fix: from_sample override logic (#3202)

src/bentoml/_internal/io_descriptors/base.py

@@ -81,7 +81,6 @@ class IODescriptor(ABC, _OpenAPIMeta, t.Generic[IOType]):
     _rpc_content_type: str = "application/grpc"
     _proto_fields: tuple[ProtoField]
     _sample: IOType | None = None
-    _initialized: bool = False
     _args: t.Sequence[t.Any]
     _kwargs: dict[str, t.Any]
 
@@ -99,27 +98,9 @@ def __init_subclass__(cls, *, descriptor_id: str | None = None):
         def __init__(self, **kwargs: t.Any) -> None:
             ...
 
-    def __getattr__(self, name: str) -> t.Any:
-        if not self._initialized:
-            self._lazy_init()
-        assert self._initialized
-        return object.__getattribute__(self, name)
-
-    def __dir__(self) -> t.Iterable[str]:
-        if not self._initialized:
-            self._lazy_init()
-        assert self._initialized
-        return object.__dir__(self)
-
     def __repr__(self) -> str:
         return self.__class__.__qualname__
 
-    def _lazy_init(self) -> None:
-        self._initialized = True
-        self.__init__(*self._args, **self._kwargs)
-        del self._args
-        del self._kwargs
-
     @property
     def sample(self) -> IOType | None:
         return self._sample
@@ -131,8 +112,7 @@ def sample(self, value: IOType) -> None:
     @classmethod
     def from_sample(cls, sample: IOType | t.Any, **kwargs: t.Any) -> Self:
         klass = cls(**kwargs)
-        sample = klass._from_sample(sample)
-        klass.sample = sample
+        klass.sample = klass._from_sample(sample)
         return klass
 
     @abstractmethod

src/bentoml/_internal/io_descriptors/file.py

@@ -135,7 +135,6 @@ def _from_sample(self, sample: FileType | str) -> FileType:
             )
         if isinstance(sample, t.IO):
             sample = FileLike[bytes](sample, "<sample>")
-            self._mime_type = filetype.guess_mime(sample)
         elif isinstance(sample, (str, os.PathLike)):
             p = resolve_user_filepath(sample, ctx=None)
             self._mime_type = filetype.guess_mime(p)

src/bentoml/_internal/io_descriptors/image.py

@@ -231,6 +231,7 @@ def _from_sample(self, sample: ImageType | str) -> ImageType:
                     sample = PIL.Image.open(f)
             except PIL.UnidentifiedImageError as err:
                 raise BadInput(f"Failed to parse sample image file: {err}") from None
+        self._mime_type = img_type.mime
         return sample
 
     def to_spec(self) -> dict[str, t.Any]:

src/bentoml/_internal/io_descriptors/numpy.py

@@ -216,15 +216,6 @@ def __init__(
         shape: tuple[int, ...] | None = None,
         enforce_shape: bool = False,
     ):
-        if enforce_dtype and not dtype:
-            raise InvalidArgument(
-                "'dtype' must be specified when 'enforce_dtype=True'"
-            ) from None
-        if enforce_shape and not shape:
-            raise InvalidArgument(
-                "'shape' must be specified when 'enforce_shape=True'"
-            ) from None
-
         if dtype and not isinstance(dtype, np.dtype):
             # Convert from primitive type or type string, e.g.: np.dtype(float) or np.dtype("float64")
             try:
@@ -440,8 +431,6 @@ async def predict(input: NDArray[np.int16]) -> NDArray[Any]:
             ) from None
         self._dtype = sample.dtype
         self._shape = sample.shape
-        self._enforce_dtype = True
-        self._enforce_shape = True
         return sample
 
     async def from_proto(self, field: pb.NDArray | bytes) -> ext.NpNDArray:

