forked from numba/numba
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compiler.py
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compiler.py
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from __future__ import absolute_import, print_function
import os
from functools import reduce, wraps
import operator
import sys
import threading
import warnings
import numpy as np
from numba import ctypes_support as ctypes
from numba import config, compiler, types, sigutils
from numba.typing.templates import AbstractTemplate, ConcreteTemplate
from numba import funcdesc, typing, utils, serialize
from numba.compiler_lock import global_compiler_lock
from .cudadrv.autotune import AutoTuner
from .cudadrv.devices import get_context
from .cudadrv import nvvm, devicearray, driver
from .errors import normalize_kernel_dimensions
from .api import get_current_device
from .args import wrap_arg
@global_compiler_lock
def compile_cuda(pyfunc, return_type, args, debug, inline):
# First compilation will trigger the initialization of the CUDA backend.
from .descriptor import CUDATargetDesc
typingctx = CUDATargetDesc.typingctx
targetctx = CUDATargetDesc.targetctx
# TODO handle debug flag
flags = compiler.Flags()
# Do not compile (generate native code), just lower (to LLVM)
flags.set('no_compile')
flags.set('no_cpython_wrapper')
if debug:
flags.set('debuginfo')
if inline:
flags.set('forceinline')
# Run compilation pipeline
cres = compiler.compile_extra(typingctx=typingctx,
targetctx=targetctx,
func=pyfunc,
args=args,
return_type=return_type,
flags=flags,
locals={})
library = cres.library
library.finalize()
return cres
@global_compiler_lock
def compile_kernel(pyfunc, args, link, debug=False, inline=False,
fastmath=False, extensions=[], max_registers=None):
cres = compile_cuda(pyfunc, types.void, args, debug=debug, inline=inline)
fname = cres.fndesc.llvm_func_name
lib, kernel = cres.target_context.prepare_cuda_kernel(cres.library, fname,
cres.signature.args,
debug=debug)
cukern = CUDAKernel(llvm_module=lib._final_module,
name=kernel.name,
pretty_name=cres.fndesc.qualname,
argtypes=cres.signature.args,
type_annotation=cres.type_annotation,
link=link,
debug=debug,
call_helper=cres.call_helper,
fastmath=fastmath,
extensions=extensions,
max_registers=max_registers)
return cukern
class DeviceFunctionTemplate(object):
"""Unmaterialized device function
"""
def __init__(self, pyfunc, debug, inline):
self.py_func = pyfunc
self.debug = debug
self.inline = inline
self._compileinfos = {}
def __reduce__(self):
glbls = serialize._get_function_globals_for_reduction(self.py_func)
func_reduced = serialize._reduce_function(self.py_func, glbls)
args = (self.__class__, func_reduced, self.debug, self.inline)
return (serialize._rebuild_reduction, args)
@classmethod
def _rebuild(cls, func_reduced, debug, inline):
func = serialize._rebuild_function(*func_reduced)
return compile_device_template(func, debug=debug, inline=inline)
def compile(self, args):
"""Compile the function for the given argument types.
Each signature is compiled once by caching the compiled function inside
this object.
Returns the `CompileResult`.
"""
if args not in self._compileinfos:
cres = compile_cuda(self.py_func, None, args, debug=self.debug,
inline=self.inline)
first_definition = not self._compileinfos
self._compileinfos[args] = cres
libs = [cres.library]
if first_definition:
# First definition
cres.target_context.insert_user_function(self, cres.fndesc,
libs)
else:
cres.target_context.add_user_function(self, cres.fndesc, libs)
else:
cres = self._compileinfos[args]
return cres
def inspect_llvm(self, args):
"""Returns the LLVM-IR text compiled for *args*.
Parameters
----------
args: tuple[Type]
Argument types.
Returns
-------
llvmir : str
"""
cres = self._compileinfos[args]
mod = cres.library._final_module
return str(mod)
def inspect_ptx(self, args, nvvm_options={}):
"""Returns the PTX compiled for *args* for the currently active GPU
Parameters
----------
args: tuple[Type]
Argument types.
nvvm_options : dict; optional
See `CompilationUnit.compile` in `numba/cuda/cudadrv/nvvm.py`.
