onnx_extended.ortops.tutorial.cpu#
get_ort_ext_libs#
- onnx_extended.ortops.tutorial.cpu.get_ort_ext_libs() List[str][source]#
Returns the list of libraries implementing new simple onnxruntime kernels implemented for the CPUExecutionProvider.
List of implemented kernels
<<<
from onnx_extended.ortops.tutorial.cpu import documentation
print("\n".join(documentation()))
>>>
onnx_extented.ortops.tutorial.cpu.CustomGemmFloat#
Implements operator Gemm for float type. Operator CustomGemmFloat16 implements the same for the float 16. CustomGemmFloat8E4M3FN allows Float8E4M3FN as inputs and floats as outputs.
Provider
CPUExecutionProvider
Attributes
to: quantized type
Inputs
A (T1): tensor of type T
B (T1): tensor of type T
C (T2): tensor of type T (optional)
scaleA (TF): scale for A (optional)
scaleB (TF): scale for B (optional)
scale (TF): scale for the result (optional)
Outputs
Y (T2): result of Gemm
Constraints
T1: float, float 16 or Float8E4M3FN
T2: float or float 16
TF: float
onnx_extented.ortops.tutorial.cpu.DynamicQuantizeLinear#
Implements DynamicQuantizeLinear opset 20.
Provider
CPUExecutionProvider
Attributes
to: quantized type
Inputs
X (T1): tensor of type T
Outputs
Y (T2): quantized X
scale (TS): scale
Y (T2): zero point
Constraints
T1: float, float 16
TS: float
T2: int8, uint8, float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz
onnx_extented.ortops.tutorial.cpu.MyCustomOp#
It does the sum of two tensors.
Provider
CPUExecutionProvider
Inputs
X (T): tensor of type T
Y (T): tensor of type T
Outputs
Z (T): addition of X, Y
Constraints
T: float
onnx_extented.ortops.tutorial.cpu.MyCustomOpWithAttributes#
It does the sum of two tensors + a constant equal to cst = att_float + att_int64 + att_string[0] + att_tensot[0].
Provider
CPUExecutionProvider
Attributes
att_float: a float
att_int64: an integer
att_tensor: a tensor of any type and shape
att_string: a string
Inputs
X (T): tensor of type T
Y (T): tensor of type T
Outputs
Z (T): addition of X, Y + cst
Constraints
T: float