cppjit/python/cppjit/numba_ext.py at main · compiler-research/cppjit · GitHub

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"""cppjit extensions for numba"""

import re

import numba

import numba.core.cgutils as nb_cgu

import numba.core.datamodel as nb_dm

import numba.core.imputils as nb_iutils

import numba.core.registry as nb_reg

import numba.core.types as nb_types

import numba.core.typing as nb_typing

import numba.core.typing.templates as nb_tmpl

import numba.extending as nb_ext

from llvmlite import ir

import cppjit

import cppjit.reflex as cpp_refl

import cppjit.types as cpp_types

# setuptools entry point for Numba

def _init_extension():

pass

class Qualified:

default = 0

value = 1

instance = 2

ir_byte = ir.IntType(8)

ir_voidptr = ir.PointerType(ir_byte) # by convention

ir_byteptr = ir_voidptr # for clarity

ir_intptr_t = ir.IntType(cppjit.sizeof("void*") * 8)

# special case access to unboxing/boxing APIs

cppjit_as_voidptr = cppjit.addressof("Instance_AsVoidPtr")

cppjit_from_voidptr = cppjit.addressof("Instance_FromVoidPtr")

_cpp2numba = {

"void": nb_types.void,

"void*": nb_types.voidptr,

"int8_t": nb_types.int8,

"uint8_t": nb_types.uint8,

"short": nb_types.short,

"unsigned short": nb_types.ushort,

"int": nb_types.intc,

"unsigned int": nb_types.uintc,

"int32_t": nb_types.int32,

"uint32_t": nb_types.uint32,

"int64_t": nb_types.int64,

"uint64_t": nb_types.uint64,

"long": nb_types.long_,

"unsigned long": nb_types.ulong,

"long long": nb_types.longlong,

"unsigned long long": nb_types.ulonglong,

"float": nb_types.float32,

"double": nb_types.float64,

"char": nb_types.char,

"unsigned char": nb_types.uchar,

"char*": nb_types.unicode_type,

}

def resolve_std_vector(val):

return re.match(r"std::vector<(.+?)>", val).group(1)

def resolve_const_types(val):

return re.match(r"const\s+(.+)\s*\*", val).group(1)

def cpp2numba(val):

if not isinstance(val, str):

# TODO: distinguish ptr/ref/byval

# TODO: Only metaclasses/proxies end up here since

# ref cases makes the RETURN_TYPE from reflex a string

return typeof_scope(val, nb_typing.typeof.Purpose.argument, Qualified.value)

elif val.startswith("std::vector"):

type_arr = getattr(numba, str(cpp2numba(resolve_std_vector(val))))[:]

return type_arr

elif val[-1] == "*" or val[-1] == "&":

if val.startswith("const"):

return nb_types.CPointer(cpp2numba(resolve_const_types(val)))

return nb_types.CPointer(_cpp2numba[val[:-2]])

return _cpp2numba[val]

_numba2cpp = dict()

for key, value in _cpp2numba.items():

_numba2cpp[value] = key

# prefer "int" in the case of intc over "int32_t"

_numba2cpp[nb_types.intc] = "int"

def numba2cpp(val):

if hasattr(val, "literal_type"):

val = val.literal_type

if val == nb_types.int64: # Python int

# TODO: this is only necessary until "best matching" is in place

val = nb_types.intc # more likely match candidate

elif isinstance(val, numba.types.CPointer):

return _numba2cpp[val.dtype]

elif isinstance(val, numba.types.RawPointer):

return _numba2cpp[nb_types.voidptr]

elif isinstance(val, numba.types.Array):

return "std::vector<" + _numba2cpp[val.dtype] + ">"

elif isinstance(val, CppClassNumbaType):

return val._scope.__cpp_name__

else:

try:

return _numba2cpp[val]

except:

raise RuntimeError("Type mapping failed from Numba to C++ for ", val)

def numba_arg_convertor(args):

args_cpp = []

for arg in list(args):

