# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import sys
from typing import TYPE_CHECKING, Any
import ml_dtypes
import numpy as np
import numpy.typing as npt
import onnx.external_data_helper
from onnx import helper
if TYPE_CHECKING:
from collections.abc import Sequence
# System is little endian
_IS_LITTLE_ENDIAN = sys.byteorder == "little"
def to_float8e8m0(
x: np.ndarray,
saturate: bool = True,
round_mode: str = "up",
) -> np.ndarray:
"""Convert float32 NumPy array to float8e8m0 representation. If the input
is not a float32 array, it will be cast to one first.
Args:
x: Input array to convert.
saturate: Whether to saturate at max/min float8e8m0 value.
round_mode: "nearest", "up", or "down".
Returns:
np.ndarray: Array of ml_dtypes.float8_e8m0fnu values.
"""
x_f32 = np.asarray(x, dtype=np.float32)
f_bits = x_f32.view(np.uint32)
# Extract exponent bits
exponent = (f_bits >> 23) & 0xFF
exponent = exponent.astype(
np.uint16
) # use uint16 to prevent overflow during computation
# Identify NaN or Inf
special_mask = exponent == 0xFF # noqa: PLR2004
output = np.zeros_like(exponent, dtype=np.uint8)
output[special_mask] = 0xFF # Preserve NaN/Inf as max exponent
# Process normal numbers
normal_mask = ~special_mask
if round_mode == "nearest":
# Get guard, round, sticky, and least significant bits
g = ((f_bits & 0x400000) > 0).astype(np.uint8)
r = ((f_bits & 0x200000) > 0).astype(np.uint8)
s = ((f_bits & 0x1FFFFF) > 0).astype(np.uint8)
lsb = (exponent > 0).astype(np.uint8)
round_up = (g == 1) & ((r == 1) | (s == 1) | (lsb == 1))
increment = np.zeros_like(exponent)
increment[round_up & normal_mask] = 1
if saturate:
max_mask = (exponent == 0xFE) & round_up & normal_mask # noqa: PLR2004
increment[max_mask] = 0 # Don't overflow past max value
exponent += increment
elif round_mode == "up":
has_fraction = (f_bits & 0x4FFFFF) > 0
round_up = has_fraction & normal_mask
if saturate:
max_mask = (exponent == 0xFE) & round_up # noqa: PLR2004
round_up[max_mask] = False
exponent += round_up.astype(np.uint16)
elif round_mode == "down":
pass # No rounding needed
else:
raise ValueError(f"Unsupported rounding mode: {round_mode}")
# Clip exponent to uint8 range
exponent = exponent.astype(np.uint8)
output[normal_mask] = exponent[normal_mask]
return output.view(ml_dtypes.float8_e8m0fnu)
def _unpack_4bit(
data: npt.NDArray[np.uint8], dims: Sequence[int]
) -> npt.NDArray[np.uint8]:
"""Convert a packed uint4 array to unpacked uint4 array represented as uint8.
Args:
data: A numpy array.
dims: The dimensions are used to reshape the unpacked buffer.
Returns:
A numpy array of int8/uint8 reshaped to dims.
"""
result = np.empty([data.size * 2], dtype=data.dtype)
array_low = data & np.uint8(0x0F)
array_high = data & np.uint8(0xF0)
array_high >>= np.uint8(4)
result[0::2] = array_low
result[1::2] = array_high
if result.size == np.prod(dims) + 1:
# handle single-element padding due to odd number of elements
result = result[:-1]
result.resize(dims, refcheck=False)
return result
def _pack_4bitx2(array: np.ndarray) -> npt.NDArray[np.uint8]:
"""Convert a numpy array to flatten, packed int4/uint4. Elements must be in the correct range."""
# Create a 1D copy
array_flat = array.ravel().view(np.uint8).copy()
size = array.size
odd_sized = size % 2 == 1
if odd_sized:
array_flat.resize([size + 1], refcheck=False)
array_flat &= 0x0F
array_flat[1::2] <<= 4
return array_flat[0::2] | array_flat[1::2] # type: ignore[return-type]
[docs]
def to_array(tensor: onnx.TensorProto, base_dir: str = "") -> np.ndarray: # noqa: PLR0911
"""Converts a tensor def object to a numpy array.
This function uses ml_dtypes if the dtype is not a native numpy dtype.
Args:
tensor: a TensorProto object.
base_dir: if external tensor exists, base_dir can help to find the path to it
Returns:
arr: the converted array.
"""
if tensor.HasField("segment"):
raise ValueError("Currently not supporting loading segments.")
if tensor.data_type == onnx.TensorProto.UNDEFINED:
raise TypeError("The element type in the input tensor is UNDEFINED.")
tensor_dtype = tensor.data_type
np_dtype = helper.tensor_dtype_to_np_dtype(tensor_dtype)
storage_np_dtype = helper.tensor_dtype_to_np_dtype(
helper.tensor_dtype_to_storage_tensor_dtype(tensor_dtype)
)
storage_field = helper.tensor_dtype_to_field(tensor_dtype)
dims = tensor.dims
if tensor.data_type == onnx.TensorProto.STRING:
utf8_strings = getattr(tensor, storage_field)
ss = [s.decode("utf-8") for s in utf8_strings]
return np.asarray(ss).astype(np_dtype).reshape(dims)
# Load raw data from external tensor if it exists
if onnx.external_data_helper.uses_external_data(tensor):
onnx.external_data_helper.load_external_data_for_tensor(tensor, base_dir)
if tensor.HasField("raw_data"):
# Raw_bytes support: using frombuffer.
raw_data = tensor.raw_data
if sys.byteorder == "big":
# Convert endian from little to big
raw_data = np.frombuffer(raw_data, dtype=np_dtype).byteswap().tobytes()
if tensor_dtype in {
onnx.TensorProto.INT4,
onnx.TensorProto.UINT4,
onnx.TensorProto.FLOAT4E2M1,
}:
data = np.frombuffer(raw_data, dtype=np.uint8)
return _unpack_4bit(data, dims).view(np_dtype)
return np.frombuffer(raw_data, dtype=np_dtype).reshape(dims)
if tensor_dtype in {
onnx.TensorProto.BFLOAT16,
onnx.TensorProto.FLOAT16,
onnx.TensorProto.INT16,
onnx.TensorProto.UINT16,
}:
return (
np.array(tensor.int32_data, dtype=np.int32)
.view(np.uint32)
.astype(np.uint16)
.reshape(dims)
.view(np_dtype)
)
if tensor_dtype in {
onnx.TensorProto.FLOAT8E4M3FN,
onnx.TensorProto.FLOAT8E4M3FNUZ,
onnx.TensorProto.FLOAT8E5M2,
onnx.TensorProto.FLOAT8E5M2FNUZ,
onnx.TensorProto.FLOAT8E8M0,
onnx.TensorProto.BOOL,
}:
return (
np.array(tensor.int32_data, dtype=np.int32)
.view(np.uint32)
.astype(np.uint8)
.view(np_dtype)
.reshape(dims)
)
if tensor_dtype in {
onnx.TensorProto.UINT4,
onnx.TensorProto.INT4,
onnx.TensorProto.FLOAT4E2M1,
}:
data = (
np.array(tensor.int32_data, dtype=np.int32).view(np.uint32).astype(np.uint8)
)
return _unpack_4bit(data, dims).view(np_dtype)
data = getattr(tensor, storage_field)
if tensor_dtype in (onnx.TensorProto.COMPLEX64, onnx.TensorProto.COMPLEX128):
return np.array(data, dtype=storage_np_dtype).view(dtype=np_dtype).reshape(dims)
return np.asarray(data, dtype=storage_np_dtype).astype(np_dtype).reshape(dims)
[docs]
def from_array(array: np.ndarray, /, name: str | None = None) -> onnx.TensorProto:
"""Converts an array into a TensorProto including
Args:
array: a numpy array.
name: (optional) the name of the tensor.
Returns:
TensorProto: the converted tensor def.
"""
tensor = onnx.TensorProto()
tensor.dims.extend(array.shape)
if name:
tensor.name = name
if array.dtype == object or np.issubdtype(array.dtype, np.str_):
# Special care for strings.
tensor.data_type = onnx.TensorProto.STRING
# TODO: Introduce full string support.
# We flatten the array in case there are n-D arrays are specified
# If you want more complex shapes then follow the below instructions.
# Unlike other types where the shape is automatically inferred from
# nested arrays of values, the only reliable way now to feed strings
# is to put them into a flat array then specify type astype(object)
# (otherwise all strings may have different types depending on their length)
# and then specify shape .reshape([x, y, z])
flat_array = array.flatten()
for e in flat_array:
if isinstance(e, str):
tensor.string_data.append(e.encode("utf-8"))
elif isinstance(e, bytes):
tensor.string_data.append(e)
else:
raise NotImplementedError(
"Unrecognized object in the object array, expect a string, or array of bytes: ",
str(type(e)),
)
return tensor
dtype = helper.np_dtype_to_tensor_dtype(array.dtype)
if dtype in {
onnx.TensorProto.INT4,
onnx.TensorProto.UINT4,
onnx.TensorProto.FLOAT4E2M1,
}:
# Pack the array into int4
array = _pack_4bitx2(array)
if not _IS_LITTLE_ENDIAN:
array = array.view(array.dtype.newbyteorder("<"))
tensor.raw_data = array.tobytes()
tensor.data_type = dtype
return tensor
[docs]
def to_list(sequence: onnx.SequenceProto) -> list[Any]:
"""Converts a sequence def to a Python list.
