fix: remove invalid boundary?

Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>

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:

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
@@ -826,15 +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

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)
@@ -112,8 +102,6 @@ def test_numpy_openapi_example():
 
 def test_verify_numpy_ndarray(caplog: LogCaptureFixture):
     partial_check = partial(from_example.validate_array, exception_cls=BentoMLException)
-    from_example._enforce_dtype = True
-    from_example._enforce_shape = True
 
     with pytest.raises(BentoMLException) as ex:
         partial_check(np.array(["asdf"]))
@@ -125,9 +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