Question

我正在学习如何使用Numba通过jit和vectorize加速功能。这段代码的jit版本没有任何问题，但是我在vectorize中遇到了索引错误。我怀疑这个question's答案是正确的想法，即存在类型错误，但是我不确定改变索引的方向。下面包括我一直在使用的功能，该功能输出斐波那契数直至序列的选定索引。索引出了什么问题？如何纠正我的代码以解决这个问题？

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。

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

错误是因为您试图vectorize可以说基本上无法向量化的功能。我认为您对@jit和@vectorize的工作方式感到困惑。为了加快您的功能，可以使用@jit，而@vectorize用于创建numpy通用函数。参见official documentation here：

使用vectorize（），将函数编写为对输入进行操作标量，而不是数组。 Numba将生成周围的循环（或内核）允许对实际输入进行有效的迭代。

因此，基本上不可能创建具有与fibonacci函数相同功能的numpy通用函数。如果您有兴趣，这是official documentation on universal functions的链接。

因此，要使用@vectorize，您需要创建一个实际上可以用作numpy通用函数的函数。为了加快代码的速度，您只需要使用@jit。

Numba索引错误：TypeError：无法在i8 *中的[0]处索引

1 个答案: