Question

我使用numba.jitclass装饰器来标记我的课程以进行优化。

我不知道如何指定我想要优化的run方法的签名。该方法将ConvertedDocument个对象数组作为参数。似乎numba无法自己弄清楚数组类型，因为当我尝试在nopython模式下调用run方法时会发生以下错误：

Traceback (most recent call last):
  File "numba_test.py", line 53, in <module>
    print run(a)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/dispatcher.py", line 310, in _compile_for_args
    raise e
numba.errors.TypingError: Caused By:
Traceback (most recent call last):
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/compiler.py", line 230, in run
    stage()
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/compiler.py", line 444, in stage_nopython_frontend
    self.locals)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/compiler.py", line 800, in type_inference_stage
    infer.propagate()
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 767, in propagate
    raise errors[0]
TypingError: Internal error at <numba.typeinfer.ExhaustIterConstraint object at 0x788cc9572d50>:
--%<-----------------------------------------------------------------
Traceback (most recent call last):
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 128, in propagate
    constraint(typeinfer)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 264, in __call__
    raise TypingError("failed to unpack {}".format(tp), loc=self.loc)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/contextlib.py", line 35, in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/errors.py", line 249, in new_error_context
    six.reraise(type(newerr), newerr, sys.exc_info()[2])
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/errors.py", line 243, in new_error_context
    yield
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 264, in __call__
    raise TypingError("failed to unpack {}".format(tp), loc=self.loc)
InternalError: local variable 'tp' referenced before assignment
[1] During: typing of exhaust iter at numba_test.py (40)
--%<-----------------------------------------------------------------

File "numba_test.py", line 40

Failed at nopython (nopython frontend)
Internal error at <numba.typeinfer.ExhaustIterConstraint object at 0x788cc9572d50>:
--%<-----------------------------------------------------------------
Traceback (most recent call last):
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 128, in propagate
    constraint(typeinfer)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 264, in __call__
    raise TypingError("failed to unpack {}".format(tp), loc=self.loc)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/contextlib.py", line 35, in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/errors.py", line 249, in new_error_context
    six.reraise(type(newerr), newerr, sys.exc_info()[2])
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/errors.py", line 243, in new_error_context
    yield
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 264, in __call__
    raise TypingError("failed to unpack {}".format(tp), loc=self.loc)
InternalError: local variable 'tp' referenced before assignment
[1] During: typing of exhaust iter at numba_test.py (40)
--%<-----------------------------------------------------------------

File "numba_test.py", line 40

This error may have been caused by the following argument(s):
- argument 0: Unsupported array dtype: object

以下是我如何指定numba装饰器：

spec = [
    ('profile', numba.typeof(numpy.asarray([1.0, 2.0]))),
    ('word_weights', numba.typeof(numpy.asarray([(1.0,2.0)])))
]

@numba.jitclass(spec)
class ConvertedDocument(object):
    def __init__(self, profile, word_weights):
        self.profile = profile
        self.word_weights = word_weights

@numba.jit(nopython=True,cache=True)
def run(docs):
    s = 0
    for doc in docs: #array of documents
        for w_id, weight in doc.word_weights: #accessing document's property
            s += weight

    return s

这就是调用run方法的方式：

x = numpy.asarray([1.0, 2.0])
y = numpy.asarray([(1.0,2.0), (3.0,4.0)])
a = numpy.asarray([ConvertedDocument(x,y)])
print run(a)

如果a numpy数组被Python列表替换，则例外情况如下：

Failed at nopython (nopython mode backend)
reflected list(instance.jitclass.ConvertedDocument#3bffb70<profile:array(float64, 1d, C),word_weights:array(float64, 2d, C)>): unsupported nested memory-managed object

有没有人知道如何在使用自定义类型时指定方法签名，或者是否支持对对象数组进行迭代？

Answer 1

问题似乎是您无法在np.nditer对象上调用jitclass，这是有道理的，因为jitclass不可迭代。它将它的数据存储为数组（和其他数据类型）的结构，而不是结构数组。您试图将其用作后者。如果除了你的两个数组属性之外你还有一堆标量数据属性或不同大小的数组，那么你如何迭代jitclass对象将是不明确的。

错误信息无疑是不清楚的。我的建议是直接迭代你需要的word_weights索引。

如何在numba中使用自定义类型为方法指定函数签名

1 个答案: