# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import sys
from typing import Any, Sequence
import numpy as np
from onnx import MapProto, OptionalProto, SequenceProto, TensorProto, helper, subbyte
from onnx.external_data_helper import load_external_data_for_tensor, uses_external_data
[docs]
def combine_pairs_to_complex(fa: Sequence[int]) -> list[complex]:
return [complex(fa[i * 2], fa[i * 2 + 1]) for i in range(len(fa) // 2)]
[docs]
def bfloat16_to_float32(
data: np.int16 | np.int32 | np.ndarray,
dims: int | Sequence[int] | None = None,
) -> np.ndarray:
"""Converts ndarray of bf16 (as uint32) to f32 (as uint32).
Args:
data: A numpy array, empty dimensions are allowed if dims is
None.
dims: If specified, the function reshapes the results.
Returns:
A numpy array of float32 with the same dimension if dims is
None, or reshaped to dims if specified
"""
shift = lambda x: x << 16 # noqa: E731
if dims is None:
if len(data.shape) == 0:
return shift(np.array([data]).astype(np.int32)).view(np.float32)[0] # type: ignore[no-any-return]
return shift(data.astype(np.int32)).view(np.float32) # type: ignore[no-any-return]
return shift(data.astype(np.int32)).reshape(dims).view(np.float32) # type: ignore[no-any-return]
def _float8e4m3_to_float32_scalar(ival: int, fn: bool, uz: bool) -> np.float32:
if not fn:
raise NotImplementedError("fn=False is not implemented.")
if ival < 0 or ival > 255: # noqa: PLR2004
raise ValueError(f"{ival} is not a float8.")
if uz:
exponent_bias = 8
if ival == 0x80: # noqa: PLR2004
return np.nan # type: ignore[return-value]
else:
exponent_bias = 7
if ival == 255: # noqa: PLR2004
return np.float32(-np.nan)
if ival == 127: # noqa: PLR2004
return np.float32(np.nan)
expo = (ival & 0x78) >> 3
mant = ival & 0x07
sign = ival & 0x80
res = sign << 24
if expo == 0:
if mant > 0:
expo = 0x7F - exponent_bias
if mant & 0x4 == 0:
mant &= 0x3
mant <<= 1
expo -= 1
if mant & 0x4 == 0:
mant &= 0x3
mant <<= 1
expo -= 1
res |= (mant & 0x3) << 21
res |= expo << 23
else:
res |= mant << 20
expo += 0x7F - exponent_bias
res |= expo << 23
f = np.uint32(res).view(np.float32)
return f
_float8e4m3_to_float32 = np.vectorize(
_float8e4m3_to_float32_scalar, excluded=["fn", "uz"]
)
[docs]
def float8e4m3_to_float32(
data: np.int16 | np.int32 | np.ndarray,
dims: int | Sequence[int] | None = None,
fn: bool = True,
uz: bool = False,
) -> np.ndarray:
"""Converts ndarray of float8, e4m3 (as uint32) to f32 (as uint32).
See :ref:`onnx-detail-float8` for technical details.
Args:
data: A numpy array, empty dimensions are allowed if dims is None.
dims: If specified, the function reshapes the results.
fn: No infinite values.
uz: No negative zero.
Returns:
A numpy array of float32 with the same dimension if dims is None,
or reshaped to dims if specified.
"""
if not fn:
raise NotImplementedError(
"float32_to_float8e4m3 not implemented with fn=False."
)
res = _float8e4m3_to_float32(data, fn=fn, uz=uz)
if dims is None:
return res # type: ignore[no-any-return]
return res.reshape(dims) # type: ignore[no-any-return]
def _float8e5m2_to_float32_scalar(ival: int, fn: bool, uz: bool) -> np.float32:
if fn and uz:
if ival == 0x80: # noqa: PLR2004
return np.float32(np.nan)
exponent_bias = 16
elif not fn and not uz:
if ival in {253, 254, 255}:
return np.float32(-np.nan)
if ival in {125, 126, 127}:
return np.float32(np.nan)
if ival == 252: # noqa: PLR2004
return np.float32(-np.inf)
if ival == 124: # noqa: PLR2004
return np.float32(np.inf)
exponent_bias = 15
else:
raise NotImplementedError("fn and uz must be both False or True.")
expo = (ival & 0x7C) >> 2
mant = ival & 0x03
sign = ival & 0x80
res = sign << 24
if expo == 0:
if mant > 0:
expo = 0x7F - exponent_bias
if mant & 0x2 == 0:
mant &= 0x1
mant <<= 1
expo -= 1
res |= (mant & 0x1) << 22
res |= expo << 23
else:
res |= mant << 21
expo += 0x7F - exponent_bias
res |= expo << 23
f = np.uint32(res).view(np.float32)
return f
_float8e5m2_to_float32 = np.vectorize(
_float8e5m2_to_float32_scalar, excluded=["fn", "uz"]
)
[docs]
def float8e5m2_to_float32(
data: np.int16 | np.int32 | np.ndarray,
dims: int | Sequence[int] | None = None,
fn: bool = False,
uz: bool = False,
) -> np.ndarray:
"""Converts ndarray of float8, e5m2 (as uint32) to f32 (as uint32).
See :ref:`onnx-detail-float8` for technical details.
Args:
data: A numpy array, empty dimensions are allowed if dims is None.
dims: If specified, the function reshapes the results.
fn: No infinite values.
uz: No negative zero.
Returns:
A numpy array of float32 with the same dimension if dims is None,
or reshaped to dims if specified
"""
res = _float8e5m2_to_float32(data, fn=fn, uz=uz)
if dims is None:
return res # type: ignore[no-any-return]
return res.reshape(dims) # type: ignore[no-any-return]
def unpack_int4(
data: np.int32 | np.ndarray,
dims: int | Sequence[int],
signed: bool,
) -> np.ndarray:
"""Converts ndarray of int4 (as packed uint8) to f32
See :ref:`onnx-detail-int4` for technical details.
Args:
data: A numpy array, empty dimensions are allowed if dims is
None.
dims: The dimensions are used to reshape the unpacked buffer
signed: Whether the 4 bit integer is signed or unsigned
Returns:
A numpy array of float32 reshaped to dims.
"""
single_func = lambda x: subbyte.unpack_single_4bitx2(x, signed) # noqa: E731
func = np.frompyfunc(single_func, 1, 2)
res_high, res_low = func(data.ravel())
res = np.empty((res_high.size + res_low.size,), dtype=np.float32)
res[0::2] = res_high
res[1::2] = res_low
if (
res.size == np.prod(dims) + 1
): # handle single-element padding due to odd number of elements
res = res.ravel()[:-1]
res = res.reshape(dims)
return res
[docs]
def to_array(tensor: TensorProto, base_dir: str = "") -> np.ndarray: # noqa: PLR0911
"""Converts a tensor def object to a numpy array.
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 == TensorProto.UNDEFINED:
raise TypeError("The element type in the input tensor is not defined.")
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 == 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 uses_external_data(tensor):
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()
# manually convert bf16 since there's no numpy support
if tensor_dtype == TensorProto.BFLOAT16:
data = np.frombuffer(raw_data, dtype=np.int16)
return bfloat16_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E4M3FN:
data = np.frombuffer(raw_data, dtype=np.int8)
return float8e4m3_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E4M3FNUZ:
data = np.frombuffer(raw_data, dtype=np.int8)
return float8e4m3_to_float32(data, dims, uz=True)
if tensor_dtype == TensorProto.FLOAT8E5M2:
data = np.frombuffer(raw_data, dtype=np.int8)
return float8e5m2_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E5M2FNUZ:
data = np.frombuffer(raw_data, dtype=np.int8)
return float8e5m2_to_float32(data, dims, fn=True, uz=True)
if tensor_dtype == TensorProto.UINT4:
data = np.frombuffer(raw_data, dtype=np.uint8)
return unpack_int4(data, dims, signed=False)
if tensor_dtype == TensorProto.INT4:
data = np.frombuffer(raw_data, dtype=np.int8)
return unpack_int4(data, dims, signed=True)
return np.frombuffer(raw_data, dtype=np_dtype).reshape(dims) # type: ignore[no-any-return]
# float16 is stored as int32 (uint16 type); Need view to get the original value
if tensor_dtype == TensorProto.FLOAT16:
return (
np.asarray(tensor.int32_data, dtype=np.uint16)
.reshape(dims)
.view(np.float16)
)
# bfloat16 is stored as int32 (uint16 type); no numpy support for bf16
if tensor_dtype == TensorProto.BFLOAT16:
data = np.asarray(tensor.int32_data, dtype=np.int32)
return bfloat16_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E4M3FN:
data = np.asarray(tensor.int32_data, dtype=np.int32)
return float8e4m3_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E4M3FNUZ:
data = np.asarray(tensor.int32_data, dtype=np.int32)
return float8e4m3_to_float32(data, dims, uz=True)
if tensor_dtype == TensorProto.FLOAT8E5M2:
data = np.asarray(tensor.int32_data, dtype=np.int32)
return float8e5m2_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E5M2FNUZ:
data = np.asarray(tensor.int32_data, dtype=np.int32)
return float8e5m2_to_float32(data, dims, fn=True, uz=True)
if tensor_dtype == TensorProto.UINT4:
data = np.asarray(tensor.int32_data, dtype=storage_np_dtype)
return unpack_int4(data, dims, signed=False)
if tensor_dtype == TensorProto.INT4:
data = np.asarray(tensor.int32_data, dtype=storage_np_dtype)
return unpack_int4(data, dims, signed=True)
data = getattr(tensor, storage_field)
if tensor_dtype in (TensorProto.COMPLEX64, TensorProto.COMPLEX128):
data = combine_pairs_to_complex(data) # type: ignore[assignment,arg-type]
return np.asarray(data, dtype=storage_np_dtype).astype(np_dtype).reshape(dims)
[docs]
def from_array(arr: np.ndarray, name: str | None = None) -> TensorProto:
"""Converts a numpy array to a tensor def.
