转换规则 No. 362 (#239)

* Add test

* Fix

paconvert/api_mapping.json

@@ -10572,6 +10572,19 @@
       "out"
     ]
   },
+  "torch.special.softmax": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.functional.softmax",
+    "args_list": [
+      "input",
+      "dim",
+      "dtype"
+    ],
+    "kwargs_change": {
+      "input": "x",
+      "dim": "axis"
+    }
+  },
   "torch.special.xlog1py": {
     "Matcher": "SpecialXLog1pYMatcher",
     "args_list": [

tests/test_special_softmax.py

@@ -0,0 +1,116 @@
+# 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.softmax")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[[2.0, 3.0, 4.0, 5.0],
+                        [3.0, 4.0, 5.0, 6.0],
+                        [7.0, 8.0, 8.0, 9.0]],
+                        [[1.0, 2.0, 3.0, 4.0],
+                        [5.0, 6.0, 7.0, 8.0],
+                        [6.0, 7.0, 8.0, 9.0]]])
+        result = torch.special.softmax(x, -1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[[2.0, 3.0, 4.0, 5.0],
+                        [3.0, 4.0, 5.0, 6.0],
+                        [7.0, 8.0, 8.0, 9.0]],
+                        [[1.0, 2.0, 3.0, 4.0],
+                        [5.0, 6.0, 7.0, 8.0],
+                        [6.0, 7.0, 8.0, 9.0]]])
+        result = torch.special.softmax(x, dim=1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[[2.0, 3.0, 4.0, 5.0],
+                        [3.0, 4.0, 5.0, 6.0],
+                        [7.0, 8.0, 8.0, 9.0]],
+                        [[1.0, 2.0, 3.0, 4.0],
+                        [5.0, 6.0, 7.0, 8.0],
+                        [6.0, 7.0, 8.0, 9.0]]])
+        result = torch.special.softmax(x, 1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[[2.0, 3.0, 4.0, 5.0],
+                        [3.0, 4.0, 5.0, 6.0],
+                        [7.0, 8.0, 8.0, 9.0]],
+                        [[1.0, 2.0, 3.0, 4.0],
+                        [5.0, 6.0, 7.0, 8.0],
+                        [6.0, 7.0, 8.0, 9.0]]])
+        result = torch.special.softmax(x, 1, dtype=torch.float64)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[[2.0, 3.0, 4.0, 5.0],
+                        [3.0, 4.0, 5.0, 6.0],
+                        [7.0, 8.0, 8.0, 9.0]],
+                        [[1.0, 2.0, 3.0, 4.0],
+                        [5.0, 6.0, 7.0, 8.0],
+                        [6.0, 7.0, 8.0, 9.0]]])
+        result = torch.special.softmax(input=x, dim=1, dtype=torch.float64)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_6():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.special as F
+        x = torch.tensor([[[2.0, 3.0, 4.0, 5.0],
+                        [3.0, 4.0, 5.0, 6.0],
+                        [7.0, 8.0, 8.0, 9.0]],
+                        [[1.0, 2.0, 3.0, 4.0],
+                        [5.0, 6.0, 7.0, 8.0],
+                        [6.0, 7.0, 8.0, 9.0]]])
+        result = torch.special.softmax(dim=1, dtype=torch.float64, input=x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])