在 numba 中，我想将配置变量作为编译时常量传递给函数。具体来说我想做的是
@njit
def physics(config):
flagA = config.flagA
flagB = config.flagB
aNumbaList = List()
for i in range(100):
if flagA:
aNumbaList.append(i)
else:
aNumbaList.append(i/10)
return aNumbaList
如果配置变量是编译时常量，这就会通过，但事实并非如此，所以它给我错误，说有两个候选者
There are 2 candidate implementations:
- Of which 2 did not match due to:
...
...
我查看了一份 numba 会议纪要，发现有一种方法可以做到这一点Numba 会议：2024-03-05 我尝试过，但仍然出现同样的错误。
这是带有错误消息的代码：
.. code:: ipython3
from numba import jit, types, njit
from numba.extending import overload
from numba.typed import List
import functools
.. code:: ipython3
class Config():
def __init__(self, flagA, flagB):
self._flagA = flagA
self._flagB = flagB
@property
def flagA(self):
return self._flagA
@property
def flagB(self):
return self._flagB
.. code:: ipython3
@functools.cache
def obj2strkeydict(obj, config_name):
# unpack object to freevars and close over them
tmp_a = obj.flagA
tmp_b = obj.flagB
assert isinstance(config_name, str)
tmp_force_heterogeneous = config_name
@njit
def configurator():
d = {'flagA': tmp_a,
'flagB': tmp_b,
'config_name': tmp_force_heterogeneous}
return d
# return a configuration function that returns a string-key-dict
# representation of the configuration object.
return configurator
.. code:: ipython3
@njit
def physics(cfig_func):
config = cfig_func()
flagA = config['flagA']
flagB = config['flagB']
aNumbaList = List()
for i in range(100):
if flagA:
aNumbaList.append(i)
else:
aNumbaList.append(i/10)
return aNumbaList
.. code:: ipython3
def demo():
configuration1 = Config(True, False)
jit_config1 = obj2strkeydict(configuration1, 'config1')
physics(jit_config1)
.. code:: ipython3
demo()
::
---------------------------------------------------------------------------
TypingError Traceback (most recent call last)
Cell In[83], line 1
----> 1 demo()
Cell In[82], line 4, in demo()
2 configuration1 = Config(True, False)
3 jit_config1 = obj2strkeydict(configuration1, 'config1')
----> 4 physics(jit_config1)
File ~/anaconda3/envs/tardis/lib/python3.11/site-packages/numba/core/dispatcher.py:468, in _DispatcherBase._compile_for_args(self, *args, **kws)
464 msg = (f"{str(e).rstrip()} \n\nThis error may have been caused "
465 f"by the following argument(s):\n{args_str}\n")
466 e.patch_message(msg)
--> 468 error_rewrite(e, 'typing')
469 except errors.UnsupportedError as e:
470 # Something unsupported is present in the user code, add help info
471 error_rewrite(e, 'unsupported_error')
File ~/anaconda3/envs/tardis/lib/python3.11/site-packages/numba/core/dispatcher.py:409, in _DispatcherBase._compile_for_args.<locals>.error_rewrite(e, issue_type)
407 raise e
408 else:
--> 409 raise e.with_traceback(None)
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
- Resolution failure for literal arguments:
No implementation of function Function(<function impl_append at 0x7fd87d253920>) found for signature:
>>> impl_append(ListType[int64], float64)
There are 2 candidate implementations:
- Of which 2 did not match due to:
Overload in function 'impl_append': File: numba/typed/listobject.py: Line 592.
With argument(s): '(ListType[int64], float64)':
Rejected as the implementation raised a specific error:
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
No implementation of function Function(<intrinsic _cast>) found for signature:
>>> _cast(float64, class(int64))
There are 2 candidate implementations:
- Of which 2 did not match due to:
Intrinsic in function '_cast': File: numba/typed/typedobjectutils.py: Line 22.
With argument(s): '(float64, class(int64))':
Rejected as the implementation raised a specific error:
TypingError: cannot safely cast float64 to int64. Please cast explicitly.
raised from /home/sam/anaconda3/envs/tardis/lib/python3.11/site-packages/numba/typed/typedobjectutils.py:75
During: resolving callee type: Function(<intrinsic _cast>)
During: typing of call at /home/sam/anaconda3/envs/tardis/lib/python3.11/site-packages/numba/typed/listobject.py (600)
File "../anaconda3/envs/tardis/lib/python3.11/site-packages/numba/typed/listobject.py", line 600:
def impl(l, item):
casteditem = _cast(item, itemty)
^
raised from /home/sam/anaconda3/envs/tardis/lib/python3.11/site-packages/numba/core/typeinfer.py:1086
- Resolution failure for non-literal arguments:
None
During: resolving callee type: BoundFunction((<class 'numba.core.types.containers.ListType'>, 'append') for ListType[int64])
During: typing of call at /tmp/ipykernel_9889/739598600.py (11)
File "../../../tmp/ipykernel_9889/739598600.py", line 11:
<source missing, REPL/exec in use?>
任何帮助或任何相关材料的参考都会对我很有帮助。 谢谢你。
在 Numba 中，全局变量是编译时常量，因此您可以使用它来执行您想要的操作。这是一个例子：
import numba as nb # v0.58.1
flagA = True
@nb.njit
def physics(flagA):
aNumbaList = nb.typed.List()
for i in range(100):
if flagA:
aNumbaList.append(i)
else:
aNumbaList.append(i/10)
return aNumbaList
在参数中传递
flagAifelse话虽这么说，全局变量在软件工程方面并不是很好，您可能希望在运行时为不同的配置编译函数（例如，基于初始化过程，同时避免写入全局变量）。
另一种解决方案是返回一个函数，该函数读取父函数中定义的变量，因此它被视为该函数的全局变量，因此被视为编译时常量。编译函数读取的变量可以作为参数传递给父函数。这是一个例子：
import numba as nb
def make_physics(flagA):
@nb.njit
def fun():
aNumbaList = nb.typed.List()
for i in range(100):
if flagA:
aNumbaList.append(i)
else:
aNumbaList.append(i/10)
return aNumbaList
return fun
physics = make_physics(True) # Compile a specialized function every time it is called
physics() # Call the compiled function generated just before
这也不会导致任何错误，并且实际上按预期工作。以下是
physicsflagA [...]
movq %rax, %r12 ; r12 = an allocated Python object (the list?)
movq 24(%rax), %rax
movq %r14, (%rax)
xorl %ebx, %ebx ; i = 0
movabsq $NRT_incref, %r13
movabsq $numba_list_append, %rbp
leaq 48(%rsp), %r15 ; (r15 is a pointer on i)
.LBB0_6: ; Main loop
movq %r12, %rcx
callq *%r13 ; Call NRT_incref(r12)
movq %rbx, 48(%rsp)
movq %r14, %rcx
movq %r15, %rdx
callq *%rbp ; Call numba_list_append(r14, pointer_of(i))
testl %eax, %eax
jne .LBB0_7 ; Stop the loop if numba_list_append returned a non-zero value
incq %rbx ; i += 1
movq %r12, %rcx
movabsq $NRT_decref, %rax
callq *%rax ; Call NRT_decref(r12)
cmpq $100, %rbx
jne .LBB0_6 ; Loop as long as i < 100
[...]
关于实际用例，记忆和 Numba 函数缓存可以帮助避免针对相同配置多次编译目标函数。