如何在numba编译函数中使用np.empty;错误消息“所有模板均被拒绝". [英] How to use np.empty inside numba compiled function; Error message "All templates rejected"

问题描述

当尝试在使用numba编译的函数定义中使用np.empty并打开nopython=True以确保优化键入有效时，遇到了这个奇怪的错误.

I ran into this weird error when trying to use np.empty in a function definition compiled with numba, and turning on nopython=True to make sure optimized typing is in effect.

这很奇怪，因为numba声称使用前两个参数支持np.empty，而我只使用前两个参数(我认为正确吗?)，所以我不知道为什么它输入不正确.

It's weird because numba claims to support np.empty with the first two arguments, and I am only using the first two arguments (correctly I think?), so I don't know why it's not typing correctly.

@jit(nopython=True)
def empty():
    return np.empty(5, np.float)

在ipython笔记本中定义上述功能后，

After defining the above function in an ipython notebook,

empty()

给出以下错误消息:

---------------------------------------------------------------------------
TypingError                               Traceback (most recent call last)
<ipython-input-88-927345c8757f> in <module>()
----> 1 empty()

~/.../lib/python3.5/site-packages/numba/dispatcher.py in _compile_for_args(self, *args, **kws)
    342                 raise e
    343             else:
--> 344                 reraise(type(e), e, None)
    345         except errors.UnsupportedError as e:
    346             # Something unsupported is present in the user code, add help info

~/.../lib/python3.5/site-packages/numba/six.py in reraise(tp, value, tb)
    656             value = tp()
    657         if value.__traceback__ is not tb:
--> 658             raise value.with_traceback(tb)
    659         raise value
    660 
TypingError: Failed at nopython (nopython frontend)
Invalid usage of Function(<built-in function empty>) with parameters (int64, Function(<class 'float'>))
 * parameterized
In definition 0:
    All templates rejected
[1] During: resolving callee type: Function(<built-in function empty>)
[2] During: typing of call at <ipython-input-87-8c7e8fa4c6eb> (3)


File "<ipython-input-87-8c7e8fa4c6eb>", line 3:
def empty():
    return np.empty(5, np.float)
    ^

This is not usually a problem with Numba itself but instead often caused by
the use of unsupported features or an issue in resolving types.

To see Python/NumPy features supported by the latest release of Numba visit:
http://numba.pydata.org/numba-doc/dev/reference/pysupported.html
and
http://numba.pydata.org/numba-doc/dev/reference/numpysupported.html

For more information about typing errors and how to debug them visit:
http://numba.pydata.org/numba-doc/latest/user/troubleshoot.html#my-code-doesn-t-compile

If you think your code should work with Numba, please report the error message
and traceback, along with a minimal reproducer at:
https://github.com/numba/numba/issues/new

推荐答案

问题是对于numba中的NumPy数组，np.float不是有效数据类型.您必须为numba提供显式dtype.这不仅是np.empty的问题，还包括其他创建数组的例程，例如np.ones，np.zeros，...在numba中.

The problem is that np.float is not a valid datatype for a NumPy array in numba. You have to provide the explicit dtype to numba. This isn't just a problem with np.empty but also for other array-creation routines like np.ones, np.zeros, ... in numba.

要使您的示例正常工作，只需做一点改动:

To make your example work only a little change is needed:

from numba import jit
import numpy as np

@jit(nopython=True)
def empty():
    return np.empty(5, np.float64)  # np.float64 instead of np.float

empty()

或速记np.float_.或者，如果您想使用32位浮点数，请改用np.float32.

Or the shorthand np.float_. Or if you want 32 bit floats use np.float32 instead.

请注意，np.float只是普通Python float的别名，因此不是 real NumPy dtype:

Note that np.float is just an alias for the normal Python float and as such not a real NumPy dtype:

>>> np.float is float
True
>>> issubclass(np.float, np.generic)
False
>>> issubclass(np.float64, np.generic)
True

同样，还有一些其他别名被解释为好像是NumPy dtypes(

Likewise there are some additional aliases that just are interpreted as if they were NumPy dtypes (source):

内置Python类型

当用于生成dtype对象时，几种python类型等效于相应的数组标量:

Built-in Python types

Several python types are equivalent to a corresponding array scalar when used to generate a dtype object:

int          int_
bool         bool_
float        float_
complex      cfloat
bytes        bytes_
str          bytes_ (Python2) or unicode_ (Python3)
unicode      unicode_
buffer       void
(all others) object_

但是numba不了解这些别名，即使不使用numba，您也最好使用

However numba doesn't know about these aliases and even when not dealing with numba you are probably better off using the real dtypes directly:

数组类型和类型之间的转换

NumPy比Python支持更多的数字类型.本节显示了哪些可用的以及如何修改数组的数据类型.

Array types and conversions between types

NumPy supports a much greater variety of numerical types than Python does. This section shows which are available, and how to modify an array’s data-type.

Data type     Description
bool_         Boolean (True or False) stored as a byte
int_          Default integer type (same as C long; normally either int64 or int32)
intc          Identical to C int (normally int32 or int64)
intp          Integer used for indexing (same as C ssize_t; normally either int32 or int64)
int8          Byte (-128 to 127)
int16         Integer (-32768 to 32767)
int32         Integer (-2147483648 to 2147483647)
int64         Integer (-9223372036854775808 to 9223372036854775807)
uint8         Unsigned integer (0 to 255)
uint16        Unsigned integer (0 to 65535)
uint32        Unsigned integer (0 to 4294967295)
uint64        Unsigned integer (0 to 18446744073709551615)
float_        Shorthand for float64.
float16       Half precision float: sign bit, 5 bits exponent, 10 bits mantissa
float32       Single precision float: sign bit, 8 bits exponent, 23 bits mantissa
float64       Double precision float: sign bit, 11 bits exponent, 52 bits mantissa
complex_      Shorthand for complex128.
complex64     Complex number, represented by two 32-bit floats (real and imaginary components)
complex128    Complex number, represented by two 64-bit floats (real and imaginary components)

请注意，其中一些是numba不支持的！

Note that some of these are not supported by numba!

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