numba @guvectorize（[（[（（float64 [：]，int64）]]，＆＃x27;（n），（） - ＆gt;（n）＆＃x27;）indexError：列表index out Range

Question

我尝试将numba应用于我的代码以提高速度。但是我遇到了一个错误“ indexError：列表以外的索引”和警告。我如何使它起作用？

我按照下面的方式运行代码：

import numpy as np
from numpy import linalg as LA
from numba import guvectorize, int64, float64, vectorize

@guvectorize([(float64[:], int64)], '(n),()->(n)')
def func(main, n):
    nprime = n-1
    off = np.random.normal(size=(nprime, nprime))
    tril = np.tril(off, -1)
    W_n = tril + tril.T
    np.fill_diagonal(W_n, main)

    eigenvalues = LA.eigvals(W_n)
    w = np.flip(np.sort(eigenvalues))
    # GOE_L12_dist[:,i] = w[0:2]
    return w

main = np.sqrt(2) * np.random.normal(size=(3-1))
func(main, 3)

然后我会遇到错误，

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Input In [16], in <cell line: 1>()
      1 @guvectorize([(float64[:], int64)], '(n),()->(n)')
----> 2 def func(main, n):
      3     nprime = n-1
      4     off = np.random.normal(size=(nprime, nprime))

File /opt/anaconda3/lib/python3.9/site-packages/numba/np/ufunc/decorators.py:197, in guvectorize.<locals>.wrap(func)
    195 if len(ftylist) > 0:
    196     guvec.disable_compile()
--> 197 return guvec.build_ufunc()

File /opt/anaconda3/lib/python3.9/site-packages/numba/np/ufunc/gufunc.py:66, in GUFunc.build_ufunc(self)
     65 def build_ufunc(self):
---> 66     self.ufunc = self.gufunc_builder.build_ufunc()
     67     return self

File /opt/anaconda3/lib/python3.9/site-packages/numba/core/compiler_lock.py:35, in _CompilerLock.__call__.<locals>._acquire_compile_lock(*args, **kwargs)
     32 @functools.wraps(func)
     33 def _acquire_compile_lock(*args, **kwargs):
     34     with self:
---> 35         return func(*args, **kwargs)

File /opt/anaconda3/lib/python3.9/site-packages/numba/np/ufunc/ufuncbuilder.py:363, in GUFuncBuilder.build_ufunc(self)
    360 nout = len(self.sout)
    362 # Pass envs to fromfuncsig to bind to the lifetime of the ufunc object
--> 363 ufunc = _internal.fromfunc(
    364     self.py_func.__name__, self.py_func.__doc__,
    365     func_list, type_list, nin, nout, datalist,
    366     keepalive, self.identity, self.signature,
    367 )
    368 return ufunc

IndexError: list index out of range

并显示警告，

/var/folders/9_/y3v35tk14nl7_b9ks8h4l79c0000gn/T/ipykernel_4300/1986471478.py:1: NumbaWarning: 
Compilation is falling back to object mode WITHOUT looplifting enabled because Function "func" failed type inference due to: No implementation of function Function(<built-in method normal of numpy.random.mtrand.RandomState object at 0x7f7c4ed6cc40>) found for signature:
 
 >>> normal(size=UniTuple(int64 x 2))
 
There are 4 candidate implementations:
      - Of which 4 did not match due to:
      Overload in function '_OverloadWrapper._build.<locals>.ol_generated': File: numba/core/overload_glue.py: Line 131.
        With argument(s): '(size=UniTuple(int64 x 2))':
       Rejected as the implementation raised a specific error:
         TypingError: Failed in nopython mode pipeline (step: nopython frontend)
       No implementation of function Function(<intrinsic stub>) found for signature:
        
        >>> stub(UniTuple(int64 x 2))
        
       There are 2 candidate implementations:
         - Of which 2 did not match due to:
         Intrinsic in function 'stub': File: numba/core/overload_glue.py: Line 35.
           With argument(s): '(UniTuple(int64 x 2))':
          Rejected as the implementation raised a specific error:
            TypingError: unsupported call signature
         raised from /opt/anaconda3/lib/python3.9/site-packages/numba/core/typing/templates.py:439
       