Returns
-------
ptx : bytes
"""
llvmir = self.inspect_llvm(args)
# Make PTX
cuctx = get_context()
device = cuctx.device
cc = device.compute_capability
arch = nvvm.get_arch_option(*cc)
ptx = nvvm.llvm_to_ptx(llvmir, opt=3, arch=arch, **nvvm_options)
return ptx
def compile_device_template(pyfunc, debug=False, inline=False):
"""Create a DeviceFunctionTemplate object and register the object to
the CUDA typing context.
"""
from .descriptor import CUDATargetDesc
dft = DeviceFunctionTemplate(pyfunc, debug=debug, inline=inline)
class device_function_template(AbstractTemplate):
key = dft
def generic(self, args, kws):
assert not kws
return dft.compile(args).signature
typingctx = CUDATargetDesc.typingctx
typingctx.insert_user_function(dft, device_function_template)
return dft
def compile_device(pyfunc, return_type, args, inline=True, debug=False):
return DeviceFunction(pyfunc, return_type, args, inline=True, debug=False)
def declare_device_function(name, restype, argtypes):
from .descriptor import CUDATargetDesc
typingctx = CUDATargetDesc.typingctx
targetctx = CUDATargetDesc.targetctx
sig = typing.signature(restype, *argtypes)
extfn = ExternFunction(name, sig)
class device_function_template(ConcreteTemplate):
key = extfn
cases = [sig]
fndesc = funcdesc.ExternalFunctionDescriptor(
name=name, restype=restype, argtypes=argtypes)
typingctx.insert_user_function(extfn, device_function_template)
targetctx.insert_user_function(extfn, fndesc)
return extfn
class DeviceFunction(object):
def __init__(self, pyfunc, return_type, args, inline, debug):
self.py_func = pyfunc
self.return_type = return_type
self.args = args
self.inline = True
self.debug = False
cres = compile_cuda(self.py_func, self.return_type, self.args,
debug=self.debug, inline=self.inline)
self.cres = cres
# Register
class device_function_template(ConcreteTemplate):
key = self
cases = [cres.signature]
cres.typing_context.insert_user_function(
self, device_function_template)
cres.target_context.insert_user_function(self, cres.fndesc,
[cres.library])
def __reduce__(self):
globs = serialize._get_function_globals_for_reduction(self.py_func)
func_reduced = serialize._reduce_function(self.py_func, globs)
args = (self.__class__, func_reduced, self.return_type, self.args,
self.inline, self.debug)
return (serialize._rebuild_reduction, args)
@classmethod
def _rebuild(cls, func_reduced, return_type, args, inline, debug):
return cls(serialize._rebuild_function(*func_reduced), return_type,
args, inline, debug)
def __repr__(self):
fmt = "<DeviceFunction py_func={0} signature={1}>"
return fmt.format(self.py_func, self.cres.signature)
class ExternFunction(object):
def __init__(self, name, sig):
self.name = name
self.sig = sig
class ForAll(object):
def __init__(self, kernel, ntasks, tpb, stream, sharedmem):
self.kernel = kernel
self.ntasks = ntasks
self.thread_per_block = tpb
self.stream = stream
self.sharedmem = sharedmem
def __call__(self, *args):
if isinstance(self.kernel, AutoJitCUDAKernel):
kernel = self.kernel.specialize(*args)
else:
kernel = self.kernel
tpb = self._compute_thread_per_block(kernel)
tpbm1 = tpb - 1
blkct = (self.ntasks + tpbm1) // tpb
return kernel.configure(blkct, tpb, stream=self.stream,
sharedmem=self.sharedmem)(*args)
def _compute_thread_per_block(self, kernel):
tpb = self.thread_per_block
# Prefer user-specified config
if tpb != 0:
return tpb
# Else, ask the driver to give a good cofnig
else:
ctx = get_context()
kwargs = dict(
func=kernel._func.get(),
b2d_func=0, # dynamic-shared memory is constant to blksz
memsize=self.sharedmem,
blocksizelimit=1024,
)
try:
# Raises from the driver if the feature is unavailable
_, tpb = ctx.get_max_potential_block_size(**kwargs)
except AttributeError:
# Fallback to table-based approach.
tpb = self._fallback_autotune_best(kernel)
raise
return tpb
def _fallback_autotune_best(self, kernel):
try:
tpb = kernel.autotune.best()
except ValueError:
warnings.warn('Could not autotune, using default tpb of 128')
tpb = 128
return tpb
class CUDAKernelBase(object):
"""Define interface for configurable kernels
"""
def __init__(self):
self.griddim = (1, 1)
self.blockdim = (1, 1, 1)
self.sharedmem = 0
self.stream = 0
def copy(self):
"""
Shallow copy the instance
"""
# Note: avoid using ``copy`` which calls __reduce__
cls = self.__class__
# new bare instance
new = cls.__new__(cls)
# update the internal states
new.__dict__.update(self.__dict__)
return new
def configure(self, griddim, blockdim, stream=0, sharedmem=0):
griddim, blockdim = normalize_kernel_dimensions(griddim, blockdim)
clone = self.copy()
clone.griddim = tuple(griddim)
clone.blockdim = tuple(blockdim)
clone.stream = stream
clone.sharedmem = sharedmem
return clone
def __getitem__(self, args):
if len(args) not in [2, 3, 4]:
raise ValueError('must specify at least the griddim and blockdim')
return self.configure(*args)
def forall(self, ntasks, tpb=0, stream=0, sharedmem=0):
"""Returns a configured kernel for 1D kernel of given number of tasks
``ntasks``.
This assumes that:
- the kernel 1-to-1 maps global thread id ``cuda.grid(1)`` to tasks.
- the kernel must check if the thread id is valid."""
return ForAll(self, ntasks, tpb=tpb, stream=stream, sharedmem=sharedmem)
def _serialize_config(self):
"""
Helper for serializing the grid, block and shared memory configuration.
CUDA stream config is not serialized.
"""
return self.griddim, self.blockdim, self.sharedmem
def _deserialize_config(self, config):
"""
Helper for deserializing the grid, block and shared memory
configuration.
"""
self.griddim, self.blockdim, self.sharedmem = config
class CachedPTX(object):
"""A PTX cache that uses compute capability as a cache key
"""
def __init__(self, name, llvmir, options):
self.name = name
self.llvmir = llvmir
self.cache = {}
self._extra_options = options.copy()
def get(self):
"""
Get PTX for the current active context.
"""
cuctx = get_context()
device = cuctx.device
cc = device.compute_capability
ptx = self.cache.get(cc)
if ptx is None:
arch = nvvm.get_arch_option(*cc)
ptx = nvvm.llvm_to_ptx(self.llvmir, opt=3, arch=arch,
**self._extra_options)
self.cache[cc] = ptx
if config.DUMP_ASSEMBLY:
print(("ASSEMBLY %s" % self.name).center(80, '-'))
print(ptx.decode('utf-8'))
print('=' * 80)
return ptx
class CachedCUFunction(object):
"""
Get or compile CUDA function for the current active context
Uses device ID as key for cache.
"""
def __init__(self, entry_name, ptx, linking, max_registers):
self.entry_name = entry_name
self.ptx = ptx
self.linking = linking
self.cache = {}
self.ccinfos = {}
self.max_registers = max_registers
def get(self):
cuctx = get_context()
device = cuctx.device
cufunc = self.cache.get(device.id)
if cufunc is None:
ptx = self.ptx.get()
# Link
linker = driver.Linker(max_registers=self.max_registers)
linker.add_ptx(ptx)
for path in self.linking:
linker.add_file_guess_ext(path)
cubin, _size = linker.complete()
compile_info = linker.info_log
module = cuctx.create_module_image(cubin)
# Load
cufunc = module.get_function(self.entry_name)
self.cache[device.id] = cufunc
self.ccinfos[device.id] = compile_info
return cufunc
def get_info(self):
self.get() # trigger compilation
cuctx = get_context()
device = cuctx.device
ci = self.ccinfos[device.id]
return ci
def __reduce__(self):
"""
Reduce the instance for serialization.
Pre-compiled PTX code string is serialized inside the `ptx` (CachedPTX).
Loaded CUfunctions are discarded. They are recreated when unserialized.
"""
if self.linking:
msg = ('cannot pickle CUDA kernel function with additional '
'libraries to link against')
raise RuntimeError(msg)
args = (self.__class__, self.entry_name, self.ptx, self.linking, self.max_registers)
return (serialize._rebuild_reduction, args)
@classmethod
def _rebuild(cls, entry_name, ptx, linking, max_registers):
"""
Rebuild an instance.
