def printf(builder, format_string, *values):
str_const = Constant.stringz(format_string)
global_str_const = get_module(builder).add_global_variable(
str_const.type, '')
global_str_const.initializer = str_const
idx = [Constant.int(Type.int(32), 0), Constant.int(Type.int(32), 0)]
str_addr = global_str_const.gep(idx)
args = []
for v in values:
if isinstance(v, int):
args.append(Constant.int(Type.int(), v))
elif isinstance(v, float):
args.append(Constant.real(Type.double(), v))
else:
args.append(v)
functype = Type.function(Type.int(32), [Type.pointer(Type.int(8))], True)
fn = get_module(builder).get_or_insert_function(functype, 'printf')
builder.call(fn, [str_addr] + args)
def __init__(self, context, builder):
"""
Note: Maybe called multiple times when lowering a function
"""
from numba.targets import boxing
self.context = context
self.builder = builder
self.module = builder.basic_block.function.module
# A unique mapping of serialized objects in this module
try:
self.module.__serialized
except AttributeError:
self.module.__serialized = {}
# Initialize types
self.pyobj = self.context.get_argument_type(types.pyobject)
self.voidptr = Type.pointer(Type.int(8))
self.long = Type.int(ctypes.sizeof(ctypes.c_long) * 8)
self.ulonglong = Type.int(ctypes.sizeof(ctypes.c_ulonglong) * 8)
self.longlong = self.ulonglong
self.double = Type.double()
self.py_ssize_t = self.context.get_value_type(types.intp)
self.cstring = Type.pointer(Type.int(8))
self.gil_state = Type.int(_helperlib.py_gil_state_size * 8)
self.py_buffer_t = ir.ArrayType(ir.IntType(8), _helperlib.py_buffer_size)
def parse_tuple_and_keywords(self, args, kws, fmt, keywords, *objs):
charptr = Type.pointer(Type.int(8))
charptrary = Type.pointer(charptr)
argtypes = [self.pyobj, self.pyobj, charptr, charptrary]
fnty = Type.function(Type.int(), argtypes, var_arg=True)
fn = self._get_function(fnty, name="PyArg_ParseTupleAndKeywords")
return self.builder.call(fn, [args, kws, fmt, keywords] + list(objs))
def make_keywords(self, kws):
strings = []
stringtype = Type.pointer(Type.int(8))
for k in kws:
strings.append(self.make_const_string(k))
strings.append(Constant.null(stringtype))
kwlist = Constant.array(stringtype, strings)
kwlist = cgutils.global_constant(self.module, ".kwlist", kwlist)
return Constant.bitcast(kwlist, Type.pointer(stringtype))
def __init__(self, context, builder, value=None, ref=None, cast_ref=False):
self._type = context.get_struct_type(self)
self._context = context
self._builder = builder
if ref is None:
self._value = alloca_once(builder, self._type)
if value is not None:
assert not is_pointer(value.type)
assert value.type == self._type, (value.type, self._type)
builder.store(value, self._value)
else:
assert value is None
assert is_pointer(ref.type)
if self._type != ref.type.pointee:
if cast_ref:
ref = builder.bitcast(ref, Type.pointer(self._type))
else:
raise TypeError(
"mismatching pointer type: got %s, expected %s"
% (ref.type.pointee, self._type))
self._value = ref
self._namemap = {}
self._fdmap = []
self._typemap = []
base = Constant.int(Type.int(), 0)
for i, (k, tp) in enumerate(self._fields):
self._namemap[k] = i
self._fdmap.append((base, Constant.int(Type.int(), i)))
self._typemap.append(tp)
def numba_array_adaptor(self, ary, ptr):
voidptr = Type.pointer(Type.int(8))
fnty = Type.function(Type.int(), [self.pyobj, voidptr])
fn = self._get_function(fnty, name="numba_adapt_ndarray")
fn.args[0].add_attribute(lc.ATTR_NO_CAPTURE)
fn.args[1].add_attribute(lc.ATTR_NO_CAPTURE)
return self.builder.call(fn, (ary, ptr))
def get_argument_type(self, ty):
if ty == types.boolean:
return self.get_data_type(ty)
elif self.is_struct_type(ty):
return Type.pointer(self.get_value_type(ty))
else:
return self.get_value_type(ty)
def call_function_pointer(self, builder, funcptr, signature, args, cconv=None):
retty = self.get_value_type(signature.return_type)
fnty = Type.function(retty, [a.type for a in args])
fnptrty = Type.pointer(fnty)
addr = self.get_constant(types.intp, funcptr)
ptr = builder.inttoptr(addr, fnptrty)
return builder.call(ptr, args, cconv=cconv)
def insert_string_const_addrspace(self, builder, string):
"""
Insert a constant string in the constant addresspace and return a
generic i8 pointer to the data.
This function attempts to deduplicate.
"""
lmod = builder.basic_block.function.module
text = Constant.stringz(string)
name = "__conststring__.%s" % string
charty = Type.int(8)
for gv in lmod.global_variables:
if gv.name == name and gv.type.pointee == text.type:
break
else:
gv = lmod.add_global_variable(text.type, name=name,
addrspace=nvvm.ADDRSPACE_CONSTANT)
gv.linkage = LINKAGE_INTERNAL
gv.global_constant = True
gv.initializer = text
constcharptrty = Type.pointer(charty, nvvm.ADDRSPACE_CONSTANT)
charptr = builder.bitcast(gv, constcharptrty)
conv = nvvmutils.insert_addrspace_conv(lmod, charty,
nvvm.ADDRSPACE_CONSTANT)
return builder.call(conv, [charptr])
def to_native_generator(self, obj, typ):
"""
Extract the generator structure pointer from a generator *obj*
(a _dynfunc.Generator instance).
"""
gen_ptr_ty = Type.pointer(self.context.get_data_type(typ))
value = self.context.get_generator_state(self.builder, obj, gen_ptr_ty)
return NativeValue(value)
def nrt_adapt_buffer_from_python(self, buf, ptr):
assert self.context.enable_nrt
fnty = Type.function(Type.void(), [Type.pointer(self.py_buffer_t),
self.voidptr])
fn = self._get_function(fnty, name="NRT_adapt_buffer_from_python")
fn.args[0].add_attribute(lc.ATTR_NO_CAPTURE)
fn.args[1].add_attribute(lc.ATTR_NO_CAPTURE)
return self.builder.call(fn, (buf, ptr))
def get_return_type(self, ty):
if isinstance(ty, types.Optional):
return self.get_return_type(ty.type)
elif self.is_struct_type(ty):
return self.get_argument_type(ty)
else:
argty = self.get_argument_type(ty)
return Type.pointer(argty)
def gil_release(self, gil):
"""
Release the acquired GIL by gil_ensure().
Must be paired with a gil_ensure().
