(l-onnx-doc-MatMul)=

# MatMul


(l-onnx-op-matmul-13)=

## MatMul - 13

### Version

- **name**: [MatMul (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#MatMul)
- **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](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).

### 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

```python
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")
```

```{toctree}
text_diff_MatMul_9_13
```

(l-onnx-op-matmul-9)=

## MatMul - 9

### Version

- **name**: [MatMul (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#MatMul)
- **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](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).

### 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.

```{toctree}
text_diff_MatMul_1_13
text_diff_MatMul_1_9
```

(l-onnx-op-matmul-1)=

## MatMul - 1

### Version

- **name**: [MatMul (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#MatMul)
- **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](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).

### 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.