转换规则 No.358/361

paconvert/api_mapping.json

@@ -5771,6 +5771,35 @@
       "A": "x"
     }
   },
+  "torch.linalg.lu_factor": {
+    "Matcher": "TupleAssignMatcher",
+    "paddle_api": "paddle.linalg.lu",
+    "args_list": [
+      "A",
+      "pivot",
+      "out"
+    ],
+    "kwargs_change": {
+      "A": "x"
+    }
+  },
+  "torch.linalg.lu_factor_ex": {
+    "Matcher": "TripleAssignMatcher",
+    "paddle_api": "paddle.linalg.lu",
+    "args_list": [
+      "A",
+      "pivot",
+      "check_errors",
+      "out"
+    ],
+    "kwargs_change": {
+      "A": "x",
+      "check_errors": ""
+    },
+    "paddle_default_kwargs": {
+      "get_infos": "True"
+    }
+  },
   "torch.linalg.matmul": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.matmul",

paconvert/api_matcher.py

@@ -3443,6 +3443,37 @@ def generate_code(self, kwargs):
             return code.strip("\n")
 
 
+class TripleAssignMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        kwargs = self.set_paddle_default_kwargs(kwargs)
+        kwargs_change = {}
+        if "kwargs_change" in self.api_mapping:
+            kwargs_change = self.api_mapping["kwargs_change"]
+
+        for k in kwargs_change:
+            if k in kwargs:
+                if kwargs_change[k]:
+                    kwargs[kwargs_change[k]] = kwargs.pop(k)
+                else:
+                    kwargs.pop(k)
+
+        if "out" in kwargs:
+            out_v = kwargs.pop("out")
+            API_TEMPLATE = textwrap.dedent(
+                """
+                out1, out2, out3 = {}({})
+                paddle.assign(out1, {}[0]), paddle.assign(out2, {}[1]), paddle.assign(out3, {}[2])
+                """
+            )
+            code = API_TEMPLATE.format(
+                self.get_paddle_api(), self.kwargs_to_str(kwargs), out_v, out_v, out_v
+            )
+            return code.strip("\n")
+        else:
+            code = "{}({})".format(self.get_paddle_api(), self.kwargs_to_str(kwargs))
+            return code.strip("\n")
+
+
 class RoundMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         if "input" not in kwargs:

tests/test_linalg_lu_factor.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.linalg.lu_factor")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=torch.float64)
+        LU, pivots = torch.linalg.lu_factor(x)
+        """
+    )
+    obj.run(pytorch_code, ["LU", "pivots"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=torch.float64)
+        LU, pivots = torch.linalg.lu_factor(A=x)
+        """
+    )
+    obj.run(pytorch_code, ["LU", "pivots"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=torch.float64)
+        LU, pivots = torch.linalg.lu_factor(pivot=True, A=x)
+        """
+    )
+    obj.run(pytorch_code, ["LU", "pivots"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=torch.float64)
+        out = (torch.tensor([], dtype=torch.float64), torch.tensor([], dtype=torch.int))
+        LU, pivots = torch.linalg.lu_factor(x, pivot=True, out=out)
+        """
+    )
+    obj.run(pytorch_code, ["LU", "pivots", "out"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=torch.float64)
+        out = (torch.tensor([], dtype=torch.float64), torch.tensor([], dtype=torch.int))
+        LU, pivots = torch.linalg.lu_factor(A=x, pivot=True, out=out)
+        """
+    )
+    obj.run(pytorch_code, ["LU", "pivots", "out"])

tests/test_linalg_lu_factor_ex.py

@@ -0,0 +1,81 @@
+# 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.lu_factor_ex")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=torch.float64)
+        LU, pivots, info = torch.linalg.lu_factor_ex(x)
+        info = info.item()
+        """
+    )
+    obj.run(pytorch_code, ["LU", "pivots", "info"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=torch.float64)
+        LU, pivots, info = torch.linalg.lu_factor_ex(A=x)
+        info = info.item()
+        """
+    )
+    obj.run(pytorch_code, ["LU", "pivots", "info"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=torch.float64)
+        LU, pivots, info = torch.linalg.lu_factor_ex(pivot=True, A=x)
+        info = info.item()
+        """
+    )
+    obj.run(pytorch_code, ["LU", "pivots", "info"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=torch.float64)
+        out = (torch.tensor([], dtype=torch.float64), torch.tensor([], dtype=torch.int), torch.tensor([], dtype=torch.int))
+        LU, pivots, info = torch.linalg.lu_factor_ex(x, pivot=True, check_errors=False, out=out)
+        info = info.item()
+        """
+    )
+    obj.run(pytorch_code, ["LU", "pivots", "info"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=torch.float64)
+        out = (torch.tensor([], dtype=torch.float64), torch.tensor([], dtype=torch.int), torch.tensor([], dtype=torch.int))
+        LU, pivots, info = torch.linalg.lu_factor_ex(A=x, pivot=True, check_errors=True, out=out)
+        info = info.item()
+        """
+    )
+    obj.run(pytorch_code, ["LU", "pivots", "info"])