Make warnings a little nicer

.github/CODEOWNERS

@@ -1 +1 @@
-* @dlangerm-stackav
+* @dlangerm-stackav @dlangerm

.pre-commit-config.yaml

@@ -1,6 +1,7 @@
 default_language_version:
   python: python3.14
 
+
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v6.0.0
@@ -38,6 +39,7 @@ repos:
         args: [--autofix, --indent, '2', --offset, '2', --line-width, '80']
       - id: pretty-format-toml
         args: [--autofix, --indent, '2']
+        exclude: uv.lock
   - repo: https://github.com/astral-sh/uv-pre-commit
       # uv version.
     rev: 0.10.0

dltype/_lib/_constants.py

@@ -29,6 +29,8 @@ class _Env(BaseSettings):
 MAX_ACCEPTABLE_EVALUATION_TIME_NS: typing.Final = int(5e9)  # 5ms
 GLOBAL_DISABLE: typing.Final = __env.DISABLE
 
+if DEBUG_MODE:
+    warnings.warn("DLType debug mode enabled", UserWarning, stacklevel=1)
 
 if GLOBAL_DISABLE:
     warnings.warn(

dltype/_lib/_core.py

@@ -60,7 +60,7 @@ def from_hint(
     ) -> tuple[DLTypeAnnotation | None, ...]:
         """Create a new _DLTypeAnnotation from a type hint."""
         if hint is None:
-            warnings.warn(f"[{name}] is missing a DLType hint", category=UserWarning, stacklevel=3)
+            warnings.warn(f"[{name}] is missing a DLType hint", category=UserWarning, stacklevel=4)
             return (None,)
 
         _logger.debug("Creating DLType from hint %r", hint)
@@ -85,19 +85,18 @@ def from_hint(
         if origin is tuple:
             return tuple(itertools.chain(*[cls.from_hint(inner_hint, name) for inner_hint in args]))
 
-        # Only process Annotated types
+        # Only process Annotated types, warn if the annotated type is a tensor
         if origin is not Annotated:
+            if any(T in hint.mro() for T in _dtypes.SUPPORTED_TENSOR_TYPES) if hint else False:
+                warnings.warn(f"[{name}] is missing a DLType hint", category=UserWarning, stacklevel=4)
             return (None,)
 
         # Ensure the annotation is a TensorTypeBase
         if len(args) < n_expected_args or not isinstance(
             args[1],
             _tensor_type_base.TensorTypeBase,
         ):
-            _logger.warning(
-                "Invalid annotated dltype hint: %r",
-                args[1:] if len(args) >= n_expected_args else None,
-            )
+            warnings.warn(f"[{name}] has an invalid DLType hint", category=UserWarning, stacklevel=4)
             return (None,)
 
         # Ensure the base type is a supported tensor type
@@ -165,6 +164,10 @@ def _resolve_value(
     return cast("tuple[Any]", value) if len(type_hint) > 1 else (value,)
 
 
+def _get_func_lineref(func: Callable[P, R]) -> str:
+    return f"Function: {func.__name__}"
+
+
 def dltyped(  # noqa: C901, PLR0915
     scope_provider: DLTypeScopeProvider | Literal["self"] | None = None,
     *,
@@ -183,7 +186,7 @@ def dltyped(  # noqa: C901, PLR0915
 
     """
 
-    def _inner_dltyped(func: Callable[P, R]) -> Callable[P, R]:  # noqa: C901, PLR0915
+    def _inner_dltyped(func: Callable[P, R]) -> Callable[P, R]:  # noqa: C901
         if _dependency_utilities.is_torch_scripting() or not enabled:
             # jit script doesn't support annotated type hints at all, we have no choice but to skip the type checking
             return func
@@ -204,9 +207,8 @@ def _inner_dltyped(func: Callable[P, R]) -> Callable[P, R]:  # noqa: C901, PLR09
 
         # if we added dltype to a method where it will have no effect, warn the user
         if dltype_hints is not None and all(all(vv is None for vv in v) for v in dltype_hints.values()):
-            _logger.warning("dltype_hints=%r", dltype_hints)
             warnings.warn(
-                "No DLType hints found, skipping type checking",
+                f"No DLType hints found for {_get_func_lineref(func)}, skipping type checking",
                 UserWarning,
                 stacklevel=2,
             )
@@ -284,12 +286,6 @@ def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:  # noqa: C901
                         maybe_return_annotation,
                     )
                     ctx.assert_context()
-                elif any(isinstance(retval, T) for T in _dtypes.SUPPORTED_TENSOR_TYPES):
-                    warnings.warn(
-                        f"[{return_key}] is missing a DLType hint",
-                        UserWarning,
-                        stacklevel=2,
-                    )
             except _errors.DLTypeError as e:
                 # include the full function signature in the error message
                 e.set_context(f"{func.__name__}{signature}")

dltype/tests/dltype_test.py

@@ -165,7 +165,7 @@ def bad_ndim_error(tensor_name: str, *, expected: int, actual: int) -> str:
             torch.ones(1, 2, 3, 4),
             incomplete_annotated_function,
             _RaisesInfo(value=torch.ones(1, 2, 3, 4)),
-            _WarnsInfo(match_text=re.escape("[return] is missing a DLType hint")),
+            None,
             id="incomplete_annotated_4D",
         ),
         pytest.param(
@@ -1064,33 +1064,38 @@ def good_function(  # pyright: ignore[reportUnusedFunction]
 
 
 def test_dimension_with_external_scope() -> None:
-    class Provider:
-        def get_dltype_scope(self) -> dict[str, int]:
-            return {"channels_in": 3, "channels_out": 4}
+    with pytest.warns(UserWarning, match=re.escape("[return] is missing a DLType hint")):
 
