diff --git a/paconvert/api_mapping.json b/paconvert/api_mapping.json
index 42f88efe6..02c198665 100644
--- a/paconvert/api_mapping.json
+++ b/paconvert/api_mapping.json
@@ -5811,6 +5811,23 @@
       "A"
     ]
   },
+  "torch.linalg.solve_triangular": {
+    "Matcher": "LinalgSolveTriangularMatcher",
+    "paddle_api": "paddle.linalg.triangular_solve",
+    "args_list": [
+      "input",
+      "B",
+      "upper",
+      "left",
+      "unitriangular",
+      "out"
+    ],
+    "kwargs_change": {
+      "input": "x",
+      "B": "y",
+      "left": "transpose"
+    }
+  },
   "torch.linalg.svdvals": {
     "Matcher": "LinalgSvdvalsMatcher",
     "paddle_api": "paddle.linalg.svd",
diff --git a/paconvert/api_matcher.py b/paconvert/api_matcher.py
index 7fa52b0d5..a76770efb 100644
--- a/paconvert/api_matcher.py
+++ b/paconvert/api_matcher.py
@@ -3886,6 +3886,18 @@ def generate_code(self, kwargs):
         return GenericMatcher.generate_code(self, kwargs)
 
 
+class LinalgSolveTriangularMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        new_kwargs = {}
+        left = kwargs.pop("left").strip("(").strip(")")
+        if left == "True":
+            new_kwargs["transpose"] = "False"
+        if left == "False":
+            new_kwargs["transpose"] = "True"
+        new_kwargs.update(kwargs)
+        return GenericMatcher.generate_code(self, new_kwargs)
+
+
 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_solve_triangular.py b/tests/test_linalg_solve_triangular.py
new file mode 100644
index 000000000..91f5ed22a
--- /dev/null
+++ b/tests/test_linalg_solve_triangular.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.solve_triangular")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        A = torch.tensor([[ 1.1527, -1.0753], [ 1.23,  0.7986]])
+        B = torch.tensor([[-0.0210,  2.3513, -1.5492], [ 1.5429,  0.7403, -1.0243]])
+        result = torch.linalg.solve_triangular(A, B, upper=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        A = torch.tensor([[ 1.1527, -1.0753], [ 1.23,  0.7986]])
+        B = torch.tensor([[-0.0210,  2.3513, -1.5492], [ 1.5429,  0.7403, -1.0243]])
+        result = torch.linalg.solve_triangular(input=A, B=B, upper=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        A = torch.tensor([[ 1.1527, -1.0753], [ 1.23,  0.7986]])
+        B = torch.tensor([[-0.0210,  2.3513, -1.5492], [ 1.5429,  0.7403, -1.0243]])
+        result = torch.linalg.solve_triangular(input=A, B=B, unitriangular=True, upper=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        A = torch.tensor([[ 1.1527, -1.0753], [ 1.23,  0.7986]])
+        B = torch.tensor([[-0.0210,  2.3513, -1.5492], [ 1.5429,  0.7403, -1.0243]])
+        out = torch.tensor([])
+        result = torch.linalg.solve_triangular(A, B, upper=True, left=True, unitriangular=False, out=out)
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        A = torch.tensor([[ 1.1527, -1.0753], [ 1.23,  0.7986]])
+        B = torch.tensor([[-0.0210,  2.3513, -1.5492], [ 1.5429,  0.7403, -1.0243]])
+        out = torch.tensor([])
+        result = torch.linalg.solve_triangular(input=A, B=B, upper=True, left=True, unitriangular=False, out=out)
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])