Using jitclass with types.List of array results in TypingError

• I am using the latest released version of Numba (most recent is visible in the change log (https://github.com/numba/numba/blob/master/CHANGE_LOG).
• I have included below a minimal working reproducer (if you are unsure how to write one see http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports).

Issue

Using jitclass() on a class with an attribute of type ListType[array(int64, 1d, A)] leads to a TypingError. However, this type is correctly printed when calling without a decorator.

Minimal example

from numba import types, typed, typeof
from numba.experimental import jitclass
import numpy as np

spec = [
    ("b", types.ListType(types.int64[:])),
]

@jitclass(spec)
class ListAttribute:
    def __init__(self):
        self.b = typed.List.empty_list(types.int64[:])
        self.b.append(np.zeros(2, dtype=np.int64))


x = ListAttribute()
print(typeof(x.b))

Trace

Traceback (most recent call last):
  File "/home/suthep/.config/JetBrains/PyCharm2020.1/scratches/scratch_1.py", line 16, in <module>
    x = ListAttribute()
  File "/home/suthep/git/uas/venv/lib/python3.8/site-packages/numba/experimental/jitclass/base.py", line 122, in __call__
    return cls._ctor(*bind.args[1:], **bind.kwargs)
  File "/home/suthep/git/uas/venv/lib/python3.8/site-packages/numba/core/dispatcher.py", line 401, in _compile_for_args
    error_rewrite(e, 'typing')
  File "/home/suthep/git/uas/venv/lib/python3.8/site-packages/numba/core/dispatcher.py", line 344, in error_rewrite
    reraise(type(e), e, None)
  File "/home/suthep/git/uas/venv/lib/python3.8/site-packages/numba/core/utils.py", line 80, in reraise
    raise value.with_traceback(tb)
numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Failed in nopython mode pipeline (step: nopython frontend)
Invalid use of Function(<built-in function getitem>) with argument(s) of type(s): (class(int64), slice<a:b>)
 * parameterized
In definition 0:
    All templates rejected with literals.
In definition 1:
    All templates rejected without literals.
In definition 2:
    All templates rejected with literals.
In definition 3:
    All templates rejected without literals.
In definition 4:
    All templates rejected with literals.
In definition 5:
    All templates rejected without literals.
In definition 6:
    All templates rejected with literals.
In definition 7:
    All templates rejected without literals.
In definition 8:
    All templates rejected with literals.
In definition 9:
    All templates rejected without literals.
In definition 10:
    All templates rejected with literals.
In definition 11:
    All templates rejected without literals.
In definition 12:
    All templates rejected with literals.
In definition 13:
    All templates rejected without literals.
In definition 14:
    All templates rejected with literals.
In definition 15:
    All templates rejected without literals.
This error is usually caused by passing an argument of a type that is unsupported by the named function.
[1] During: typing of intrinsic-call at /home/suthep/.config/JetBrains/PyCharm2020.1/scratches/scratch_1.py (12)
[2] During: typing of static-get-item at /home/suthep/.config/JetBrains/PyCharm2020.1/scratches/scratch_1.py (12)

File "scratch_1.py", line 12:
    def __init__(self):
        self.b = typed.List.empty_list(types.int64[:])
        ^

[1] During: resolving callee type: jitclass.ListAttribute#7f7dfdb8b910<b:ListType[array(int64, 1d, A)]>
[2] During: typing of call at <string> (3)

[3] During: resolving callee type: jitclass.ListAttribute#7f7dfdb8b910<b:ListType[array(int64, 1d, A)]>
[4] During: typing of call at <string> (3)


File "<string>", line 3:
<source missing, REPL/exec in use?>

Environment:

python==3.8.2

attrs==19.3.0
coverage==5.1
cycler==0.10.0
decorator==4.4.2
imagecodecs==2020.2.18
imageio==2.8.0
joblib==0.14.1
kiwisolver==1.2.0
llvmlite==0.32.1
matplotlib==3.2.1
more-itertools==8.2.0
networkx==2.4
numba==0.49.1
numpy==1.18.4
packaging==20.3
Pillow==7.1.2
pluggy==0.13.1
py==1.8.1
pyparsing==2.4.7
pytest==5.4.2
python-dateutil==2.8.1
PyWavelets==1.1.1
scikit-image==0.17.2
scikit-learn==0.23.0
scipy==1.4.1
six==1.14.0
threadpoolctl==2.0.0
tifffile==2020.5.11

[esc]

@spomjaksilp thanks for asking about this on the Numba issue tracker. Specifying types involving jitclasses, typed-lists and arrays can be a bit quirky at times. Here is a snippet that should get you what you want:

from numba import types, typed, typeof
from numba.experimental import jitclass
import numpy as np

array_type = types.int64[:]

spec = [
    ("b", types.ListType(array_type)),
]


@jitclass(spec)
class ListAttribute:
    def __init__(self):
        self.b = typed.List.empty_list(array_type)
        self.b.append(np.zeros(2, dtype=np.int64))


x = ListAttribute()
print(typeof(x.b))

[esc]

@andreapiso thanks, that’s some very helpful input, we appreciate it!

Perhaps the following could be used to make the type spec for the Array:

In [14]: numba.types.Array(numba.types.int64, ndim=1, layout="C")
Out[14]: array(int64, 1d, C)