mindtorch_v2: align std and var dim schema errors

src/mindtorch_v2/_dispatch/schema.py

@@ -350,6 +350,20 @@ def _validate_count_nonzero_dim(value):
                 return
             _raise_invalid_combo()
 
+        def _validate_std_var_dim(value):
+            if value is None:
+                return
+            if isinstance(value, bool):
+                _raise_invalid_combo_with_got("(Tensor, dim=bool)")
+                return
+            if isinstance(value, str):
+                if value.isidentifier():
+                    raise _dimname_not_found(value, bound.get("input"))
+                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"
@@ -623,6 +637,9 @@ def _validate_transpose_dims(dim0, dim1):
             if op_short_name == "count_nonzero" and param.name == "dim":
                 _validate_count_nonzero_dim(value)
                 continue
+            if op_short_name in {"std", "var"} and param.name == "dim":
+                _validate_std_var_dim(value)
+                continue
             if op_short_name == "view" and param.name == "shape":
                 _validate_view_shape(value)
                 continue

src/mindtorch_v2/_dispatch/schemas.py

@@ -179,6 +179,12 @@ def register_schemas():
         },
     )
     registry.register_schema("std", "std(Tensor input, int[]? dim=None, bool keepdim=False, bool unbiased=True) -> Tensor")
+    registry.register_error_overrides(
+        "std",
+        {
+            "unexpected": "{name}() received an invalid combination of arguments - got {got}, but expected one of:\n * (Tensor input, tuple of ints dim, bool unbiased = True, bool keepdim = False, *, Tensor out = None)\n * (Tensor input, tuple of ints dim = None, *, Number correction = None, bool keepdim = False, Tensor out = None)\n * (Tensor input, bool unbiased = True)\n      didn't match because some of the keywords were incorrect: dim\n * (Tensor input, tuple of names dim, bool unbiased = True, bool keepdim = False, *, Tensor out = None)\n * (Tensor input, tuple of names dim, *, Number correction = None, bool keepdim = False, Tensor out = None)\n",
+        },
+    )
 
     registry.register_schema("reshape", "reshape(Tensor(a) input, int[] shape) -> Tensor(a)")
     registry.register_error_overrides(
@@ -392,6 +398,12 @@ def register_schemas():
     registry.register_schema("scatter_add_", "scatter_add_(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor")
 
     registry.register_schema("var", "var(Tensor input, int[]? dim=None, bool unbiased=True, bool keepdim=False) -> Tensor")
+    registry.register_error_overrides(
+        "var",
+        {
+            "unexpected": "{name}() received an invalid combination of arguments - got {got}, but expected one of:\n * (Tensor input, tuple of ints dim, bool unbiased = True, bool keepdim = False, *, Tensor out = None)\n * (Tensor input, tuple of ints dim = None, *, Number correction = None, bool keepdim = False, Tensor out = None)\n * (Tensor input, bool unbiased = True)\n      didn't match because some of the keywords were incorrect: dim\n * (Tensor input, tuple of names dim, bool unbiased = True, bool keepdim = False, *, Tensor out = None)\n * (Tensor input, tuple of names dim, *, Number correction = None, bool keepdim = False, Tensor out = None)\n",
+        },
+    )
     registry.register_schema("norm", "norm(Tensor input, Any p=2, int[]? dim=None, bool keepdim=False) -> Tensor")
     registry.register_error_overrides(
         "norm",

tests/mindtorch_v2/contract/test_schema_dim_validation.py

@@ -591,3 +591,51 @@ def th_call():
         pt.count_nonzero(pt.tensor([[1.0, 0.0], [0.0, 1.0]]), dim="0")
 
     assert_torch_error(mt_call, th_call)
+
+
+def test_dispatch_std_rejects_bool_dim_matches_torch():
+    mt_x = torch.tensor([1.0, 0.0])
+
+    def mt_call():
+        dispatch("std", mt_x.device.type, mt_x, dim=True)
+
+    def th_call():
+        pt.std(pt.tensor([1.0, 0.0]), dim=True)
+
+    assert_torch_error(mt_call, th_call)
+
+
+def test_dispatch_std_rejects_str_dim_matches_torch():
+    mt_x = torch.tensor([1.0, 0.0])
+
+    def mt_call():
+        dispatch("std", mt_x.device.type, mt_x, dim="0")
+
+    def th_call():
+        pt.std(pt.tensor([1.0, 0.0]), dim="0")
+
+    assert_torch_error(mt_call, th_call)
+
+
+def test_dispatch_var_rejects_bool_dim_matches_torch():
+    mt_x = torch.tensor([1.0, 0.0])
+
+    def mt_call():
+        dispatch("var", mt_x.device.type, mt_x, dim=True)
+
+    def th_call():
+        pt.var(pt.tensor([1.0, 0.0]), dim=True)
+
+    assert_torch_error(mt_call, th_call)
+
+
+def test_dispatch_var_rejects_str_dim_matches_torch():
+    mt_x = torch.tensor([1.0, 0.0])
+
+    def mt_call():
+        dispatch("var", mt_x.device.type, mt_x, dim="0")
+
+    def th_call():
+        pt.var(pt.tensor([1.0, 0.0]), dim="0")
+
+    assert_torch_error(mt_call, th_call)