CuPy 数据上的“无法确定 Numba 类型"错误 [英] 'Cannot determine Numba type' error on CuPy data

问题描述

我正在尝试使用 Numba 代码处理由 CuPy 逻辑生成的一些数据.但得到不同的错误.例如简化示例

I am trying to process some data, produced by CuPy logic, with Numba code. But getting different errors. For example for simplified example

import cupy as cp
import numba
from numba import void, int32, int64, float32, float64
import numpy as np

@numba.jit
def numba_test(a, b, n, m):
    for i in range(n):
        for j in range(m):
            a[i, j] += b[i, j]

a = cp.zeros((100, 10), dtype=np.float32)
b = cp.zeros((100, 10), dtype=np.float32)
numba_test(a, b, 100, 10)

我要了

<ipython-input-1-d30521cb61ff>:6: NumbaWarning: 
Compilation is falling back to object mode WITH looplifting enabled because Function "numba_test" failed type inference due to: non-precise type pyobject
[1] During: typing of argument at <ipython-input-1-d30521cb61ff> (8)

File "<ipython-input-1-d30521cb61ff>", line 8:
def numba_test(a, b, n, m):
    for i in range(n):
    ^

  @numba.jit
<ipython-input-1-d30521cb61ff>:6: NumbaWarning: 
Compilation is falling back to object mode WITHOUT looplifting enabled because Function "numba_test" failed type inference due to: cannot determine Numba type of <class 'numba.dispatcher.LiftedLoop'>

File "<ipython-input-1-d30521cb61ff>", line 8:
def numba_test(a, b, n, m):
    for i in range(n):
    ^

  @numba.jit
/usr/local/lib/python3.6/dist-packages/numba/object_mode_passes.py:178: NumbaWarning: Function "numba_test" was compiled in object mode without forceobj=True, but has lifted loops.

File "<ipython-input-1-d30521cb61ff>", line 8:
def numba_test(a, b, n, m):
    for i in range(n):
    ^

  state.func_ir.loc))
/usr/local/lib/python3.6/dist-packages/numba/object_mode_passes.py:188: NumbaDeprecationWarning: 
Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.

For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit

File "<ipython-input-1-d30521cb61ff>", line 8:
def numba_test(a, b, n, m):
    for i in range(n):
    ^

  state.func_ir.loc))
<ipython-input-1-d30521cb61ff>:6: NumbaWarning: 
Compilation is falling back to object mode WITHOUT looplifting enabled because Function "numba_test" failed type inference due to: non-precise type pyobject
[1] During: typing of argument at <ipython-input-1-d30521cb61ff> (8)

File "<ipython-input-1-d30521cb61ff>", line 8:
def numba_test(a, b, n, m):
    for i in range(n):
    ^

  @numba.jit
/usr/local/lib/python3.6/dist-packages/numba/object_mode_passes.py:178: NumbaWarning: Function "numba_test" was compiled in object mode without forceobj=True.

File "<ipython-input-1-d30521cb61ff>", line 8:
def numba_test(a, b, n, m):
    for i in range(n):
    ^

  state.func_ir.loc))
/usr/local/lib/python3.6/dist-packages/numba/object_mode_passes.py:188: NumbaDeprecationWarning: 
Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.

For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit

File "<ipython-input-1-d30521cb61ff>", line 8:
def numba_test(a, b, n, m):
    for i in range(n):
    ^

  state.func_ir.loc))

我在 Google Colab 上运行代码，之前能够运行一些 CuPy 代码和大量 Numba 代码.

I am running the code on Google Colab, was able to run some CuPy code and considerable amount of Numba code before.

在@numba.jit 之后添加 (void(float64[:, :], float64[:, :], int64, int64)) 没有帮助，会产生类似无效参数类型 pyObject 的错误.用 cp.asarray(a) 替换 a 和 b 也无济于事.如何运行它?

Adding (void(float64[:, :], float64[:, :], int64, int64)) after @numba.jit doesn't help, generating error like invalid argument type pyObject. Replacing a and b with cp.asarray(a) don't help too. How to run this?

开发人员编写他们提供必要的接口 https://docs.cupy.dev/en/stable/reference/interoperability.html

Developers write they provide necessary interface https://docs.cupy.dev/en/stable/reference/interoperability.html

@cuda.jit
def add(x, out):
        start = cuda.grid(1)
        stride = cuda.gridsize(1)
        for i in range(start, x.shape[0], stride):
                out[i] = x[i] + 2

a = cupy.arange(10)
out = cupy.zeros_like(a)

add[1, 32](a, out)

作品

推荐答案

您正在调用 CPU JIT 编译器.您应该按照您链接的说明页面进行操作，并执行

You are calling the CPU JIT compiler. You should follow the instruction pages that you linked, and do

from numba import cuda

@cuda.jit
def ...

使用 CUDA JIT 编译器.

to use the CUDA JIT compiler.

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