# If the user explicitly passes an argument using numba CPointer, the regex match is used

# to detect the pass by reference since the dispatcher always returns typeref[val*]

match = re.search(r"typeref\[(.*?)\*\]", str(arg))

if match:

literal_val = match.group(1)

arg_type = numba.typeof(eval(literal_val))

args_cpp.append(to_ref(numba2cpp(arg_type)))

else:

args_cpp.append(numba2cpp(arg))

return tuple(args_cpp)

def to_ref(type_list):

ref_list = []

for l in type_list: # noqa: E741

ref_list.append(l + " &")

return ref_list

# TODO: looks like Numba treats unsigned types as signed when lowering,

# which seems to work as they're just reinterpret_casts

_cpp2ir = {

"char *": ir_byteptr,

"int8_t": ir.IntType(8),

"uint8_t": ir.IntType(8),

"short": ir.IntType(nb_types.short.bitwidth),

"unsigned short": ir.IntType(nb_types.ushort.bitwidth),

"int": ir.IntType(nb_types.intc.bitwidth),

"unsigned int": ir.IntType(nb_types.uintc.bitwidth),

"int32_t": ir.IntType(32),

"uint32_t": ir.IntType(32),

"int64_t": ir.IntType(64),

"uint64_t": ir.IntType(64),

"long": ir.IntType(nb_types.long_.bitwidth),

"unsigned long": ir.IntType(nb_types.ulong.bitwidth),

"long long": ir.IntType(nb_types.longlong.bitwidth),

"unsigned long long": ir.IntType(nb_types.ulonglong.bitwidth),

"float": ir.FloatType(),

"double": ir.DoubleType(),

}

def cpp2ir(val):

try:

return _cpp2ir[val]

except KeyError:

if val.startswith("std::vector"):

## TODO should be possible to obtain the vector length from the CPPDataMember val

type_arr = ir.VectorType(cpp2ir(resolve_std_vector(val)), 3)

return type_arr

elif val != "char *" and val[-1] == "*":

if val.startswith("const"):

return ir.PointerType(cpp2ir(resolve_const_types(val)))

type_2 = _cpp2ir[val[:-2]]

return ir.PointerType(type_2)

# C++ function pointer -> Numba

class CppFunctionNumbaType(nb_types.Callable):

targetdescr = nb_reg.cpu_target

requires_gil = False

def __init__(self, func, is_method=False):

super(CppFunctionNumbaType, self).__init__("CppFunction(%s)" % str(func))

self.sig = None

self._func = func

self._is_method = is_method

self._signatures = list()

self._impl_keys = dict()

self._arg_set_matched = tuple()

self.ret_type = None

def is_precise(self):

return True # by definition

def get_call_type(self, context, args, kwds):

try:

return self._impl_keys[args].sig

except KeyError:

pass

ol = CppFunctionNumbaType(

self._func.__overload__(numba_arg_convertor(args)), self._is_method

)

thistype = None

if self._is_method:

thistype = nb_types.voidptr

self.ret_type = cpp2numba(ol._func.__cpp_reflex__(cpp_refl.RETURN_TYPE))

ol.sig = nb_typing.Signature(

return_type=self.ret_type, args=args, recvr=thistype

)

extsig = ol.sig

if self._is_method:

self.ret_type = ol.sig.return_type

args = (nb_types.voidptr, *args)

extsig = nb_typing.Signature(

return_type=ol.sig.return_type, args=args, recvr=None

)

self._impl_keys[args] = ol

self._arg_set_matched = numba_arg_convertor(args)

@nb_iutils.lower_builtin(ol, *args)

def lower_external_call(

context,

builder,

sig,

args,

ty=nb_types.ExternalFunctionPointer(extsig, ol.get_pointer),

pyval=self._func,

is_method=self._is_method,

ptrty = context.get_function_pointer_type(ty)

ptrval = context.add_dynamic_addr(

builder, ty.get_pointer(pyval), info=str(pyval)

)

fptr = builder.bitcast(ptrval, ptrty)

return context.call_function_pointer(builder, fptr, args)

return ol.sig

def get_call_signatures(self):

return list(self._signatures), False

def get_impl_key(self, sig):

return self._impl_keys[sig.args]

# TODO: Remove the redundancy of __overload__ matching and use this function

# to only obtain the address given the matched overload

def get_pointer(self, func):

if func is None:

func = self._func

ol = func.__overload__(numba_arg_convertor(self.sig.args))

address = cppjit.addressof(ol)

if not address:

raise RuntimeError("unresolved address for %s" % str(ol))

return address

@property

def key(self):

return self._func

@nb_ext.typeof_impl.register(cpp_types.Function)

def typeof_function(val, c):

return CppFunctionNumbaType(val)