-        @dltype.dltyped(scope_provider="self")
-        def forward(
-            self,
+        class Provider:
+            def get_dltype_scope(self) -> dict[str, int]:
+                return {"channels_in": 3, "channels_out": 4}
+
+            @dltype.dltyped(scope_provider="self")
+            def forward(
+                self,
+                tensor: Annotated[
+                    torch.Tensor,
+                    dltype.FloatTensor["batch channels_in channels_out"],
+                ],
+            ) -> torch.Tensor:
+                return tensor
+
+    with pytest.warns(UserWarning, match=re.escape("[return] is missing a DLType hint")):
+
+        @dltype.dltyped(scope_provider=Provider())
+        def good_function(
             tensor: Annotated[
                 torch.Tensor,
-                dltype.FloatTensor["batch channels_in channels_out"],
+                dltype.IntTensor["batch channels_in channels_out"],
             ],
         ) -> torch.Tensor:
             return tensor
 
-    @dltype.dltyped(scope_provider=Provider())
-    def good_function(
-        tensor: Annotated[
-            torch.Tensor,
-            dltype.IntTensor["batch channels_in channels_out"],
-        ],
-    ) -> torch.Tensor:
-        return tensor
-
-    with pytest.warns(UserWarning, match=re.escape("[return] is missing a DLType hint")):
+    with pytest.WarningsRecorder() as rec:
         good_function(torch.ones(1, 3, 4).int())
-    with pytest.warns(UserWarning, match=re.escape("[return] is missing a DLType hint")):
+    with pytest.WarningsRecorder() as rec:
         good_function(torch.ones(4, 3, 4).int())
+        assert len(rec.list) == 0
 
     with pytest.raises(dltype.DLTypeShapeError):
         good_function(torch.ones(1, 3, 5).int())
@@ -1099,10 +1104,8 @@ def good_function(
 
     provider = Provider()
 
-    with pytest.warns(UserWarning, match=re.escape("[return] is missing a DLType hint")):
-        provider.forward(torch.ones(1, 3, 4))
-    with pytest.warns(UserWarning, match=re.escape("[return] is missing a DLType hint")):
-        provider.forward(torch.ones(4, 3, 4))
+    provider.forward(torch.ones(1, 3, 4))
+    provider.forward(torch.ones(4, 3, 4))
 
     with pytest.raises(dltype.DLTypeShapeError):
         provider.forward(torch.ones(1, 3, 5))
@@ -1114,23 +1117,23 @@ def test_optional_type_handling() -> None:
     """Test that dltyped correctly handles Optional tensor types."""
 
     # Test with a function with optional parameter
-    @dltype.dltyped()
-    def optional_tensor_func(
-        tensor: Annotated[torch.Tensor, dltype.FloatTensor["b c h w"]] | None,
-    ) -> torch.Tensor:
-        if tensor is None:
-            return torch.zeros(1, 3, 5, 5)
-        return tensor
+    with pytest.warns(UserWarning, match=re.escape("[return] is missing a DLType hint")):
+
+        @dltype.dltyped()
+        def optional_tensor_func(
+            tensor: Annotated[torch.Tensor, dltype.FloatTensor["b c h w"]] | None,
+        ) -> torch.Tensor:
+            if tensor is None:
+                return torch.zeros(1, 3, 5, 5)
+            return tensor
 
     # Should work with None
-    with pytest.warns(UserWarning, match=re.escape("[return] is missing a DLType hint")):
-        result = optional_tensor_func(None)
+    result = optional_tensor_func(None)
     assert result.shape == (1, 3, 5, 5)
 
     # Should work with correct tensor
     input_tensor = torch.rand(2, 3, 4, 4)
-    with pytest.warns(UserWarning, match=re.escape("[return] is missing a DLType hint")):
-        torch.testing.assert_close(optional_tensor_func(input_tensor), input_tensor)
+    torch.testing.assert_close(optional_tensor_func(input_tensor), input_tensor)
 
     # Should fail with incorrect shape
     with pytest.raises(dltype.DLTypeNDimsError):
@@ -1359,26 +1362,35 @@ def create(
 
 def test_warning_if_decorator_has_no_annotations_to_check() -> None:
     with pytest.warns(
-        UserWarning,
-        match="No DLType hints found, skipping type checking",
+        UserWarning, match="No DLType hints found for Function: no_annotations, skipping type checking"
     ):
 
         @dltype.dltyped()
         def no_annotations(tensor: torch.Tensor) -> torch.Tensor:  # pyright: ignore[reportUnusedFunction]
             return tensor
 
     # should warn if some tensors are untyped
-    @dltype.dltyped()
-    def some_annotations(
-        tensor: Annotated[torch.Tensor, dltype.FloatTensor["1 2 3"]],
-    ) -> torch.Tensor:
-        return tensor
 
     with pytest.warns(
         UserWarning,
         match=re.escape("[return] is missing a DLType hint"),
     ):
-        some_annotations(torch.rand(1, 2, 3))
+
+        @dltype.dltyped()
+        def some_annotations(
+            tensor: Annotated[torch.Tensor, dltype.FloatTensor["1 2 3"]],
+        ) -> torch.Tensor:
+            return tensor
+
+    some_annotations(torch.rand(1, 2, 3))
+
+    with pytest.warns(UserWarning, match=re.escape("[tensor] has an invalid DLType hint")):
+
+        @dltype.dltyped()
+        def some_annotations(
+            tensor: Annotated[torch.Tensor, 5],
+        ) -> torch.Tensor:
+            return tensor
 
 
 def test_scalar() -> None: