Numba 和多维添加 - 不适用于 numpy.newaxis？

Question

尝试在 python 上加速 DP 算法，numba 似乎是一个合适的候选者。

我正在用一个提供 3 维数组的一维数组减去一个二维数组。然后，我沿第 3 个维度使用 .argmin() 来获得二维数组。这适用于 numpy，但不适用于 numba。

重现问题的玩具代码：

from numba import jit
import numpy as np

inflow      = np.arange(1,0,-0.01)                  # Dim [T]
actions     = np.arange(0,1,0.05)                   # Dim [M]
start_lvl   = np.random.rand(500).reshape(-1,1)*49  # Dim [Nx1]
disc_lvl    = np.arange(0,1000)                     # Dim [O]

@jit(nopython=True)
def my_func(disc_lvl, actions, start_lvl, inflow):
    for i in range(0,100):
        # Calculate new level at time i
        new_lvl = start_lvl + inflow[i] + actions       # Dim [N x M]

        # For each new_level element, find closest discretized level
        diff    = (disc_lvl-new_lvl[:,:,np.newaxis])    # Dim [N x M x O]
        idx_lvl = abs(diff).argmin(axis=2)              # Dim [N x M]

        return True

# function works fine without numba
success = my_func(disc_lvl, actions, start_lvl, inflow)

为什么上面的代码没有运行？它在取出 @jit(nopython=True) 时起作用。是否有工作轮使以下计算与 numba 一起工作？

我尝试了带有 numpy 重复和 expand_dims 的变体，以及明确定义 jit 函数的输入类型但没有成功。

Answer 1

您需要进行一些更改才能使其正常工作：

使用 arr[:, :, None] 添加维度：对于 Numba，它看起来像 getitem 所以更喜欢使用 reshape
使用 np.abs 而不是内置的 abs
带有 axis 关键字参数的 argmin 是 not implemented。更喜欢使用 Numba 旨在优化的循环。

所有这些都修复后，您可以运行 jitted 函数：

from numba import jit
import numpy as np

inflow = np.arange(1,0,-0.01)  # Dim [T]
actions = np.arange(0,1,0.05)  # Dim [M]
start_lvl = np.random.rand(500).reshape(-1,1)*49  # Dim [Nx1]
disc_lvl = np.arange(0,1000)  # Dim [O]

@jit(nopython=True)
def my_func(disc_lvl, actions, start_lvl, inflow):
    for i in range(0,100):
        # Calculate new level at time i
        new_lvl = start_lvl + inflow[i] + actions  # Dim [N x M]

        # For each new_level element, find closest discretized level
        new_lvl_3d = new_lvl.reshape(*new_lvl.shape, 1)
        diff = np.abs(disc_lvl - new_lvl_3d)  # Dim [N x M x O]

        idx_lvl = np.empty(new_lvl.shape)
        for i in range(diff.shape[0]):
            for j in range(diff.shape[1]):
                idx_lvl[i, j] = diff[i, j, :].argmin()

        return True

# function works fine without numba
success = my_func(disc_lvl, actions, start_lvl, inflow)

Answer 2

在下面找到我的第一个 post 的更正代码，您可以使用和不使用 numba 库的 jitted 模式执行（通过删除以 @jit 开头的行）。对于这个例子，我观察到速度增加了 2 倍。

from numba import jit
import numpy as np
import datetime as dt

inflow = np.arange(1,0,-0.01)                       # Dim [T]
nbTime = np.shape(inflow)[0]
actions = np.arange(0,1,0.01)                       # Dim [M]
start_lvl = np.random.rand(500).reshape(-1,1)*49    # Dim [Nx1]
disc_lvl = np.arange(0,1000)                        # Dim [O]

@jit(nopython=True)
def my_func(nbTime, disc_lvl, actions, start_lvl, inflow):
    # Initialize result 
    res = np.empty((nbTime,np.shape(start_lvl)[0],np.shape(actions)[0]))

    for t in range(0,nbTime):
        # Calculate new level at time t
        new_lvl = start_lvl + inflow[t] + actions  # Dim [N x M]      
        print(t)

        # For each new_level element, find closest discretized level
        new_lvl_3d = new_lvl.reshape(*new_lvl.shape, 1)
        diff = np.abs(disc_lvl - new_lvl_3d)  # Dim [N x M x O]

        idx_lvl = np.empty(new_lvl.shape)
        for i in range(diff.shape[0]):
            for j in range(diff.shape[1]):
                idx_lvl[i, j] = diff[i, j, :].argmin()

        res[t,:,:] = idx_lvl

    return res

# Call function and print running time
start_time = dt.datetime.now()
result = my_func(nbTime, disc_lvl, actions, start_lvl, inflow)
print('Execution time :',(dt.datetime.now() - start_time))

Numba 和多维添加 - 不适用于 numpy.newaxis？

Numba and multidimensions additions - not working with numpy.newaxis?

python

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subtraction

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