Args:
arr: a numpy array.
name: (optional) the name of the tensor.
Returns:
TensorProto: the converted tensor def.
"""
if not isinstance(arr, (np.ndarray, np.generic)):
raise TypeError(
f"arr must be of type np.generic or np.ndarray, got {type(arr)}"
)
tensor = TensorProto()
tensor.dims.extend(arr.shape)
if name:
tensor.name = name
if arr.dtype == object:
# Special care for strings.
tensor.data_type = helper.np_dtype_to_tensor_dtype(arr.dtype)
# TODO: Introduce full string support.
# We flatten the array in case there are 2-D arrays are specified
# We throw the error below if we have a 3-D array or some kind of other
# object. 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 = arr.flatten()
for e in flat_array:
if isinstance(e, str):
tensor.string_data.append(e.encode("utf-8"))
elif isinstance(e, np.ndarray):
for s in e:
if isinstance(s, str):
tensor.string_data.append(s.encode("utf-8"))
elif isinstance(s, bytes):
tensor.string_data.append(s)
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
# For numerical types, directly use numpy raw bytes.
try:
dtype = helper.np_dtype_to_tensor_dtype(arr.dtype)
except KeyError as e:
raise RuntimeError(f"Numpy data type not understood yet: {arr.dtype!r}") from e
tensor.data_type = dtype
tensor.raw_data = arr.tobytes() # note: tobytes() is only after 1.9.
if sys.byteorder == "big":
# Convert endian from big to little
convert_endian(tensor)
return tensor
[docs]
def to_list(sequence: 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 == SequenceProto.TENSOR:
return [to_array(v) for v in sequence.tensor_values] # type: ignore[arg-type]
if elem_type == SequenceProto.SPARSE_TENSOR:
return [to_array(v) for v in sequence.sparse_tensor_values] # type: ignore[arg-type]
if elem_type == SequenceProto.SEQUENCE:
return [to_list(v) for v in sequence.sequence_values]
if elem_type == 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
) -> 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 = 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 = SequenceProto.MAP
elif isinstance(first_elem, list):
elem_type = SequenceProto.SEQUENCE
else:
elem_type = SequenceProto.TENSOR
else:
# if empty input list and no dtype specified
# choose sequence of tensors on default
elem_type = 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 == SequenceProto.TENSOR:
for tensor in lst:
sequence.tensor_values.extend([from_array(tensor)])
elif elem_type == SequenceProto.SEQUENCE:
for seq in lst:
sequence.sequence_values.extend([from_list(seq)])
elif elem_type == 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: 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 == 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) -> 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 = 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 = [
TensorProto.INT8,
TensorProto.INT16,
TensorProto.INT32,
TensorProto.INT64,
TensorProto.UINT8,
TensorProto.UINT16,
TensorProto.UINT32,
TensorProto.UINT64,
]
if not (
all(
np.result_type(key) == raw_key_type # type: ignore[arg-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 == 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: 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 == OptionalProto.UNDEFINED:
return None
if elem_type == OptionalProto.TENSOR:
return to_array(optional.tensor_value)
if elem_type == OptionalProto.SPARSE_TENSOR:
return to_array(optional.sparse_tensor_value) # type: ignore[arg-type]
if elem_type == OptionalProto.SEQUENCE:
return to_list(optional.sequence_value)
if elem_type == OptionalProto.MAP:
return to_dict(optional.map_value)
if elem_type == 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
) -> 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 = OptionalProto()
if name:
optional.name = name
if dtype:
# dtype must be a valid OptionalProto.DataType
valid_dtypes = list(OptionalProto.DataType.values())
if dtype not in valid_dtypes:
raise TypeError(f"{dtype} must be a valid OptionalProto.DataType.")
elem_type = dtype
elif isinstance(opt, dict):
elem_type = OptionalProto.MAP
elif isinstance(opt, list):
elem_type = OptionalProto.SEQUENCE
elif opt is None:
elem_type = OptionalProto.UNDEFINED
else:
elem_type = OptionalProto.TENSOR
optional.elem_type = elem_type
if opt is not None:
if elem_type == OptionalProto.TENSOR:
optional.tensor_value.CopyFrom(from_array(opt))
elif elem_type == OptionalProto.SEQUENCE:
optional.sequence_value.CopyFrom(from_list(opt))
elif elem_type == 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
[docs]
def convert_endian(tensor: TensorProto) -> None:
"""Call to convert endianness of raw data in tensor.
Args:
tensor: TensorProto to be converted.
"""
tensor_dtype = tensor.data_type
np_dtype = helper.tensor_dtype_to_np_dtype(tensor_dtype)
tensor.raw_data = (
np.frombuffer(tensor.raw_data, dtype=np_dtype).byteswap().tobytes()
)
[docs]
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.")