转换规则 No. 323/333 (#198)

Add tests

paconvert/api_mapping.json

@@ -9844,6 +9844,18 @@
       "out"
     ]
   },
+  "torch.special.polygamma": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.polygamma",
+    "args_list": [
+      "n",
+      "input",
+      "out"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
+  },
   "torch.special.psi": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.digamma",

tests/test_nn_GLU.py

@@ -0,0 +1,73 @@
+# 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.GLU")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        m = torch.nn.GLU()
+        result = m(x)
+        """
+    )
+    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([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        m = torch.nn.GLU(dim=-1)
+        result = m(x)
+        """
+    )
+    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([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        m = torch.nn.GLU(dim=2)
+        result = m(x)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )

tests/test_special_polygamma.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.special.polygamma")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.special.polygamma(1, torch.tensor([1, 0.5]))
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([1, 0.5])
+        result = torch.special.polygamma(1, a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = [1, 0.5]
+        out = torch.tensor(a)
+        result = torch.special.polygamma(1, torch.tensor(a), out=out)
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])