def __init__(self):
# Lazily load the libHLC library
bitcode_path = os.path.join(sys.prefix, 'share', 'rocmtools')
assert os.path.exists(bitcode_path) and os.path.isdir(bitcode_path)
self.bitcode_path = bitcode_path
dev_ctx = devices.get_context()
target_cpu = dev_ctx.agent.name
self.target_cpu = target_cpu
if self.hlc is None:
try:
hlc = CDLL(os.path.join(sys.prefix, 'lib', 'librocmlite.so'))
except OSError:
raise ImportError("librocmlite.so cannot be found. Please "
"install the roctools package by: "
"conda install -c numba roctools")
else:
hlc.ROC_ParseModule.restype = moduleref_ptr
hlc.ROC_ParseBitcode.restype = moduleref_ptr
hlc.ROC_ModuleEmitBRIG.restype = c_size_t
hlc.ROC_Initialize()
weakref.finalize(hlc, hlc.ROC_Finalize)
hlc.ROC_SetCommandLineOption.argtypes = [
c_int,
c_void_p,
]
type(self).hlc = hlc
def _initialize(self):
if not self.initialized:
dev_ctx = devices.get_context()
target_cpu = dev_ctx.agent.name.decode('UTF-8')
self.target_cpu = "-mcpu %s" % target_cpu
self.CMD_OPT = ' '.join([
self.opt, "-O3", self.triple_flag, self.target_cpu,
"-disable-simplify-libcalls", "-verify", "-S", "-o {fout}", "{fin}"
])
self.CMD_VERIFY = ' '.join([
self.opt, "-verify", self.triple_flag, self.target_cpu, "-S",
"-o {fout}", "{fin}"
])
self.CMD_GEN_HSAIL = ' '.join([
self.llc, "-O2", self.triple_flag, self.target_cpu,
"-filetype=asm", "-o {fout}", "{fin}"
])
self.CMD_GEN_BRIG = ' '.join([
self.llc, "-O2", self.triple_flag, self.target_cpu,
"-filetype=obj", "-o {fout}", "{fin}"
])
self.CMD_LINK_BUILTINS = ' '.join(
[self.llvm_link, "-S", "-o {fout}", "{fin}", "{lib}"])
self.CMD_LINK_LIBS = ' '.join(
[self.llvm_link, "-S", "-o {fout}", "{fin}"])
self.CMD_LINK_BRIG = ' '.join(
[self.ld_lld, "-shared", "-o {fout}", "{fin}"])
def to_device(obj, stream=None, context=None, copy=True, to=None):
"""to_device(obj, context, copy=True, to=None)
Allocate and transfer a numpy ndarray or structured scalar to the device.
To copy host->device a numpy array::
ary = numpy.arange(10)
d_ary = roc.to_device(ary)
The resulting ``d_ary`` is a ``DeviceNDArray``.
To copy device->host::
hary = d_ary.copy_to_host()
To copy device->host to an existing array::
ary = numpy.empty(shape=d_ary.shape, dtype=d_ary.dtype)
d_ary.copy_to_host(ary)
"""
context = context or get_context()
if to is None:
to = devicearray.from_array_like(obj)
if copy:
to.copy_to_device(obj, stream=stream, context=context)
return to
def to_device(obj, stream=None, context=None, copy=True, to=None):
"""to_device(obj, context, copy=True, to=None)
Allocate and transfer a numpy ndarray or structured scalar to the device.
To copy host->device a numpy array::
ary = numpy.arange(10)
d_ary = roc.to_device(ary)
The resulting ``d_ary`` is a ``DeviceNDArray``.
To copy device->host::
hary = d_ary.copy_to_host()
To copy device->host to an existing array::
ary = numpy.empty(shape=d_ary.shape, dtype=d_ary.dtype)
d_ary.copy_to_host(ary)
"""
context = context or get_context()
if to is None:
to = devicearray.from_array_like(obj)
if copy:
to.copy_to_device(obj, stream=stream, context=context)
return to
def _initialize(self):
if not self.initialized:
dev_ctx = devices.get_context()
target_cpu = dev_ctx.agent.name.decode('UTF-8')
self.target_cpu = "-mcpu %s" % target_cpu
self.CMD_OPT = ' '.join([
self.opt,
"-O3",
self.triple_flag,
self.target_cpu,
"-disable-simplify-libcalls",
"-verify",
"-S",
"-o {fout}",
"{fin}"])
self.CMD_VERIFY = ' '.join([
self.opt,
"-verify",
self.triple_flag,
self.target_cpu,
"-S",
"-o {fout}",
"{fin}"])
self.CMD_GEN_HSAIL = ' '.join([self.llc,
"-O2",
self.triple_flag,
self.target_cpu,
"-filetype=asm",
"-o {fout}",
"{fin}"])
self.CMD_GEN_BRIG = ' '.join([self.llc,
"-O2",
self.triple_flag,
self.target_cpu,
"-filetype=obj",
"-o {fout}",
"{fin}"])
self.CMD_LINK_BUILTINS = ' '.join([
self.llvm_link,
"-S",
"-o {fout}",
"{fin}",
"{lib}"])
self.CMD_LINK_LIBS = ' '.join([self.llvm_link,
"-S",
"-o {fout}",
"{fin}"])
self.CMD_LINK_BRIG = ' '.join([self.ld_lld,
"-shared",
"-o {fout}",
"{fin}"])
def __init__(self, shape, strides, dtype, dgpu_data=None):
"""
Args
----
shape
array shape.
strides
array strides.
dtype
data type as numpy.dtype.
dgpu_data
user provided device memory for the ndarray data buffer
"""
if isinstance(shape, (int, long)):
shape = (shape,)
if isinstance(strides, (int, long)):
strides = (strides,)
self.ndim = len(shape)
if len(strides) != self.ndim:
raise ValueError("strides not match ndim")
self._dummy = dummyarray.Array.from_desc(0, shape, strides, dtype.itemsize)
self.shape = tuple(shape)
self.strides = tuple(strides)
self.dtype = np.dtype(dtype)
self.size = int(np.prod(self.shape))
# prepare dgpu memory
if self.size > 0:
if dgpu_data is None:
from numba.roc.api import _memory_size_from_info
self.alloc_size = _memory_size_from_info(
self.shape, self.strides, self.dtype.itemsize
)
# find a coarse region on the dGPU
dgpu_data = devices.get_context().mempoolalloc(self.alloc_size)
else: # we have some preallocated dgpu_memory
sz = getattr(dgpu_data, "_hsa_memsize_", None)
if sz is None:
raise ValueError("dgpu_data as no _hsa_memsize_ attribute")
assert sz >= 0
self.alloc_size = sz
else:
dgpu_data = None
self.alloc_size = 0
self.dgpu_data = dgpu_data