Args:
sequence: a SequenceProto object.
Returns:
list: the converted list.
"""
elem_type = sequence.elem_type
if elem_type == onnx.SequenceProto.TENSOR:
return [to_array(v) for v in sequence.tensor_values]
if elem_type == onnx.SequenceProto.SPARSE_TENSOR:
return [to_array(v) for v in sequence.sparse_tensor_values] # type: ignore[arg-type]
if elem_type == onnx.SequenceProto.SEQUENCE:
return [to_list(v) for v in sequence.sequence_values]
if elem_type == onnx.SequenceProto.MAP:
return [to_dict(v) for v in sequence.map_values]
raise TypeError("The element type in the input sequence is not supported.")
[docs]
def from_list(
lst: list[Any], name: str | None = None, dtype: int | None = None
) -> onnx.SequenceProto:
"""Converts a list into a sequence def.
Args:
lst: a Python list
name: (optional) the name of the sequence.
dtype: (optional) type of element in the input list, used for specifying
sequence values when converting an empty list.
Returns:
SequenceProto: the converted sequence def.
"""
sequence = onnx.SequenceProto()
if name:
sequence.name = name
if dtype:
elem_type = dtype
elif len(lst) > 0:
first_elem = lst[0]
if isinstance(first_elem, dict):
elem_type = onnx.SequenceProto.MAP
elif isinstance(first_elem, list):
elem_type = onnx.SequenceProto.SEQUENCE
else:
elem_type = onnx.SequenceProto.TENSOR
else:
# if empty input list and no dtype specified
# choose sequence of tensors on default
elem_type = onnx.SequenceProto.TENSOR
sequence.elem_type = elem_type
if (len(lst) > 0) and not all(isinstance(elem, type(lst[0])) for elem in lst):
raise TypeError(
"The element type in the input list is not the same "
"for all elements and therefore is not supported as a sequence."
)
if elem_type == onnx.SequenceProto.TENSOR:
for tensor in lst:
sequence.tensor_values.extend([from_array(np.asarray(tensor))])
elif elem_type == onnx.SequenceProto.SEQUENCE:
for seq in lst:
sequence.sequence_values.extend([from_list(seq)])
elif elem_type == onnx.SequenceProto.MAP:
for mapping in lst:
sequence.map_values.extend([from_dict(mapping)])
else:
raise TypeError(
"The element type in the input list is not a tensor, "
"sequence, or map and is not supported."
)
return sequence
[docs]
def to_dict(map_proto: onnx.MapProto) -> dict[Any, Any]:
"""Converts a map def to a Python dictionary.
Args:
map_proto: a MapProto object.
Returns:
The converted dictionary.
"""
key_list: list[Any] = []
if map_proto.key_type == onnx.TensorProto.STRING:
key_list = list(map_proto.string_keys)
else:
key_list = list(map_proto.keys)
value_list = to_list(map_proto.values)
if len(key_list) != len(value_list):
raise IndexError(
"Length of keys and values for MapProto (map name: ",
map_proto.name,
") are not the same.",
)
dictionary = dict(zip(key_list, value_list))
return dictionary
[docs]
def from_dict(dict_: dict[Any, Any], name: str | None = None) -> onnx.MapProto:
"""Converts a Python dictionary into a map def.
Args:
dict_: Python dictionary
name: (optional) the name of the map.
Returns:
MapProto: the converted map def.
"""
map_proto = onnx.MapProto()
if name:
map_proto.name = name
keys = list(dict_)
raw_key_type = np.result_type(keys[0])
key_type = helper.np_dtype_to_tensor_dtype(raw_key_type)
valid_key_int_types = {
onnx.TensorProto.INT8,
onnx.TensorProto.INT16,
onnx.TensorProto.INT32,
onnx.TensorProto.INT64,
onnx.TensorProto.UINT8,
onnx.TensorProto.UINT16,
onnx.TensorProto.UINT32,
onnx.TensorProto.UINT64,
}
if not (all(np.result_type(key) == raw_key_type for key in keys)):
raise TypeError(
"The key type in the input dictionary is not the same "
"for all keys and therefore is not valid as a map."