@nb_ext.typeof_impl.register(cpp_types.Template)

def typeof_template(val, c):

if hasattr(val, "__overload__"):

return CppFunctionNumbaType(val)

raise RuntimeError("only function templates supported")

@nb_ext.register_model(CppFunctionNumbaType)

class CppFunctionModel(nb_dm.models.PrimitiveModel):

def __init__(self, dmm, fe_type):

# the function pointer of this overload can not be exactly typed, but

# only the storage size is relevant, so simply use a void*

be_type = ir.PointerType(dmm.lookup(nb_types.void).get_value_type())

super(CppFunctionModel, self).__init__(dmm, fe_type, be_type)

@nb_iutils.lower_constant(CppFunctionNumbaType)

def constant_function_pointer(context, builder, ty, pyval):

# TODO: needs to exist for the proper flow, but why? The lowering of the

# actual overload is handled dynamically.

return

# C++ method / data member -> Numba

class CppDataMemberInfo(object):

__slots__ = ["f_name", "f_offset", "f_nbtype", "f_irtype"]

def __init__(self, name, offset, cpptype):

self.f_name = name

self.f_offset = offset

self.f_nbtype = cpp2numba(cpptype)

self.f_irtype = cpp2ir(cpptype)

# C++ class -> Numba

class CppClassNumbaType(CppFunctionNumbaType):

def __init__(self, scope, qualifier):

addr = None

cppinstance_val = None

if qualifier == Qualified.instance:

addr = cppjit.addressof(scope)

cppinstance_val = scope

scope = type(scope)

qualifier = Qualified.default

super(CppClassNumbaType, self).__init__(scope.__init__)

self.name = "CppClass(%s)" % scope.__cpp_name__ # overrides value in Type

self._scope = scope

self._qualifier = qualifier

self._cppinstanceval = cppinstance_val

self._addr = addr

def get_scope(self):

return self._scope

def get_qualifier(self):

return self._qualifier

def get_call_type(self, context, args, kwds):

sig = super(CppClassNumbaType, self).get_call_type(context, args, kwds)

self.sig = sig

return sig

def is_precise(self):

return True

@property

def key(self):

return (self._scope, self._qualifier)

@nb_tmpl.infer_getattr

class CppClassFieldResolver(nb_tmpl.AttributeTemplate):

key = CppClassNumbaType

def generic_resolve(self, typ, attr):

ft = typ.__dict__.get(attr, None)

if ft is not None:

return ft

try:

f = getattr(typ._scope, attr)

if isinstance(f, cpp_types.Function):

ft = CppFunctionNumbaType(f, is_method=True)

except AttributeError:

pass

try:

f = typ._scope.__dict__[attr]

if isinstance(f, cpp_types.DataMember):

ct = f.__cpp_reflex__(cpp_refl.TYPE)

ft = cpp2numba(ct)

except AttributeError:

pass

if ft is not None:

typ.__dict__[attr] = ft

return ft

@nb_iutils.lower_getattr_generic(CppClassNumbaType)

def cppclass_getattr_impl(context, builder, typ, val, attr):

# TODO: the following relies on the fact that numba will first lower the

# field access, then immediately lower the call; and that the `val` loads

# the struct representing the C++ object. Neither need be stable.

if attr in typ._scope.__dict__ and isinstance(

typ._scope.__dict__[attr], cpp_types.DataMember

dm = typ._scope.__dict__[attr]

ct = dm.__cpp_reflex__(cpp_refl.TYPE)

offset = dm.__cpp_reflex__(cpp_refl.OFFSET)

q = typ.get_qualifier()

if q == Qualified.default:

llval = builder.bitcast(val, ir_byteptr)

pfc = builder.gep(llval, [ir.Constant(ir_intptr_t, offset)])

pf = builder.bitcast(pfc, ir.PointerType(cpp2ir(ct)))

return builder.load(pf)

elif q == Qualified.value:

model = nb_dm.default_manager.lookup(typ)

return model.get(builder, val, attr)

else:

assert not "unknown qualified type"

# TODO: easier with inttoptr and ptrtoint (cgutils.pointer_add)?

llval = builder.bitcast(val, ir_byteptr)

pfc = builder.gep(llval, [ir.Constant(ir_intptr_t, offset)])

pf = builder.bitcast(pfc, ir.PointerType(cpp2ir(ct)))

return builder.load(pf)