"""
gilptrty = Type.pointer(self.gil_state)
fnty = Type.function(Type.void(), [gilptrty])
fn = self._get_function(fnty, "numba_gil_release")
return self.builder.call(fn, [gil])
def unpack_tuple(self, args, name, n_min, n_max, *objs):
charptr = Type.pointer(Type.int(8))
argtypes = [self.pyobj, charptr, self.py_ssize_t, self.py_ssize_t]
fnty = Type.function(Type.int(), argtypes, var_arg=True)
fn = self._get_function(fnty, name="PyArg_UnpackTuple")
n_min = Constant.int(self.py_ssize_t, n_min)
n_max = Constant.int(self.py_ssize_t, n_max)
if isinstance(name, str):
name = self.context.insert_const_string(self.builder.module, name)
return self.builder.call(fn, [args, name, n_min, n_max] + list(objs))
def __init__(self, context, builder):
"""
Note: Maybe called multiple times when lowering a function
"""
fix_python_api()
self.context = context
self.builder = builder
self.module = builder.basic_block.function.module
# Initialize types
self.pyobj = self.context.get_argument_type(types.pyobject)
self.voidptr = Type.pointer(Type.int(8))
self.long = Type.int(ctypes.sizeof(ctypes.c_long) * 8)
self.ulonglong = Type.int(ctypes.sizeof(ctypes.c_ulonglong) * 8)
self.longlong = self.ulonglong
self.double = Type.double()
self.py_ssize_t = self.context.get_value_type(types.intp)
self.cstring = Type.pointer(Type.int(8))
self.gil_state = Type.int(_helperlib.py_gil_state_size * 8)
def frexp_impl(context, builder, sig, args):
val, = args
fltty = context.get_data_type(sig.args[0])
intty = context.get_data_type(sig.return_type[1])
expptr = cgutils.alloca_once(builder, intty, name="exp")
fnty = Type.function(fltty, (fltty, Type.pointer(intty)))
fname = {"float": "numba_frexpf", "double": "numba_frexp"}[str(fltty)]
fn = builder.module.get_or_insert_function(fnty, name=fname)
res = builder.call(fn, (val, expptr))
res = cgutils.make_anonymous_struct(builder, (res, builder.load(expptr)))
return impl_ret_untracked(context, builder, sig.return_type, res)
def print_charseq(context, builder, sig, args):
[x] = args
py = context.get_python_api(builder)
xp = cgutils.alloca_once(builder, x.type)
builder.store(x, xp)
byteptr = builder.bitcast(xp, Type.pointer(Type.int(8)))
size = context.get_constant(types.intp, x.type.elements[0].count)
cstr = py.bytes_from_string_and_size(byteptr, size)
py.print_object(cstr)
py.decref(cstr)
return context.get_dummy_value()
def gil_ensure(self):
"""
Ensure the GIL is acquired.
The returned value must be consumed by gil_release().
"""
gilptrty = Type.pointer(self.gil_state)
fnty = Type.function(Type.void(), [gilptrty])
fn = self._get_function(fnty, "numba_gil_ensure")
gilptr = cgutils.alloca_once(self.builder, self.gil_state)
self.builder.call(fn, [gilptr])
return gilptr
def get_value_type(self, ty):
if ty == types.boolean:
return Type.int(1)
dataty = self.get_data_type(ty)
if isinstance(ty, types.Record):
# Record data are passed by refrence
memory = dataty.elements[0]
return Type.struct([Type.pointer(memory)])
return dataty
def from_native_generator(self, val, typ, env=None):
"""
Make a Numba generator (a _dynfunc.Generator instance) from a
generator structure pointer *val*.
*env* is an optional _dynfunc.Environment instance to be wrapped
in the generator.
"""
llty = self.context.get_data_type(typ)
assert not llty.is_pointer
gen_struct_size = self.context.get_abi_sizeof(llty)
gendesc = self.context.get_generator_desc(typ)
# This is the PyCFunctionWithKeywords generated by PyCallWrapper
genfnty = Type.function(self.pyobj, [self.pyobj, self.pyobj, self.pyobj])
genfn = self._get_function(genfnty, name=gendesc.llvm_cpython_wrapper_name)
# This is the raw finalizer generated by _lower_generator_finalize_func()
finalizerty = Type.function(Type.void(), [self.voidptr])
if typ.has_finalizer:
finalizer = self._get_function(finalizerty, name=gendesc.llvm_finalizer_name)
else:
finalizer = Constant.null(Type.pointer(finalizerty))
# PyObject *numba_make_generator(state_size, initial_state, nextfunc, finalizer, env)
fnty = Type.function(self.pyobj, [self.py_ssize_t,
self.voidptr,
Type.pointer(genfnty),
Type.pointer(finalizerty),
self.voidptr])
fn = self._get_function(fnty, name="numba_make_generator")
state_size = ir.Constant(self.py_ssize_t, gen_struct_size)
initial_state = self.builder.bitcast(val, self.voidptr)
if env is None:
env = self.get_null_object()
env = self.builder.bitcast(env, self.voidptr)
return self.builder.call(fn,
(state_size, initial_state, genfn, finalizer, env))
def ptx_cmem_arylike(context, builder, sig, args):
lmod = builder.module
[arr] = args
flat = arr.flatten(order='A')
aryty = sig.return_type
dtype = aryty.dtype
if isinstance(dtype, types.Complex):
elemtype = (types.float32
if dtype == types.complex64
else types.float64)
constvals = []
for i in range(flat.size):
elem = flat[i]
real = context.get_constant(elemtype, elem.real)
imag = context.get_constant(elemtype, elem.imag)
constvals.extend([real, imag])
elif dtype in types.number_domain:
constvals = [context.get_constant(dtype, flat[i])
for i in range(flat.size)]
else:
raise TypeError("unsupport type: %s" % dtype)
constary = lc.Constant.array(constvals[0].type, constvals)
addrspace = nvvm.ADDRSPACE_CONSTANT
gv = lmod.add_global_variable(constary.type, name="_cudapy_cmem",
addrspace=addrspace)
gv.linkage = lc.LINKAGE_INTERNAL
gv.global_constant = True
gv.initializer = constary
# Convert to generic address-space
conv = nvvmutils.insert_addrspace_conv(lmod, Type.int(8), addrspace)
addrspaceptr = gv.bitcast(Type.pointer(Type.int(8), addrspace))
genptr = builder.call(conv, [addrspaceptr])
# Create array object
ary = context.make_array(aryty)(context, builder)
kshape = [context.get_constant(types.intp, s) for s in arr.shape]
kstrides = [context.get_constant(types.intp, s) for s in arr.strides]
context.populate_array(ary,
data=builder.bitcast(genptr, ary.data.type),
shape=cgutils.pack_array(builder, kshape),
strides=cgutils.pack_array(builder, kstrides),
itemsize=ary.itemsize,
parent=ary.parent,
meminfo=None)
return ary._getvalue()
def pack_value(self, builder, ty, value, ptr):
"""Pack data for array storage
"""
if isinstance(ty, types.Record):
pdata = cgutils.get_record_data(builder, value)
databuf = builder.load(pdata)
casted = builder.bitcast(ptr, Type.pointer(databuf.type))
builder.store(databuf, casted)
return
if ty == types.boolean:
value = cgutils.as_bool_byte(builder, value)
assert value.type == ptr.type.pointee
builder.store(value, ptr)
def to_native_array(self, typ, ary):
# TODO check matching dtype.
# currently, mismatching dtype will still work and causes
# potential memory corruption
voidptr = Type.pointer(Type.int(8))
nativearycls = self.context.make_array(typ)
nativeary = nativearycls(self.context, self.builder)
aryptr = nativeary._getpointer()
ptr = self.builder.bitcast(aryptr, voidptr)
errcode = self.numba_array_adaptor(ary, ptr)
failed = cgutils.is_not_null(self.builder, errcode)
with cgutils.if_unlikely(self.builder, failed):
# TODO
self.builder.unreachable()
return self.builder.load(aryptr)
def __init__(self, context, builder, args, steps, i, argtype):
# Get data
p = builder.gep(args, [context.get_constant(types.intp, i)])
if cgutils.is_struct_ptr(argtype):
self.byref = True
self.data = builder.bitcast(builder.load(p), argtype)
else:
self.byref = False
self.data = builder.bitcast(builder.load(p), Type.pointer(argtype))
# Get step
p = builder.gep(steps, [context.get_constant(types.intp, i)])
abisize = context.get_constant(types.intp,
context.get_abi_sizeof(argtype))
self.step = builder.load(p)
self.is_unit_strided = builder.icmp(ICMP_EQ, abisize, self.step)
self.builder = builder
def _generic_array(context,
builder,
shape,
dtype,
symbol_name,
addrspace,
can_dynsized=False):
elemcount = reduce(operator.mul, shape)
lldtype = context.get_data_type(dtype)
laryty = Type.array(lldtype, elemcount)
if addrspace == nvvm.ADDRSPACE_LOCAL:
# Special case local addrespace allocation to use alloca
# NVVM is smart enough to only use local memory if no register is
# available
dataptr = cgutils.alloca_once(builder, laryty, name=symbol_name)
else:
lmod = builder.module
# Create global variable in the requested address-space
gvmem = lmod.add_global_variable(laryty, symbol_name, addrspace)
# Specify alignment to avoid misalignment bug
gvmem.align = context.get_abi_sizeof(lldtype)
if elemcount <= 0:
if can_dynsized: # dynamic shared memory
gvmem.linkage = lc.LINKAGE_EXTERNAL
else:
raise ValueError("array length <= 0")
else:
## Comment out the following line to workaround a NVVM bug
## which generates a invalid symbol name when the linkage
## is internal and in some situation.
## See _get_unique_smem_id()
# gvmem.linkage = lc.LINKAGE_INTERNAL
gvmem.initializer = lc.Constant.undef(laryty)
if dtype not in types.number_domain:
raise TypeError("unsupported type: %s" % dtype)
# Convert to generic address-space
conv = nvvmutils.insert_addrspace_conv(lmod, Type.int(8), addrspace)
addrspaceptr = gvmem.bitcast(Type.pointer(Type.int(8), addrspace))
dataptr = builder.call(conv, [addrspaceptr])
return _make_array(context, builder, dataptr, dtype, shape)
def make_constant_array(self, builder, aryty, arr):
"""
Unlike the parent version. This returns a a pointer in the constant
addrspace.
"""
lmod = builder.module
constvals = [
self.get_constant(types.byte, i)
for i in iter(arr.tobytes(order='A'))
]
constary = lc.Constant.array(Type.int(8), constvals)
addrspace = nvvm.ADDRSPACE_CONSTANT
gv = lmod.add_global_variable(constary.type,
name="_cudapy_cmem",
addrspace=addrspace)
gv.linkage = lc.LINKAGE_INTERNAL
gv.global_constant = True
gv.initializer = constary
# Preserve the underlying alignment
lldtype = self.get_data_type(aryty.dtype)
align = self.get_abi_sizeof(lldtype)
gv.align = 2**(align - 1).bit_length()
# Convert to generic address-space
conv = nvvmutils.insert_addrspace_conv(lmod, Type.int(8), addrspace)
addrspaceptr = gv.bitcast(Type.pointer(Type.int(8), addrspace))
genptr = builder.call(conv, [addrspaceptr])
# Create array object
ary = self.make_array(aryty)(self, builder)
kshape = [self.get_constant(types.intp, s) for s in arr.shape]
kstrides = [self.get_constant(types.intp, s) for s in arr.strides]
self.populate_array(ary,
data=builder.bitcast(genptr, ary.data.type),
shape=kshape,
strides=kstrides,
itemsize=ary.itemsize,
parent=ary.parent,
meminfo=None)
return ary._getvalue()
def ptx_cmem_arylike(context, builder, sig, args):
lmod = builder.module
[arr] = args
aryty = sig.return_type
constvals = [
context.get_constant(types.byte, i)
for i in iter(arr.tobytes(order="A"))
]
constary = lc.Constant.array(Type.int(8), constvals)
addrspace = nvvm.ADDRSPACE_CONSTANT
gv = lmod.add_global_variable(constary.type,
name="_cudapy_cmem",
addrspace=addrspace)
gv.linkage = lc.LINKAGE_INTERNAL
gv.global_constant = True
gv.initializer = constary
# Preserve the underlying alignment
lldtype = context.get_data_type(aryty.dtype)
align = context.get_abi_sizeof(lldtype)
gv.align = 2**(align - 1).bit_length()
# Convert to generic address-space
conv = nvvmutils.insert_addrspace_conv(lmod, Type.int(8), addrspace)
addrspaceptr = gv.bitcast(Type.pointer(Type.int(8), addrspace))
genptr = builder.call(conv, [addrspaceptr])
# Create array object
ary = context.make_array(aryty)(context, builder)
kshape = [context.get_constant(types.intp, s) for s in arr.shape]
kstrides = [context.get_constant(types.intp, s) for s in arr.strides]
context.populate_array(
ary,
data=builder.bitcast(genptr, ary.data.type),
shape=cgutils.pack_array(builder, kshape),
strides=cgutils.pack_array(builder, kstrides),
itemsize=ary.itemsize,
parent=ary.parent,
meminfo=None,
)
return ary._getvalue()
def _generic_array(context, builder, shape, dtype, symbol_name, addrspace,
can_dynsized=False):
elemcount = reduce(operator.mul, shape)
lldtype = context.get_data_type(dtype)
laryty = Type.array(lldtype, elemcount)
if addrspace == nvvm.ADDRSPACE_LOCAL:
# Special case local addrespace allocation to use alloca
# NVVM is smart enough to only use local memory if no register is
# available
dataptr = cgutils.alloca_once(builder, laryty, name=symbol_name)
else:
lmod = builder.module
# Create global variable in the requested address-space
gvmem = lmod.add_global_variable(laryty, symbol_name, addrspace)
# Specify alignment to avoid misalignment bug
gvmem.align = context.get_abi_sizeof(lldtype)
if elemcount <= 0:
if can_dynsized: # dynamic shared memory
gvmem.linkage = lc.LINKAGE_EXTERNAL
else:
raise ValueError("array length <= 0")
else:
## Comment out the following line to workaround a NVVM bug
## which generates a invalid symbol name when the linkage
## is internal and in some situation.
## See _get_unique_smem_id()
# gvmem.linkage = lc.LINKAGE_INTERNAL
gvmem.initializer = lc.Constant.undef(laryty)
if dtype not in types.number_domain:
raise TypeError("unsupported type: %s" % dtype)
# Convert to generic address-space
conv = nvvmutils.insert_addrspace_conv(lmod, Type.int(8), addrspace)
addrspaceptr = gvmem.bitcast(Type.pointer(Type.int(8), addrspace))
dataptr = builder.call(conv, [addrspaceptr])
return _make_array(context, builder, dataptr, dtype, shape)
def test_inline_rsqrt(self):
mod = Module.new(__name__)
fnty = Type.function(Type.void(), [Type.pointer(Type.float())])
fn = mod.add_function(fnty, "cu_rsqrt")
bldr = Builder.new(fn.append_basic_block("entry"))
rsqrt_approx_fnty = Type.function(Type.float(), [Type.float()])
inlineasm = InlineAsm.get(rsqrt_approx_fnty, "rsqrt.approx.f32 $0, $1;", "=f,f", side_effect=True)
val = bldr.load(fn.args[0])
res = bldr.call(inlineasm, [val])
bldr.store(res, fn.args[0])
bldr.ret_void()
# generate ptx
nvvm.fix_data_layout(mod)
nvvm.set_cuda_kernel(fn)
nvvmir = str(mod)
ptx = nvvm.llvm_to_ptx(nvvmir)
self.assertTrue("rsqrt.approx.f32" in str(ptx))
def real_divmod(context, builder, x, y):
assert x.type == y.type
floatty = x.type
module = builder.module
fname = context.mangler(".numba.python.rem", [x.type])
fnty = Type.function(floatty, (floatty, floatty, Type.pointer(floatty)))
fn = module.get_or_insert_function(fnty, fname)
if fn.is_declaration:
fn.linkage = lc.LINKAGE_LINKONCE_ODR
fnbuilder = lc.Builder(fn.append_basic_block('entry'))
fx, fy, pmod = fn.args
div, mod = real_divmod_func_body(context, fnbuilder, fx, fy)
fnbuilder.store(mod, pmod)
fnbuilder.ret(div)
pmod = cgutils.alloca_once(builder, floatty)
quotient = builder.call(fn, (x, y, pmod))
return quotient, builder.load(pmod)
def real_divmod(context, builder, x, y):
assert x.type == y.type
floatty = x.type
module = builder.module
fname = ".numba.python.rem.%s" % x.type
fnty = Type.function(floatty, (floatty, floatty, Type.pointer(floatty)))
fn = module.get_or_insert_function(fnty, fname)
if fn.is_declaration:
fn.linkage = lc.LINKAGE_LINKONCE_ODR
fnbuilder = lc.Builder.new(fn.append_basic_block('entry'))
fx, fy, pmod = fn.args
div, mod = real_divmod_func_body(context, fnbuilder, fx, fy)
fnbuilder.store(mod, pmod)
fnbuilder.ret(div)
pmod = cgutils.alloca_once(builder, floatty)
quotient = builder.call(fn, (x, y, pmod))
return quotient, builder.load(pmod)
def printf(builder, format_string, *values):
str_const = Constant.stringz(format_string)
global_str_const = get_module(builder).add_global_variable(str_const.type,
'')
global_str_const.initializer = str_const
idx = [Constant.int(Type.int(32), 0), Constant.int(Type.int(32), 0)]
str_addr = global_str_const.gep(idx)
args = []
for v in values:
if isinstance(v, int):
args.append(Constant.int(Type.int(), v))
elif isinstance(v, float):
args.append(Constant.real(Type.double(), v))
else:
args.append(v)
functype = Type.function(Type.int(32), [Type.pointer(Type.int(8))], True)
fn = get_module(builder).get_or_insert_function(functype, 'printf')
builder.call(fn, [str_addr] + args)
def ptx_cmem_arylike(context, builder, sig, args):
lmod = builder.module
[arr] = args
aryty = sig.return_type
constvals = [
context.get_constant(types.byte, i)
for i in six.iterbytes(arr.tobytes(order='A'))
]
constary = lc.Constant.array(Type.int(8), constvals)
addrspace = nvvm.ADDRSPACE_CONSTANT
gv = lmod.add_global_variable(constary.type, name="_cudapy_cmem",
addrspace=addrspace)
gv.linkage = lc.LINKAGE_INTERNAL
gv.global_constant = True
gv.initializer = constary
# Preserve the underlying alignment
lldtype = context.get_data_type(aryty.dtype)
align = context.get_abi_sizeof(lldtype)
gv.align = 2 ** (align - 1).bit_length()
# Convert to generic address-space
conv = nvvmutils.insert_addrspace_conv(lmod, Type.int(8), addrspace)
addrspaceptr = gv.bitcast(Type.pointer(Type.int(8), addrspace))
genptr = builder.call(conv, [addrspaceptr])
# Create array object
ary = context.make_array(aryty)(context, builder)
kshape = [context.get_constant(types.intp, s) for s in arr.shape]
kstrides = [context.get_constant(types.intp, s) for s in arr.strides]
context.populate_array(ary,
data=builder.bitcast(genptr, ary.data.type),
shape=cgutils.pack_array(builder, kshape),
strides=cgutils.pack_array(builder, kstrides),
itemsize=ary.itemsize,
parent=ary.parent,
meminfo=None)
return ary._getvalue()
def test_inline_rsqrt(self):
mod = Module.new(__name__)
fnty = Type.function(Type.void(), [Type.pointer(Type.float())])
fn = mod.add_function(fnty, 'cu_rsqrt')
bldr = Builder.new(fn.append_basic_block('entry'))
rsqrt_approx_fnty = Type.function(Type.float(), [Type.float()])
inlineasm = InlineAsm.get(rsqrt_approx_fnty,
'rsqrt.approx.f32 $0, $1;',
'=f,f', side_effect=True)
val = bldr.load(fn.args[0])
res = bldr.call(inlineasm, [val])
bldr.store(res, fn.args[0])
bldr.ret_void()
# generate ptx
nvvm.fix_data_layout(mod)
nvvm.set_cuda_kernel(fn)
nvvmir = str(mod)
ptx = nvvm.llvm_to_ptx(nvvmir)
self.assertTrue('rsqrt.approx.f32' in str(ptx))
def parse_tuple(self, args, fmt, *objs):
charptr = Type.pointer(Type.int(8))
argtypes = [self.pyobj, charptr]
fnty = Type.function(Type.int(), argtypes, var_arg=True)
fn = self._get_function(fnty, name="PyArg_ParseTuple")
return self.builder.call(fn, [args, fmt] + list(objs))
def _generic_array(context,
builder,
shape,
dtype,
symbol_name,
addrspace,
can_dynsized=False):
elemcount = reduce(operator.mul, shape, 1)
# Check for valid shape for this type of allocation.
# Only 1d arrays can be dynamic.
dynamic_smem = elemcount <= 0 and can_dynsized and len(shape) == 1
if elemcount <= 0 and not dynamic_smem:
raise ValueError("array length <= 0")
# Check that we support the requested dtype
other_supported_type = isinstance(dtype, (types.Record, types.Boolean))
if dtype not in types.number_domain and not other_supported_type:
raise TypeError("unsupported type: %s" % dtype)
lldtype = context.get_data_type(dtype)
laryty = Type.array(lldtype, elemcount)
if addrspace == nvvm.ADDRSPACE_LOCAL:
# Special case local address space allocation to use alloca
# NVVM is smart enough to only use local memory if no register is
# available
dataptr = cgutils.alloca_once(builder, laryty, name=symbol_name)
else:
lmod = builder.module
# Create global variable in the requested address space
gvmem = lmod.add_global_variable(laryty, symbol_name, addrspace)
# Specify alignment to avoid misalignment bug
align = context.get_abi_sizeof(lldtype)
# Alignment is required to be a power of 2 for shared memory. If it is
# not a power of 2 (e.g. for a Record array) then round up accordingly.
gvmem.align = 1 << (align - 1).bit_length()
if dynamic_smem:
gvmem.linkage = lc.LINKAGE_EXTERNAL
else:
## Comment out the following line to workaround a NVVM bug
## which generates a invalid symbol name when the linkage
## is internal and in some situation.
## See _get_unique_smem_id()
# gvmem.linkage = lc.LINKAGE_INTERNAL
gvmem.initializer = lc.Constant.undef(laryty)
# Convert to generic address-space
conv = nvvmutils.insert_addrspace_conv(lmod, Type.int(8), addrspace)
addrspaceptr = gvmem.bitcast(Type.pointer(Type.int(8), addrspace))
dataptr = builder.call(conv, [addrspaceptr])
targetdata = _get_target_data(context)
lldtype = context.get_data_type(dtype)
itemsize = lldtype.get_abi_size(targetdata)
# Compute strides
laststride = itemsize
rstrides = []
for i, lastsize in enumerate(reversed(shape)):
rstrides.append(laststride)
laststride *= lastsize
strides = [s for s in reversed(rstrides)]
kstrides = [context.get_constant(types.intp, s) for s in strides]
# Compute shape
if dynamic_smem:
# Compute the shape based on the dynamic shared memory configuration.
# Unfortunately NVVM does not provide an intrinsic for the
# %dynamic_smem_size register, so we must read it using inline
# assembly.
get_dynshared_size = InlineAsm.get(Type.function(Type.int(), []),
"mov.u32 $0, %dynamic_smem_size;",
'=r',
side_effect=True)
dynsmem_size = builder.zext(builder.call(get_dynshared_size, []),
Type.int(width=64))
# Only 1-D dynamic shared memory is supported so the following is a
# sufficient construction of the shape
kitemsize = context.get_constant(types.intp, itemsize)
kshape = [builder.udiv(dynsmem_size, kitemsize)]
else:
kshape = [context.get_constant(types.intp, s) for s in shape]
# Create array object
ndim = len(shape)
aryty = types.Array(dtype=dtype, ndim=ndim, layout='C')
ary = context.make_array(aryty)(context, builder)
context.populate_array(ary,
data=builder.bitcast(dataptr, ary.data.type),
shape=kshape,
strides=kstrides,
itemsize=context.get_constant(types.intp, itemsize),
meminfo=None)
return ary._getvalue()
def get_record_member(builder, record, offset, typ):
pval = inbound_gep(builder, record, 0, offset)
assert not is_pointer(pval.type.pointee)
return builder.bitcast(pval, Type.pointer(typ))
def get_data_type(self, ty):
"""
Get a LLVM data representation of the Numba type *ty* that is safe
for storage. Record data are stored as byte array.
The return value is a llvmlite.ir.Type object, or None if the type
is an opaque pointer (???).
"""
try:
fac = type_registry.match(ty)
except KeyError:
pass
else:
return fac(self, ty)
if (isinstance(ty, types.Dummy) or isinstance(ty, types.Module)
or isinstance(ty, types.Function)
or isinstance(ty, types.Dispatcher)
or isinstance(ty, types.Object)
or isinstance(ty, types.Macro)):
return PYOBJECT
elif isinstance(ty, types.CPointer):
dty = self.get_data_type(ty.dtype)
return Type.pointer(dty)
elif isinstance(ty, types.Optional):
return self.get_struct_type(self.make_optional(ty))
elif isinstance(ty, types.Array):
return self.get_struct_type(self.make_array(ty))
elif isinstance(ty, types.UniTuple):
dty = self.get_value_type(ty.dtype)
return Type.array(dty, ty.count)
elif isinstance(ty, types.Tuple):
dtys = [self.get_value_type(t) for t in ty]
return Type.struct(dtys)
elif isinstance(ty, types.Record):
# Record are represented as byte array
return Type.struct([Type.array(Type.int(8), ty.size)])
elif isinstance(ty, types.UnicodeCharSeq):
charty = Type.int(numpy_support.sizeof_unicode_char * 8)
return Type.struct([Type.array(charty, ty.count)])
elif isinstance(ty, types.CharSeq):
charty = Type.int(8)
return Type.struct([Type.array(charty, ty.count)])
elif ty in STRUCT_TYPES:
return self.get_struct_type(STRUCT_TYPES[ty])
else:
try:
impl = struct_registry.match(ty)
except KeyError:
pass
else:
return self.get_struct_type(impl(ty))
if isinstance(ty, types.Pair):
pairty = self.make_pair(ty.first_type, ty.second_type)
return self.get_struct_type(pairty)
else:
return LTYPEMAP[ty]
def get_record_member(builder, record, offset, typ):
pdata = get_record_data(builder, record)
pval = inbound_gep(builder, pdata, 0, offset)
assert not is_pointer(pval.type.pointee)
return builder.bitcast(pval, Type.pointer(typ))
import sys
import ctypes
import struct as struct_
from llvmlite.llvmpy.core import Type, Constant
_trace_refs_ = hasattr(sys, "getobjects")
_plat_bits = struct_.calcsize("@P") * 8
_int8 = Type.int(8)
_int32 = Type.int(32)
_void_star = Type.pointer(_int8)
_int8_star = _void_star
_sizeof_py_ssize_t = ctypes.sizeof(getattr(ctypes, "c_size_t"))
_llvm_py_ssize_t = Type.int(_sizeof_py_ssize_t * 8)
if _trace_refs_:
_pyobject_head = Type.struct(
[_void_star, _void_star, _llvm_py_ssize_t, _void_star])
_pyobject_head_init = Constant.struct([
Constant.null(_void_star), # _ob_next
Constant.null(_void_star), # _ob_prev
Constant.int(_llvm_py_ssize_t, 1), # ob_refcnt
Constant.null(_void_star), # ob_type
])
else:
_pyobject_head = Type.struct([_llvm_py_ssize_t, _void_star])
_pyobject_head_init = Constant.struct([
def build_ufunc_wrapper(library, context, func, signature, objmode, env):
"""
Wrap the scalar function with a loop that iterates over the arguments
"""
byte_t = Type.int(8)
byte_ptr_t = Type.pointer(byte_t)
byte_ptr_ptr_t = Type.pointer(byte_ptr_t)
intp_t = context.get_value_type(types.intp)
intp_ptr_t = Type.pointer(intp_t)
fnty = Type.function(Type.void(),
[byte_ptr_ptr_t, intp_ptr_t, intp_ptr_t, byte_ptr_t])
wrapper_module = library.create_ir_module('')
if objmode:
func_type = context.call_conv.get_function_type(
types.pyobject, [types.pyobject] * len(signature.args))
else:
func_type = context.call_conv.get_function_type(
signature.return_type, signature.args)
oldfunc = func
func = wrapper_module.add_function(func_type, name=func.name)
func.attributes.add("alwaysinline")
wrapper = wrapper_module.add_function(fnty, "__ufunc__." + func.name)
arg_args, arg_dims, arg_steps, arg_data = wrapper.args
arg_args.name = "args"
arg_dims.name = "dims"
arg_steps.name = "steps"
arg_data.name = "data"
builder = Builder.new(wrapper.append_basic_block("entry"))
loopcount = builder.load(arg_dims, name="loopcount")
actual_args = context.call_conv.get_arguments(func)
# Prepare inputs
arrays = []
for i, typ in enumerate(signature.args):
arrays.append(
UArrayArg(context, builder, arg_args, arg_steps, i,
context.get_argument_type(typ)))
# Prepare output
valty = context.get_data_type(signature.return_type)
out = UArrayArg(context, builder, arg_args, arg_steps, len(actual_args),
valty)
# Setup indices
offsets = []
zero = context.get_constant(types.intp, 0)
for _ in arrays:
p = cgutils.alloca_once(builder, intp_t)
offsets.append(p)
builder.store(zero, p)
store_offset = cgutils.alloca_once(builder, intp_t)
builder.store(zero, store_offset)
unit_strided = cgutils.true_bit
for ary in arrays:
unit_strided = builder.and_(unit_strided, ary.is_unit_strided)
if objmode:
# General loop
pyapi = context.get_python_api(builder)
gil = pyapi.gil_ensure()
with cgutils.for_range(builder, loopcount, intp=intp_t):
slowloop = build_obj_loop_body(context, func, builder, arrays, out,
offsets, store_offset, signature,
pyapi, env)
pyapi.gil_release(gil)
builder.ret_void()
else:
with cgutils.ifelse(builder,
unit_strided) as (is_unit_strided, is_strided):
with is_unit_strided:
with cgutils.for_range(builder, loopcount, intp=intp_t) as ind:
fastloop = build_fast_loop_body(context, func, builder,
arrays, out, offsets,
store_offset, signature,
ind)
builder.ret_void()
with is_strided:
# General loop
with cgutils.for_range(builder, loopcount, intp=intp_t):
slowloop = build_slow_loop_body(context, func, builder,
arrays, out, offsets,
store_offset, signature)
builder.ret_void()
builder.ret_void()
del builder
# Run optimizer
library.add_ir_module(wrapper_module)
wrapper = library.get_function(wrapper.name)
oldfunc.linkage = LINKAGE_INTERNAL
return wrapper
def _prepare_call_to_object_mode(context, builder, func, signature, args, env):
mod = cgutils.get_module(builder)
thisfunc = cgutils.get_function(builder)
bb_core_return = thisfunc.append_basic_block('ufunc.core.return')
pyapi = context.get_python_api(builder)
# Call to
# PyObject* ndarray_new(int nd,
# npy_intp *dims, /* shape */
# npy_intp *strides,
# void* data,
# int type_num,
# int itemsize)
ll_int = context.get_value_type(types.int32)
ll_intp = context.get_value_type(types.intp)
ll_intp_ptr = Type.pointer(ll_intp)
ll_voidptr = context.get_value_type(types.voidptr)
ll_pyobj = context.get_value_type(types.pyobject)
fnty = Type.function(
ll_pyobj,
[ll_int, ll_intp_ptr, ll_intp_ptr, ll_voidptr, ll_int, ll_int])
fn_array_new = mod.get_or_insert_function(fnty, name="numba_ndarray_new")
# Convert each llarray into pyobject
error_pointer = cgutils.alloca_once(builder, Type.int(1), name='error')
builder.store(cgutils.true_bit, error_pointer)
ndarray_pointers = []
ndarray_objects = []
for i, (arr, arrtype) in enumerate(zip(args, signature.args)):
ptr = cgutils.alloca_once(builder, ll_pyobj)
ndarray_pointers.append(ptr)
builder.store(Constant.null(ll_pyobj), ptr) # initialize to NULL
arycls = context.make_array(arrtype)
array = arycls(context, builder, ref=arr)
