pythonnumpyarrayfire

Arrayfire python rowwise addition and multiplication


I'm trying to learn the Arrayfire idioms by translating some vectorised numpy code.

For example, this is valid rowwise addition and multiplication in numpy,

>>> a = np.array([1,2,3])
>>> a
array([1, 2, 3])
>>> b = np.arange(9).reshape((3, 3))
>>> b
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
>>> a + b
array([[ 1,  3,  5],
       [ 4,  6,  8],
       [ 7,  9, 11]])
>>> a * b
array([[ 0,  2,  6],
       [ 3,  8, 15],
       [ 6, 14, 24]])

Do all arrays in arrayfire need to be the same shape? This generates an error.

>>> a = af.from_ndarray(a)
>>> b = af.from_ndarray(b)
>>> a + b
Invalid dimension for argument 1
Expected: ldims == rdims
>>> a * b
Invalid dimension for argument 1
Expected: ldims == rdims

From @pradeep answer, you can do this using tile until the added as a feature request.

>>> a = np.array([1,2,3])
>>> a = af.tile(af.transpose(af.from_ndarray(a)),3,1)
>>> af.display(a)

[3 3 1 1]
         1          2          3 
         1          2          3 
         1          2          3 

>>> b = np.arange(9).reshape((3, 3))
>>> b = af.from_ndarray(b)
>>> af.display(a + b)

[3 3 1 1]
         1          3          5 
         4          6          8 
         7          9         11 

>>> af.display(a * b)

[3 3 1 1]
         0          2          6 
         3          8         15 
         6         14         24 

Solution

  • As of writing this response, yes that is correct. Array's need to be of same shape. But I would like to point out that we are already working on broadcasting feature for binary operations - here is the PR - we will try to get this feature into a release as soon as we can.

    However, even with current release, this limitation can be easily worked around using tile function. Since tile will be a JIT operation for such broadcast operations, it won't allocate any additional memory. The arithmetic operation and tiling operation will be combined into an efficient single launch kernel.