1.4. Creating Numpy universal functions

1.4.1. The @vectorize decorator

Numba’s vectorize allows Python functions taking scalar input arguments to be used as NumPy ufuncs. Creating a traditional NumPy ufunc is not not the most straightforward process and involves writing some C code. Numba makes this easy. Using the vectorize() decorator, Numba can compile a pure Python function into a ufunc that operates over NumPy arrays as fast as traditional ufuncs written in C.

Using vectorize(), you write your function as operating over input scalars, rather than arrays. Numba will generate the surrounding loop (or kernel) allowing efficient iteration over the actual inputs.

The vectorize() decorator needs you to pass a list of signatures you want to support. In the basic case, only one signature will be passed:

from numba import vectorize, float64

@vectorize([float64(float64, float64)])
def f(x, y):
    return x + y

If you pass several signatures, beware that you have to pass most specific signatures before least specific ones (e.g., single-precision floats before double-precision floats), otherwise type-based dispatching will not work as expected:

@vectorize([int32(int32, int32),
            int64(int64, int64),
            float32(float32, float32),
            float64(float64, float64)])
def f(x, y):
    return x + y

The function will work as expected over the specified array types:

>>> a = np.arange(6)
>>> f(a, a)
array([ 0,  2,  4,  6,  8, 10])
>>> a = np.linspace(0, 1, 6)
>>> f(a, a)
array([ 0. ,  0.4,  0.8,  1.2,  1.6,  2. ])

but it will fail working on other types:

>>> a = np.linspace(0, 1+1j, 6)
>>> f(a, a)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: ufunc 'ufunc' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

You might ask yourself, “why would I go through this instead of compiling a simple iteration loop using the @jit decorator?”. The answer is that NumPy ufuncs automatically get other features such as reduction, accumulation or broadcasting. Using the example above:

>>> a = np.arange(12).reshape(3, 4)
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> f.reduce(a, axis=0)
array([12, 15, 18, 21])
>>> f.reduce(a, axis=1)
array([ 6, 22, 38])
>>> f.accumulate(a)
array([[ 0,  1,  2,  3],
       [ 4,  6,  8, 10],
       [12, 15, 18, 21]])
>>> f.accumulate(a, axis=1)
array([[ 0,  1,  3,  6],
       [ 4,  9, 15, 22],
       [ 8, 17, 27, 38]])

See also

Standard features of ufuncs (NumPy documentation).

1.4.2. The @guvectorize decorator

While vectorize() allows you to write ufuncs that work on one element at a time, the guvectorize() decorator takes the concept one step further and allows you to write ufuncs that will work on an arbitrary number of elements of input arrays, and take and return arrays of differing dimensions. The typical example is a running median or a convolution filter.

Contrary to vectorize() functions, guvectorize() functions don’t return their result value: their take it as an array argument, which must be filled in by the function. This is because the array is actually allocated by NumPy’s dispatch mechanism, which calls into the Numba-generated code.

Here is a very simple example:

@guvectorize([(int64[:], int64[:], int64[:])], '(n),()->(n)')
def g(x, y, res):
    for i in range(x.shape[0]):
        res[i] = x[i] + y[0]

The underlying Python function simply adds a given scalar (y) to all elements of a 1-dimension array. What’s more interesting is the declaration. There are two things there:

  • the declaration of input and output layouts, in symbolic form: (n),()->(n) tells NumPy that the function takes a n-element one-dimension array, a scalar (symbolically denoted by the empty tuple ()) and returns a n-element one-dimension array;
  • the list of supported concrete signatures as in @vectorize; here we only support int64 arrays.


The concrete signature does not allow for scalar values, even though the layout may mention them. In this example, the second argument is declared as int64[:], not int64. This is why it must be dereferenced by fetching y[0].

We can now check what the compiled ufunc does, over a simple example:

>>> a = np.arange(5)
>>> a
array([0, 1, 2, 3, 4])
>>> g(a, 2)
array([2, 3, 4, 5, 6])

The nice thing is that NumPy will automatically dispatch over more complicated inputs, depending on their shapes:

>>> a = np.arange(6).reshape(2, 3)
>>> a
array([[0, 1, 2],
       [3, 4, 5]])
>>> g(a, 10)
array([[10, 11, 12],
       [13, 14, 15]])
>>> g(a, np.array([10, 20]))
array([[10, 11, 12],
       [23, 24, 25]])


Both vectorize() and guvectorize() support passing nopython=True as in the @jit decorator. Use it to ensure the generated code does not fallback to object mode.