"""
return cls(entry_name, ptx, linking, max_registers)
class CUDAKernel(CUDAKernelBase):
'''
CUDA Kernel specialized for a given set of argument types. When called, this
object will validate that the argument types match those for which it is
specialized, and then launch the kernel on the device.
'''
def __init__(self, llvm_module, name, pretty_name, argtypes, call_helper,
link=(), debug=False, fastmath=False, type_annotation=None,
extensions=[], max_registers=None):
super(CUDAKernel, self).__init__()
# initialize CUfunction
options = {'debug': debug}
if fastmath:
options.update(dict(ftz=True,
prec_sqrt=False,
prec_div=False,
fma=True))
ptx = CachedPTX(pretty_name, str(llvm_module), options=options)
cufunc = CachedCUFunction(name, ptx, link, max_registers)
# populate members
self.entry_name = name
self.argument_types = tuple(argtypes)
self.linking = tuple(link)
self._type_annotation = type_annotation
self._func = cufunc
self.debug = debug
self.call_helper = call_helper
self.extensions = list(extensions)
@classmethod
def _rebuild(cls, name, argtypes, cufunc, link, debug, call_helper, extensions, config):
"""
Rebuild an instance.
"""
instance = cls.__new__(cls)
# invoke parent constructor
super(cls, instance).__init__()
# populate members
instance.entry_name = name
instance.argument_types = tuple(argtypes)
instance.linking = tuple(link)
instance._type_annotation = None
instance._func = cufunc
instance.debug = debug
instance.call_helper = call_helper
instance.extensions = extensions
# update config
instance._deserialize_config(config)
return instance
def __reduce__(self):
"""
Reduce the instance for serialization.
Compiled definitions are serialized in PTX form.
Type annotation are discarded.
Thread, block and shared memory configuration are serialized.
Stream information is discarded.
"""
config = self._serialize_config()
args = (self.__class__, self.entry_name, self.argument_types,
self._func, self.linking, self.debug, self.call_helper,
self.extensions, config)
return (serialize._rebuild_reduction, args)
def __call__(self, *args, **kwargs):
assert not kwargs
self._kernel_call(args=args,
griddim=self.griddim,
blockdim=self.blockdim,
stream=self.stream,
sharedmem=self.sharedmem)
def bind(self):
"""
Force binding to current CUDA context
"""
self._func.get()
@property
def ptx(self):
'''
PTX code for this kernel.
'''
return self._func.ptx.get().decode('utf8')
@property
def device(self):
"""
Get current active context
"""
return get_current_device()
def inspect_llvm(self):
'''
Returns the LLVM IR for this kernel.
'''
return str(self._func.ptx.llvmir)
def inspect_asm(self):
'''
Returns the PTX code for this kernel.
'''
return self._func.ptx.get().decode('ascii')
def inspect_types(self, file=None):
'''
Produce a dump of the Python source of this function annotated with the
corresponding Numba IR and type information. The dump is written to
*file*, or *sys.stdout* if *file* is *None*.