src/bentoml/_internal/io_descriptors/pandas.py

@@ -325,19 +325,6 @@ def __init__(
         enforce_shape: bool = False,
         default_format: t.Literal["json", "parquet", "csv"] = "json",
     ):
-        if enforce_dtype and dtype is None:
-            raise ValueError(
-                "'dtype' must be specified if 'enforce_dtype' is True"
-            ) from None
-        if enforce_shape and shape is None:
-            raise ValueError(
-                "'shape' must be specified if 'enforce_shape' is True"
-            ) from None
-        if apply_column_names and columns is None:
-            raise ValueError(
-                "'columns' must be specified if 'apply_column_names' is True"
-            ) from None
-
         self._orient: ext.DataFrameOrient = orient
         self._columns = columns
         self._apply_column_names = apply_column_names
@@ -431,10 +418,8 @@ def predict(inputs: pd.DataFrame) -> pd.DataFrame: ...
                 ) from None
         self._shape = sample.shape
         self._columns = [str(i) for i in list(sample.columns)]
-        self._dtype = True
-        self._enforce_dtype = True
-        self._enforce_shape = True
-        self._apply_column_names = True
+        if self._dtype is None:
+            self._dtype = True  # infer dtype automatically
         return sample
 
     def _convert_dtype(
@@ -828,15 +813,6 @@ def __init__(
         shape: tuple[int, ...] | None = None,
         enforce_shape: bool = False,
     ):
-        if enforce_dtype and dtype is None:
-            raise ValueError(
-                "'dtype' must be specified if 'enforce_dtype' is True"
-            ) from None
-        if enforce_shape and shape is None:
-            raise ValueError(
-                "'shape' must be specified if 'enforce_shape' is True"
-            ) from None
-
         self._orient: ext.SeriesOrient = orient
         self._dtype = dtype
         self._enforce_dtype = enforce_dtype
@@ -885,8 +861,6 @@ def predict(inputs: pd.Series) -> pd.Series: ...
             sample = pd.Series(sample)
         self._dtype = sample.dtype
         self._shape = sample.shape
-        self._enforce_dtype = True
-        self._enforce_shape = True
         return sample
 
     def input_type(self) -> LazyType[ext.PdSeries]:

tests/unit/_internal/io/test_numpy.py

@@ -10,7 +10,6 @@
 
 from bentoml.io import NumpyNdarray
 from bentoml.exceptions import BadInput
-from bentoml.exceptions import InvalidArgument
 from bentoml.exceptions import BentoMLException
 from bentoml._internal.service.openapi.specification import Schema
 
@@ -29,7 +28,7 @@ class ExampleGeneric(str, np.generic):
 
 
 example = np.zeros((2, 2, 3, 2))
-from_example = NumpyNdarray.from_sample(example)
+from_example = NumpyNdarray.from_sample(example, enforce_dtype=True, enforce_shape=True)
 
 
 def test_invalid_dtype():
@@ -43,15 +42,6 @@ def test_invalid_dtype():
     assert "expects a 'numpy.array'" in str(e.value)
 
 
-def test_invalid_init():
-    with pytest.raises(InvalidArgument) as exc_info:
-        NumpyNdarray(enforce_dtype=True)
-    assert "'dtype' must be specified" in str(exc_info.value)
-    with pytest.raises(InvalidArgument) as exc_info:
-        NumpyNdarray(enforce_shape=True)
-    assert "'shape' must be specified" in str(exc_info.value)
-
-
 @pytest.mark.parametrize("dtype, expected", [("float", "number"), (">U8", "integer")])
 def test_numpy_to_openapi_types(dtype: str, expected: str):
     assert NumpyNdarray(dtype=dtype)._openapi_types() == expected  # type: ignore (private functions warning)
@@ -123,9 +113,6 @@ def test_verify_numpy_ndarray(caplog: LogCaptureFixture):
 
     # test cases where reshape is failed
     example = NumpyNdarray.from_sample(np.ones((2, 2, 3)))
-    # Note that from_sample now lazy load the IO descriptor
-    example._enforce_shape = False
-    example._enforce_dtype = False
     with caplog.at_level(logging.DEBUG):
         example.validate_array(np.array("asdf"))
     assert "Failed to reshape" in caplog.text