def test_d2d(self):
hst = np.arange(100, dtype=np.uint32)
hst2 = np.empty_like(hst)
sz = hst.size * hst.dtype.itemsize
dev1 = self.context.memalloc(sz)
dev2 = self.context.memalloc(sz)
driver.host_to_device(dev1, hst, sz)
driver.device_to_device(dev2, dev1, sz)
driver.device_to_host(hst2, dev2, sz)
self.assertTrue(np.all(hst == hst2))
def test_d2d(self):
hst = np.arange(100, dtype=np.uint32)
hst2 = np.empty_like(hst)
sz = hst.size * hst.dtype.itemsize
dev1 = self.context.memalloc(sz)
dev2 = self.context.memalloc(sz)
driver.host_to_device(dev1, hst, sz)
driver.device_to_device(dev2, dev1, sz)
driver.device_to_host(hst2, dev2, sz)
self.assertTrue(np.all(hst == hst2))
def gpu_slice(arr, col):
"""
Missing feature in NumbaPro
"""
from numba.cuda.cudadrv.driver import device_to_host
view, size = gpu_slice_view(arr, col)
host = np.empty(shape=arr.shape[0], dtype=arr.dtype)
device_to_host(host, view, size)
return host
def test_memcpy(self):
hstary = np.arange(100, dtype=np.uint32)
hstary2 = np.arange(100, dtype=np.uint32)
sz = hstary.size * hstary.dtype.itemsize
devary = self.context.memalloc(sz)
driver.host_to_device(devary, hstary, sz)
driver.device_to_host(hstary2, devary, sz)
self.assertTrue(np.all(hstary == hstary2))
def test_memcpy(self):
hstary = np.arange(100, dtype=np.uint32)
hstary2 = np.arange(100, dtype=np.uint32)
sz = hstary.size * hstary.dtype.itemsize
devary = self.context.memalloc(sz)
driver.host_to_device(devary, hstary, sz)
driver.device_to_host(hstary2, devary, sz)
self.assertTrue(np.all(hstary == hstary2))
def test_memset(self):
dtype = np.dtype('uint32')
n = 10
sz = dtype.itemsize * 10
devary = self.context.memalloc(sz)
driver.device_memset(devary, 0xab, sz)
hstary = np.empty(n, dtype=dtype)
driver.device_to_host(hstary, devary, sz)
hstary2 = np.array([0xabababab] * n, dtype=np.dtype('uint32'))
self.assertTrue(np.all(hstary == hstary2))
def test_memset(self):
dtype = np.dtype('uint32')
n = 10
sz = dtype.itemsize * 10
devary = self.context.memalloc(sz)
driver.device_memset(devary, 0xab, sz)
hstary = np.empty(n, dtype=dtype)
driver.device_to_host(hstary, devary, sz)
hstary2 = np.array([0xabababab] * n, dtype=np.dtype('uint32'))
self.assertTrue(np.all(hstary == hstary2))
def copy_to_host(self, ary=None, stream=0):
"""Copy ``self`` to ``ary`` or create a new Numpy ndarray
if ``ary`` is ``None``.
If a CUDA ``stream`` is given, then the transfer will be made
asynchronously as part as the given stream. Otherwise, the transfer is
synchronous: the function returns after the copy is finished.
Always returns the host array.
Example::
import numpy as np
from numba import cuda
arr = np.arange(1000)
d_arr = cuda.to_device(arr)
my_kernel[100, 100](d_arr)
result_array = d_arr.copy_to_host()
"""
if any(s < 0 for s in self.strides):
msg = "D->H copy not implemented for negative strides: {}"
raise NotImplementedError(msg.format(self.strides))
assert self.alloc_size >= 0, "Negative memory size"
stream = self._default_stream(stream)
if ary is None:
hostary = np.empty(shape=self.alloc_size, dtype=np.byte)
else:
check_array_compatibility(self, ary)
hostary = ary
if self.alloc_size != 0:
_driver.device_to_host(hostary,
self,
self.alloc_size,
stream=stream)
if ary is None:
if self.size == 0:
hostary = np.ndarray(shape=self.shape,
dtype=self.dtype,
buffer=hostary)
else:
hostary = np.ndarray(
shape=self.shape,
dtype=self.dtype,
strides=self.strides,
buffer=hostary,
)
return hostary
def test_cuda_driver_basic(self):
module = self.context.create_module_ptx(self.ptx)
function = module.get_function('_Z10helloworldPi')
array = (c_int * 100)()
memory = self.context.memalloc(sizeof(array))
host_to_device(memory, array, sizeof(array))
function = function.configure((1, ), (100, ))
function(memory)
device_to_host(array, memory, sizeof(array))
for i, v in enumerate(array):
self.assertEqual(i, v)
module.unload()
def test_cuda_driver_basic(self):
module = self.context.create_module_ptx(self.ptx)
function = module.get_function('_Z10helloworldPi')
array = (c_int * 100)()
memory = self.context.memalloc(sizeof(array))
host_to_device(memory, array, sizeof(array))
function = function.configure((1,), (100,))
function(memory)
device_to_host(array, memory, sizeof(array))
for i, v in enumerate(array):
self.assertEqual(i, v)
module.unload()
def test_cuda_driver_stream(self):
module = self.context.create_module_ptx(self.ptx)
function = module.get_function('_Z10helloworldPi')
array = (c_int * 100)()
stream = self.context.create_stream()
with stream.auto_synchronize():
memory = self.context.memalloc(sizeof(array))
host_to_device(memory, array, sizeof(array), stream=stream)
function = function.configure((1, ), (100, ), stream=stream)
function(memory)
device_to_host(array, memory, sizeof(array), stream=stream)
for i, v in enumerate(array):
self.assertEqual(i, v)
def test_cuda_driver_stream(self):
module = self.context.create_module_ptx(self.ptx)
function = module.get_function('_Z10helloworldPi')
array = (c_int * 100)()
stream = self.context.create_stream()
with stream.auto_synchronize():
memory = self.context.memalloc(sizeof(array))
host_to_device(memory, array, sizeof(array), stream=stream)
function = function.configure((1,), (100,), stream=stream)
function(memory)
device_to_host(array, memory, sizeof(array), stream=stream)
for i, v in enumerate(array):
self.assertEqual(i, v)
def test_host_alloc_driver(self):
n = 32
mem = cuda.current_context().memhostalloc(n, mapped=True)
dtype = np.dtype(np.uint8)
ary = np.ndarray(shape=n // dtype.itemsize, dtype=dtype, buffer=mem)
magic = 0xab
driver.device_memset(mem, magic, n)
self.assertTrue(np.all(ary == magic))
ary.fill(n)
recv = np.empty_like(ary)
driver.device_to_host(recv, mem, ary.size)
self.assertTrue(np.all(ary == recv))
self.assertTrue(np.all(recv == n))
def test_host_alloc_driver(self):
n = 32
mem = cuda.current_context().memhostalloc(n, mapped=True)
dtype = np.dtype(np.uint8)
ary = np.ndarray(shape=n // dtype.itemsize, dtype=dtype, buffer=mem)
magic = 0xAB
driver.device_memset(mem, magic, n)
self.assertTrue(np.all(ary == magic))
ary.fill(n)
recv = np.empty_like(ary)
driver.device_to_host(recv, mem, ary.size)
self.assertTrue(np.all(ary == recv))
self.assertTrue(np.all(recv == n))
def test_cuda_driver_basic(self):
module = self.context.create_module_ptx(self.ptx)
function = module.get_function('_Z10helloworldPi')
array = (c_int * 100)()
memory = self.context.memalloc(sizeof(array))
host_to_device(memory, array, sizeof(array))
launch_kernel(function.handle, # Kernel
1, 1, 1, # gx, gy, gz
100, 1, 1, # bx, by, bz
0, # dynamic shared mem
0, # stream
[memory]) # arguments
device_to_host(array, memory, sizeof(array))
for i, v in enumerate(array):
self.assertEqual(i, v)
module.unload()
def _do_getitem(self, item, stream=0):
stream = self._default_stream(stream)
typ, offset = self.dtype.fields[item]
newdata = self.gpu_data.view(offset)
if typ.shape == ():
if typ.names is not None:
return DeviceRecord(dtype=typ, stream=stream,
gpu_data=newdata)
else:
hostary = np.empty(1, dtype=typ)
_driver.device_to_host(dst=hostary, src=newdata,
size=typ.itemsize,
stream=stream)
return hostary[0]
else:
shape, strides, dtype = \
prepare_shape_strides_dtype(typ.shape,
None,
typ.subdtype[0], 'C')
return DeviceNDArray(shape=shape, strides=strides,
dtype=dtype, gpu_data=newdata,
stream=stream)
def test_cuda_driver_stream_operations(self):
module = self.context.create_module_ptx(self.ptx)
function = module.get_function('_Z10helloworldPi')
array = (c_int * 100)()
stream = self.context.create_stream()
with stream.auto_synchronize():
memory = self.context.memalloc(sizeof(array))
host_to_device(memory, array, sizeof(array), stream=stream)
launch_kernel(function.handle, # Kernel
1, 1, 1, # gx, gy, gz
100, 1, 1, # bx, by, bz
0, # dynamic shared mem
stream.handle, # stream
[memory]) # arguments
device_to_host(array, memory, sizeof(array), stream=stream)
for i, v in enumerate(array):
self.assertEqual(i, v)
def _do_getitem(self, item, stream=0):
stream = self._default_stream(stream)
arr = self._dummy.__getitem__(item)
extents = list(arr.iter_contiguous_extent())
cls = type(self)
if len(extents) == 1:
newdata = self.gpu_data.view(*extents[0])
if not arr.is_array:
# Element indexing
hostary = np.empty(1, dtype=self.dtype)
_driver.device_to_host(dst=hostary, src=newdata,
size=self._dummy.itemsize,
stream=stream)
return hostary[0]
else:
return cls(shape=arr.shape, strides=arr.strides,
dtype=self.dtype, gpu_data=newdata, stream=stream)
else:
newdata = self.gpu_data.view(*arr.extent)
return cls(shape=arr.shape, strides=arr.strides,
dtype=self.dtype, gpu_data=newdata, stream=stream)
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
def launch(self, args, griddim, blockdim, stream=0, sharedmem=0):
# Prepare kernel
cufunc = self._codelibrary.get_cufunc()
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)
if driver.USE_NV_BINDING:
zero_stream = driver.binding.CUstream(0)
else:
zero_stream = None
stream_handle = stream and stream.handle or zero_stream
# Invoke kernel
driver.launch_kernel(cufunc.handle,
*griddim,
*blockdim,
sharedmem,
stream_handle,
kernelargs,
cooperative=self.cooperative)
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.abspath(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()
