 2)
+        loss = out2 * 2 + out1 * 1
+        loss.backward()
+        self.assertEqual(model.linear.weight.grad, 5)
+
+    def test_order_1(self):
+        """
+        loss = 2 * w * 2  + 1 * w * 1
+        delta_w = 5
+        """
+        model = paddle.jit.to_static(
+            function=MyLayer(),
+            input_spec=[
+                paddle.static.InputSpec(
+                    shape=[None, None], dtype=paddle.float32
+                )
+            ],
+        )
+        model.clear_gradients()
+        inp = paddle.ones([1, 1])
+        out1 = model(inp * 1)
+        out2 = model(inp * 2)
+        loss = out1 * 1 + out2 * 2
+        loss.backward()
+        self.assertEqual(model.linear.weight.grad, 5)
+
+
+if __name__ == '__main__':
+    unittest.main()