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和矢量化的函数。我对这段代码的jit版本没有任何问题，但是我在vectorize中遇到了一个索引错误。我怀疑这个question's答案是正确的，即存在类型错误，但我不确定应该朝哪个方向改变索引。下面是我一直在使用的函数，它将斐波那契数输出到序列的选定索引。索引出了什么问题，如何更正代码来解释它？你知道吗

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}))

这将导致以下错误输出。你知道吗

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

这个错误是因为您试图vectorize一个函数，您可以说它本质上是不可矢量化的。我认为您混淆了@jit和@vectorize如何工作的功能。为了加快函数的速度，可以使用@jit，而@vectorize用于创建numpy通用函数。参见official documentation here：

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.

因此，基本上不可能创建一个numpy通用函数，它具有与fibonacci函数相同的功能。如果您感兴趣，这里有official documentation on universal functions的链接。你知道吗

所以为了使用@vectorize，您需要创建一个基本上可以用作numpy通用函数的函数。为了加快代码的速度，只需使用@jit。你知道吗