Numba data type error: Cannot unify array

Question

我正在使用 Numba 加速一系列功能，如下所示。如果我将函数 PosMomentSingle 中的 step_size 变量设置为浮点数（例如 step_size = 0.5），而不是整数（例如 step_size = 1.0），我会收到以下错误：

Cannot unify array(float32, 1d, C) and array(float64, 1d, C) for 'axle_coords.2', defined at <ipython-input-182-37c789ca2187> (12)

File "<ipython-input-182-37c789ca2187>", line 12:
def nbSimpleSpanMoment(L, axles, spacings, step_size):
    <source elided>
    
    while np.min(axle_coords) < L:

我发现很难理解问题出在哪里，但我的猜测是 @jit (nbSimpleSpanMoment) 之后的函数有问题，数据类型不匹配。我尝试将所有变量设置为 float32，然后设置为 float64（例如 L = np.float32(L)），但无论我尝试什么都会产生一组新的错误。由于错误消息非常含糊，我无法调试问题。有 numba 经验的人可以解释我在这里做错了什么吗？

我将我的代码放在下面以重现问题。

感谢您的帮助！

import numba as nb
import numpy as np

@nb.vectorize(nopython=True)
def nbvectMoment(L,x):
    if x<L/2.0:
        return 0.5*x
    else:
        return 0.5*(L-x)

@nb.jit(nopython=True)
def nbSimpleSpanMoment(L, axles, spacings, step_size):
    travel = L + np.sum(spacings)
    maxmoment = 0
    axle_coords = -np.cumsum(spacings)
    moment_inf = np.empty_like(axles)
    while np.min(axle_coords) < L:
        axle_coords = axle_coords + step_size
        y = nbvectMoment(L,axle_coords)
        for k in range(y.shape[0]):
            if axle_coords[k] >=0 and axle_coords[k] <= L:
                moment_inf[k] = y[k]
            else:
                moment_inf[k] = 0.0   
        moment = np.sum(moment_inf * axles)
        if maxmoment < moment:
            maxmoment = moment
    return np.around(maxmoment,1)

def PosMomentSingle(current_axles, current_spacings):
    data_list = []
    for L in range (1,201):
        L=float(L)        
        if L <= 40:
            step_size = 0.5
        else:
            step_size = 0.5            
        axles = np.array(current_axles, dtype='f')
        spacings = np.array(current_spacings, dtype='f')            
        axles_inv = axles[::-1]
        spacings_inv = spacings[::-1]           
        spacings = np.insert(spacings,0,0)
        spacings_inv = np.insert(spacings_inv,0,0)            
        left_to_right = nbSimpleSpanMoment(L, axles, spacings, step_size)
        right_to_left = nbSimpleSpanMoment(L, axles_inv, spacings_inv, step_size)            
        data_list.append(max(left_to_right, right_to_left))
    return data_list

load_effects = []
for v in range(14,31):
    load_effects.append(PosMomentSingle([8, 32, 32], [14, v]))
load_effects = np.array(load_effects)

Answer 1

删除代码中的所有类型转换后，返回以下错误

TypingError: Cannot unify array(int64, 1d, C) and array(float64, 1d, C) for 'axle_coords.2'

这帮助我将错误追溯到 spacings 的 dtype。在您的代码中，它初始化为 C 兼容 single，这似乎与 python float32 不同，请参阅 here。将其更改为 np.float64 后，代码现在运行。

下面的代码现在可以运行并且不会再出现统一错误。

import numba as nb
import numpy as np

@nb.vectorize(nopython=True)
def nbvectMoment(L,x):
    if x<L/2.0:
        return 0.5*x
    else:
        return 0.5*(L-x)

@nb.jit(nopython=True)
def nbSimpleSpanMoment(L, axles, spacings, step_size):
    travel = L + np.sum(spacings)
    maxmoment = 0
    axle_coords = -np.cumsum(spacings)
    moment_inf = np.empty_like(axles)
    while np.min(axle_coords) < L:
        axle_coords = axle_coords + step_size
        y = nbvectMoment(L,axle_coords)
        for k in range(y.shape[0]):
            if axle_coords[k] >=0 and axle_coords[k] <= L:
                moment_inf[k] = y[k]
            else:
                moment_inf[k] = 0.0
        moment = np.sum(moment_inf * axles)
        if maxmoment < moment:
            maxmoment = moment
    return np.around(maxmoment,1)

def PosMomentSingle(current_axles, current_spacings):
    data_list = []
    for L in range (1,201):
        L=float(L)
        if L <= 40:
            step_size = 0.5
        else:
            step_size = 0.5
        axles = np.array(current_axles, np.float32)
        spacings = np.array(current_spacings, dtype=np.float64)
        axles_inv = axles[::-1]
        spacings_inv = spacings[::-1]
        spacings = np.insert(spacings,0,0)
        spacings_inv = np.insert(spacings_inv,0,0)
        left_to_right = nbSimpleSpanMoment(L, axles, spacings, step_size)
        right_to_left = nbSimpleSpanMoment(L, axles_inv, spacings_inv, step_size)
        data_list.append(max(left_to_right, right_to_left))
    return data_list

load_effects = []
for v in range(14,31):
    load_effects.append(PosMomentSingle([8, 32, 32], [14, v]))
load_effects = np.array(load_effects)

Numba data type error: Cannot unify array

Numba data type error: Cannot unify array

python

performance

numpy

vectorization

numba