Gemm

Gemm - 13

Version

  • name: Gemm (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

General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3

  • A’ = transpose(A) if transA else A

  • B’ = transpose(B) if transB else B

Compute Y = alpha * A’ * B’ + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports unidirectional broadcasting (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check Broadcasting in ONNX. This operator has optional inputs/outputs. See ONNX IR for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument’s name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.

Attributes

  • alpha - FLOAT (default is '1.0'):

    Scalar multiplier for the product of input tensors A * B.

  • beta - FLOAT (default is '1.0'):

    Scalar multiplier for input tensor C.

  • transA - INT (default is '0'):

    Whether A should be transposed

  • transB - INT (default is '0'):

    Whether B should be transposed

Inputs

Between 2 and 3 inputs.

  • A (heterogeneous) - T:

    Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.

  • B (heterogeneous) - T:

    Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.

  • C (optional, heterogeneous) - T:

    Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).

Outputs

  • Y (heterogeneous) - T:

    Output tensor of shape (M, N).

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 float/int tensors.

Gemm - 11

Version

  • name: Gemm (GitHub)

  • domain: main

  • since_version: 11

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 11.

Summary

General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3

A’ = transpose(A) if transA else A

B’ = transpose(B) if transB else B

Compute Y = alpha * A’ * B’ + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports unidirectional broadcasting (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check Broadcasting in ONNX. This operator has optional inputs/outputs. See ONNX IR for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument’s name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.

Attributes

  • alpha - FLOAT (default is '1.0'):

    Scalar multiplier for the product of input tensors A * B.

  • beta - FLOAT (default is '1.0'):

    Scalar multiplier for input tensor C.

  • transA - INT (default is '0'):

    Whether A should be transposed

  • transB - INT (default is '0'):

    Whether B should be transposed

Inputs

Between 2 and 3 inputs.

  • A (heterogeneous) - T:

    Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.

  • B (heterogeneous) - T:

    Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.

  • C (optional, heterogeneous) - T:

    Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).

Outputs

  • Y (heterogeneous) - T:

    Output tensor of shape (M, N).

Type Constraints

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

    Constrain input and output types to float/int tensors.

Gemm - 9

Version

  • name: Gemm (GitHub)

  • domain: main

  • since_version: 9

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 9.

Summary

General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3

A’ = transpose(A) if transA else A

B’ = transpose(B) if transB else B

Compute Y = alpha * A’ * B’ + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports unidirectional broadcasting (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check Broadcasting in ONNX.

Attributes

  • alpha - FLOAT (default is '1.0'):

    Scalar multiplier for the product of input tensors A * B.

  • beta - FLOAT (default is '1.0'):

    Scalar multiplier for input tensor C.

  • transA - INT (default is '0'):

    Whether A should be transposed

  • transB - INT (default is '0'):

    Whether B should be transposed

Inputs

  • A (heterogeneous) - T:

    Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.

  • B (heterogeneous) - T:

    Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.

  • C (heterogeneous) - T:

    Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).

Outputs

  • Y (heterogeneous) - T:

    Output tensor of shape (M, N).

Type Constraints

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

    Constrain input and output types to float/int tensors.

Gemm - 7

Version

  • name: Gemm (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

General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3

A’ = transpose(A) if transA else A

B’ = transpose(B) if transB else B

Compute Y = alpha * A’ * B’ + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports unidirectional broadcasting (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check Broadcasting in ONNX.

Attributes

  • alpha - FLOAT (default is '1.0'):

    Scalar multiplier for the product of input tensors A * B.

  • beta - FLOAT (default is '1.0'):

    Scalar multiplier for input tensor C.

  • transA - INT (default is '0'):

    Whether A should be transposed

  • transB - INT (default is '0'):

    Whether B should be transposed

Inputs

  • A (heterogeneous) - T:

    Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.

  • B (heterogeneous) - T:

    Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.

  • C (heterogeneous) - T:

    Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).

Outputs

  • Y (heterogeneous) - T:

    Output tensor of shape (M, N).

Type Constraints

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

    Constrain input and output types to float tensors.

Gemm - 6

Version

  • name: Gemm (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

General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 Compute Y = alpha * A * B + beta * C, where input tensor A has dimension (M X K), input tensor B has dimension (K X N), input tensor C and output tensor Y have dimension (M X N). If attribute broadcast is non-zero, input tensor C will be broadcasted to match the dimension requirement. A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB.

Attributes

  • alpha - FLOAT (default is '1.0'):

    Scalar multiplier for the product of input tensors A * B, the default value is 1.0.

  • beta - FLOAT (default is '1.0'):

    Scalar multiplier for input tensor C, the default value is 1.0.

  • broadcast - INT (default is '0'):

    Whether C should be broadcasted

  • transA - INT (default is '0'):

    Whether A should be transposed

  • transB - INT (default is '0'):

    Whether B should be transposed

Inputs

  • A (heterogeneous) - T:

    Input tensor A

  • B (heterogeneous) - T:

    Input tensor B

  • C (heterogeneous) - T:

    Input tensor C

Outputs

  • Y (heterogeneous) - T:

    Output tensor.

Type Constraints

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

    Constrain input and output types to float tensors.

Gemm - 1

Version

  • name: Gemm (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

General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 Compute Y = alpha * A * B + beta * C, where input tensor A has dimension (M X K), input tensor B has dimension (K X N), input tensor C and output tensor Y have dimension (M X N). If attribute broadcast is non-zero, input tensor C will be broadcasted to match the dimension requirement. A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB.

Attributes

  • alpha - FLOAT (default is '1.0'):

    Scalar multiplier for the product of input tensors A * B, the default value is 1.0.

  • beta - FLOAT (default is '1.0'):

    Scalar multiplier for input tensor C, the default value is 1.0.

  • broadcast - INT (default is '0'):

    Whether C should be broadcasted

  • transA - INT (default is '0'):

    Whether A should be transposed

  • transB - INT (default is '0'):

    Whether B should be transposed

Inputs

  • A (heterogeneous) - T:

    Input tensor A

  • B (heterogeneous) - T:

    Input tensor B

  • C (heterogeneous) - T:

    Input tensor C, can be inplace.

Outputs

  • Y (heterogeneous) - T:

    Output tensor.

Type Constraints

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

    Constrain input and output types to float tensors.