'''
if self._type_annotation is None:
raise ValueError("Type annotation is not available")
if file is None:
file = sys.stdout
print("%s %s" % (self.entry_name, self.argument_types), file=file)
print('-' * 80, file=file)
print(self._type_annotation, file=file)
print('=' * 80, file=file)
def _kernel_call(self, args, griddim, blockdim, stream=0, sharedmem=0):
# Prepare kernel
cufunc = self._func.get()
if self.debug:
excname = cufunc.name + "__errcode__"
excmem, excsz = cufunc.module.get_global_symbol(excname)
assert excsz == ctypes.sizeof(ctypes.c_int)
excval = ctypes.c_int()
excmem.memset(0, stream=stream)
# Prepare arguments
retr = [] # hold functors for writeback
kernelargs = []
for t, v in zip(self.argument_types, args):
self._prepare_args(t, v, stream, retr, kernelargs)
# Configure kernel
cu_func = cufunc.configure(griddim, blockdim,
stream=stream,
sharedmem=sharedmem)
# Invoke kernel
cu_func(*kernelargs)
if self.debug:
driver.device_to_host(ctypes.addressof(excval), excmem, excsz)
if excval.value != 0:
# An error occurred
def load_symbol(name):
mem, sz = cufunc.module.get_global_symbol("%s__%s__" %
(cufunc.name,
name))
val = ctypes.c_int()
driver.device_to_host(ctypes.addressof(val), mem, sz)
return val.value
tid = [load_symbol("tid" + i) for i in 'zyx']
ctaid = [load_symbol("ctaid" + i) for i in 'zyx']
code = excval.value
exccls, exc_args, loc = self.call_helper.get_exception(code)
# Prefix the exception message with the source location
if loc is None:
locinfo = ''
else:
sym, filepath, lineno = loc
filepath = os.path.relpath(filepath)
locinfo = 'In function %r, file %s, line %s, ' % (
sym, filepath, lineno,
)
# Prefix the exception message with the thread position
prefix = "%stid=%s ctaid=%s" % (locinfo, tid, ctaid)
if exc_args:
exc_args = ("%s: %s" % (prefix, exc_args[0]),) + exc_args[1:]
else:
exc_args = prefix,
raise exccls(*exc_args)
# retrieve auto converted arrays
for wb in retr:
wb()
def _prepare_args(self, ty, val, stream, retr, kernelargs):
"""
Convert arguments to ctypes and append to kernelargs
"""
# map the arguments using any extension you've registered
for extension in reversed(self.extensions):
ty, val = extension.prepare_args(
ty,
val,
stream=stream,
retr=retr)
if isinstance(ty, types.Array):
devary = wrap_arg(val).to_device(retr, stream)
c_intp = ctypes.c_ssize_t
meminfo = ctypes.c_void_p(0)
parent = ctypes.c_void_p(0)
nitems = c_intp(devary.size)
itemsize = c_intp(devary.dtype.itemsize)
data = ctypes.c_void_p(driver.device_pointer(devary))
kernelargs.append(meminfo)
kernelargs.append(parent)
kernelargs.append(nitems)
kernelargs.append(itemsize)
kernelargs.append(data)
for ax in range(devary.ndim):
kernelargs.append(c_intp(devary.shape[ax]))
for ax in range(devary.ndim):
kernelargs.append(c_intp(devary.strides[ax]))
elif isinstance(ty, types.Integer):
cval = getattr(ctypes, "c_%s" % ty)(val)
kernelargs.append(cval)
elif ty == types.float64:
cval = ctypes.c_double(val)
kernelargs.append(cval)
elif ty == types.float32:
cval = ctypes.c_float(val)
kernelargs.append(cval)
elif ty == types.boolean:
cval = ctypes.c_uint8(int(val))
kernelargs.append(cval)
elif ty == types.complex64:
kernelargs.append(ctypes.c_float(val.real))
kernelargs.append(ctypes.c_float(val.imag))
elif ty == types.complex128:
kernelargs.append(ctypes.c_double(val.real))
kernelargs.append(ctypes.c_double(val.imag))
elif isinstance(ty, (types.NPDatetime, types.NPTimedelta)):
kernelargs.append(ctypes.c_int64(val.view(np.int64)))
elif isinstance(ty, types.Record):
devrec = wrap_arg(val).to_device(retr, stream)
kernelargs.append(devrec)
else:
raise NotImplementedError(ty, val)
@property
def autotune(self):
"""Return the autotuner object associated with this kernel."""
warnings.warn(_deprec_warn_msg.format('autotune'), DeprecationWarning)
has_autotune = hasattr(self, '_autotune')
if has_autotune and self._autotune.dynsmem == self.sharedmem:
return self._autotune
else:
# Get CUDA Function
cufunc = self._func.get()
at = AutoTuner(info=cufunc.attrs, cc=cufunc.device.compute_capability)
self._autotune = at
return self._autotune
@property
def occupancy(self):
"""Occupancy is the ratio of the number of active warps per multiprocessor to the maximum
number of warps that can be active on the multiprocessor at once.
Calculate the theoretical occupancy of the kernel given the
current configuration."""
warnings.warn(_deprec_warn_msg.format('occupancy'), DeprecationWarning)
thread_per_block = reduce(operator.mul, self.blockdim, 1)
return self.autotune.closest(thread_per_block)
_deprec_warn_msg = ("The .{} attribute is is deprecated and will be "
"removed in a future release")
class AutoJitCUDAKernel(CUDAKernelBase):
'''
CUDA Kernel object. When called, the kernel object will specialize itself
for the given arguments (if no suitable specialized version already exists)
& compute capability, and launch on the device associated with the current
context.
