mars.tensor.multiply(x1, x2, out=None, where=None, **kwargs)[source]#

Multiply arguments element-wise.

  • x1 (array_like) – Input arrays to be multiplied.

  • x2 (array_like) – Input arrays to be multiplied.

  • out (Tensor, None, or tuple of Tensor and None, optional) – A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated tensor is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

  • where (array_like, optional) – Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.

  • **kwargs


y – The product of x1 and x2, element-wise. Returns a scalar if both x1 and x2 are scalars.

Return type



Equivalent to x1 * x2 in terms of array broadcasting.


>>> import mars.tensor as mt
>>> mt.multiply(2.0, 4.0).execute()
>>> x1 = mt.arange(9.0).reshape((3, 3))
>>> x2 = mt.arange(3.0)
>>> mt.multiply(x1, x2).execute()
array([[  0.,   1.,   4.],
       [  0.,   4.,  10.],
       [  0.,   7.,  16.]])