chore: import upstream snapshot with attribution

test/dygraph_to_static/test_isinstance.py

@@ -0,0 +1,124 @@
+# 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.
+
+# 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,
+    enable_to_static_guard,
+)
+
+import paddle
+from paddle import nn
+
+
+class SimpleReturnLayer(nn.Layer):
+    def forward(self, x):
+        return x
+
+
+class AddAttrLayer(nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.attr = None
+
+    def forward(self, x):
+        out = x + self.attr
+        return out
+
+
+class IsInstanceLayer(nn.Layer):
+    def __init__(self, layer):
+        super().__init__()
+        self.layer = layer
+
+    def forward(self, x):
+        if isinstance(self.layer, (AddAttrLayer,)):
+            self.layer.attr = x
+        res = self.layer(x)
+        return res
+
+
+class SequentialLayer(nn.Layer):
+    def __init__(self, layers):
+        super().__init__()
+        self.layers = nn.LayerList(layers)
+
+    def forward(self, x):
+        res = x
+        for layer in self.layers:
+            if isinstance(layer, AddAttrLayer):
+                layer.attr = x
+            res = layer(res)
+        return res
+
+
+def train(model, to_static):
+    with enable_to_static_guard(to_static):
+        x = paddle.ones(shape=[2, 3], dtype='int32')
+        out = model(x)
+
+    return out.numpy()
+
+
+class TestIsinstance(Dy2StTestBase):
+    def test_isinstance_simple_return_layer(self):
+        model_creator = lambda: paddle.jit.to_static(
+            IsInstanceLayer(SimpleReturnLayer())
+        )
+        self._test_model(model_creator)
+
+    def test_isinstance_add_attr_layer(self):
+        model_creator = lambda: paddle.jit.to_static(
+            IsInstanceLayer(AddAttrLayer())
+        )
+        self._test_model(model_creator)
+
+    def test_sequential_layer(self):
+        def model_creator():
+            layers = []
+            for i in range(5):
+                layers.append(SimpleReturnLayer())
+                layers.append(AddAttrLayer())
+            return paddle.jit.to_static(SequentialLayer(layers))
+
+        self._test_model(model_creator)
+
+    def _test_model(self, model_creator):
+        st_model = model_creator()
+        st_out = train(st_model, to_static=True)
+        dy_model = model_creator()
+        dy_out = train(dy_model, to_static=False)
+        np.testing.assert_allclose(
+            dy_out,
+            st_out,
+            rtol=1e-05,
+            err_msg=f'dy_out:\n {dy_out}\n st_out:\n{st_out}',
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()