Kernel objects are not to be constructed by the user, but instead are
created using the :func:`numba.cuda.jit` decorator.
'''
def __init__(self, func, bind, targetoptions):
super(AutoJitCUDAKernel, self).__init__()
self.py_func = func
self.bind = bind
# keyed by a `(compute capability, args)` tuple
self.definitions = {}
self.targetoptions = targetoptions
# defensive copy
self.targetoptions['extensions'] = \
list(self.targetoptions.get('extensions', []))
from .descriptor import CUDATargetDesc
self.typingctx = CUDATargetDesc.typingctx
@property
def extensions(self):
'''
A list of objects that must have a `prepare_args` function. When a
specialized kernel is called, each argument will be passed through
to the `prepare_args` (from the last object in this list to the
first). The arguments to `prepare_args` are:
- `ty` the numba type of the argument
- `val` the argument value itself
- `stream` the CUDA stream used for the current call to the kernel
- `retr` a list of zero-arg functions that you may want to append
post-call cleanup work to.
The `prepare_args` function must return a tuple `(ty, val)`, which
will be passed in turn to the next right-most `extension`. After all
the extensions have been called, the resulting `(ty, val)` will be
passed into Numba's default argument marshalling logic.
'''
return self.targetoptions['extensions']
def __call__(self, *args):
'''
Specialize and invoke this kernel with *args*.
'''
kernel = self.specialize(*args)
cfg = kernel[self.griddim, self.blockdim, self.stream, self.sharedmem]
cfg(*args)
def specialize(self, *args):
'''
Compile and bind to the current context a version of this kernel
specialized for the given *args*.
'''
argtypes = tuple(
[self.typingctx.resolve_argument_type(a) for a in args])
kernel = self.compile(argtypes)
return kernel
def compile(self, sig):
'''
Compile and bind to the current context a version of this kernel
specialized for the given signature.
'''
argtypes, return_type = sigutils.normalize_signature(sig)
assert return_type is None
cc = get_current_device().compute_capability
kernel = self.definitions.get((cc, argtypes))
if kernel is None:
if 'link' not in self.targetoptions:
self.targetoptions['link'] = ()
kernel = compile_kernel(self.py_func, argtypes,
**self.targetoptions)
self.definitions[(cc, argtypes)] = kernel
if self.bind:
kernel.bind()
return kernel
def inspect_llvm(self, signature=None, compute_capability=None):
'''
Return the LLVM IR for all signatures encountered thus far, or the LLVM
IR for a specific signature and compute_capability if given.
'''
cc = compute_capability or get_current_device().compute_capability
if signature is not None:
return self.definitions[(cc, signature)].inspect_llvm()
else:
return dict((sig, defn.inspect_llvm())
for sig, defn in self.definitions.items())
def inspect_asm(self, signature=None, compute_capability=None):
'''
Return the generated assembly code for all signatures encountered thus
far, or the LLVM IR for a specific signature and compute_capability
if given.
'''
cc = compute_capability or get_current_device().compute_capability
if signature is not None:
return self.definitions[(cc, signature)].inspect_asm()
else:
return dict((sig, defn.inspect_asm())
for sig, defn in self.definitions.items())
def inspect_types(self, file=None):
'''
Produce a dump of the Python source of this function annotated with the
corresponding Numba IR and type information. The dump is written to
*file*, or *sys.stdout* if *file* is *None*.
'''
if file is None:
file = sys.stdout
for _, defn in utils.iteritems(self.definitions):
defn.inspect_types(file=file)
@classmethod
def _rebuild(cls, func_reduced, bind, targetoptions, config):
"""
Rebuild an instance.
"""
func = serialize._rebuild_function(*func_reduced)
instance = cls(func, bind, targetoptions)
instance._deserialize_config(config)
return instance
def __reduce__(self):
"""
Reduce the instance for serialization.
Compiled definitions are discarded.
"""
glbls = serialize._get_function_globals_for_reduction(self.py_func)
func_reduced = serialize._reduce_function(self.py_func, glbls)
config = self._serialize_config()
args = (self.__class__, func_reduced, self.bind, self.targetoptions,
config)
return (serialize._rebuild_reduction, args)