zero = Constant.int(ll_int, 0)
# Extract members of the llarray
nd = Constant.int(ll_int, arrtype.ndim)
dims = builder.gep(array._get_ptr_by_name('shape'), [zero, zero])
strides = builder.gep(array._get_ptr_by_name('strides'), [zero, zero])
data = builder.bitcast(array.data, ll_voidptr)
dtype = np.dtype(str(arrtype.dtype))
# Prepare other info for reconstruction of the PyArray
type_num = Constant.int(ll_int, dtype.num)
itemsize = Constant.int(ll_int, dtype.itemsize)
# Call helper to reconstruct PyArray objects
obj = builder.call(fn_array_new,
[nd, dims, strides, data, type_num, itemsize])
builder.store(obj, ptr)
ndarray_objects.append(obj)
obj_is_null = cgutils.is_null(builder, obj)
builder.store(obj_is_null, error_pointer)
cgutils.cbranch_or_continue(builder, obj_is_null, bb_core_return)
# Call ufunc core function
object_sig = [types.pyobject] * len(ndarray_objects)
status, retval = context.call_conv.call_function(builder,
func,
ll_pyobj,
object_sig,
ndarray_objects,
env=env)
builder.store(status.is_error, error_pointer)
# Release returned object
pyapi.decref(retval)
builder.branch(bb_core_return)
# At return block
builder.position_at_end(bb_core_return)
# Release argument object
for ndary_ptr in ndarray_pointers:
pyapi.decref(builder.load(ndary_ptr))
innercall = status.code
return innercall, builder.load(error_pointer)
def from_native_value(self, val, typ):
if typ == types.pyobject:
return val
elif typ == types.boolean:
longval = self.builder.zext(val, self.long)
return self.bool_from_long(longval)
elif typ in types.unsigned_domain:
ullval = self.builder.zext(val, self.ulonglong)
return self.long_from_ulonglong(ullval)
elif typ in types.signed_domain:
ival = self.builder.sext(val, self.longlong)
return self.long_from_longlong(ival)
elif typ == types.float32:
dbval = self.builder.fpext(val, self.double)
return self.float_from_double(dbval)
elif typ == types.float64:
return self.float_from_double(val)
elif typ == types.complex128:
cmplxcls = self.context.make_complex(typ)
cval = cmplxcls(self.context, self.builder, value=val)
return self.complex_from_doubles(cval.real, cval.imag)
elif typ == types.complex64:
cmplxcls = self.context.make_complex(typ)
cval = cmplxcls(self.context, self.builder, value=val)
freal = self.context.cast(self.builder, cval.real, types.float32,
types.float64)
fimag = self.context.cast(self.builder, cval.imag, types.float32,
types.float64)
return self.complex_from_doubles(freal, fimag)
elif isinstance(typ, types.NPDatetime):
return self.create_np_datetime(val, typ.unit_code)
elif isinstance(typ, types.NPTimedelta):
return self.create_np_timedelta(val, typ.unit_code)
elif typ == types.none:
ret = self.make_none()
return ret
elif isinstance(typ, types.Optional):
return self.from_native_return(val, typ.type)
elif isinstance(typ, types.Array):
return self.from_native_array(typ, val)
elif isinstance(typ, types.Record):
# Note we will create a copy of the record
# This is the only safe way.
pdata = cgutils.get_record_data(self.builder, val)
size = Constant.int(Type.int(), pdata.type.pointee.count)
ptr = self.builder.bitcast(pdata, Type.pointer(Type.int(8)))
# Note: this will only work for CPU mode
# The following requires access to python object
dtype_addr = Constant.int(self.py_ssize_t, id(typ.dtype))
dtypeobj = dtype_addr.inttoptr(self.pyobj)
return self.recreate_record(ptr, size, dtypeobj)
elif isinstance(typ, (types.Tuple, types.UniTuple)):
return self.from_tuple(typ, val)
raise NotImplementedError(typ)
def _prepare_call_to_object_mode(context, builder, pyapi, func, signature,
args, env):
mod = builder.module
bb_core_return = builder.append_basic_block('ufunc.core.return')
# Call to
# PyObject* ndarray_new(int nd,
# npy_intp *dims, /* shape */
# npy_intp *strides,
# void* data,
# int type_num,
# int itemsize)
ll_int = context.get_value_type(types.int32)
ll_intp = context.get_value_type(types.intp)
ll_intp_ptr = Type.pointer(ll_intp)
ll_voidptr = context.get_value_type(types.voidptr)
ll_pyobj = context.get_value_type(types.pyobject)
fnty = Type.function(
ll_pyobj,
[ll_int, ll_intp_ptr, ll_intp_ptr, ll_voidptr, ll_int, ll_int])
fn_array_new = mod.get_or_insert_function(fnty, name="numba_ndarray_new")
# Convert each llarray into pyobject
error_pointer = cgutils.alloca_once(builder, Type.int(1), name='error')
builder.store(cgutils.true_bit, error_pointer)
# The PyObject* arguments to the kernel function
object_args = []
object_pointers = []
for i, (arg, argty) in enumerate(zip(args, signature.args)):
# Allocate NULL-initialized slot for this argument
objptr = cgutils.alloca_once(builder, ll_pyobj, zfill=True)
object_pointers.append(objptr)
if isinstance(argty, types.Array):
# Special case arrays: we don't need full-blown NRT reflection
# since the argument will be gone at the end of the kernel
arycls = context.make_array(argty)
array = arycls(context, builder, value=arg)
zero = Constant.int(ll_int, 0)
# Extract members of the llarray
nd = Constant.int(ll_int, argty.ndim)
dims = builder.gep(array._get_ptr_by_name('shape'), [zero, zero])
strides = builder.gep(array._get_ptr_by_name('strides'),
[zero, zero])
data = builder.bitcast(array.data, ll_voidptr)
dtype = np.dtype(str(argty.dtype))
# Prepare other info for reconstruction of the PyArray
type_num = Constant.int(ll_int, dtype.num)
itemsize = Constant.int(ll_int, dtype.itemsize)
# Call helper to reconstruct PyArray objects
obj = builder.call(fn_array_new,
[nd, dims, strides, data, type_num, itemsize])
else:
# Other argument types => use generic boxing
obj = pyapi.from_native_value(argty, arg)
builder.store(obj, objptr)
object_args.append(obj)
obj_is_null = cgutils.is_null(builder, obj)
builder.store(obj_is_null, error_pointer)
cgutils.cbranch_or_continue(builder, obj_is_null, bb_core_return)
# Call ufunc core function
object_sig = [types.pyobject] * len(object_args)
status, retval = context.call_conv.call_function(builder,
func,
types.pyobject,
object_sig,
object_args,
env=env)
builder.store(status.is_error, error_pointer)
# Release returned object
pyapi.decref(retval)
builder.branch(bb_core_return)
# At return block
builder.position_at_end(bb_core_return)
# Release argument objects
for objptr in object_pointers:
pyapi.decref(builder.load(objptr))
innercall = status.code
return innercall, builder.load(error_pointer)
from functools import singledispatch
from llvmlite.llvmpy.core import Type
from numba.core import types, cgutils
from numba.core.imputils import Registry
from numba.cuda import nvvmutils
registry = Registry()
lower = registry.lower
voidptr = Type.pointer(Type.int(8))
# NOTE: we don't use @lower here since print_item() doesn't return a LLVM value
@singledispatch
def print_item(ty, context, builder, val):
"""
Handle printing of a single value of the given Numba type.
A (format string, [list of arguments]) is returned that will allow
forming the final printf()-like call.
"""
raise NotImplementedError("printing unimplemented for values of type %s" %
(ty, ))
@print_item.register(types.Integer)
@print_item.register(types.IntegerLiteral)
def int_print_impl(ty, context, builder, val):
if ty in types.unsigned_domain:
rawfmt = "%llu"
dsttype = types.uint64
def build_ufunc_wrapper(library, context, fname, signature, objmode, envptr,
env):
"""
Wrap the scalar function with a loop that iterates over the arguments
"""
assert isinstance(fname, str)
byte_t = Type.int(8)
byte_ptr_t = Type.pointer(byte_t)
byte_ptr_ptr_t = Type.pointer(byte_ptr_t)
intp_t = context.get_value_type(types.intp)
intp_ptr_t = Type.pointer(intp_t)
fnty = Type.function(Type.void(),
[byte_ptr_ptr_t, intp_ptr_t, intp_ptr_t, byte_ptr_t])
wrapperlib = context.codegen().create_library('ufunc_wrapper')
wrapper_module = wrapperlib.create_ir_module('')
if objmode:
func_type = context.call_conv.get_function_type(
types.pyobject, [types.pyobject] * len(signature.args))
else:
func_type = context.call_conv.get_function_type(
signature.return_type, signature.args)
func = wrapper_module.add_function(func_type, name=fname)
func.attributes.add("alwaysinline")
wrapper = wrapper_module.add_function(fnty, "__ufunc__." + func.name)
arg_args, arg_dims, arg_steps, arg_data = wrapper.args
arg_args.name = "args"
arg_dims.name = "dims"
arg_steps.name = "steps"
arg_data.name = "data"
builder = Builder(wrapper.append_basic_block("entry"))
loopcount = builder.load(arg_dims, name="loopcount")
# Prepare inputs
arrays = []
for i, typ in enumerate(signature.args):
arrays.append(UArrayArg(context, builder, arg_args, arg_steps, i, typ))
# Prepare output
out = UArrayArg(context, builder, arg_args, arg_steps, len(arrays),
signature.return_type)
# Setup indices
offsets = []
zero = context.get_constant(types.intp, 0)
for _ in arrays:
p = cgutils.alloca_once(builder, intp_t)
offsets.append(p)
builder.store(zero, p)
store_offset = cgutils.alloca_once(builder, intp_t)
builder.store(zero, store_offset)
unit_strided = cgutils.true_bit
for ary in arrays:
unit_strided = builder.and_(unit_strided, ary.is_unit_strided)
pyapi = context.get_python_api(builder)
if objmode:
# General loop
gil = pyapi.gil_ensure()
with cgutils.for_range(builder, loopcount, intp=intp_t):
slowloop = build_obj_loop_body(context, func, builder, arrays, out,
offsets, store_offset, signature,
pyapi, envptr, env)
pyapi.gil_release(gil)
builder.ret_void()
else:
with builder.if_else(unit_strided) as (is_unit_strided, is_strided):
with is_unit_strided:
with cgutils.for_range(builder, loopcount,
intp=intp_t) as loop:
fastloop = build_fast_loop_body(context, func, builder,
arrays, out, offsets,
store_offset, signature,
loop.index, pyapi)
with is_strided:
# General loop
with cgutils.for_range(builder, loopcount, intp=intp_t):
slowloop = build_slow_loop_body(context, func, builder,
arrays, out, offsets,
store_offset, signature,
pyapi)
builder.ret_void()
del builder
# Link and finalize
wrapperlib.add_ir_module(wrapper_module)
wrapperlib.add_linking_library(library)
return wrapperlib.get_pointer_to_function(wrapper.name)
from llvmlite import ir as llvmir
import llvmlite.llvmpy.core as lc
from llvmlite.llvmpy.core import Type, Constant, LLVMException
import llvmlite.binding as ll
from numba import types, utils, cgutils, typing, funcdesc, debuginfo
from numba import _dynfunc, _helperlib
from numba.compiler_lock import global_compiler_lock
from numba.pythonapi import PythonAPI
from . import arrayobj, builtins, imputils
from .imputils import (user_function, user_generator,
builtin_registry, impl_ret_borrowed,
RegistryLoader)
from numba import datamodel
GENERIC_POINTER = Type.pointer(Type.int(8))
PYOBJECT = GENERIC_POINTER
void_ptr = GENERIC_POINTER
class OverloadSelector(object):
"""
An object matching an actual signature against a registry of formal
signatures and choosing the best candidate, if any.
In the current implementation:
- a "signature" is a tuple of type classes or type instances
- the "best candidate" is the most specific match
"""
def __init__(self):
def recreate_record(self, pdata, size, dtypeaddr):
fnty = Type.function(
self.pyobj, [Type.pointer(Type.int(8)),
Type.int(), self.pyobj])
fn = self._get_function(fnty, name="numba_recreate_record")
return self.builder.call(fn, [pdata, size, dtypeaddr])
def get_record_member(builder, record, offset, typ):
pval = gep_inbounds(builder, record, 0, offset)
assert not is_pointer(pval.type.pointee)
return builder.bitcast(pval, Type.pointer(typ))
from llvmlite import ir as llvmir
import llvmlite.llvmpy.core as lc
from llvmlite.llvmpy.core import Type, Constant, LLVMException
import llvmlite.binding as ll
from numba import types, utils, cgutils, typing, funcdesc, debuginfo
from numba import _dynfunc, _helperlib
from numba.pythonapi import PythonAPI
from . import arrayobj, builtins, imputils
from .imputils import (user_function, user_generator,
builtin_registry, impl_ret_borrowed,
RegistryLoader)
from numba import datamodel
GENERIC_POINTER = Type.pointer(Type.int(8))
PYOBJECT = GENERIC_POINTER
void_ptr = GENERIC_POINTER
class OverloadSelector(object):
"""
An object matching an actual signature against a registry of formal
signatures and choosing the best candidate, if any.
In the current implementation:
- a "signature" is a tuple of type classes or type instances
- the "best candidate" is the most specific match
"""
def __init__(self):
def _python_array_obj_to_native_list(typ, obj, c, size, listptr, errorptr):
"""
Construct a new native list from a Python array of objects.
copied from _python_list_to_native but list_getitem is converted to array
getitem.
"""
def check_element_type(nth, itemobj, expected_typobj):
typobj = nth.typeof(itemobj)
# Check if *typobj* is NULL
with c.builder.if_then(
cgutils.is_null(c.builder, typobj),
likely=False,
):
c.builder.store(cgutils.true_bit, errorptr)
loop.do_break()
# Mandate that objects all have the same exact type
type_mismatch = c.builder.icmp_signed('!=', typobj, expected_typobj)
with c.builder.if_then(type_mismatch, likely=False):
c.builder.store(cgutils.true_bit, errorptr)
c.pyapi.err_format(
"PyExc_TypeError",
"can't unbox heterogeneous list: %S != %S",
expected_typobj, typobj,
)
c.pyapi.decref(typobj)
loop.do_break()
c.pyapi.decref(typobj)
# Allocate a new native list
ok, list = listobj.ListInstance.allocate_ex(c.context, c.builder, typ, size)
# Array getitem call
arr_get_fnty = LLType.function(LLType.pointer(c.pyapi.pyobj), [c.pyapi.pyobj, c.pyapi.py_ssize_t])
arr_get_fn = c.pyapi._get_function(arr_get_fnty, name="array_getptr1")
with c.builder.if_else(ok, likely=True) as (if_ok, if_not_ok):
with if_ok:
list.size = size
zero = lir.Constant(size.type, 0)
with c.builder.if_then(c.builder.icmp_signed('>', size, zero),
likely=True):
# Traverse Python list and unbox objects into native list
with _NumbaTypeHelper(c) as nth:
# Note: *expected_typobj* can't be NULL
# TODO: enable type checking when emty list item in
# list(list(str)) case can be handled
# expected_typobj = nth.typeof(c.builder.load(
# c.builder.call(arr_get_fn, [obj, zero])))
with cgutils.for_range(c.builder, size) as loop:
itemobj = c.builder.call(arr_get_fn, [obj, loop.index])
# extra load since we have ptr to object
itemobj = c.builder.load(itemobj)
# c.pyapi.print_object(itemobj)
# check_element_type(nth, itemobj, expected_typobj)
# XXX we don't call native cleanup for each
# list element, since that would require keeping
# of which unboxings have been successful.
native = c.unbox(typ.dtype, itemobj)
with c.builder.if_then(native.is_error, likely=False):
c.builder.store(cgutils.true_bit, errorptr)
loop.do_break()
# The object (e.g. string) is stored so incref=True
list.setitem(loop.index, native.value, incref=True)
# c.pyapi.decref(expected_typobj)
if typ.reflected:
list.parent = obj
# Stuff meminfo pointer into the Python object for
# later reuse.
with c.builder.if_then(c.builder.not_(c.builder.load(errorptr)),
likely=False):
c.pyapi.object_set_private_data(obj, list.meminfo)
list.set_dirty(False)
c.builder.store(list.value, listptr)
with if_not_ok:
c.builder.store(cgutils.true_bit, errorptr)
# If an error occurred, drop the whole native list
with c.builder.if_then(c.builder.load(errorptr)):
c.context.nrt.decref(c.builder, typ, list.value)
def recreate_record(self, pdata, size, dtype, env_manager):
fnty = Type.function(self.pyobj, [Type.pointer(Type.int(8)),
Type.int(), self.pyobj])
fn = self._get_function(fnty, name="numba_recreate_record")
dtypeaddr = env_manager.read_const(env_manager.add_const(dtype))
return self.builder.call(fn, [pdata, size, dtypeaddr])
def _build_wrapper(self, library, name):
"""
The LLVM IRBuilder code to create the gufunc wrapper.
The *library* arg is the CodeLibrary for which the wrapper should
be added to. The *name* arg is the name of the wrapper function being
created.
"""
byte_t = Type.int(8)
byte_ptr_t = Type.pointer(byte_t)
byte_ptr_ptr_t = Type.pointer(byte_ptr_t)
intp_t = self.context.get_value_type(types.intp)
intp_ptr_t = Type.pointer(intp_t)
fnty = Type.function(
Type.void(), [byte_ptr_ptr_t, intp_ptr_t, intp_ptr_t, byte_ptr_t])
wrapper_module = library.create_ir_module('')
func_type = self.call_conv.get_function_type(self.fndesc.restype,
self.fndesc.argtypes)
fname = self.fndesc.llvm_func_name
func = wrapper_module.add_function(func_type, name=fname)
func.attributes.add("alwaysinline")
wrapper = wrapper_module.add_function(fnty, name)
arg_args, arg_dims, arg_steps, arg_data = wrapper.args
arg_args.name = "args"
arg_dims.name = "dims"
arg_steps.name = "steps"
arg_data.name = "data"
builder = Builder(wrapper.append_basic_block("entry"))
loopcount = builder.load(arg_dims, name="loopcount")
pyapi = self.context.get_python_api(builder)
# Unpack shapes
unique_syms = set()
for grp in (self.sin, self.sout):
for syms in grp:
unique_syms |= set(syms)
sym_map = {}
for syms in self.sin:
for s in syms:
if s not in sym_map:
sym_map[s] = len(sym_map)
sym_dim = {}
for s, i in sym_map.items():
sym_dim[s] = builder.load(
builder.gep(arg_dims,
[self.context.get_constant(types.intp, i + 1)]))
# Prepare inputs
arrays = []
step_offset = len(self.sin) + len(self.sout)
for i, (typ, sym) in enumerate(
zip(self.signature.args, self.sin + self.sout)):
ary = GUArrayArg(self.context, builder, arg_args, arg_steps, i,
step_offset, typ, sym, sym_dim)
step_offset += len(sym)
arrays.append(ary)
bbreturn = builder.append_basic_block('.return')
# Prologue
self.gen_prologue(builder, pyapi)
# Loop
with cgutils.for_range(builder, loopcount, intp=intp_t) as loop:
args = [a.get_array_at_offset(loop.index) for a in arrays]
innercall, error = self.gen_loop_body(builder, pyapi, func, args)
# If error, escape
cgutils.cbranch_or_continue(builder, error, bbreturn)
builder.branch(bbreturn)
builder.position_at_end(bbreturn)
# Epilogue
self.gen_epilogue(builder, pyapi)
builder.ret_void()
# Link
library.add_ir_module(wrapper_module)
library.add_linking_library(self.library)
def build(self):
byte_t = Type.int(8)
byte_ptr_t = Type.pointer(byte_t)
byte_ptr_ptr_t = Type.pointer(byte_ptr_t)
intp_t = self.context.get_value_type(types.intp)
intp_ptr_t = Type.pointer(intp_t)
fnty = Type.function(
Type.void(), [byte_ptr_ptr_t, intp_ptr_t, intp_ptr_t, byte_ptr_t])
wrapper_module = self.library.create_ir_module('')
func_type = self.call_conv.get_function_type(self.fndesc.restype,
self.fndesc.argtypes)
func = wrapper_module.add_function(func_type, name=self.func.name)
func.attributes.add("alwaysinline")
wrapper = wrapper_module.add_function(fnty,
"__gufunc__." + self.func.name)
arg_args, arg_dims, arg_steps, arg_data = wrapper.args
arg_args.name = "args"
arg_dims.name = "dims"
arg_steps.name = "steps"
arg_data.name = "data"
builder = Builder.new(wrapper.append_basic_block("entry"))
loopcount = builder.load(arg_dims, name="loopcount")
# Unpack shapes
unique_syms = set()
for grp in (self.sin, self.sout):
for syms in grp:
unique_syms |= set(syms)
sym_map = {}
for syms in self.sin:
for s in syms:
if s not in sym_map:
sym_map[s] = len(sym_map)
sym_dim = {}
for s, i in sym_map.items():
sym_dim[s] = builder.load(
builder.gep(arg_dims,
[self.context.get_constant(types.intp, i + 1)]))
# Prepare inputs
arrays = []
step_offset = len(self.sin) + len(self.sout)
for i, (typ, sym) in enumerate(
zip(self.signature.args, self.sin + self.sout)):
ary = GUArrayArg(self.context, builder, arg_args, arg_dims,
arg_steps, i, step_offset, typ, sym, sym_dim)
if not ary.as_scalar:
step_offset += ary.ndim
arrays.append(ary)
bbreturn = cgutils.get_function(builder).append_basic_block('.return')
# Prologue
self.gen_prologue(builder)
# Loop
with cgutils.for_range(builder, loopcount, intp=intp_t) as ind:
args = [a.array_value for a in arrays]
innercall, error = self.gen_loop_body(builder, func, args)
# If error, escape
cgutils.cbranch_or_continue(builder, error, bbreturn)
for a in arrays:
a.next(ind)
builder.branch(bbreturn)
builder.position_at_end(bbreturn)
# Epilogue
self.gen_epilogue(builder)
builder.ret_void()
self.library.add_ir_module(wrapper_module)
wrapper = self.library.get_function(wrapper.name)
# Set core function to internal so that it is not generated
self.func.linkage = LINKAGE_INTERNAL
return wrapper, self.env