How to use np.empty inside numba compiled function; Error message “All templates rejected”

Question

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.

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)

After defining the above function in an ipython notebook,

empty()

Gives the following error message:

---------------------------------------------------------------------------
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

Answer 1

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.

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()

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

---

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

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

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_

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:

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)

Note that some of these are not supported by numba!