---
title: "matmul(_:)"
framework: coreml
role: symbol
role_heading: Instance Method
path: "coreml/mltensor/matmul(_:)"
---

# matmul(_:)

Multiplies two tensors together using matrix multiplication.

## Declaration

```swift
func matmul(_ other: MLTensor) -> MLTensor
```

## Parameters

- `other`: The right-hand side tensor to be multiplied.

## Return Value

Return Value The result of the matrix multiplication.

## Discussion

Discussion The specific behaviour is determined by the dimensionality of the inputs, as described below: If both tensors are 1-dimensional, the dot-product is returned. If the first is 2-dimensional and other 1-dimensional, the matrix-vector product is returned. If both have a rank of 2, the matrix-matrix product is returned and when the rank is greater than 2, the batched matrix multiply is returned For example: let v1 = MLTensor([1.0, 2.0, 3.0, 4.0]) let v2 = MLTensor([5.0, 6.0, 7.0, 8.0]) let v3 = v1.matmul(v2) v3.shape // is [] await v3.shapedArray(of: Float.self) // is 70.0

let m1 = MLTensor(shape: [2, 3], scalars: [     1, 2, 3,     4, 5, 6 ], scalarType: Float.self) let m2 = MLTensor(shape: [3, 2], scalars: [      7,  8,      9, 10,     11, 12 ], scalarType: Float.self) let m3 = t1.matmul(r2) m3.shape // is [2, 2] await m3.shapedArray(of: Float.self) // is [[58, 64], [139, 154]]

// Supports broadcasting let m4 = MLTensor(randomNormal: [3, 1, 1, 4], scalarType: Float.self) let m5 = MLTensor(randomNormal: [4, 2], scalarType: Float.self) let m6 = t4.matmul(t5) m6.shape // is [3, 1, 1, 2]

## See Also

### Computing the exponent, pow and square root

- [exp()](coreml/mltensor/exp().md)
- [exp2()](coreml/mltensor/exp2().md)
- [pow(_:)](coreml/mltensor/pow(_:).md)
- [rsqrt()](coreml/mltensor/rsqrt().md)
- [squared()](coreml/mltensor/squared().md)
- [squareRoot()](coreml/mltensor/squareroot().md)
- [log()](coreml/mltensor/log().md)
- [round()](coreml/mltensor/round().md)