转换规则 No.252/256/267 (#148)

Add tests

paconvert/api_mapping.json

@@ -3699,12 +3699,15 @@
     }
   },
   "torch.exp": {
-    "Matcher": "ExpMatcher",
+    "Matcher": "GenericMatcher",
     "paddle_api": "paddle.exp",
     "args_list": [
       "input",
       "out"
-    ]
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
   },
   "torch.exp2": {
     "Matcher": "Exp2Matcher",
@@ -3714,7 +3717,7 @@
     ]
   },
   "torch.expm1": {
-    "Matcher": "ExpMatcher",
+    "Matcher": "GenericMatcher",
     "paddle_api": "paddle.expm1",
     "args_list": [
       "input",
@@ -4746,6 +4749,17 @@
       "input": "x"
     }
   },
+  "torch.linalg.multi_dot": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.linalg.multi_dot",
+    "args_list": [
+      "tensors",
+      "out"
+    ],
+    "kwargs_change": {
+      "tensors": "x"
+    }
+  },
   "torch.linalg.norm": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.linalg.norm",
@@ -8660,6 +8674,17 @@
       "input": "x"
     }
   },
+  "torch.special.expm1": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.expm1",
+    "args_list": [
+      "input",
+      "out"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
+  },
   "torch.special.log1p": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.log1p",

paconvert/api_matcher.py

@@ -2419,22 +2419,6 @@ def generate_code(self, kwargs):
         return code
 
 
-class ExpMatcher(BaseMatcher):
-    def generate_code(self, kwargs):
-        if "input" in kwargs:
-            kwargs["x"] = "(" + kwargs.pop("input").strip("\n") + ").astype('float32')"
-
-        if "out" in kwargs and kwargs["out"] is not None:
-            out_v = kwargs.pop("out").strip("\n")
-            code = "paddle.assign({}({}), output={})".format(
-                self.get_paddle_api(), self.kwargs_to_str(kwargs), out_v
-            )
-        else:
-            code = "{}({})".format(self.get_paddle_api(), self.kwargs_to_str(kwargs))
-
-        return code
-
-
 class TensorSVDMatcher(BaseMatcher):
     def generate_code(self, kwargs):
 

tests/test_dsplit.py

@@ -0,0 +1,72 @@
+# 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.dsplit")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.arange(16.0).reshape(2, 2, 4)
+        result = torch.dsplit(a, 2)
+        """
+    )
+    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
+        a = torch.arange(16.0).reshape(2, 2, 4)
+        result = torch.dsplit(a, [2,2])
+        if len(result) > 2:
+            result = (result[0], result[2])
+        """
+    )
+    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
+        a = torch.arange(12).reshape(3, 2, 2)
+        result = torch.dsplit(a, indices=[1,1])
+        if len(result) > 2:
+            result = (result[0], result[2])
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )

tests/test_linalg_multi_dot.py

@@ -0,0 +1,54 @@
+# 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.linalg.multi_dot")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = [torch.tensor([1, 2], dtype=torch.float32), torch.tensor([2, 3], dtype=torch.float32)]
+        result = torch.linalg.multi_dot(input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = [torch.tensor([1, 2], dtype=torch.float32), torch.tensor([2, 3], dtype=torch.float32)]
+        result = torch.linalg.multi_dot(tensors=input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = [torch.tensor([1, 2], dtype=torch.float32), torch.tensor([2, 3], dtype=torch.float32)]
+        out = torch.tensor([])
+        result = torch.linalg.multi_dot(input, out=out)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_special_expm1.py

@@ -0,0 +1,64 @@
+# 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.expm1")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.special.expm1(torch.tensor([0., -2., 3.]))
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([-1., -2., 3.])
+        result = torch.special.expm1(a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = [-1, -2, 3]
+        out = torch.tensor(a, dtype=torch.float32)
+        result = torch.special.expm1(torch.tensor(a), out=out)
+        """
+    )
+    obj.run(pytorch_code, ["out"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([-1, -2, 3])
+        result = torch.special.expm1(input=a)
+        """
+    )
+    obj.run(pytorch_code, ["result"])