cunumeric.matmul#

cunumeric.matmul(a: ndarray, b: ndarray, /, out: ndarray | None = None, *, casting: CastingKind = 'same_kind', dtype: np.dtype[Any] | None = None) → ndarray#

Matrix product of two arrays.

Parameters:

a (array_like) – Input arrays, scalars not allowed.
b (array_like) – Input arrays, scalars not allowed.
out (ndarray, optional) – A location into which the result is stored. If provided, it must have a shape that matches the signature (n,k),(k,m)->(n,m).
casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional) –
Controls what kind of data casting may occur.
- ’no’ means the data types should not be cast at all.
- ’equiv’ means only byte-order changes are allowed.
- ’safe’ means only casts which can preserve values are allowed.
- ’same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.
- ’unsafe’ means any data conversions may be done.
Default is ‘same_kind’.
dtype (data-type, optional) – If provided, forces the calculation to use the data type specified. Note that you may have to also give a more liberal casting parameter to allow the conversions. Default is None.

Returns:

output – The matrix product of the inputs. This is a scalar only when both a, b are 1-d vectors. If out is given, then it is returned.

Return type:

ndarray

Notes

The behavior depends on the arguments in the following way.

If both arguments are 2-D they are multiplied like conventional matrices.
If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly.
If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.

matmul differs from dot in two important ways:

Multiplication by scalars is not allowed, use * instead.

Stacks of matrices are broadcast together as if the matrices were elements, respecting the signature (n,k),(k,m)->(n,m):

>>> a = ones([9, 5, 7, 4])
>>> c = ones([9, 5, 4, 3])
>>> dot(a: ndarray, c).shape
(9, 5, 7, 9, 5, 3)
>>> matmul(a: ndarray, c).shape
(9, 5, 7, 3)
>>> # n is 7, k is 4, m is 3

The cuNumeric implementation is a little more liberal than NumPy in terms of allowed broadcasting, e.g. matmul(ones((3,1)), ones((4,5))) is allowed.

Only floating-point types are supported.