onnx · justinchuby · Jan 22, 2025 · Jan 10, 2025 · Jan 11, 2025 · Jan 11, 2025
@@ -6,136 +6,9 @@
 .. currentmodule:: onnx.helper
 ```
 
-```{eval-rst}
-.. autosummary::
-
-    find_min_ir_version_for
-    get_all_tensor_dtypes
-    get_attribute_value
-    get_node_attr_value
-    set_metadata_props
-    set_model_props
-    float32_to_bfloat16
-    float32_to_float8e4m3
-    float32_to_float8e5m2
-    make_attribute
-    make_attribute_ref
-    make_empty_tensor_value_info
-    make_function
-    make_graph
-    make_map
-    make_map_type_proto
-    make_model
-    make_node
-    make_operatorsetid
-    make_opsetid
-    make_model_gen_version
-    make_optional
-    make_optional_type_proto
-    make_sequence
-    make_sequence_type_proto
-    make_sparse_tensor
-    make_sparse_tensor_type_proto
-    make_sparse_tensor_value_info
-    make_tensor
-    make_tensor_sequence_value_info
-    make_tensor_type_proto
-    make_training_info
-    make_tensor_value_info
-    make_value_info
-    np_dtype_to_tensor_dtype
-    printable_attribute
-    printable_dim
-    printable_graph
-    printable_node
-    printable_tensor_proto
-    printable_type
-    printable_value_info
-    split_complex_to_pairs
-    create_op_set_id_version_map
-    strip_doc_string
-    pack_float32_to_4bit
-    tensor_dtype_to_np_dtype
-    tensor_dtype_to_storage_tensor_dtype
-    tensor_dtype_to_string
-    tensor_dtype_to_field
-```
-
-## getter
-
-```{eval-rst}
-.. autofunction:: onnx.helper.get_attribute_value
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.get_node_attr_value
-```
-
-## setter
-
-```{eval-rst}
-.. autofunction:: onnx.helper.set_metadata_props
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.set_model_props
-```
-
-## print
-
-```{eval-rst}
-.. autofunction:: onnx.helper.printable_attribute
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.printable_dim
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.printable_graph
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.printable_node
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.printable_tensor_proto
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.printable_type
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.printable_value_info
-```
-
-## tools
-
-```{eval-rst}
-.. autofunction:: onnx.helper.find_min_ir_version_for
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.split_complex_to_pairs
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.create_op_set_id_version_map
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.strip_doc_string
-```
-
-```{eval-rst}
-.. autofunction:: onnx.helper.pack_float32_to_4bit
-```
-
 (l-onnx-make-function)=
 
-## make function
+## Helper functions to make ONNX graph components
 
 All functions used to create an ONNX graph.
 
@@ -239,7 +112,7 @@ All functions used to create an ONNX graph.
 .. autofunction:: onnx.helper.make_value_info
 ```
 
-## type mappings
+## Type Mappings
 
 ```{eval-rst}
 .. autofunction:: onnx.helper.get_all_tensor_dtypes
@@ -265,16 +138,30 @@ All functions used to create an ONNX graph.
 .. autofunction:: onnx.helper.tensor_dtype_to_string
 ```
 
-## cast
+## Tools
 
 ```{eval-rst}
-.. autofunction:: onnx.helper.float32_to_bfloat16
+.. autofunction:: onnx.helper.find_min_ir_version_for
 ```
 
 ```{eval-rst}
-.. autofunction:: onnx.helper.float32_to_float8e4m3
+.. autofunction:: onnx.helper.create_op_set_id_version_map
 ```
 
+## Other functions
+
 ```{eval-rst}
-.. autofunction:: onnx.helper.float32_to_float8e5m2
+.. autosummary::
+
+    get_attribute_value
+    get_node_attr_value
+    set_metadata_props
+    set_model_props
+    printable_attribute
+    printable_dim
+    printable_graph
+    printable_node
+    printable_tensor_proto
+    printable_type
+    printable_value_info
 ```
@@ -7,9 +7,6 @@
 ```{eval-rst}
 .. autosummary::
 
-    bfloat16_to_float32
-    float8e4m3_to_float32
-    float8e5m2_to_float32
     from_array
     from_dict
     from_list
@@ -65,35 +62,3 @@ these two functions use a custom dtype defined in :mod:`onnx._custom_element_typ
 ```{eval-rst}
 .. autofunction:: onnx.numpy_helper.from_optional
 ```
-
-## tools
-
-```{eval-rst}
-.. autofunction:: onnx.numpy_helper.convert_endian
-```
-
-```{eval-rst}
-.. autofunction:: onnx.numpy_helper.combine_pairs_to_complex
-```
-
-```{eval-rst}
-.. autofunction:: onnx.numpy_helper.create_random_int
-```
-
-```{eval-rst}
-.. autofunction:: onnx.numpy_helper.unpack_int4
-```
-
-## cast
-
-```{eval-rst}
-.. autofunction:: onnx.numpy_helper.bfloat16_to_float32
-```
-
-```{eval-rst}
-.. autofunction:: onnx.numpy_helper.float8e4m3_to_float32
-```
-
-```{eval-rst}
-.. autofunction:: onnx.numpy_helper.float8e5m2_to_float32
-```
@@ -18,6 +18,7 @@
 
 import google.protobuf.message
 import numpy as np
+import typing_extensions
 
 import onnx._custom_element_types as custom_np_types
 from onnx import (
@@ -356,20 +357,28 @@
     set_metadata_props(model, dict_value)
 
 
-def split_complex_to_pairs(ca: Sequence[np.complex64]) -> Sequence[int]:
+def _split_complex_to_pairs(ca: Sequence[np.complex64]) -> Sequence[int]:
     return [
         (ca[i // 2].real if (i % 2 == 0) else ca[i // 2].imag)  # type: ignore[misc]
         for i in range(len(ca) * 2)
     ]
 
 
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=FutureWarning,
+)
+def float32_to_bfloat16(*args, **kwargs) -> int:
+    return _float32_to_bfloat16(*args, **kwargs)
+
+
 # convert a float32 value to a bfloat16 (as int)
 # By default, this conversion rounds-to-nearest-even and supports NaN
 # Setting `truncate` to True enables a simpler conversion. In this mode the
 # conversion is performed by simply dropping the 2 least significant bytes of
 # the significand. In this mode an error of up to 1 bit may be introduced and
 # preservation of NaN values is not be guaranteed.
-def float32_to_bfloat16(fval: float, truncate: bool = False) -> int:
+def _float32_to_bfloat16(fval: float, truncate: bool = False) -> int:
     ival = int.from_bytes(struct.pack("<f", fval), "little")
     if truncate:
         return ival >> 16
@@ -382,7 +391,15 @@
     return (ival + rounded) >> 16
 
 
-def float32_to_float8e4m3(  # noqa: PLR0911
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=FutureWarning,
+)
+def float32_to_float8e4m3(*args, **kwargs) -> int:
+    return _float32_to_float8e4m3(*args, **kwargs)
+
+
+def _float32_to_float8e4m3(  # noqa: PLR0911
     fval: float,
     scale: float = 1.0,
     fn: bool = True,
@@ -516,7 +533,15 @@
         return int(ret)
 
 
-def float32_to_float8e5m2(  # noqa: PLR0911
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=FutureWarning,
+)
+def float32_to_float8e5m2(*args: Any, **kwargs: Any) -> int:
+    return _float32_to_float8e5m2(*args, **kwargs)
+
+
+def _float32_to_float8e5m2(  # noqa: PLR0911
     fval: float,
     scale: float = 1.0,
     fn: bool = False,
@@ -642,7 +667,15 @@
         raise NotImplementedError("fn and uz must be both False or True.")
 
 
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=FutureWarning,
+)
 def pack_float32_to_4bit(array: np.ndarray | Sequence, signed: bool) -> np.ndarray:
+    return _pack_float32_to_4bit(array, signed)
+
+
+def _pack_float32_to_4bit(array: np.ndarray | Sequence, signed: bool) -> np.ndarray:
     """Convert an array of float32 value to a 4bit data-type and pack every two concecutive elements in a byte.
