ortops.tutorial#

CPU: onnx_extented.ortops.tutorial.cpu#

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.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

CUDA: onnx_extented.ortops.tutorial.cuda#

onnx_extended.ortops.tutorial.cuda.get_ort_ext_libs() → List[str][source]#

Returns the list of libraries implementing new simple onnxruntime kernels implemented for the CUDAExecutionProvider.

List of implemented kernels

<<<

from onnx_extended.ortops.tutorial.cuda import documentation

print("\n".join(documentation()))

>>>

onnx_extented.ortops.tutorial.cuda.CustomGemm#

It calls CUDA library for Gemm \alpha A B + \beta C.

Provider

CUDAExecutionProvider

Inputs

  • A (T): tensor of type T

  • B (T): tensor of type T

  • C (T): tensor of type T

  • D (T): tensor of type T

  • E (T): tensor of type T

Outputs

  • Z (T): \alpha A B + \beta C

Constraints

  • T: float, float16, bfloat16