转换规则 No.16/17/18 (#142)

* 规则转换 No.16/17/18.

* 增加cummin，searchsorted转换测试。

* fix error

* fix error at SearchsortedMatcher

* fix code style error

paconvert/api_mapping.json

@@ -3398,6 +3398,19 @@
       "device"
     ]
   },
+  "torch.cummin": {
+    "Matcher": "TupleAssignMatcher",
+    "paddle_api": "paddle.cummin",
+    "args_list": [
+      "input",
+      "dim",
+      "out"
+    ],
+    "kwargs_change": {
+      "input": "x",
+      "dim": "axis"
+    }
+  },
   "torch.cumprod": {
     "Matcher": "CumprodMatcher",
     "paddle_api": "paddle.cumprod",
@@ -8418,6 +8431,18 @@
       "dtype": "paddle.float32"
     }
   },
+  "torch.searchsorted": {
+    "Matcher": "SearchsortedMatcher",
+    "args_list": [
+      "sorted_sequence",
+      "values",
+      "out_int32",
+      "right",
+      "side",
+      "out",
+      "sorter"
+    ]
+  },
   "torch.seed": {
     "Matcher": "SeedMatcher"
   },
@@ -9214,7 +9239,18 @@
       "tensor": "x"
     }
   },
-  "torch.vander": {},
+  "torch.vander": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.vander",
+    "args_list": [
+      "x",
+      "N",
+      "increasing"
+    ],
+    "kwargs_change": {
+      "N": "n"
+    }
+  },
   "torch.var": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.var",

paconvert/api_matcher.py

@@ -2867,6 +2867,30 @@ def generate_code(self, kwargs):
         return code
 
 
+class SearchsortedMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+
+        if "side" in kwargs:
+            kwargs["right"] = kwargs.pop("side").strip("\n") + "== 'right'"
+
+        if "sorter" in kwargs and kwargs["sorter"] is not None:
+            kwargs[
+                "sorted_sequence"
+            ] += ".take_along_axis(axis=-1, indices = {})".format(
+                kwargs.pop("sorter").strip("\n")
+            )
+
+        code = "paddle.searchsorted({})".format(self.kwargs_to_str(kwargs))
+
+        if "out" in kwargs and kwargs["out"] is not None:
+            out_v = kwargs.pop("out").strip("\n")
+            code = "paddle.assign(paddle.searchsorted({}), output={})".format(
+                self.kwargs_to_str(kwargs), out_v
+            )
+
+        return code
+
+
 class SincMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         if "input" not in kwargs:

tests/test_cummin.py

@@ -0,0 +1,118 @@
+# 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.cummin")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 1.0, 1.0],
+                        [2.0, 2.0, 2.0],
+                        [3.0, 3.0, 3.0]])
+        result = torch.cummin(x, 0)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 1.0, 1.0],
+                        [2.0, 2.0, 2.0],
+                        [3.0, 3.0, 3.0]])
+        result = torch.cummin(x, dim=1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 1.0, 1.0],
+                        [2.0, 2.0, 2.0],
+                        [3.0, 3.0, 3.0]])
+        result = torch.cummin(input=x, dim=1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 1.0, 1.0],
+                        [2.0, 2.0, 2.0],
+                        [3.0, 3.0, 3.0]])
+        values = torch.tensor([[1.0, 1.0, 1.0],
+                        [2.0, 2.0, 2.0],
+                        [3.0, 3.0, 3.0]]).float()
+        indices = torch.tensor([[1, 1, 1],
+                        [2, 2, 2],
+                        [3, 3, 3]])
+        out = (values, indices)
+        result = torch.cummin(x, 0, out=(values, indices))
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 1.0, 1.0],
+                        [2.0, 2.0, 2.0],
+                        [3.0, 3.0, 3.0]])
+        values = torch.tensor([[1.0, 1.0, 1.0],
+                        [2.0, 2.0, 2.0],
+                        [3.0, 3.0, 3.0]]).float()
+        indices = torch.tensor([[1, 1, 1],
+                        [2, 2, 2],
+                        [3, 3, 3]])
+        out = (values, indices)
+        result = torch.cummin(x, dim = 0, out=(values, indices))
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])
+
+
+def test_case_6():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 1.0, 1.0],
+                        [2.0, 2.0, 2.0],
+                        [3.0, 3.0, 3.0]])
+        values = torch.tensor([[1.0, 1.0, 1.0],
+                        [2.0, 2.0, 2.0],
+                        [3.0, 3.0, 3.0]]).float()
+        indices = torch.tensor([[1, 1, 1],
+                        [2, 2, 2],
+                        [3, 3, 3]])
+        out = (values, indices)
+        result = torch.cummin(input = x, dim =0, out=(values, indices))
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])

tests/test_searchsorted.py

@@ -0,0 +1,137 @@
+# 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.searchsorted")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1,  3,  5,  7,  9],
+                          [ 2,  4,  6,  8, 10]])
+        values = torch.tensor([[3, 6, 9],
+                               [3, 6, 9]])
+        result = torch.searchsorted(x, values)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1,  3,  5,  7,  9],
+                          [ 2,  4,  6,  8, 10]])
+        values = torch.tensor([[3, 6, 9],
+                               [3, 6, 9]])
+        result = torch.searchsorted(x, values, out_int32 = True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1,  3,  5,  7,  9],
+                          [ 2,  4,  6,  8, 10]])
+        values = torch.tensor([[3, 6, 9],
+                               [3, 6, 9]])
+        result = torch.searchsorted(x, values, right = True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1,  3,  5,  7,  9],
+                          [ 2,  4,  6,  8, 10]])
+        values = torch.tensor([[3, 6, 9],
+                               [3, 6, 9]])
+        result = torch.searchsorted(x, values, side = 'right')
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1,  3,  5,  7,  9],
+                          [ 2,  4,  6,  8, 10]])
+        values = torch.tensor([[3, 6, 9],
+                               [3, 6, 9]])
+        out = torch.tensor([[3, 6, 9],
+                            [3, 6, 9]])
+        result = torch.searchsorted(x, values, out = out)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_6():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1,  3,  9,  7,  5],
+                          [ 2,  4,  6,  8, 10]])
+        values = torch.tensor([[3, 6, 9],
+                               [3, 6, 9]])
+        sorter = torch.argsort(x)
+        result = torch.searchsorted(x, values, sorter = sorter)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_7():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1,  3,  9,  7,  5],
+                          [ 2,  4,  6,  8, 10]])
+        values = torch.tensor([[3, 6, 9],
+                               [3, 6, 9]])
+        out = torch.tensor([[3, 6, 9],
+                            [3, 6, 9]])
+        sorter = torch.argsort(x)
+        result = torch.searchsorted(x, values, right = True, side = 'right', out = out, sorter = sorter)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1,  3,  5,  7,  9],
+                          [ 2,  4,  6,  8, 10]])
+        values = torch.tensor([[3, 6, 9],
+                               [3, 6, 9]])
+        result = torch.searchsorted(x, values, right = False, side = 'right')
+        """
+    )
+    obj.run(pytorch_code, ["result"])