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# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import numpy as np
from onnx import subbyte
from onnx.helper import (
float32_to_bfloat16,
float32_to_float8e4m3,
float32_to_float8e5m2,
tensor_dtype_to_np_dtype,
)
from onnx.numpy_helper import (
bfloat16_to_float32,
float8e4m3_to_float32,
float8e5m2_to_float32,
)
from onnx.onnx_pb import TensorProto
from onnx.reference.custom_element_types import (
bfloat16,
float8e4m3fn,
float8e4m3fnuz,
float8e5m2,
float8e5m2fnuz,
int4,
uint4,
)
from onnx.reference.op_run import OpRun
def cast_to(x, to, saturate): # noqa: PLR0911
if x.dtype == bfloat16 and x.dtype.descr[0][0] == "bfloat16":
if to == TensorProto.BFLOAT16:
return x
xr = x.ravel()
xf = np.empty(xr.shape[0], dtype=np.float32)
for i in range(xr.shape[0]):
el = bfloat16_to_float32(xr[i])
xf[i] = el
dtype = tensor_dtype_to_np_dtype(to)
return xf.astype(dtype).reshape(x.shape)
f8 = {
(float8e4m3fn, "e4m3fn", TensorProto.FLOAT8E4M3FN): float8e4m3_to_float32,
(
float8e4m3fnuz,
"e4m3fnuz",
TensorProto.FLOAT8E4M3FNUZ,
): lambda *args: float8e4m3_to_float32(*args, uz=True),
(float8e5m2, "e5m2", TensorProto.FLOAT8E5M2): float8e5m2_to_float32,
(
float8e5m2fnuz,
"e5m2fnuz",
TensorProto.FLOAT8E5M2FNUZ,
): lambda *args: float8e5m2_to_float32(*args, fn=True, uz=True),
}
for (dt, st, proto_type), cvt in f8.items():
if x.dtype == dt and x.dtype.descr[0][0] == st:
if to == proto_type:
return x
xr = x.ravel()
xf = np.empty(xr.shape[0], dtype=np.float32)
for i in range(xr.shape[0]):
el = cvt(xr[i])
xf[i] = el
dtype = tensor_dtype_to_np_dtype(to)
return xf.astype(dtype).reshape(x.shape)
if to == TensorProto.BFLOAT16:
xf = x.astype(np.float32).ravel()
y = np.empty(xf.shape, dtype=bfloat16).ravel()
for i in range(y.shape[0]):
el = float32_to_bfloat16(xf[i], truncate=True) # type: ignore[assignment]
y[i] = el
return y.reshape(x.shape)
i4 = [
(uint4, "uint4", TensorProto.UINT4, False),
(int4, "int4", TensorProto.INT4, True),
]
for np_type, np_desc, tensor_type, signed in i4:
if x.dtype == np_type and x.dtype.descr[0][0] == np_desc:
if to == tensor_type:
return x
to_type = tensor_dtype_to_np_dtype(to)
return x.astype(to_type)
if to == tensor_type:
xf = x.astype(np.float32).ravel()
y = np.empty(xf.shape, dtype=np_type).ravel()
for i in range(y.shape[0]):
el = subbyte.float32_to_4bit_unpacked(xf[i], signed=signed)
y[i] = el
return y.reshape(x.shape)
f8back = {
TensorProto.FLOAT8E4M3FN: (
float8e4m3fn,
lambda *args: float32_to_float8e4m3(*args, saturate=saturate),
),
TensorProto.FLOAT8E4M3FNUZ: (
float8e4m3fnuz,
lambda *args: float32_to_float8e4m3(*args, uz=True, saturate=saturate),
),
TensorProto.FLOAT8E5M2: (
float8e5m2,
lambda *args: float32_to_float8e5m2(*args, saturate=saturate),
),
TensorProto.FLOAT8E5M2FNUZ: (
float8e5m2fnuz,
lambda *args: float32_to_float8e5m2(
*args, fn=True, uz=True, saturate=saturate
),
),
}
for dt, (npdt, cvt) in f8back.items():
if to == dt:
xf = x.astype(np.float32).ravel()
y = np.empty(xf.shape, dtype=npdt).ravel()
for i in range(y.shape[0]):
el = cvt(xf[i]) # type: ignore[assignment]
y[i] = el
return y.reshape(x.shape)
if to == TensorProto.STRING:
return x.astype(np.str_)
dtype = tensor_dtype_to_np_dtype(to)
return x.astype(dtype)
class Cast_1(OpRun):
def _run(self, x, to=None): # type: ignore
return (cast_to(x, to, saturate=True),)
class Cast_19(OpRun):
def _run(self, x, to=None, saturate=None): # type: ignore
return (cast_to(x, to, saturate),)
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