Mul

Mul - 14

Version

  • name: Mul (GitHub)

  • domain: main

  • since_version: 14

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 14.

Summary

Performs element-wise binary multiplication (with Numpy-style broadcasting support).

This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.

Inputs

  • A (heterogeneous) - T:

    First operand.

  • B (heterogeneous) - T:

    Second operand.

Outputs

  • C (heterogeneous) - T:

    Result, has same element type as two inputs

Type Constraints

  • T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

    Constrain input and output types to all numeric tensors.

Mul - 13

Version

  • name: Mul (GitHub)

  • domain: main

  • since_version: 13

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 13.

Summary

Performs element-wise binary multiplication (with Numpy-style broadcasting support).

This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Inputs

  • A (heterogeneous) - T:

    First operand.

  • B (heterogeneous) - T:

    Second operand.

Outputs

  • C (heterogeneous) - T:

    Result, has same element type as two inputs

Type Constraints

  • T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):

    Constrain input and output types to high-precision numeric tensors.

Mul - 7

Version

  • name: Mul (GitHub)

  • domain: main

  • since_version: 7

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 7.

Summary

Performs element-wise binary multiplication (with Numpy-style broadcasting support).

This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Inputs

  • A (heterogeneous) - T:

    First operand.

  • B (heterogeneous) - T:

    Second operand.

Outputs

  • C (heterogeneous) - T:

    Result, has same element type as two inputs

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):

    Constrain input and output types to high-precision numeric tensors.

Mul - 6

Version

  • name: Mul (GitHub)

  • domain: main

  • since_version: 6

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 6.

Summary

Performs element-wise binary multiplication (with limited broadcast support).

If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.

For example, the following tensor shapes are supported (with broadcast=1):

shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0

Attribute broadcast=1 needs to be passed to enable broadcasting.

Attributes

  • axis - INT :

    If set, defines the broadcast dimensions. See doc for details.

  • broadcast - INT (default is '0'):

    Pass 1 to enable broadcasting

Inputs

  • A (heterogeneous) - T:

    First operand, should share the type with the second operand.

  • B (heterogeneous) - T:

    Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.

Outputs

  • C (heterogeneous) - T:

    Result, has same dimensions and type as A

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):

    Constrain input and output types to high-precision numeric tensors.

Mul - 1

Version

  • name: Mul (GitHub)

  • domain: main

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: False

This version of the operator has been available since version 1.

Summary

Performs element-wise binary multiplication (with limited broadcast support).

If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.

For example, the following tensor shapes are supported (with broadcast=1):

shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0

Attribute broadcast=1 needs to be passed to enable broadcasting.

Attributes

  • axis - INT :

    If set, defines the broadcast dimensions. See doc for details.

  • broadcast - INT (default is '0'):

    Pass 1 to enable broadcasting

  • consumed_inputs - INTS :

    legacy optimization attribute.

Inputs

  • A (heterogeneous) - T:

    First operand, should share the type with the second operand.

  • B (heterogeneous) - T:

    Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.

Outputs

  • C (heterogeneous) - T:

    Result, has same dimensions and type as A

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ):

    Constrain input and output types to float tensors.