def _make_array(context, builder, dataptr, dtype, shape, layout='C'):
ndim = len(shape)
# Create array object
aryty = types.Array(dtype=dtype, ndim=ndim, layout='C')
ary = context.make_array(aryty)(context, builder)
targetdata = _get_target_data(context)
lldtype = context.get_data_type(dtype)
itemsize = lldtype.get_abi_size(targetdata)
# Compute strides
rstrides = [itemsize]
for i, lastsize in enumerate(reversed(shape[1:])):
rstrides.append(lastsize * rstrides[-1])
strides = [s for s in reversed(rstrides)]
kshape = [context.get_constant(types.intp, s) for s in shape]
kstrides = [context.get_constant(types.intp, s) for s in strides]
context.populate_array(ary,
data=builder.bitcast(dataptr, ary.data.type),
shape=cgutils.pack_array(builder, kshape),
strides=cgutils.pack_array(builder, kstrides),
itemsize=context.get_constant(types.intp, itemsize),
meminfo=None)
return ary._getvalue()
def _shape_and_strides(self, context, builder):
# Set shape and strides for a 1D size 1 array
one = context.get_constant(types.intp, 1)
zero = context.get_constant(types.intp, 0)
shape = cgutils.pack_array(builder, [one])
strides = cgutils.pack_array(builder, [zero])
return shape, strides
def cuda_gridsize(context, builder, sig, args):
restype = sig.return_type
nx = _nthreads_for_dim(builder, 'x')
if restype == types.int32:
return nx
elif isinstance(restype, types.UniTuple):
ny = _nthreads_for_dim(builder, 'y')
if restype.count == 2:
return cgutils.pack_array(builder, (nx, ny))
elif restype.count == 3:
nz = _nthreads_for_dim(builder, 'z')
return cgutils.pack_array(builder, (nx, ny, nz))
# Fallthrough to here indicates unexpected return type or tuple length
raise ValueError('Unexpected return type %s of cuda.gridsize' % restype)
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=cgutils.pack_array(builder, kshape),
strides=cgutils.pack_array(builder, kstrides),
itemsize=ary.itemsize,
parent=ary.parent,
meminfo=None)
return ary._getvalue()
def cuda_grid(context, builder, sig, args):
restype = sig.return_type
if restype == types.int32:
return nvvmutils.get_global_id(builder, dim=1)
elif isinstance(restype, types.UniTuple):
ids = nvvmutils.get_global_id(builder, dim=restype.count)
return cgutils.pack_array(builder, ids)
else:
raise ValueError('Unexpected return type %s from cuda.grid' % restype)
def unituple_constant(context, builder, ty, pyval):
"""
Create a homogeneous tuple constant.
"""
consts = [context.get_constant_generic(builder, ty.dtype, v)
for v in pyval]
return impl_ret_borrowed(
context, builder, ty, cgutils.pack_array(builder, consts),
)
def ptx_gridsize2d(context, builder, sig, args):
assert len(args) == 1
ntidx = nvvmutils.call_sreg(builder, "ntid.x")
nctaidx = nvvmutils.call_sreg(builder, "nctaid.x")
ntidy = nvvmutils.call_sreg(builder, "ntid.y")
nctaidy = nvvmutils.call_sreg(builder, "nctaid.y")
r1 = builder.mul(ntidx, nctaidx)
r2 = builder.mul(ntidy, nctaidy)
return cgutils.pack_array(builder, [r1, r2])
def core(context, builder, sig, args):
lmod = builder.module
fargtys = []
for arg in prototype_args:
ty = context.get_value_type(arg.ty)
if arg.is_ptr:
ty = ty.as_pointer()
fargtys.append(ty)
fretty = context.get_value_type(retty)
fnty = Type.function(fretty, fargtys)
fn = lmod.get_or_insert_function(fnty, name=func)
# For returned values that are returned through a pointer, we need to
# allocate variables on the stack and pass a pointer to them.
actual_args = []
virtual_args = []
arg_idx = 0
for arg in prototype_args:
if arg.is_ptr:
# Allocate space for return value and add to args
tmp_arg = cgutils.alloca_once(builder,
context.get_value_type(arg.ty))
actual_args.append(tmp_arg)
virtual_args.append(tmp_arg)
else:
actual_args.append(args[arg_idx])
arg_idx += 1
ret = builder.call(fn, actual_args)
# Following the call, we need to assemble the returned values into a
# tuple for returning back to the caller.
tuple_args = []
if retty != types.void:
tuple_args.append(ret)
for arg in virtual_args:
tuple_args.append(builder.load(arg))
if isinstance(nb_retty, types.UniTuple):
return cgutils.pack_array(builder, tuple_args)
else:
return cgutils.pack_struct(builder, tuple_args)
def real_divmod_impl(context, builder, sig, args, loc=None):
x, y = args
quot = cgutils.alloca_once(builder, x.type, name="quot")
rem = cgutils.alloca_once(builder, x.type, name="rem")
with builder.if_else(cgutils.is_scalar_zero(builder, y),
likely=False) as (if_zero, if_non_zero):
with if_zero:
if not context.error_model.fp_zero_division(
builder, ("modulo by zero", ), loc):
# No exception raised => compute the nan result,
# and set the FP exception word for Numpy warnings.
q = builder.fdiv(x, y)
r = builder.frem(x, y)
builder.store(q, quot)
builder.store(r, rem)
with if_non_zero:
q, r = real_divmod(context, builder, x, y)
builder.store(q, quot)
builder.store(r, rem)
return cgutils.pack_array(builder, (builder.load(quot), builder.load(rem)))
def as_data(self, builder, value):
values = [
builder.load(cgutils.gep_inbounds(builder, value, i))
for i in range(self._fe_type.count)
]
return cgutils.pack_array(builder, values)
def _shape_and_strides(self, context, builder):
shape = cgutils.pack_array(builder, self.shape)
strides = cgutils.pack_array(builder, self.strides)
return shape, strides
def int_divmod_impl(context, builder, sig, args):
quot, rem = _int_divmod_impl(context, builder, sig, args,
"integer divmod by zero")
return cgutils.pack_array(builder, (builder.load(quot), builder.load(rem)))
def _generic_array(context,
builder,
shape,
dtype,
symbol_name,
addrspace,
can_dynsized=False):
elemcount = reduce(operator.mul, shape)
# Check for valid shape for this type of allocation
dynamic_smem = elemcount <= 0 and can_dynsized
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
rstrides = [itemsize]
for i, lastsize in enumerate(reversed(shape[1:])):
rstrides.append(lastsize * rstrides[-1])
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=cgutils.pack_array(builder, kshape),
strides=cgutils.pack_array(builder, kstrides),
itemsize=context.get_constant(types.intp, itemsize),
meminfo=None,
)
return ary._getvalue()
def ptx_grid3d(context, builder, sig, args):
assert len(args) == 1
r1, r2, r3 = nvvmutils.get_global_id(builder, dim=3)
return cgutils.pack_array(builder, [r1, r2, r3])