       During: resolving callee type: Function(<intrinsic stub>)
       During: typing of call at <string> (3)
       
       
       File "<string>", line 3:
       <source missing, REPL/exec in use?>

  raised from /opt/anaconda3/lib/python3.9/site-packages/numba/core/typeinfer.py:1086

During: resolving callee type: Function(<built-in method normal of numpy.random.mtrand.RandomState object at 0x7f7c4ed6cc40>)
During: typing of call at /var/folders/9_/y3v35tk14nl7_b9ks8h4l79c0000gn/T/ipykernel_4300/1986471478.py (4)


File "../../../../../../../../var/folders/9_/y3v35tk14nl7_b9ks8h4l79c0000gn/T/ipykernel_4300/1986471478.py", line 4:
<source missing, REPL/exec in use?>

  @guvectorize([(float64[:], int64)], '(n),()->(n)')
/opt/anaconda3/lib/python3.9/site-packages/numba/core/object_mode_passes.py:151: NumbaWarning: Function "func" was compiled in object mode without forceobj=True.

File "../../../../../../../../var/folders/9_/y3v35tk14nl7_b9ks8h4l79c0000gn/T/ipykernel_4300/1986471478.py", line 1:
<source missing, REPL/exec in use?>

  warnings.warn(errors.NumbaWarning(warn_msg,
/opt/anaconda3/lib/python3.9/site-packages/numba/core/object_mode_passes.py:161: 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 https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit

File "../../../../../../../../var/folders/9_/y3v35tk14nl7_b9ks8h4l79c0000gn/T/ipykernel_4300/1986471478.py", line 1:
<source missing, REPL/exec in use?>

  warnings.warn(errors.NumbaDeprecationWarning(msg,

但是当我按照下面的内容编写代码时，它可以与警告一起使用。但是重复输入，因此没有使用main_2。

@guvectorize([(float64[:], int64, float64[:])], '(n),()->(n)')
def func(main, n, main_2):
    nprime = n-1
    off = np.random.normal(size=(nprime, nprime))
    tril = np.tril(off, -1)
    W_n = tril + tril.T
    np.fill_diagonal(W_n, main)

    eigenvalues = LA.eigvals(W_n)
    w = np.flip(np.sort(eigenvalues))
    # GOE_L12_dist[:,i] = w[0:2]
    return w

main = np.sqrt(2) * np.random.normal(size=(3-1))
func(main, 3, main)

Answer 1

@guvectorize期望在功能定义中否返回。
来自@guvectorize 文档

与vectorize（）函数相反，guvectorize（）函数不返回其结果值：它们将其作为数组参数，必须由函数填充

最后一个参数不必包含在函数调用中。 np.random.normal如果提供了所有参数，则不会引起警告。

import numpy as np
from numpy import linalg as LA
from numba import guvectorize, int64, float64          # tested with numba 0.55.2

@guvectorize([(float64[:], int64, float64[:])], '(n),()->(n)')
def func(main, n, res):
    nprime = n-1
    off = np.random.normal(0., 1., (nprime, nprime))   # all arguments provided
    tril = np.tril(off, -1)
    W_n = tril + tril.T
    np.fill_diagonal(W_n, main)

    eigenvalues = LA.eigvals(W_n)
    res = np.flip(np.sort(eigenvalues))                # res filled with the result
                                                       # return removed
main = np.sqrt(2) * np.random.normal(size=(3-1))
func(main, 3)                                          # call without last argument

输出

array([-1.60235493,  0.95716831])

---

如果函数仅取决于 n （请注意，此实施模仿了期望 n 的决定），并且无需推广到更多不需要两个维度，@guvectorize不需要。

from numba import njit

@njit
def func(n):
    nprime = n-1
    main = np.sqrt(2.) * np.random.normal(0., 1., (nprime))
    off = np.random.normal(0., 1., (nprime, nprime))
    tril = np.tril(off, -1)
    W_n = tril + tril.T
    np.fill_diagonal(W_n, main)

    eigenvalues = LA.eigvals(W_n)
    return np.flip(np.sort(eigenvalues))

func(3)

输出

array([ 1.06510406, -3.08375554])