Fix lu_factor_ex

paconvert/api_mapping.json

@@ -5816,7 +5816,7 @@
     }
   },
   "torch.linalg.lu_factor_ex": {
-    "Matcher": "TripleAssignMatcher",
+    "Matcher": "LinalgLufactorexMatcher",
     "paddle_api": "paddle.linalg.lu",
     "args_list": [
       "A",

paconvert/api_matcher.py

@@ -3492,6 +3492,47 @@ def generate_code(self, kwargs):
             return code.strip("\n")
 
 
+class LinalgLufactorexMatcher(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 = {}({})
+                out3 = paddle.to_tensor(out3.item(), dtype='int32')
+                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:
+            API_TEMPLATE = textwrap.dedent(
+                """
+                out1, out2, out3 = {}({})
+                out3 = paddle.to_tensor(out3.item(), dtype='int32')
+                (out1, out2, out3)
+                """
+            )
+            code = API_TEMPLATE.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_ex.py

@@ -25,7 +25,6 @@ def test_case_1():
         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"])
@@ -37,7 +36,6 @@ def test_case_2():
         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"])
@@ -49,7 +47,6 @@ def test_case_3():
         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"])
@@ -62,7 +59,6 @@ def test_case_4():
         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"])
@@ -75,7 +71,6 @@ def test_case_5():
         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"])