Python High Performance - Second Edition

Type specializations

As shown earlier, the nb.jit decorator works by compiling a specialized version of the function once it encounters a new argument type. To better understand how this works, we can inspect the decorated function in the sum_sq example.

Numba exposes the specialized types using the signatures attribute. Right after the sum_sq definition, we can inspect the available specialization by accessing the sum_sq.signatures, as follows:

    sum_sq.signatures    # Output:    # []

If we call this function with a specific argument, for instance, an array of float64 numbers, we can see how Numba compiles a specialized version on the fly. If we also apply the function on an array of float32, we can see how a new entry is added to the sum_sq.signatures ...