)
values = list(dict_.values())
raw_value_type = np.result_type(values[0])
if not all(np.result_type(val) == raw_value_type for val in values):
raise TypeError(
"The value type in the input dictionary is not the same "
"for all values and therefore is not valid as a map."
)
value_seq = from_list(values)
map_proto.key_type = key_type
if key_type == onnx.TensorProto.STRING:
map_proto.string_keys.extend(keys)
elif key_type in valid_key_int_types:
map_proto.keys.extend(keys)
map_proto.values.CopyFrom(value_seq)
return map_proto
[docs]
def to_optional(optional: onnx.OptionalProto) -> Any | None:
"""Converts an optional def to a Python optional.
Args:
optional: an OptionalProto object.
Returns:
opt: the converted optional.
"""
elem_type = optional.elem_type
if elem_type == onnx.OptionalProto.UNDEFINED:
return None
if elem_type == onnx.OptionalProto.TENSOR:
return to_array(optional.tensor_value)
if elem_type == onnx.OptionalProto.SPARSE_TENSOR:
return to_array(optional.sparse_tensor_value) # type: ignore[arg-type]
if elem_type == onnx.OptionalProto.SEQUENCE:
return to_list(optional.sequence_value)
if elem_type == onnx.OptionalProto.MAP:
return to_dict(optional.map_value)
if elem_type == onnx.OptionalProto.OPTIONAL:
return to_optional(optional.optional_value)
raise TypeError("The element type in the input optional is not supported.")
[docs]
def from_optional(
opt: Any | None, name: str | None = None, dtype: int | None = None
) -> onnx.OptionalProto:
"""Converts an optional value into a Optional def.
Args:
opt: a Python optional
name: (optional) the name of the optional.
dtype: (optional) type of element in the input, used for specifying
optional values when converting empty none. dtype must
be a valid OptionalProto.DataType value
Returns:
optional: the converted optional def.
"""
# TODO: create a map and replace conditional branches
optional = onnx.OptionalProto()
if name:
optional.name = name
if dtype is not None:
# dtype must be a valid onnx.OptionalProto.DataType
if dtype not in onnx.OptionalProto.DataType.values():
raise TypeError(f"{dtype} must be a valid OptionalProto.DataType.")
elem_type = dtype
elif isinstance(opt, dict):
elem_type = onnx.OptionalProto.MAP
elif isinstance(opt, list):
elem_type = onnx.OptionalProto.SEQUENCE
elif opt is None:
elem_type = onnx.OptionalProto.UNDEFINED
else:
elem_type = onnx.OptionalProto.TENSOR
optional.elem_type = elem_type
if opt is not None:
if elem_type == onnx.OptionalProto.TENSOR:
optional.tensor_value.CopyFrom(from_array(opt))
elif elem_type == onnx.OptionalProto.SEQUENCE:
optional.sequence_value.CopyFrom(from_list(opt))
elif elem_type == onnx.OptionalProto.MAP:
optional.map_value.CopyFrom(from_dict(opt))
else:
raise TypeError(
"The element type in the input is not a tensor, "
"sequence, or map and is not supported."
)
return optional
def create_random_int(
input_shape: tuple[int], dtype: np.dtype, seed: int = 1
) -> np.ndarray:
"""Create random integer array for backend/test/case/node.
Args:
input_shape: The shape for the returned integer array.
dtype: The NumPy data type for the returned integer array.
seed: The seed for np.random.
Returns:
np.ndarray: Random integer array.
"""
np.random.seed(seed)
if dtype in (
np.uint8,
np.uint16,
np.uint32,
np.uint64,
np.int8,
np.int16,
np.int32,
np.int64,
):
# the range of np.random.randint is int32; set a fixed boundary if overflow
end = min(np.iinfo(dtype).max, np.iinfo(np.int32).max)
start = max(np.iinfo(dtype).min, np.iinfo(np.int32).min)
return np.random.randint(start, end, size=input_shape).astype(dtype)
else:
raise TypeError(f"{dtype} is not supported by create_random_int.")
def saturate_cast(x: np.ndarray, dtype: np.dtype) -> np.ndarray:
"""Saturate cast for numeric types.
This function ensures that values outside the representable range
of the target dtype are clamped to the maximum or minimum representable
value of that dtype.
"""
if np.issubdtype(dtype, np.integer) or dtype in (ml_dtypes.int4, ml_dtypes.uint4):
info = ml_dtypes.iinfo(dtype)
x = np.round(x)
else:
info = ml_dtypes.finfo(dtype) # type: ignore[assignment]
return np.clip(x, info.min, info.max).astype(dtype)