# assume this is a method

q = typ.get_qualifier()

if q == Qualified.default:

return builder.bitcast(val, ir_voidptr)

elif q == Qualified.value:

return None

assert not "unknown qualified type"

return None

class ImplAggregateValueModel(nb_dm.models.StructModel):

def get(self, builder, val, pos):

"""Get a field at the given position/field name"""

if isinstance(pos, str):

pos = self.get_field_position(pos)

# Use the offsets for direct addressing, rather than getting the elements

# from the struct type.

dmi = self._data_members[pos]

stack = nb_cgu.alloca_once(builder, self.get_data_type())

builder.store(val, stack)

llval = builder.bitcast(stack, ir_byteptr)

pfc = builder.gep(llval, [ir.Constant(ir_intptr_t, dmi.f_offset)])

pf = builder.bitcast(pfc, ir.PointerType(dmi.f_irtype))

return builder.load(pf)

class ImplClassValueModel(ImplAggregateValueModel):

# TODO : Should the address have to be passed here and stored in meminfo

# value: representation inside function body. Maybe stored in stack.

# The representation here are flexible.

def get_value_type(self):

return self.get_data_type()

# data: representation used when storing into containers (e.g. arrays).

def get_data_type(self):

# The struct model relies on data being a POD, but for C++ objects, there

# can be hidden data (e.g. vtable, thunks, or simply private members), and

# the alignment of Cling and Numba also need not be the same. Therefore, the

# struct is split in a series of byte members to get the total size right

# and to allow addressing at the correct offsets.

if self._data_type is None:

self._data_type = ir.LiteralStructType(

[ir_byte for i in range(self._sizeof)], packed=True

)

return self._data_type

# return: representation used for return argument.

def get_return_type(self):

return self.get_data_type()

scope_numbatypes = (dict(), dict())

@nb_ext.typeof_impl.register(cpp_types.Scope)

def typeof_scope(val, c, q=Qualified.default):

is_instance = False

cppinstance_val = None

if q == Qualified.instance:

cppinstance_val = val

val = type(val)

q = Qualified.default

is_instance = True

global scope_numbatypes

try:

return scope_numbatypes[q][val]

except KeyError:

pass

if val.__cpp_reflex__(cpp_refl.IS_NAMESPACE):

cnt = nb_types.Module(val)

scope_numbatypes[Qualified.default][val] = cnt

return cnt

class ImplClassType(CppClassNumbaType):

pass

if is_instance:

cnt = ImplClassType(cppinstance_val, Qualified.instance)

else:

cnt = ImplClassType(val, q)

scope_numbatypes[q][val] = cnt

# declare data members to Numba

data_members = list()

member_methods = dict()

for name, field in val.__dict__.items():

if isinstance(field, cpp_types.DataMember):

data_members.append(

CppDataMemberInfo(

name,

field.__cpp_reflex__(cpp_refl.OFFSET),

field.__cpp_reflex__(cpp_refl.TYPE),

)

elif isinstance(field, cpp_types.Function):

member_methods[name] = field.__cpp_reflex__(cpp_refl.RETURN_TYPE)

# TODO: this refresh is needed b/c the scope type is registered as a

# callable after the tracing started; no idea of the side-effects ...

nb_reg.cpu_target.typing_context.refresh()

# create a model description for Numba

if q == Qualified.default:

@nb_ext.register_model(ImplClassType)

class ImplClassModel(nb_dm.models.StructModel):

def __init__(self, dmm, fe_type):

self._data_members = data_members

self._member_methods = member_methods

# TODO: eventually we need not derive from StructModel

members = [(dmi.f_name, dmi.f_nbtype) for dmi in data_members]

nb_dm.models.StructModel.__init__(self, dmm, fe_type, members)

# proxies are always accessed by pointer, which are not composites

def traverse(self, builder):

return []

def traverse_models(self):

return []

def traverse_types(self):

return [self._fe_type] # from StructModel

# data: representation used when storing into containers (e.g. arrays).

# TODO ...

# value: representation inside function body. Maybe stored in stack.

# The representation here are flexible.

def get_value_type(self):

