Add tests

tests/test_nn_Module_bfloat16.py

@@ -0,0 +1,38 @@
+# Copyright (c) 2023 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.Module.bfloat16")
+
+
+def _test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1., 2., 3.])
+        module1 = torch.nn.Module()
+        module1.register_buffer('buffer', x)
+        module1.bfloat16()
+        result = module1.buffer
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )

tests/test_nn_Module_double.py

@@ -0,0 +1,38 @@
+# Copyright (c) 2023 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.Module.double")
+
+
+def _test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1., 2., 3.])
+        module1 = torch.nn.Module()
+        module1.register_buffer('buffer', x)
+        module1.double()
+        result = module1.buffer
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )

tests/test_nn_Module_float.py

@@ -0,0 +1,38 @@
+# Copyright (c) 2023 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.Module.float")
+
+
+def _test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1., 2., 3.])
+        module1 = torch.nn.Module()
+        module1.register_buffer('buffer', x)
+        module1.float()
+        result = module1.buffer
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )

tests/test_nn_Module_half.py

@@ -0,0 +1,38 @@
+# Copyright (c) 2023 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.Module.half")
+
+
+def _test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1., 2., 3.])
+        module1 = torch.nn.Module()
+        module1.register_buffer('buffer', x)
+        module1.half()
+        result = module1.buffer
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )

tests/test_nn_Module_register_full_backward_hook.py

@@ -0,0 +1,53 @@
+# Copyright (c) 2023 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.Module.register_full_backward_hook")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        import torch.nn.functional as F
+
+        def backward_after_hook(module, data_input, data_output):
+            print("I am function after forward function.")
+
+        class MyModule(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv1 = nn.Conv2d(1, 3, 3)
+                self.conv2 = nn.Conv2d(3, 3, 3)
+
+            def forward(self, x):
+                x = F.relu(self.conv1(x))
+                x = F.relu(self.conv2(x))
+                return x
+
+        my_module = MyModule()
+        my_module.register_full_backward_hook(backward_after_hook)
+        result = None
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )

tests/test_nn_Module_register_full_backward_pre_hook.py

@@ -0,0 +1,53 @@
+# Copyright (c) 2023 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.Module.register_full_backward_pre_hook")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        import torch.nn.functional as F
+
+        def backward_pre_hook(module, data_input):
+            print("I am function before forward function.")
+
+        class MyModule(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv1 = nn.Conv2d(1, 3, 3)
+                self.conv2 = nn.Conv2d(3, 3, 3)
+
+            def forward(self, x):
+                x = F.relu(self.conv1(x))
+                x = F.relu(self.conv2(x))
+                return x
+
+        my_module = MyModule()
+        my_module.register_full_backward_pre_hook(backward_pre_hook)
+        result = None
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )

tests/test_nn_Module_requires_grad_.py

@@ -0,0 +1,57 @@
+# Copyright (c) 2023 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.Module.requires_grad_")
+
+
+def _test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1., 2., 3.])
+        module1 = torch.nn.Module()
+        module1.register_buffer('buffer', x)
+        module1.requires_grad_(True)
+        result = None
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )
+
+
+def _test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1., 2., 3.])
+        module1 = torch.nn.Module()
+        module1.register_buffer('buffer', x)
+        module1.requires_grad_(requires_grad=True)
+        result = None
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )

tests/test_nn_Module_to.py

@@ -0,0 +1,76 @@
+# Copyright (c) 2023 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.Module.to")
+
+
+def _test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1., 2., 3.])
+        module1 = torch.nn.Module()
+        module1.register_buffer('buffer', x)
+        module1.to(dtype=torch.float32)
+        result = module1.buffer
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )
+
+
+def _test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1., 2., 3.])
+        module1 = torch.nn.Module()
+        module1.register_buffer('buffer', x)
+        module1.to(device="cpu")
+        result = module1.buffer
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )
+
+
+def _test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1., 2., 3.])
+        module1 = torch.nn.Module()
+        module1.register_buffer('buffer', x)
+        module1.to(device="cpu", non_blocking=False)
+        result = module1.buffer
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )