MatMul

MatMul - 13

Version

  • name: MatMul (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

Matrix product that behaves like numpy.matmul.

Inputs

  • A (heterogeneous) - T:

    N-dimensional matrix A

  • B (heterogeneous) - T:

    N-dimensional matrix B

Outputs

  • Y (heterogeneous) - T:

    Matrix multiply results from A * B

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.

Examples

default

import numpy as np
import onnx

node = onnx.helper.make_node(
    "MatMul",
    inputs=["a", "b"],
    outputs=["c"],
)

# 2d
a = np.random.randn(3, 4).astype(np.float32)
b = np.random.randn(4, 3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_2d")

# 3d
a = np.random.randn(2, 3, 4).astype(np.float32)
b = np.random.randn(2, 4, 3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_3d")

# 4d
a = np.random.randn(1, 2, 3, 4).astype(np.float32)
b = np.random.randn(1, 2, 4, 3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d")

# broadcasting
a = np.random.randn(3, 1, 3, 4).astype(np.float32)
b = np.random.randn(1, 2, 4, 2).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_bcast")

# 1d + 3d
a = np.random.randn(4).astype(np.float32)
b = np.random.randn(2, 4, 1).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_1d_3d")

# 3d + 1d
a = np.random.randn(1, 2, 4, 3).astype(np.float32)
b = np.random.randn(3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d_1d")

# 1d + 1d
a = np.random.randn(3).astype(np.float32)
b = np.random.randn(3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_1d_1d")

MatMul - 9

Version

  • name: MatMul (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

Matrix product that behaves like numpy.matmul.

Inputs

  • A (heterogeneous) - T:

    N-dimensional matrix A

  • B (heterogeneous) - T:

    N-dimensional matrix B

Outputs

  • Y (heterogeneous) - T:

    Matrix multiply results from A * B

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.

MatMul - 1

Version

  • name: MatMul (GitHub)

  • domain: main

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

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

Summary

Matrix product that behaves like numpy.matmul.

Inputs

  • A (heterogeneous) - T:

    N-dimensional matrix A

  • B (heterogeneous) - T:

    N-dimensional matrix B

Outputs

  • Y (heterogeneous) - T:

    Matrix multiply results from A * B

Type Constraints

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

    Constrain input and output types to float tensors.