mindtorch_v2: align logsumexp dim schema errors

src/mindtorch_v2/_dispatch/schema.py

@@ -380,6 +380,20 @@ def _validate_nan_reduction_dim(value):
                 f"{op_short_name}(): argument 'dim' must be tuple of ints, not {type(value).__name__}"
             )
 
+        def _validate_logsumexp_dim(value, input_tensor):
+            if isinstance(value, bool):
+                _raise_invalid_combo_with_got("(Tensor, dim=bool)")
+                return
+            if isinstance(value, int) and not isinstance(value, bool):
+                return
+            if isinstance(value, str):
+                if value.isidentifier():
+                    raise _dimname_not_found(value, input_tensor)
+                raise RuntimeError(
+                    "Invalid name: a valid identifier contains only digits, alphabetical characters, "
+                    f"and/or underscore and starts with a non-digit. got: '{value}'."
+                )
+
         def _type_label(value):
             if isinstance(value, bool):
                 return "bool"
@@ -659,6 +673,9 @@ def _validate_transpose_dims(dim0, dim1):
             if op_short_name in {"nansum", "nanmean"} and param.name == "dim":
                 _validate_nan_reduction_dim(value)
                 continue
+            if op_short_name == "logsumexp" and param.name == "dim":
+                _validate_logsumexp_dim(value, bound.get("input"))
+                continue
             if op_short_name == "view" and param.name == "shape":
                 _validate_view_shape(value)
                 continue

src/mindtorch_v2/_dispatch/schemas.py

@@ -511,6 +511,12 @@ def register_schemas():
 
     # New GROUP C ops for Tensor API alignment
     registry.register_schema("logsumexp", "logsumexp(Tensor input, int dim, bool keepdim=False) -> Tensor")
+    registry.register_error_overrides(
+        "logsumexp",
+        {
+            "unexpected": "{name}() received an invalid combination of arguments - got {got}, but expected one of:\n * (Tensor input, tuple of ints dim, bool keepdim = False, *, Tensor out = None)\n * (Tensor input, tuple of names dim, bool keepdim = False, *, Tensor out = None)\n",
+        },
+    )
     registry.register_schema("trace", "trace(Tensor input) -> Tensor")
     registry.register_schema("det", "det(Tensor input) -> Tensor")
     registry.register_schema("matrix_power", "matrix_power(Tensor input, int n) -> Tensor")

tests/mindtorch_v2/contract/test_schema_dim_validation.py

@@ -687,3 +687,39 @@ def th_call():
         pt.nanmean(pt.tensor([1.0, 0.0]), dim="0")
 
     assert_torch_error(mt_call, th_call)
+
+
+def test_dispatch_logsumexp_rejects_bool_dim_matches_torch():
+    mt_x = torch.tensor([1.0, 0.0])
+
+    def mt_call():
+        dispatch("logsumexp", mt_x.device.type, mt_x, dim=True)
+
+    def th_call():
+        pt.logsumexp(pt.tensor([1.0, 0.0]), dim=True)
+
+    assert_torch_error(mt_call, th_call)
+
+
+def test_dispatch_logsumexp_rejects_invalid_name_dim_matches_torch():
+    mt_x = torch.tensor([1.0, 0.0])
+
+    def mt_call():
+        dispatch("logsumexp", mt_x.device.type, mt_x, dim="0")
+
+    def th_call():
+        pt.logsumexp(pt.tensor([1.0, 0.0]), dim="0")
+
+    assert_torch_error(mt_call, th_call)
+
+
+def test_dispatch_logsumexp_rejects_missing_name_dim_matches_torch():
+    mt_x = torch.tensor([1.0, 0.0])
+
+    def mt_call():
+        dispatch("logsumexp", mt_x.device.type, mt_x, dim="x")
+
+    def th_call():
+        pt.logsumexp(pt.tensor([1.0, 0.0]), dim="x")
+
+    assert_torch_error(mt_call, th_call)