diff --git a/paconvert/api_mapping.json b/paconvert/api_mapping.json
index dd045688a..f806122be 100644
--- a/paconvert/api_mapping.json
+++ b/paconvert/api_mapping.json
@@ -5771,6 +5771,25 @@
       "A": "x"
     }
   },
+  "torch.linalg.lu_factor": {
+    "Matcher": "LinalgLufactorMatcher",
+    "paddle_api": "paddle.linalg.lu",
+    "args_list": [
+      "A",
+      "pivot",
+      "out"
+    ]
+  },
+  "torch.linalg.lu_factor_ex": {
+    "Matcher": "LinalgLufactorexMatcher",
+    "paddle_api": "paddle.linalg.lu",
+    "args_list": [
+      "A",
+      "pivot",
+      "check_errors",
+      "out"
+    ]
+  },
   "torch.linalg.matmul": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.matmul",
diff --git a/paconvert/api_matcher.py b/paconvert/api_matcher.py
index 4951d4000..7fa23ef69 100644
--- a/paconvert/api_matcher.py
+++ b/paconvert/api_matcher.py
@@ -4061,6 +4061,60 @@ def generate_code(self, kwargs):
         return code
 
 
+class LinalgLufactorMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        out_v = kwargs.pop("out") if "out" in kwargs else None
+        new_kwargs = {}
+        new_kwargs["x"] = kwargs.pop("A")
+        new_kwargs.update(kwargs)
+        if out_v:
+            API_TEMPLATE = textwrap.dedent(
+                """
+                tmp_lu, tmp_p = {}({})
+                paddle.assign(tmp_lu, {}[0]), paddle.assign(tmp_p, {}[1])
+                """
+            )
+            code = API_TEMPLATE.format(
+                self.get_paddle_api(), self.kwargs_to_str(new_kwargs), out_v, out_v
+            )
+        else:
+            code = "{}({})".format(
+                self.get_paddle_api(), self.kwargs_to_str(new_kwargs)
+            )
+        return code
+
+
+class LinalgLufactorexMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        out_v = kwargs.pop("out") if "out" in kwargs else None
+        if "check_errors" in kwargs:
+            kwargs.pop("check_errors")
+
+        new_kwargs = {}
+        new_kwargs["x"] = kwargs.pop("A")
+        new_kwargs.update(kwargs)
+        new_kwargs["get_infos"] = "True"
+        if out_v:
+            API_TEMPLATE = textwrap.dedent(
+                """
+                tmp_lu, tmp_p, tmp_info = {}({})
+                paddle.assign(tmp_lu, {}[0]), paddle.assign(tmp_p, {}[1]), paddle.assign(tmp_info, {}[2])
+                """
+            )
+            code = API_TEMPLATE.format(
+                self.get_paddle_api(),
+                self.kwargs_to_str(new_kwargs),
+                out_v,
+                out_v,
+                out_v,
+            )
+        else:
+            code = "{}({})".format(
+                self.get_paddle_api(), self.kwargs_to_str(new_kwargs)
+            )
+        return code
+
+
 class QrMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         some_v = kwargs.pop("some") if "some" in kwargs else None
diff --git a/tests/test_linalg_lu_factor.py b/tests/test_linalg_lu_factor.py
new file mode 100644
index 000000000..2e2f83f22
--- /dev/null
+++ b/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"])
diff --git a/tests/test_linalg_lu_factor_ex.py b/tests/test_linalg_lu_factor_ex.py
new file mode 100644
index 000000000..3d4f61357
--- /dev/null
+++ b/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"])