Numba索引错误：TypeError：无法在i8 *中的[0]处索引

Question

我正在学习如何使用Numba来加速jit和vectorize的功能。我对此代码的jit版本没有任何问题，但我在使用vectorize时遇到索引错误。我怀疑这个question's的答案是正确的想法，有一个类型错误，但我不知道改变索引的方向。下面包括我一直在玩的函数，它将斐波纳契数输出到序列的选定索引。索引出了什么问题，以及如何纠正我的代码以解决它？

from numba import vectorize
import numpy as np
from timeit import timeit

@vectorize
def fib(n):
    '''
    Adjusted from:
    https://lectures.quantecon.org/py/numba.html
    https://en.wikipedia.org/wiki/Fibonacci_number
    https://www.geeksforgeeks.org/program-for-nth-fibonacci-number/
    '''

    if n == 1:
        return np.ones(1)
    elif n > 1:
        x = np.empty(n)
        x[0] = 1
        x[1] = 1
        for i in range(2,n):
            x[i] =  x[i-1] + x[i-2]
        return x
    else:
        print('WARNING: Check validity of input.')


print(timeit('fib(10)', globals={'fib':fib}))

这导致以下错误输出。

Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/llvmlite/ir/instructions.py", line 619, in __init__
    typ = typ.elements[i]
AttributeError: 'PointerType' object has no attribute 'elements'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/galen/Projects/myjekyllblog/test_code/quantecon_2.py", line 27, in <module>
    print(timeit('fib(10)', globals={'fib':fib}))
  File "/usr/lib/python3.6/timeit.py", line 233, in timeit
    return Timer(stmt, setup, timer, globals).timeit(number)
  File "/usr/lib/python3.6/timeit.py", line 178, in timeit
    timing = self.inner(it, self.timer)
  File "<timeit-src>", line 6, in inner
  File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/dufunc.py", line 166, in _compile_for_args
    return self._compile_for_argtys(tuple(argtys))
  File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/dufunc.py", line 188, in _compile_for_argtys
    cres, actual_sig)
  File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/ufuncbuilder.py", line 157, in _build_element_wise_ufunc_wrapper
    cres.objectmode, cres)
  File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/wrappers.py", line 220, in build_ufunc_wrapper
    env=envptr)
  File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/wrappers.py", line 130, in build_fast_loop_body
    env=env)
  File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/wrappers.py", line 23, in _build_ufunc_loop_body
    store(retval)
  File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/wrappers.py", line 126, in store
    out.store_aligned(retval, ind)
  File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/wrappers.py", line 276, in store_aligned
    self.context.pack_value(self.builder, self.fe_type, value, ptr)
  File "/usr/local/lib/python3.6/dist-packages/numba/targets/base.py", line 482, in pack_value
    dataval = self.data_model_manager[ty].as_data(builder, value)
  File "/usr/local/lib/python3.6/dist-packages/numba/datamodel/models.py", line 558, in as_data
    elems = self._as("as_data", builder, value)
  File "/usr/local/lib/python3.6/dist-packages/numba/datamodel/models.py", line 530, in _as
    self.get(builder, value, i)))
  File "/usr/local/lib/python3.6/dist-packages/numba/datamodel/models.py", line 558, in as_data
    elems = self._as("as_data", builder, value)
  File "/usr/local/lib/python3.6/dist-packages/numba/datamodel/models.py", line 530, in _as
    self.get(builder, value, i)))
  File "/usr/local/lib/python3.6/dist-packages/numba/datamodel/models.py", line 624, in get
    name="extracted." + self._fields[pos])
  File "/usr/local/lib/python3.6/dist-packages/llvmlite/ir/builder.py", line 911, in extract_value
    instr = instructions.ExtractValue(self.block, agg, idx, name=name)
  File "/usr/local/lib/python3.6/dist-packages/llvmlite/ir/instructions.py", line 622, in __init__
    % (list(indices), agg.type))
TypeError: Can't index at [0] in i8*

Answer 1

错误是因为你试图vectorize一个你可以说基本上不可矢量化的函数。我认为你混淆了@jit和@vectorize如何工作的功能。为了加速你的功能，你使用@jit，而@vectorize用于创建numpy通用函数。见official documentation here：

使用vectorize（），您可以将函数编写为通过输入标量而不是数组进行操作。 Numba将生成周围的循环（或内核），允许对实际输入进行有效迭代。

所以基本上不可能创建一个与你的斐波那契函数具有相同功能的numpy通用函数。如果您有兴趣，这里是official documentation on universal functions的链接。

因此，为了使用@vectorize，您需要创建一个可以基本上用作numpy通用函数的函数。出于加速代码的目的，您只需使用@jit。