# the C++ object, b/c through a proxy, is always accessed by pointer; it is

# represented as a pointer to POD to allow indexing by Numba for data member

# type checking, but the address offsetting for loading data member values is

# independent (see get(), below), so the exact layout need not match a POD

# TODO: this doesn't work for real PODs, b/c those are unpacked into their elements

# and passed through registers

return ir.PointerType(super(ImplClassModel, self).get_value_type())

# argument: representation used for function argument. Needs to be builtin type,

# but unlike other Numba composites, C++ proxies are not flattened.

def get_argument_type(self):

return self.get_value_type()

def as_argument(self, builder, value):

return value

def from_argument(self, builder, value):

return value

# return: representation used for return argument.

# TODO ...

# access to public data members

def get(self, builder, val, pos):

"""Get a field at the given position/field name"""

if isinstance(pos, str):

pos = self.get_field_position(pos)

dmi = self._data_members[pos]

llval = builder.bitcast(val, ir_byteptr)

pfc = builder.gep(llval, [ir.Constant(ir_intptr_t, dmi.f_offset)])

pf = builder.bitcast(pfc, ir.PointerType(dmi.f_irtype))

return builder.load(pf)

elif q == Qualified.value:

if val.__cpp_reflex__(cpp_refl.IS_AGGREGATE):

@nb_ext.register_model(ImplClassType)

class ImplClassModel(ImplAggregateValueModel):

pass

else:

@nb_ext.register_model(ImplClassType)

class ImplClassModel(ImplClassValueModel):

pass

def init(self, dmm, fe_type, sz=cppjit.sizeof(val)):

self._data_members = data_members

self._member_methods = member_methods

self._sizeof = sz

# TODO: this code exists purely to be able to use the indexing and hierarchy

# of the base class StructModel, which isn't much of a reason

members = [(dmi.f_name, dmi.f_nbtype) for dmi in data_members]

nb_dm.models.StructModel.__init__(self, dmm, fe_type, members)

ImplClassModel.__init__ = init

else:

assert not "unknown qualified type"

# Python proxy unwrapping for arguments into the Numba trace

@nb_ext.unbox(ImplClassType)

def unbox_instance(typ, obj, c):

global cppjit_as_voidptr

ptrty = ir.PointerType(ir.FunctionType(ir_voidptr, [ir_voidptr]))

ptrval = c.context.add_dynamic_addr(

c.builder, cppjit_as_voidptr, info="Instance_AsVoidPtr"

)

fp = c.builder.bitcast(ptrval, ptrty)

vptr = c.context.call_function_pointer(c.builder, fp, [obj])

model = nb_dm.default_manager.lookup(typ)

pobj = c.builder.bitcast(vptr, model.get_argument_type())

return nb_ext.NativeValue(pobj, is_error=None, cleanup=None)

def make_implclass(context, builder, typ, **kwargs):

return nb_cgu.create_struct_proxy(typ)(context, builder, **kwargs)

# C++ object to Python proxy wrapping for returns from Numba trace

@nb_ext.box(ImplClassType)

def box_instance(typ, val, c):

assert not "requires object model and passing of intact object, not memberwise copy"

global cppjit_from_voidptr

if isinstance(val, ir.Constant):

if val.constant == ir.Undefined:

assert not "Value passed to instance boxing is undefined"

return NULL # noqa: F821

implclass = make_implclass(c.context, c.builder, typ)

classobj = c.pyapi.unserialize(c.pyapi.serialize_object(cpp_types.Instance))

box_list = []

model = implclass._datamodel # noqa: F841

cfr = CppClassFieldResolver(c.context)

for i in typ._scope.__dict__:

if isinstance(cfr.generic_resolve(typ, i), nb_types.Type):

box_list.append(

c.box(cfr.generic_resolve(typ, i), getattr(implclass, i))

)

box_res = c.pyapi.call_function_objargs(classobj, tuple(box_list))

# Required for nopython mode, numba nrt requres each member box call to decref

# since it steals the reference

for i in box_list:

c.pyapi.decref(i)

return box_res

return cnt

# C++ instance -> Numba

@nb_ext.typeof_impl.register(cpp_types.Instance)

def typeof_instance(val, c):

global scope_numbatypes

try:

return scope_numbatypes[Qualified.default][type(val)]

except KeyError:

pass

# Pass the val itself to obtain Cling address of the CPPInstance for reference to C++ objects

return typeof_scope(val, c, Qualified.instance)

cppjit/python/cppjit/numba_ext.py at main · compiler-research/cppjit · GitHub

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