     See :ref:`onnx-detail-int4` for technical details.
 
@@ -662,15 +695,23 @@
         array_flat = np.append(array_flat, np.array([0]))
 
     def single_func(x, y) -> np.ndarray:
-        return subbyte.float32x2_to_4bitx2(x, y, signed)
+        return subbyte._float32x2_to_4bitx2(x, y, signed)
 
     func = np.frompyfunc(single_func, 2, 1)
 
     arr: np.ndarray = func(array_flat[0::2], array_flat[1::2])
     return arr.astype(np.uint8)
 
 
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=FutureWarning,
+)
 def pack_float32_to_float4e2m1(array: np.ndarray | Sequence) -> np.ndarray:
+    return _pack_float32_to_float4e2m1(array)
+
+
+def _pack_float32_to_float4e2m1(array: np.ndarray | Sequence) -> np.ndarray:
     """Convert an array of float32 value to float4e2m1 and pack every two concecutive elements in a byte.
     See :ref:`onnx-detail-float4` for technical details.
 
@@ -688,7 +729,7 @@
     if is_odd_volume:
         array_flat = np.append(array_flat, np.array([0]))
 
-    arr = subbyte.float32x2_to_float4e2m1x2(array_flat[0::2], array_flat[1::2])
+    arr = subbyte._float32x2_to_float4e2m1x2(array_flat[0::2], array_flat[1::2])
     return arr.astype(np.uint8)
 
 
@@ -759,7 +800,7 @@
         tensor.raw_data = vals
     else:
         if data_type in (TensorProto.COMPLEX64, TensorProto.COMPLEX128):
-            vals = split_complex_to_pairs(vals)
+            vals = _split_complex_to_pairs(vals)
         elif data_type == TensorProto.FLOAT16:
             vals = (
                 np.array(vals).astype(np_dtype).view(dtype=np.uint16).flatten().tolist()
@@ -772,13 +813,13 @@
             TensorProto.FLOAT8E5M2FNUZ,
         ):
             fcast = {
-                TensorProto.BFLOAT16: float32_to_bfloat16,
-                TensorProto.FLOAT8E4M3FN: float32_to_float8e4m3,
-                TensorProto.FLOAT8E4M3FNUZ: lambda *args: float32_to_float8e4m3(  # type: ignore[misc]
+                TensorProto.BFLOAT16: _float32_to_bfloat16,
+                TensorProto.FLOAT8E4M3FN: _float32_to_float8e4m3,
+                TensorProto.FLOAT8E4M3FNUZ: lambda *args: _float32_to_float8e4m3(  # type: ignore[misc]
                     *args, uz=True
                 ),
-                TensorProto.FLOAT8E5M2: float32_to_float8e5m2,
-                TensorProto.FLOAT8E5M2FNUZ: lambda *args: float32_to_float8e5m2(  # type: ignore[misc]
+                TensorProto.FLOAT8E5M2: _float32_to_float8e5m2,
+                TensorProto.FLOAT8E5M2FNUZ: lambda *args: _float32_to_float8e5m2(  # type: ignore[misc]
                     *args, fn=True, uz=True
                 ),
             }[
@@ -801,9 +842,9 @@
             # to uint8 regardless of the value of 'signed'. Using int8 would cause
             # the size of int4 tensors to increase ~5x if the tensor contains negative values (due to
             # the way negative values are serialized by protobuf).
-            vals = pack_float32_to_4bit(vals, signed=signed).flatten().tolist()
+            vals = _pack_float32_to_4bit(vals, signed=signed).flatten().tolist()
         elif data_type == TensorProto.FLOAT4E2M1:
-            vals = pack_float32_to_float4e2m1(vals).flatten().tolist()
+            vals = _pack_float32_to_float4e2m1(vals).flatten().tolist()
         elif data_type == TensorProto.BOOL:
             vals = np.array(vals).astype(int)
         elif data_type == TensorProto.STRING: