def test_nan_arithmetic(ctx_getter):
context = ctx_getter()
queue = cl.CommandQueue(context)
def make_nan_contaminated_vector(size):
shape = (size,)
a = numpy.random.randn(*shape).astype(numpy.float32)
#for i in range(0, shape[0], 3):
#a[i] = float('nan')
from random import randrange
for i in range(size//10):
a[randrange(0, size)] = float('nan')
return a
size = 1 << 20
a = make_nan_contaminated_vector(size)
a_gpu = cl_array.to_device(context, queue, a)
b = make_nan_contaminated_vector(size)
b_gpu = cl_array.to_device(context, queue, b)
ab = a*b
ab_gpu = (a_gpu*b_gpu).get()
for i in range(size):
assert numpy.isnan(ab[i]) == numpy.isnan(ab_gpu[i])
def _make_inputs(self, queue, pixel_size):
mf = cl.mem_flags
v_1 = cl_array.to_device(queue, self._make_vertices(0, pixel_size[1]))
v_2 = cl_array.to_device(queue, self._make_vertices(1, pixel_size[0]))
v_3 = cl_array.to_device(queue, self._make_vertices(2, pixel_size[1]))
return v_1, v_2, v_3
def build(self, coords, values, base):
"""Use OpenCL to build the arrays."""
lenbase = base.shape[0]
lencoords = coords.shape[0]
coords_array = cla.to_device(self.queue, coords)
values_array = cla.to_device(self.queue, values)
base_array = cla.to_device(self.queue, base)
template_array = cla.zeros(self.queue, (lenbase), dtype=np.int32)
event = self.program.nearest(
self.queue,
base.shape,
None,
coords_array.data,
values_array.data,
base_array.data,
template_array.data,
np.int32(lencoords),
self.nnear,
self.usemajority,
)
try:
event.wait()
except cl.RuntimeError, inst:
errstr = inst.__str__()
if errstr == "clWaitForEvents failed: out of resources":
print "OpenCL timed out, probably due to the display manager."
print "Disable your display manager and try again!"
print "If that does not work, rerun with OpenCL disabled."
else:
raise cl.RuntimeError, inst
sys.exit(1)
def compute_preconditioners(self):
"""
Create a diagonal preconditioner for the projection and backprojection
operator.
Each term of the diagonal is the sum of the projector/backprojector
along rows [1], i.e the projection/backprojection of an array of ones.
[1] Jens Gregor and Thomas Benson,
Computational Analysis and Improvement of SIRT,
IEEE transactions on medical imaging, vol. 27, no. 7, 2008
"""
# r_{i,i} = 1/(sum_j a_{i,j})
slice_ones = np.ones(self.backprojector.slice_shape, dtype=np.float32)
R = 1./self.projector.projection(slice_ones) # could be all done on GPU, but I want extra checks
R[np.logical_not(np.isfinite(R))] = 1. # In the case where the rotation axis is excentred
self.d_R = parray.to_device(self.queue, R)
# c_{j,j} = 1/(sum_i a_{i,j})
sino_ones = np.ones(self.sino_shape, dtype=np.float32)
C = 1./self.backprojector.backprojection(sino_ones)
C[np.logical_not(np.isfinite(C))] = 1. # In the case where the rotation axis is excentred
self.d_C = parray.to_device(self.queue, C)
self.add_to_cl_mem({
"d_R": self.d_R,
"d_C": self.d_C
})
def computeEnergy(self, x, y, z, q):
xd = cl_array.to_device(self.queue, x)
yd = cl_array.to_device(self.queue, y)
zd = cl_array.to_device(self.queue, z)
qd = cl_array.to_device(self.queue, q)
coulombEnergy = cl_array.zeros_like(xd)
prec = x.dtype
if prec == numpy.float32:
self.compEnergyF.calc_potential_energy(self.queue,
(x.size, ), None,
xd.data, yd.data, zd.data,
qd.data, coulombEnergy.data, numpy.int32(len(x)),
numpy.float32(self.k),numpy.float32(self.impactFact),
g_times_l = False)
elif prec == numpy.float64:
self.compEnergyD.calc_potential_energy(self.queue,
(x.size, ), None,
xd.data, yd.data, zd.data,
qd.data, coulombEnergy.data, numpy.int32(len(x)) ,
numpy.float64(self.k),numpy.float64(self.impactFact),
g_times_l = False)
else:
print("Unknown float type.")
return numpy.sum(coulombEnergy.get(self.queue))
def computeEnergy(self, x, y, z, q):
coulombEnergy = cl_array.zero_like(q)
xd = cl_array.to_device(self.queue, x)
yd = cl_array.to_device(self.queue, y)
zd = cl_array.to_device(self.queue, z)
qd = cl_array.to_device(self.queue, q)
prec = x.dtype
if prec == numpy.float32:
self.compEnergyF.calc_potential_energy(
self.queue, (x.size, ),
None,
xd.data,
yd.data,
zd.data,
qd.data,
coulombEnergy.data,
g_time_l=False)
elif prec == numpy.float64:
self.compEnergyD.calc_potential_energy(
self.queue, (x.size, ),
None,
xd.data,
yd.data,
zd.data,
qd.data,
coulombEnergy.data,
g_time_l=False)
else:
print("Unknown float type.")
return np.sum(coulombEnergy.get(self.queue))
def test_count_1(self):
nrepeats = 3
shape = [5, 5, 5]
np_interspace = randint(2, size=shape).astype(np.int32)
np_access_interspace = randint(nrepeats, size=shape).astype(np.int32)
np_count = np.ones([nrepeats] + shape, dtype=np.float32)
weight = 0.5
expected = np.ones_like(np_count)
tmp = expected[0]
tmp[np_interspace == 1] += weight
for i in range(1, nrepeats):
tmp = expected[i]
tmp[np_access_interspace == i] += weight
cl_interspace = cl_array.to_device(self.queue, np_interspace)
cl_access_interspace = cl_array.to_device(self.queue, np_access_interspace)
cl_count = cl_array.to_device(self.queue, np_count)
self.kernels.count(self.queue, cl_interspace, cl_access_interspace, weight, cl_count)
self.assertTrue(np.allclose(expected, cl_count.get()))
def test_fancy_indexing(ctx_factory):
if _PYPY:
pytest.xfail("numpypy: multi value setting is not supported")
context = ctx_factory()
queue = cl.CommandQueue(context)
n = 2 ** 20 + 2**18 + 22
numpy_dest = np.zeros(n, dtype=np.int32)
numpy_idx = np.arange(n, dtype=np.int32)
np.random.shuffle(numpy_idx)
numpy_src = 20000+np.arange(n, dtype=np.int32)
cl_dest = cl_array.to_device(queue, numpy_dest)
cl_idx = cl_array.to_device(queue, numpy_idx)
cl_src = cl_array.to_device(queue, numpy_src)
numpy_dest[numpy_idx] = numpy_src
cl_dest[cl_idx] = cl_src
assert np.array_equal(numpy_dest, cl_dest.get())
numpy_dest = numpy_src[numpy_idx]
cl_dest = cl_src[cl_idx]
assert np.array_equal(numpy_dest, cl_dest.get())
def compute_preconditioners(self):
"""
Create a diagonal preconditioner for the projection and backprojection
operator.
Each term of the diagonal is the sum of the projector/backprojector
along rows [2],
i.e the projection/backprojection of an array of ones.
[2] T. Pock, A. Chambolle,
Diagonal preconditioning for first order primal-dual algorithms in
convex optimization,
International Conference on Computer Vision, 2011
"""
# Compute the diagonal preconditioner "Sigma"
slice_ones = np.ones(self.backprojector.slice_shape, dtype=np.float32)
Sigma_k = 1./self.projector.projection(slice_ones)
Sigma_k[np.logical_not(np.isfinite(Sigma_k))] = 1.
self.d_Sigma_k = parray.to_device(self.queue, Sigma_k)
self.d_Sigma_kp1 = self.d_Sigma_k + 1 # TODO: memory vs computation
self.Sigma_grad = 1/2.0 # For discrete gradient, sum|D_i,j| = 2 along lines or cols
# Compute the diagonal preconditioner "Tau"
sino_ones = np.ones(self.sino_shape, dtype=np.float32)
C = self.backprojector.backprojection(sino_ones)
Tau = 1./(C + 2.)
self.d_Tau = parray.to_device(self.queue, Tau)
self.add_to_cl_mem({
"d_Sigma_k": self.d_Sigma_k,
"d_Sigma_kp1": self.d_Sigma_kp1,
"d_Tau": self.d_Tau
})
def get_binned_data_angular(self,limits=((-1,1),(-1,1)),points=500): """ Azimuth/elevation map measured ray endpoints to a circle and bin them on the CL DEV. This linearly maps elevation to the circle's radius and azimuth to phi. nice for cross-section plots of directivity. Binning is done with points number of points within limits=((xmin,xmax),(ymin,ymax)).""" (pos0,pwr0) = self.get_measured_rays() pos0_dev = cl_array.to_device(self.queue,pos0.astype(np.float32)) x_dev = cl_array.zeros(self.queue,pwr0.shape,dtype=np.float32) y_dev = cl_array.zeros(self.queue,pwr0.shape,dtype=np.float32) pwr0_dev = cl_array.to_device(self.queue,pwr0.astype(np.float32)) pwr_dev = cl_array.zeros(self.queue,pwr0.shape,dtype=np.float32) pivot = cl_array.to_device(self.queue,np.array([0,0,0,0],dtype=np.float32)) time1 = time() R_dev = cl_array.to_device(self.queue,np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,0]]).astype(np.float32)) evt = self.prg.angular_project(self.queue, pwr0.shape, None, pos0_dev.data,pwr0_dev.data,R_dev.data,pivot.data,x_dev.data,y_dev.data,pwr_dev.data) evt.wait() x=x_dev.get() y=y_dev.get() pwr=np.float64(pwr_dev.get()) time2 = time() dx = np.float64(limits[0][1]-limits[0][0])/np.float64(points) dy = np.float64(limits[1][1]-limits[1][0])/np.float64(points) pwr = pwr / (dx * dy) (H,x_coord,y_coord)=np.histogram2d(x=x.flatten(),y=y.flatten(),bins=points,range=limits,weights=pwr.flatten()) self.hist_data = (H,x_coord,y_coord) return self.hist_data
def __init__(self, target, queue, laplace=False):
super(GPUCorrelator, self).__init__(target, laplace=laplace)
self._queue = queue
self._ctx = self._queue.context
self._gpu = self._queue.device
self._allocate_arrays()
self._build_ffts()
self._generate_kernels()
target = self._target
if self._laplace:
target = self._laplace_filter(self._target)
# move some arrays to the GPU
self._gtarget = cl_array.to_device(self._queue, target.astype(np.float32))
self._lcc_mask = cl_array.to_device(self._queue, self._lcc_mask.astype(np.int32))
# Do some one-time precalculations
self._rfftn(self._gtarget, self._ft_target)
self._k.multiply(self._gtarget, self._gtarget, self._target2)
self._rfftn(self._target2, self._ft_target2)
self._gcenter = np.asarray(list(self._center) + [0], dtype=np.float32)
self._gshape = np.asarray(
list(self._target.shape) + [np.product(self._target.shape)],
dtype=np.int32)
def get_binned_data_stereographic(self,limits=((-1,1),(-1,1)),points=500): #project data stereographically onto xy plane and bin it """ stereographically project measured ray endpoints and bin them on the CL DEV. This is a lot faster when you have loads of data. Binning is done with points number of points within limits=((xmin,xmax),(ymin,ymax)).""" (pos0,pwr0) = self.get_measured_rays() pos0_dev = cl_array.to_device(self.queue,pos0.astype(np.float32)) x_dev = cl_array.zeros(self.queue,pwr0.shape,dtype=np.float32) y_dev = cl_array.zeros(self.queue,pwr0.shape,dtype=np.float32) pwr0_dev = cl_array.to_device(self.queue,pwr0.astype(np.float32)) pwr_dev = cl_array.zeros(self.queue,pwr0.shape,dtype=np.float32) pivot = cl_array.to_device(self.queue,np.array([0,0,0,0],dtype=np.float32)) time1 = time() R_dev = cl_array.to_device(self.queue,np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,0]]).astype(np.float32)) evt = self.prg.stereograph_project(self.queue, pwr0.shape, None, pos0_dev.data,pwr0_dev.data,R_dev.data,pivot.data,x_dev.data,y_dev.data,pwr_dev.data) evt.wait() x=x_dev.get() y=y_dev.get() pwr=np.float64(pwr_dev.get()) time2 = time() dx = np.float64(limits[0][1]-limits[0][0])/np.float64(points) dy = np.float64(limits[1][1]-limits[1][0])/np.float64(points) pwr = pwr / (dx * dy) (H,x_coord,y_coord)=np.histogram2d(x=x.flatten(),y=y.flatten(),bins=points,range=limits,weights=pwr.flatten()) self.hist_data = (H,x_coord,y_coord) return self.hist_data
def __init__(self, ctx, queue, dtype=np.float32):
self.ctx = ctx
self.queue = queue
sobel_c = np.array([1., 0., -1.]).astype(dtype)
sobel_r = np.array([1., 2., 1.]).astype(dtype)
self.sobel_c = cl_array.to_device(self.queue, sobel_c)
self.sobel_r = cl_array.to_device(self.queue, sobel_r)
self.scratch = None
self.sepconv_rc = LocalMemorySeparableCorrelation(self.ctx, self.queue, sobel_r, sobel_c)
self.sepconv_cr = LocalMemorySeparableCorrelation(self.ctx, self.queue, sobel_c, sobel_r)
TYPE = ""
if dtype == np.float32:
TYPE = "float"
elif dtype == np.uint8:
TYPE = "unsigned char"
elif dtype == np.uint16:
TYPE = "unsigned short"
self.mag = ElementwiseKernel(ctx,
"float *result, %s *imgx, %s *imgy" % (TYPE, TYPE),
"result[i] = sqrt((float)imgx[i]*imgx[i] + (float)imgy[i]*imgy[i])",
"mag")
def allocate_arrays(self):
"""
Allocate various types of arrays for the tests
"""
# numpy images
self.grad = np.zeros(self.image.shape, dtype=np.complex64)
self.grad2 = np.zeros((2,) + self.image.shape, dtype=np.float32)
self.grad_ref = gradient(self.image)
self.div_ref = divergence(self.grad_ref)
self.image2 = np.zeros_like(self.image)
# Device images
self.gradient_parray = parray.zeros(self.la.queue, self.image.shape, np.complex64)
# we should be using cl.Buffer(self.la.ctx, cl.mem_flags.READ_WRITE, size=self.image.nbytes*2),
# but platforms not suporting openCL 1.2 have a problem with enqueue_fill_buffer,
# so we use the parray "fill" utility
self.gradient_buffer = self.gradient_parray.data
# Do the same for image
self.image_parray = parray.to_device(self.la.queue, self.image)
self.image_buffer = self.image_parray.data
# Refs
tmp = np.zeros(self.image.shape, dtype=np.complex64)
tmp.real = np.copy(self.grad_ref[0])
tmp.imag = np.copy(self.grad_ref[1])
self.grad_ref_parray = parray.to_device(self.la.queue, tmp)
self.grad_ref_buffer = self.grad_ref_parray.data
def test_pthomas():
nz = 3
ny = 4
nx = 5
a = np.random.rand(nx)
b = np.random.rand(nx)
c = np.random.rand(nx)
d = np.random.rand(nz, ny, nx)
d_copy = d.copy()
solver = pthomas.PThomas(context, queue, (nz, ny, nx))
a_d = cl_array.to_device(queue, a)
b_d = cl_array.to_device(queue, b)
c_d = cl_array.to_device(queue, c)
c2_d = cl_array.to_device(queue, c)
d_d = cl_array.to_device(queue, d)
evt = solver.solve(a_d, b_d, c_d, c2_d, d_d)
d = d_d.get()
for i in range(nz):
for j in range(ny):
x_true = scipy_solve_banded(a, b, c, d_copy[i,j,:])
assert_allclose(x_true, d[i,j,:])
print 'pass'
def CalcF(ctx, queue, m2, r2):
# Define dimensions
xdim = ydim = m2.shape[0]
# m2 = np.float32(m2)
# r2 = np.float32(r2)
# Get the compiled kernel
kernel = get_kernel(ctx, xdim)
# Move data to the GPU
gpu_m2 = cl_array.to_device(queue, m2)
gpu_r2 = cl_array.to_device(queue, r2)
gpu_result = cl_array.zeros(queue, (ydim, xdim), np.float32)
# Define grid shape (the same as the matrix dimensions)
grid_shape = (ydim, xdim)
# Get group shape based on the matrix dimensions and the actual hardware
group_shape = (16, 16)
event = kernel.CalcF(queue, grid_shape, group_shape, gpu_result.data, gpu_m2.data, gpu_r2.data)
event.wait()
result = gpu_result.get()
queue.finish()
return result
def get_array(data, queue=None):
"""Get pyopencl.array.Array from *data* which can be a numpy array, a pyopencl.array.Array or a
pyopencl.Image. *queue* is an OpenCL command queue.
"""
if not queue:
queue = cfg.OPENCL.queue
if isinstance(data, cl_array.Array):
result = data
elif isinstance(data, np.ndarray):
if data.dtype.kind == 'c':
if data.dtype.itemsize != cfg.PRECISION.cl_cplx:
data = data.astype(cfg.PRECISION.np_cplx)
result = cl_array.to_device(queue, data.astype(cfg.PRECISION.np_cplx))
else:
if data.dtype.kind != 'f' or data.dtype.itemsize != cfg.PRECISION.cl_float:
data = data.astype(cfg.PRECISION.np_float)
result = cl_array.to_device(queue, data.astype(cfg.PRECISION.np_float))
elif isinstance(data, cl.Image):
result = cl_array.empty(queue, data.shape[::-1], np.float32)
cl.enqueue_copy(queue, result.data, data, offset=0, origin=(0, 0),
region=result.shape[::-1])
if result.dtype.itemsize != cfg.PRECISION.cl_float:
result = result.astype(cfg.PRECISION.np_float)
else:
raise TypeError('Unsupported data type {}'.format(type(data)))
return result
def _gpu_init(self):
"""Method to initialize all the data for GPU-accelerate search"""
self.gpu_data = {}
g = self.gpu_data
d = self.data
q = self.queue
# move data to the GPU. All should be float32, as these is the native
# lenght for GPUs
g['rcore'] = cl_array.to_device(q, float32array(d['rcore'].array))
g['rsurf'] = cl_array.to_device(q, float32array(d['rsurf'].array))
# Make the scanning chain object an Image, as this is faster to rotate
g['im_lsurf'] = cl.image_from_array(q.context, float32array(d['lsurf'].array))
g['sampler'] = cl.Sampler(q.context, False, cl.addressing_mode.CLAMP,
cl.filter_mode.LINEAR)
if self.distance_restraints:
g['restraints'] = cl_array.to_device(q, float32array(d['restraints']))
# Allocate arrays on the GPU
g['lsurf'] = cl_array.zeros_like(g['rcore'])
g['clashvol'] = cl_array.zeros_like(g['rcore'])
g['intervol'] = cl_array.zeros_like(g['rcore'])
g['interspace'] = cl_array.zeros(q, d['shape'], dtype=np.int32)
g['restspace'] = cl_array.zeros_like(g['interspace'])
g['access_interspace'] = cl_array.zeros_like(g['interspace'])
g['best_access_interspace'] = cl_array.zeros_like(g['interspace'])
# arrays for counting
# Reductions are typically tedious on GPU, and we need to define the
# workgroupsize to allocate the correct amount of data
WORKGROUPSIZE = 32
nsubhists = int(np.ceil(g['rcore'].size/WORKGROUPSIZE))
g['subhists'] = cl_array.zeros(q, (nsubhists, d['nrestraints'] + 1), dtype=np.float32)
g['viol_counter'] = cl_array.zeros(q, (nsubhists, d['nrestraints'], d['nrestraints']), dtype=np.float32)
# complex arrays
g['ft_shape'] = list(d['shape'])
g['ft_shape'][0] = d['shape'][0]//2 + 1
g['ft_rcore'] = cl_array.zeros(q, g['ft_shape'], dtype=np.complex64)
g['ft_rsurf'] = cl_array.zeros_like(g['ft_rcore'])
g['ft_lsurf'] = cl_array.zeros_like(g['ft_rcore'])
g['ft_clashvol'] = cl_array.zeros_like(g['ft_rcore'])
g['ft_intervol'] = cl_array.zeros_like(g['ft_rcore'])
# other miscellanious data
g['nrot'] = d['nrot']
g['max_clash'] = d['max_clash']
g['min_interaction'] = d['min_interaction']
# kernels
g['k'] = Kernels(q.context)
g['k'].rfftn = pyclfft.RFFTn(q.context, d['shape'])
g['k'].irfftn = pyclfft.iRFFTn(q.context, d['shape'])
# initial calculations
g['k'].rfftn(q, g['rcore'], g['ft_rcore'])
g['k'].rfftn(q, g['rsurf'], g['ft_rsurf'])
def gs_mod_gpu(idata,itera=10,osize=256):
cut=osize//2
pl=cl.get_platforms()[0]
devices=pl.get_devices(device_type=cl.device_type.GPU)
ctx = cl.Context(devices=[devices[0]])
queue = cl.CommandQueue(ctx)
plan = Plan(idata.shape, queue=queue,dtype=complex128) #no funciona con "complex128"
src = str(Template(KERNEL).render(
double_support=all(
has_double_support(dev) for dev in devices),
amd_double_support=all(
has_amd_double_support(dev) for dev in devices)
))
prg = cl.Program(ctx,src).build()
idata_gpu=cl_array.to_device(queue, ifftshift(idata).astype("complex128"))
fdata_gpu=cl_array.empty_like(idata_gpu)
rdata_gpu=cl_array.empty_like(idata_gpu)
plan.execute(idata_gpu.data,fdata_gpu.data)
mask=exp(2.j*pi*random(idata.shape))
mask[512-cut:512+cut,512-cut:512+cut]=0
idata_gpu=cl_array.to_device(queue, ifftshift(idata+mask).astype("complex128"))
fdata_gpu=cl_array.empty_like(idata_gpu)
rdata_gpu=cl_array.empty_like(idata_gpu)
error_gpu=cl_array.to_device(ctx, queue, zeros(idata_gpu.shape).astype("double"))
plan.execute(idata_gpu.data,fdata_gpu.data)
e=1000
ea=1000
for i in range (itera):
prg.norm(queue, fdata_gpu.shape, None,fdata_gpu.data)
plan.execute(fdata_gpu.data,rdata_gpu.data,inverse=True)
#~ prg.norm1(queue, rdata_gpu.shape,None,rdata_gpu.data,idata_gpu.data,error_gpu.data, int32(cut))
norm1=prg.norm1
norm1.set_scalar_arg_dtypes([None, None, None, int32])
norm1(queue, rdata_gpu.shape,None,rdata_gpu.data,idata_gpu.data,error_gpu.data, int32(cut))
e= sqrt(cl_array.sum(error_gpu).get())/(2*cut)
#~ if e>ea:
#~
#~ break
#~ ea=e
plan.execute(rdata_gpu.data,fdata_gpu.data)
fdata=fdata_gpu.get()
fdata=ifftshift(fdata)
fdata=exp(1.j*angle(fdata))
return fdata
def test_touch(self):
MAX_CLASH = 100 + 0.9
MIN_INTER = 300 + 0.9
NROT = np.random.randint(self.rotations.shape[0] + 1)
rotmat = self.rotations[0]
cpu_lsurf = np.zeros_like(self.im_lsurf.array)
disvis.libdisvis.rotate_image3d(self.im_lsurf.array, self.vlength, np.linalg.inv(rotmat), self.im_center, cpu_lsurf)
cpu_clashvol = numpy.fft.irfftn(numpy.fft.rfftn(cpu_lsurf).conj() * numpy.fft.rfftn(self.rcore.array))
gpu_rcore = cl_array.to_device(self.queue, np.asarray(self.rcore.array, dtype=np.float32))
gpu_im_lsurf = cl.image_from_array(self.queue.context, np.asarray(self.im_lsurf.array, dtype=np.float32))
gpu_lsurf = cl_array.zeros(self.queue, self.shape, dtype=np.float32)
self.kernels.rotate_image3d(self.queue, self.sampler, gpu_im_lsurf, rotmat, gpu_lsurf, self.im_center)
gpu_ft_lsurf = cl_array.zeros(self.queue, self.ft_shape, dtype=np.complex64)
gpu_ft_rcore = cl_array.zeros(self.queue, self.ft_shape, dtype=np.complex64)
gpu_ft_clashvol = cl_array.zeros(self.queue, self.ft_shape, dtype=np.complex64)
gpu_clashvol = cl_array.zeros(self.queue, self.shape, dtype=np.float32)
self.kernels.rfftn(self.queue, gpu_rcore, gpu_ft_rcore)
self.kernels.rfftn(self.queue, gpu_lsurf, gpu_ft_lsurf)
self.kernels.c_conj_multiply(self.queue, gpu_ft_lsurf, gpu_ft_rcore, gpu_ft_clashvol)
self.kernels.irfftn(self.queue, gpu_ft_clashvol, gpu_clashvol)
cpu_intervol = numpy.fft.irfftn(numpy.fft.rfftn(cpu_lsurf).conj() * numpy.fft.rfftn(self.rsurf.array))
gpu_rsurf = cl_array.to_device(self.queue, np.asarray(self.rsurf.array, dtype=np.float32))
gpu_ft_rsurf = cl_array.zeros(self.queue, self.ft_shape, dtype=np.complex64)
gpu_ft_intervol = cl_array.zeros(self.queue, self.ft_shape, dtype=np.complex64)
gpu_intervol = cl_array.zeros(self.queue, self.shape, dtype=np.float32)
cpu_interspace = np.zeros(self.shape, dtype=np.int32)
gpu_interspace = cl_array.zeros(self.queue, self.shape, dtype=np.int32)
self.kernels.rfftn(self.queue, gpu_rsurf, gpu_ft_rsurf)
self.kernels.rfftn(self.queue, gpu_lsurf, gpu_ft_lsurf)
self.kernels.c_conj_multiply(self.queue, gpu_ft_lsurf, gpu_ft_rsurf, gpu_ft_intervol)
self.kernels.irfftn(self.queue, gpu_ft_intervol, gpu_intervol)
self.kernels.touch(self.queue, gpu_clashvol, MAX_CLASH, gpu_intervol, MIN_INTER, gpu_interspace)
np.logical_and(cpu_clashvol < MAX_CLASH, cpu_intervol > MIN_INTER, cpu_interspace)
disvis.volume.Volume(cpu_interspace, self.im_lsurf.voxelspacing, self.im_lsurf.origin).tofile('cpu_interspace.mrc')
disvis.volume.Volume(gpu_interspace.get(), self.im_lsurf.voxelspacing, self.im_lsurf.origin).tofile('gpu_interspace.mrc')
disvis.volume.Volume(cpu_interspace - gpu_interspace.get(), self.im_lsurf.voxelspacing, self.im_lsurf.origin).tofile('diff.mrc')
print()
print(cpu_interspace.sum(), gpu_interspace.get().sum())
print(np.abs(cpu_interspace - gpu_interspace.get()).sum())
self.assertTrue(np.allclose(gpu_interspace.get(), cpu_interspace))
def main():
# Allocate the first GPU
ctx = cl.create_some_context(0)#use device 0, the GPU
queue = cl.CommandQueue(ctx)
# Define dimensions
ydim = 1024
xdim = 1024
# Create random matrix
matrix = np.random.random((ydim, xdim))
matrix = np.float32(matrix)
# Create random matrix2
matrix2 = np.random.random((ydim, xdim))
matrix2 = np.float32(matrix2)
# Get the compiled kernel
kernel = get_kernel(ctx, xdim)
# Start timing
t1 = time.time()
# Move data to the GPU
gpu_matrix = cl_array.to_device(queue, matrix)
gpu_matrix2 = cl_array.to_device(queue, matrix2)
gpu_result = cl_array.zeros(queue, (ydim, xdim), np.float32)
# Define grid shape (the same as the matrix dimensions)
grid_shape = (ydim, xdim)
# Get group shape based on the matrix dimensions and the actual hardware
group_shape = (16,16)#(32,16)
# Execute the kernel
event = kernel.add(queue,
grid_shape, group_shape,
gpu_result.data,
gpu_matrix.data,
gpu_matrix2.data)
# Wait for the kernel to finish
event.wait()
# Move the result from GPU to CPU
result = gpu_result.get()
# Measure end time
t2 = time.time()
# Print result and execution time
print result
print "Elapsed: %f seconds " % (t2-t1)
# Free the GPU resource
queue.finish()
def test(self):
a = numpy.random.randn(4, 4).astype(numpy.float32)
b = numpy.random.randn(4, 4).astype(numpy.float32)
c = numpy.random.randn(4, 4).astype(numpy.float32)
a_gpu = cl_array.to_device(self.ctx, queue, a)
b_gpu = cl_array.to_device(self.ctx, queue, b)
c_gpu = cl_array.to_device(self.ctx, queue, c)
dest_gpu = cl_array.empty_like(a_gpu)
def sum_solutions(self, line_da, x_R_d, x_UH, x_LH, alpha, beta):
x_UH_d = cl_array.to_device(self.queue, x_UH)
x_LH_d = cl_array.to_device(self.queue, x_LH)
alpha_d = cl_array.to_device(self.queue, alpha)
beta_d = cl_array.to_device(self.queue, beta)
evt = self.sum_solutions_kernel(self.queue, (line_da.nx, line_da.ny, line_da.nz), None,
x_R_d.data, x_UH_d.data,
x_LH_d.data, alpha_d.data, beta_d.data,
np.int32(line_da.nx), np.int32(line_da.ny),
np.int32(line_da.nz))
def prepare_dev_data(self):
ldis = self.ldis
# differentiation matrix
drds_dev = np.empty((ldis.Np, ldis.Np, 2), dtype=np.float32)
drds_dev[:, :, 0] = ldis.Dr.T
drds_dev[:, :, 1] = ldis.Ds.T
mf = cl.mem_flags
self.diffmatrices_img = cl.Image(
self.ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
cl.ImageFormat(cl.channel_order.RG, cl.channel_type.FLOAT),
shape=drds_dev.shape[:2],
hostbuf=drds_dev,
)
# geometric coefficients
drdx_dev = np.empty((self.K, self.dimensions ** 2), dtype=np.float32)
drdx_dev[:, 0] = self.rx[:, 0]
drdx_dev[:, 1] = self.ry[:, 0]
drdx_dev[:, 2] = self.sx[:, 0]
drdx_dev[:, 3] = self.sy[:, 0]
self.drdx_dev = cl_array.to_device(self.queue, drdx_dev)
# lift matrix
lift_dev = np.zeros((ldis.Np, ldis.Nfp, 4), dtype=np.float32)
partitioned_lift = ldis.LIFT.reshape(ldis.Np, -1, ldis.Nfaces)
lift_dev[:, :, : ldis.Nfaces] = partitioned_lift
self.lift_img = cl.Image(
self.ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
cl.ImageFormat(cl.channel_order.RGBA, cl.channel_type.FLOAT),
shape=(ldis.Nfp, ldis.Np),
hostbuf=lift_dev,
)
# surface info
surfinfo_dev = np.empty((self.K, 6, ldis.Nafp), dtype=np.float32)
el_p, face_i_p = divmod(self.vmapP.reshape(-1, ldis.Nafp), ldis.Np)
el_m, face_i_m = divmod(self.vmapM.reshape(-1, ldis.Nafp), ldis.Np)
ind_p = el_p * self.block_size + face_i_p
ind_m = el_m * self.block_size + face_i_m
surfinfo_dev[:, 0, :] = ind_m
surfinfo_dev[:, 1, :] = ind_p
surfinfo_dev[:, 2, :] = self.Fscale
surfinfo_dev[:, 3, :] = np.where(ind_m == ind_p, -1, 1)
surfinfo_dev[:, 4, :] = self.nx
surfinfo_dev[:, 5, :] = self.ny
self.surfinfo_dev = cl_array.to_device(self.queue, surfinfo_dev)
def test_multiply(self):
np_in1 = np.arange(10, dtype=np.float32)
np_in2 = np.arange(10, dtype=np.float32)
np_out = np_in1 * np_in2
cl_in1 = cl_array.to_device(self.queue, np_in1)
cl_out = cl_array.to_device(self.queue, np.zeros(10, dtype=np.float32))
cl_in2 = cl_array.to_device(self.queue, np_in2)
self.k.multiply(cl_in1, cl_in2, cl_out)
self.assertTrue(np.allclose(np_out, cl_out.get()))
def _build_block_index(discr,
nblks=10,
factor=1.0,
method='elements',
use_tree=True):
from pytential.linalg.proxy import (
partition_by_nodes, partition_by_elements)
if method == 'elements':
factor = 1.0
if method == 'nodes':
nnodes = discr.nnodes
else:
nnodes = discr.mesh.nelements
max_particles_in_box = nnodes // nblks
# create index ranges
if method == 'nodes':
indices = partition_by_nodes(discr,
use_tree=use_tree,
max_nodes_in_box=max_particles_in_box)
elif method == 'elements':
indices = partition_by_elements(discr,
use_tree=use_tree,
max_elements_in_box=max_particles_in_box)
else:
raise ValueError('unknown method: {}'.format(method))
# randomly pick a subset of points
if abs(factor - 1.0) > 1.0e-14:
with cl.CommandQueue(discr.cl_context) as queue:
indices = indices.get(queue)
indices_ = np.empty(indices.nblocks, dtype=np.object)
for i in range(indices.nblocks):
iidx = indices.block_indices(i)
isize = int(factor * len(iidx))
isize = max(1, min(isize, len(iidx)))
indices_[i] = np.sort(
np.random.choice(iidx, size=isize, replace=False))
ranges_ = to_device(queue,
np.cumsum([0] + [r.shape[0] for r in indices_]))
indices_ = to_device(queue, np.hstack(indices_))
indices = BlockIndexRanges(discr.cl_context,
indices_.with_queue(None),
ranges_.with_queue(None))
return indices
def test_multiply_array(ctx_getter):
"""Test the multiplication of two arrays."""
context = ctx_getter()
queue = cl.CommandQueue(context)
a = numpy.array([1,2,3,4,5,6,7,8,9,10]).astype(numpy.float32)
a_gpu = cl_array.to_device(context, queue, a)
b_gpu = cl_array.to_device(context, queue, a)
a_squared = (b_gpu*a_gpu).get()
assert (a*a == a_squared).all()
def send_arrays_to_device(self, field, field_temp,
field_interaction, factor):
""" Move numpy arrays onto compute device. """
self.shape = field.shape
self.buf_field = cl_array.to_device(
self.queue, field.astype(self.np_complex))
self.buf_temp = cl_array.to_device(
self.queue, field_temp.astype(self.np_complex))
self.buf_interaction = cl_array.to_device(
self.queue, field_interaction.astype(self.np_complex))
self.buf_factor = cl_array.to_device(
self.queue, factor.astype(self.np_complex))
def work(x,y,n):
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
arr =cl_array.to_device(queue, numpy.zeros(n).astype(numpy.float16))
ris = cl_array.to_device(queue,numpy.zeros(1).astype(numpy.float32))
summ = ElementwiseKernel(ctx,
"float a,float b, float *x,float *c ",
"c[0]=a+b ")
prod = ElementwiseKernel(ctx,
"float a,float b, float *x, float *c ",
"c[0]=a*b ")
summ (x,y,arr,ris)
prod(x,y,arr,ris)
return ris
def alter_sum(): ctx = cl_init() queue = cl.CommandQueue(ctx) n = 10**6 a_gpu = cl_array.to_device( queue, np.random.randn(n).astype(np.float32)) b_gpu = cl_array.to_device( queue, np.random.randn(n).astype(np.float32)) cl_sum = cl_array.sum(a_gpu).get() numpy_sum = np.sum(a_gpu.get()) print cl_sum, numpy_sum
def test_divide_scalar(ctx_factory):
"""Test the division of an array and a scalar."""
context = ctx_factory()
queue = cl.CommandQueue(context)
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
a_gpu = cl_array.to_device(queue, a)
result = (a_gpu / 2).get()
assert (a / 2 == result).all()
result = (2 / a_gpu).get()
assert (np.abs(2 / a - result) < 1e-5).all()
def _allocate_memory(self, mode):
self.mode = mode or "reflect"
option_array_names = {
"allocate_input_array": "data_in",
"allocate_output_array": "data_out",
"allocate_tmp_array": "data_tmp",
}
# Nonseparable transforms do not need tmp array
if not (self.separable):
self.extra_options["allocate_tmp_array"] = False
# Allocate arrays
for option_name, array_name in option_array_names.items():
if self.extra_options[option_name]:
value = parray.empty(self.queue, self.shape, np.float32)
value.fill(np.float32(0.0))
else:
value = None
setattr(self, array_name, value)
if isinstance(self.kernel, np.ndarray):
self.d_kernel = parray.to_device(self.queue, self.kernel)
else:
if not (isinstance(self.kernel, parray.Array)):
raise ValueError(
"kernel must be either numpy array or pyopencl array")
self.d_kernel = self.kernel
self._old_input_ref = None
self._old_output_ref = None
if self.use_textures:
self._allocate_textures()
self._c_modes_mapping = {
"periodic": 2,
"wrap": 2,
"nearest": 1,
"replicate": 1,
"reflect": 0,
"constant": 3,
}
mp = self._c_modes_mapping
if self.mode.lower() not in mp:
raise ValueError("""
Mode %s is not available for textures. Available modes are:
%s
""" % (self.mode, str(mp.keys())))
# TODO
if not (self.use_textures) and self.mode.lower() == "constant":
raise NotImplementedError(
"mode='constant' is not implemented without textures yet")
#
self._c_conv_mode = mp[self.mode]
def test_nan_arithmetic(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
def make_nan_contaminated_vector(size):
shape = (size, )
a = np.random.randn(*shape).astype(np.float32)
from random import randrange
for i in range(size // 10):
a[randrange(0, size)] = float('nan')
return a
size = 1 << 20
a = make_nan_contaminated_vector(size)
a_gpu = cl_array.to_device(queue, a)
b = make_nan_contaminated_vector(size)
b_gpu = cl_array.to_device(queue, b)
ab = a * b
ab_gpu = (a_gpu * b_gpu).get()
assert (np.isnan(ab) == np.isnan(ab_gpu)).all()
def process(self, ibuf):
if isinstance(ibuf, np.ndarray):
ibuf = cla.to_device(self._queue, ibuf)
sz = ibuf.shape[0]
k2 = False
if len(ibuf.shape) > 1:
if ibuf.shape[1] == 1:
pass
elif ibuf.shape[1] == 2:
k2 = True
else:
raise ValueError('invalid dimensionality')
max_lag = self._nlags * self._lag_step + self._lag_base
max_pre = max_lag + self._win_length
offset = max_pre
count = sz - offset
obuf = cla.empty(self._queue,
((count + self._interval - 1) // self._interval,
self._nlags // 64),
dtype=splice_point)
if k2:
ev = self._program.eval_state_2(self._queue, (self._nlags, ),
(64, ),
ibuf.data,
obuf.data,
np.int32(offset),
self._win_length,
self._lag_base,
self._lag_step,
self._interval,
np.int32(count),
wait_for=None)
else:
ev = self._program.eval_state_1(self._queue, (self._nlags, ),
(64, ),
ibuf.data,
obuf.data,
np.int32(offset),
self._win_length,
self._lag_base,
self._lag_step,
self._interval,
np.int32(count),
wait_for=None)
ev.wait()
return obuf.get()
def solve(self, A, B, x0=None, tol=10e-6, iters=300):
r""" Solve linear system of equations by a Jacobi
iterative method.
@param A Linear system matrix.
@param B Linear system independent term.
@param x0 Initial aproximation of the solution.
@param tol Relative error tolerance: \n
\$ \vert\vert B - A \, x \vert \vert_\infty /
\vert\vert B \vert \vert_\infty \$
@param iters Maximum number of iterations.
"""
# Create/set OpenCL buffers
self.setBuffers(A, B, x0)
# Get dimensions for OpenCL execution
n = np.uint32(len(B))
gSize = (clUtils.globalSize(n), )
# Get a norm to can compare later for valid result
B_cl = cl_array.to_device(self.context, self.queue, B)
bnorm2 = self.dot(B_cl, B_cl).get()
FreeCAD.Console.PrintMessage(bnorm2)
FreeCAD.Console.PrintMessage("\n")
# Iterate while the result converges or maximum number
# of iterations is reached.
for i in range(0, iters):
# Compute residues
kernelargs = (self.A, self.B, self.X0, self.R.data, n)
# Test if the final result has been reached
self.program.r(self.queue, gSize, None, *(kernelargs))
rnorm2 = self.dot(self.R, self.R).get()
FreeCAD.Console.PrintMessage("\t")
FreeCAD.Console.PrintMessage(rnorm2)
FreeCAD.Console.PrintMessage("\n")
if np.sqrt(rnorm2 / bnorm2) <= tol:
break
# Iterate
kernelargs = (self.A, self.R.data, self.AR.data, n)
self.program.dot_mat_vec(self.queue, gSize, None, *(kernelargs))
AR_R = self.dot(self.AR, self.R).get()
AR_AR = self.dot(self.AR, self.AR).get()
kernelargs = (self.A, self.R.data, self.X, self.X0, AR_R, AR_AR, n)
self.program.minres(self.queue, gSize, None, *(kernelargs))
# Swap variables
swap = self.X
self.X = self.X0
self.X0 = swap
# Return result computed
x = np.zeros((n), dtype=np.float32)
cl.enqueue_read_buffer(self.queue, self.X0, x).wait()
return (x, np.sqrt(rnorm2 / bnorm2), i)
def test_divide_inplace_array(ctx_factory):
"""Test inplace division of arrays."""
context = ctx_factory()
queue = cl.CommandQueue(context)
dtypes = (np.uint8, np.uint16, np.uint32,
np.int8, np.int16, np.int32,
np.float32, np.complex64)
from pyopencl.characterize import has_double_support
if has_double_support(queue.device):
dtypes = dtypes + (np.float64, np.complex128)
from itertools import product
for dtype_a, dtype_b in product(dtypes, repeat=2):
print(dtype_a, dtype_b)
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]).astype(dtype_a)
b = np.array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10]).astype(dtype_b)
a_gpu = cl_array.to_device(queue, a)
b_gpu = cl_array.to_device(queue, b)
# ensure the same behavior as inplace numpy.ndarray division
try:
a_gpu /= b_gpu
except TypeError:
# pass for now, as numpy casts differently for in-place and out-place
# true_divide
pass
# with np.testing.assert_raises(TypeError):
# a /= b
else:
a /= b
assert (np.abs(a_gpu.get() - a) < 1e-3).all()
assert a_gpu.dtype is a.dtype
def test_ones_matrix_arange_vector():
inp_layer = np.arange(inp_size).astype(np.float32)
inp_layer = pycl_array.to_device(clsingle.queue, inp_layer)
matrix = clsingle.ones((out_size, inp_size))
out_layer = clsingle.zeros(out_size)
code.program.matrix_vector_mul(clsingle.queue, (out_size, TS), (WPT, TS),
inp_size, RESET_OUTPUT, inp_layer.data,
matrix.data, out_layer.data)
approx_val = pytest.approx(np.sum(inp_layer.get()))
out_layer = out_layer.get()
for i in range(out_size):
assert out_layer[i] == approx_val
def shuffle(x_data, rows, cols):
"""
Odd sized row count will not have 1 row shuffled
:param x_data:
:param rows:
:param cols:
:param swaps_g:
:return:
"""
swaps_np = np.arange(rows, dtype=cltypes.uint)
np.random.shuffle(swaps_np)
swaps_g = array.to_device(queue, swaps_np, allocator=read_only_arr)
e1 = shuffle_krnl(queue, (cols, len(swaps_np) // 2), None, x_data, swaps_g.data)
e1.wait()
return swaps_g
def cg_solve(self, x, iters):
x = clarray.to_device(self.queue, np.require(x, requirements="C"))
b = clarray.empty(self.queue,
(self.NScan, 1, self.NSlice, self.dimY, self.dimX),
DTYPE, "C")
Ax = clarray.empty(self.queue,
(self.NScan, 1, self.NSlice, self.dimY, self.dimX),
DTYPE, "C")
data = clarray.to_device(self.queue, self.data)
self.operator_rhs(b, data)
res = b
p = res
delta = np.linalg.norm(res.get())**2/np.linalg.norm(b.get())**2
self.res.append(delta)
print("Initial Residuum: ", delta)
for i in range(iters):
self.operator_lhs(Ax, p)
Ax = Ax + self.reco_par["lambd"]*p
alpha = (clarray.vdot(res, res)/(clarray.vdot(p, Ax))).real.get()
x[i+1] = (x[i] + alpha*p)
res_new = res - alpha*Ax
delta = np.linalg.norm(res_new.get())**2/np.linalg.norm(b.get())**2
self.res.append(delta)
if delta < self.reco_par["tol"]:
print("Converged after %i iterations to %1.3e." % (i, delta))
return x.get()[:i+1, ...]
if not np.mod(i, 1):
print("Residuum at iter %i : %1.3e" % (i, delta), end='\r')
beta = (clarray.vdot(res_new, res_new) /
clarray.vdot(res, res)).real.get()
p = res_new+beta*p
(res, res_new) = (res_new, res)
return x.get()
def test_outoforderqueue_reductions(ctx_factory):
context = ctx_factory()
try:
queue = cl.CommandQueue(context,
properties=cl.command_queue_properties.OUT_OF_ORDER_EXEC_MODE_ENABLE)
except Exception:
pytest.skip("out-of-order queue not available")
# 0/1 values to avoid accumulated rounding error
a = (np.random.rand(10**6) > 0.5).astype(np.dtype('float32'))
a[800000] = 10 # all<5 looks true until near the end
a_gpu = cl_array.to_device(queue, a)
b1 = cl_array.sum(a_gpu).get()
b2 = cl_array.dot(a_gpu, 3 - a_gpu).get()
b3 = (a_gpu < 5).all().get()
assert b1 == a.sum() and b2 == a.dot(3 - a) and b3 == 0
def test_adj_inplace(self):
inpgrad = clarray.to_device(self.queue, self.symgradin)
inpdiv = clarray.to_device(self.queue, self.symdivin)
outgrad = clarray.zeros_like(inpdiv)
outdiv = clarray.zeros_like(inpgrad)
outgrad.add_event(self.symgrad.fwd(outgrad, inpgrad))
outdiv.add_event(self.symgrad.adj(outdiv, inpdiv))
outgrad = outgrad.get()
outdiv = outdiv.get()
a1 = np.vdot(outgrad[..., :3].flatten(),
self.symdivin[..., :3].flatten())/self.symgradin.size*4
a2 = 2*np.vdot(outgrad[..., 3:6].flatten(),
self.symdivin[..., 3:6].flatten())/self.symgradin.size*4
a = a1+a2
b = np.vdot(self.symgradin.flatten(),
-outdiv.flatten())/self.symgradin.size*4
print("Adjointness: %.2e +1j %.2e" % ((a - b).real, (a - b).imag))
np.testing.assert_allclose(a, b, rtol=RTOL, atol=ATOL)
def test_1d_out_of_place(self, ctx):
queue = cl.CommandQueue(ctx)
nd_data = np.arange(32, dtype=np.complex64)
cl_data = cla.to_device(queue, nd_data)
cl_data_transformed = cla.zeros_like(cl_data)
transform = FFT(ctx, queue,
cl_data,
cl_data_transformed
)
transform.enqueue()
assert np.allclose(cl_data_transformed.get(),
np.fft.fft(nd_data))
def setupClVariables(self):
self.nrOfDetectionAngleSteps = self.configReader.nrOfDetectionAngleSteps
self.host_mostRecentMembraneCoordinatesX = np.zeros(
shape=self.nrOfDetectionAngleSteps, dtype=np.float64)
self.dev_mostRecentMembraneCoordinatesX = cl_array.to_device(
self.managementQueue, self.host_mostRecentMembraneCoordinatesX)
self.host_mostRecentMembraneCoordinatesY = np.zeros(
shape=self.nrOfDetectionAngleSteps, dtype=np.float64)
self.dev_mostRecentMembraneCoordinatesY = cl_array.to_device(
self.managementQueue, self.host_mostRecentMembraneCoordinatesY)
self.host_mostRecentMembraneNormalVectorsX = np.zeros(
shape=self.nrOfDetectionAngleSteps, dtype=np.float64)
self.dev_mostRecentMembraneNormalVectorsX = cl_array.to_device(
self.managementQueue, self.host_mostRecentMembraneNormalVectorsX)
self.host_mostRecentMembraneNormalVectorsY = np.zeros(
shape=self.nrOfDetectionAngleSteps, dtype=np.float64)
self.dev_mostRecentMembraneNormalVectorsY = cl_array.to_device(
self.managementQueue, self.host_mostRecentMembraneNormalVectorsY)
self.host_contourCenter = np.zeros(1, cl.array.vec.double2)
self.dev_mostRecentContourCenter = cl_array.to_device(
self.managementQueue, self.host_contourCenter)
pass
def __init__(self,
data: Union[cl.array.Array, list, np.ndarray],
gpu: bool = False) -> None:
"""Initialize variables."""
self._gpu: bool = gpu
if isinstance(data, list):
self._data: np.ndarray = np.array(data, dtype=np.float32)
if self._gpu:
self._data = clarray.to_device(QUEUE, self._data)
elif isinstance(data, np.ndarray):
if data.dtype != np.float32:
# NOTE: The NumPy array has to be converted into a list first.
# Otherwise, the operations on cpu and gpu produce
# different results. This behavior can be caused by many
# reasons including OpenCL and even the operating system
# itself. Some research is needed to figure out cause and
# eliminate extra work for rebuilding the array.
self._data: np.ndarray = np.array(data.tolist(), np.float32)
else:
self._data: np.ndarray = data
if self._gpu:
self._data = clarray.to_device(QUEUE, self._data)
elif isinstance(data, cl.array.Array):
self._data: cl.array.Array = data
self._gpu: bool = True
else:
raise TypeError(
"Expected `list`, `np.ndarray`, or `pyopencl.array.Array` got "
f"`{type(data)}`")
def __init__(self, ctx, queue, shape, coeffs):
'''
Create context for the Cyclic Reduction Solver
that solves a "near-toeplitz"
tridiagonal system with
diagonals:
a = (_, ai, ai .... an)
b[:] = (b1, bi, bi, bi... bn)
c[:] = (c1, ci, ci, ... _)
Parameters
----------
ctx: PyOpenCL context
queue: PyOpenCL command queue
shape: The size of the tridiagonal system.
coeffs: A list of coefficients that make up the tridiagonal matrix:
[b1, c1, ai, bi, ci, an, bn]
'''
self.ctx = ctx
self.queue = queue
self.device = self.ctx.devices[0]
self.platform = self.device.platform
self.nz, self.ny, self.nx = shape
self.coeffs = coeffs
mf = cl.mem_flags
# check that system_size is a power of 2:
assert np.int(np.log2(self.nx)) == np.log2(self.nx)
# compute coefficients a, b, etc.,
a, b, c, k1, k2, b_first, k1_first, k1_last = self._precompute_coefficients(
)
self.a_d = cl_array.to_device(queue, a)
self.b_d = cl_array.to_device(queue, b)
self.c_d = cl_array.to_device(queue, c)
self.k1_d = cl_array.to_device(queue, k1)
self.k2_d = cl_array.to_device(queue, k2)
self.b_first_d = cl_array.to_device(queue, b_first)
self.k1_first_d = cl_array.to_device(queue, k1_first)
self.k1_last_d = cl_array.to_device(queue, k1_last)
self.forward_reduction, self.back_substitution = kernels.get_funcs(
self.ctx, 'kernels.cl', 'globalForwardReduction',
'globalBackSubstitution')
def clfftn(data):
""" OpenCL FFT 3D
"""
clear_first_arg_caches()
#ctx = cl.create_some_context(interactive=False)
#queue = cl.CommandQueue(ctx)
ctx, queue = clinit()
plan = Plan(data.shape, normalize=True, queue=queue)
# forward transform on device
gpu_data = cl_array.to_device(queue, data)
# forward transform
plan.execute(gpu_data.data)
#result = gpu_data.get()
result = gpu_data.get()
return result
def mhd_gamma_calc(queue, G, P, loc=Loci.CENT, out=None):
"""Find relativistic gamma-factor w.r.t. normal observer"""
s = G.slices
sh = G.shapes
global g3
if g3 is None:
g3 = cl_array.to_device(queue, G.gcov[loc.value, 1:, 1:].copy())
if out is None:
out = cl_array.empty(queue, sh.grid_scalar, dtype=np.float64)
evt, _ = G.dot2geom2(queue, g=g3, u=P[s.U3VEC], v=P[s.U3VEC], out=out)
out = clm.sqrt(1. + out)
return out
def test_zero_size_array(ctx_factory, empty_shape):
context = ctx_factory()
queue = cl.CommandQueue(context)
if queue.device.platform.name == "Intel(R) OpenCL":
pytest.xfail("size-0 arrays fail on Intel CL")
a = cl_array.zeros(queue, empty_shape, dtype=np.float32)
b = cl_array.zeros(queue, empty_shape, dtype=np.float32)
b.fill(1)
c = a + b
c_host = c.get()
cl_array.to_device(queue, c_host)
assert c.flags.c_contiguous == c_host.flags.c_contiguous
assert c.flags.f_contiguous == c_host.flags.f_contiguous
for order in "CF":
c_flat = c.reshape(-1, order=order)
c_host_flat = c_host.reshape(-1, order=order)
assert c_flat.shape == c_host_flat.shape
assert c_flat.strides == c_host_flat.strides
assert c_flat.flags.c_contiguous == c_host_flat.flags.c_contiguous
assert c_flat.flags.f_contiguous == c_host_flat.flags.f_contiguous
def __init__(self, ary, backend=None):
self.backend = get_backend(backend)
self.data = ary
self._convert = False
if self.backend == 'opencl':
use_double = get_config().use_double
self._dtype = np.float64 if use_double else np.float32
if np.issubdtype(self.data.dtype, np.float):
self._convert = True
from pyopencl.array import to_device
from .opencl import get_queue
self.q = get_queue()
self.dev = to_device(self.q, self._get_data())
else:
self.dev = self.data
def project_metaballs_naive(metaballs,
shape,
pixel_size,
offset=None,
z_step=None,
queue=None,
out=None,
block=False):
"""Project a list of :class:`.MetaBall` on an image plane with *shape*, *pixel_size*. *z_step*
is the physical step in the z-dimension, if not specified it is the same as *pixel_size*.
*offset* is the physical spatial body offset as (y, x). Use OpenCL *queue* and *out* pyopencl
Array instance for returning the result. If *block* is True, wait for the kernel to finish.
"""
def get_extrema(sgn):
func = np.max if sgn > 0 else np.min
x_ps = util.make_tuple(pixel_size)[1]
res = [(ball.position[2] + sgn *
(2 * ball.radius + x_ps)).simplified.magnitude
for ball in metaballs]
return func(res)
if offset is None:
offset = (0, 0) * q.m
if not queue:
queue = cfg.OPENCL.queue
if out is None:
out = cl_array.Array(queue, shape, cfg.PRECISION.np_float)
string = ''.join([body.pack() for body in metaballs])
data = np.fromstring(string, dtype=np.float32)
data = cl_array.to_device(queue, data)
n, m = shape
ps = util.make_tuple(pixel_size.simplified.magnitude)
z_step = ps[1] if z_step is None else z_step.simplified.magnitude
z_range = get_extrema(-1), get_extrema(1)
offset = g_util.make_vfloat2(*offset.simplified.magnitude[::-1])
ev = cfg.OPENCL.programs['geometry'].naive_metaballs(
cfg.OPENCL.queue, (m, n), None, out.data, data.data,
np.int32(len(metaballs)), offset, g_util.make_vfloat2(*z_range),
cfg.PRECISION.np_float(z_step), g_util.make_vfloat2(*ps[::-1]),
np.int32(True))
if block:
ev.wait()
return out
def test_hankel_01_complex(ctx_factory, ref_src):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
if not has_double_support(ctx.devices[0]):
from pytest import skip
skip(
"no double precision support--cannot test complex bessel function")
n = 10**6
np.random.seed(11)
z = (np.logspace(-5, 2, n) * np.exp(1j * 2 * np.pi * np.random.rand(n)))
def get_err(check, ref):
return np.max(np.abs(check - ref)) / np.max(np.abs(ref))
if ref_src == "pyfmmlib":
pyfmmlib = pytest.importorskip("pyfmmlib")
h0_ref, h1_ref = pyfmmlib.hank103_vec(z, ifexpon=1)
elif ref_src == "scipy":
spec = pytest.importorskip("scipy.special")
h0_ref = spec.hankel1(0, z)
h1_ref = spec.hankel1(1, z)
else:
raise ValueError("ref_src")
z_dev = cl_array.to_device(queue, z)
h0_dev, h1_dev = clmath.hankel_01(z_dev)
rel_err_h0 = np.abs(h0_dev.get() - h0_ref) / np.abs(h0_ref)
rel_err_h1 = np.abs(h1_dev.get() - h1_ref) / np.abs(h1_ref)
max_rel_err_h0 = np.max(rel_err_h0)
max_rel_err_h1 = np.max(rel_err_h1)
print("H0", max_rel_err_h0)
print("H1", max_rel_err_h1)
assert max_rel_err_h0 < 4e-13
assert max_rel_err_h1 < 2e-13
if 0:
import matplotlib.pyplot as pt
pt.loglog(np.abs(z), rel_err_h0)
pt.loglog(np.abs(z), rel_err_h1)
pt.show()
def step(self, delta_time):
if pause:
return
centers = np.ndarray((self.galaxy_count, 4), dtype=np.float32)
for i in range(self.galaxy_count):
centers[i][:3] = self.galaxies[i].position
centers[i][3] = self.galaxies[i].mass
centers_buffer = clarray.to_device(cl_queue, centers)
gl.glFlush()
gl.glFinish()
for i, galaxy in enumerate(self.galaxies):
cl.enqueue_acquire_gl_objects(cl_queue, [
galaxy.body_positions_cl_buffer,
galaxy.body_velocities_cl_buffer
])
kernel_step(cl_queue, (galaxy.body_count, ), None,
galaxy.body_positions_cl_buffer,
galaxy.body_velocities_cl_buffer, centers_buffer.data,
np.uint(galaxy.body_count), np.uint(self.galaxy_count),
np.float32(self.dt * delta_time), np.float32(self.G))
cl.enqueue_release_gl_objects(cl_queue, [
galaxy.body_positions_cl_buffer,
galaxy.body_velocities_cl_buffer
])
cl_queue.finish()
centers = [
mathutils.Vector((galaxy.position.x, galaxy.position.y,
galaxy.position.z, galaxy.mass))
for galaxy in self.galaxies
]
for i in range(self.galaxy_count):
this_galaxy = self.galaxies[i]
f = mathutils.Vector((0, 0, 0))
#f = np.zeros((4,), dtype=np.float32)
for j in self.others(i):
delta_pos = mathutils.Vector(centers[j] - centers[i]).xyz
length = max(1.0, delta_pos.length_squared)
f += delta_pos.normalized() * self.G * centers[i][3] * centers[
j][3] / delta_pos.length_squared
this_galaxy.velocity += f * delta_time * self.dt
this_galaxy.position += this_galaxy.velocity * delta_time * self.dt
def setUp(self):
parser = tmpArgs()
parser.streamed = False
parser.devices = -1
parser.use_GPU = True
par = {}
pyqmri.pyqmri._setupOCL(parser, par)
setupPar(par)
if DTYPE == np.complex128:
file = resource_filename(
'pyqmri', 'kernels/OpenCL_Kernels_double.c')
else:
file = resource_filename(
'pyqmri', 'kernels/OpenCL_Kernels.c')
prg = []
for j in range(len(par["ctx"])):
with open(file) as myfile:
prg.append(Program(
par["ctx"][j],
myfile.read()))
prg = prg[0]
self.op = pyqmri.operator.OperatorImagespace(
par, prg,
DTYPE=DTYPE,
DTYPE_real=DTYPE_real)
self.opinfwd = np.random.randn(par["unknowns"], par["NSlice"],
par["dimY"], par["dimX"]) +\
1j * np.random.randn(par["unknowns"], par["NSlice"],
par["dimY"], par["dimX"])
self.opinadj = np.random.randn(par["NScan"], 1, par["NSlice"],
par["dimY"], par["dimX"]) +\
1j * np.random.randn(par["NScan"], 1, par["NSlice"],
par["dimY"], par["dimX"])
self.model_gradient = np.random.randn(par["unknowns"], par["NScan"],
par["NSlice"],
par["dimY"], par["dimX"]) + \
1j * np.random.randn(par["unknowns"], par["NScan"],
par["NSlice"],
par["dimY"], par["dimX"])
self.model_gradient = self.model_gradient.astype(DTYPE)
self.opinfwd = self.opinfwd.astype(DTYPE)
self.opinadj = self.opinadj.astype(DTYPE)
self.queue = par["queue"][0]
self.grad_buf = clarray.to_device(self.queue, self.model_gradient)
def transfer(thickness,
refractive_index,
wavelength,
exponent=False,
queue=None,
out=None,
check=True,
block=False):
"""Transfer *thickness* (can be either a numpy or pyopencl array) with *refractive_index* and
given *wavelength*. If *exponent* is True, compute the exponent of the function without applying
the wavenumber. Use command *queue* for computation and *out* pyopencl array. If *block* is
True, wait for the kernel to finish. If *check* is True, the function is checked for aliasing
artefacts. Returned *out* array is different from the input one because of the pyopencl.clmath
behavior.
"""
if queue is None:
queue = cfg.OPENCL.queue
if isinstance(thickness, cl_array.Array):
thickness_mem = thickness
else:
prep = thickness.simplified.magnitude.astype(cfg.PRECISION.np_float)
thickness_mem = cl_array.to_device(queue, prep)
if out is None:
out = cl_array.Array(queue, thickness_mem.shape, cfg.PRECISION.np_cplx)
if exponent or check:
wavenumber = cfg.PRECISION.np_float(2 * np.pi /
wavelength.simplified.magnitude)
ev = cfg.OPENCL.programs['physics'].transmission_add(
queue, thickness_mem.shape[::-1], None,
out.data, thickness_mem.data,
cfg.PRECISION.np_cplx(refractive_index), wavenumber, np.int32(1))
if check and not is_wavefield_sampling_ok(out, queue=queue):
LOG.error('Insufficient transmission function sampling')
if not exponent:
# Apply the exponent
out = clmath.exp(out, queue=queue)
else:
ev = cfg.OPENCL.programs['physics'].transfer(
queue, thickness_mem.shape[::-1], None, out.data,
thickness_mem.data, cfg.PRECISION.np_cplx(refractive_index),
cfg.PRECISION.np_float(wavelength.simplified.magnitude))
if block:
ev.wait()
return out
def test_outoforderqueue_copy(ctx_factory):
context = ctx_factory()
try:
queue = cl.CommandQueue(context,
properties=cl.command_queue_properties.OUT_OF_ORDER_EXEC_MODE_ENABLE)
except Exception:
pytest.skip("out-of-order queue not available")
a = np.random.rand(10**6).astype(np.dtype('float32'))
a_gpu = cl_array.to_device(queue, a)
c_gpu = a_gpu**2 - 7
b_gpu = c_gpu.copy() # testing that this waits for and creates events
b_gpu *= 10
queue.finish()
b1 = b_gpu.get()
b = 10 * (a**2 - 7)
assert np.abs(b1 - b).mean() < 1e-5
def probabilities(self):
"""Gets the squared absolute value of each of the amplitudes"""
out = pycl_array.to_device(
self.queue,
np.zeros(2**self.num_qubits, dtype=np.float32)
)
program.calculate_probabilities(
self.queue,
out.shape,
None,
self.buffer.data,
out.data
)
return out.get()
def _init_reference_field(self, scale_ref=1):
# clear object patches
for subfield, mask in zip(self.object_multiareafield.subfields,
self.object_multiareafield.subfields_masks):
np.copyto(subfield.field, 0, where=mask)
# obtain reference field
self.propagator_object_to_farfield.propagator_full_field.propagate()
self.object_field_ref = self.object_multiareafield.field.copy()
self.far_field.field *= scale_ref
self.far_field_ref = self.far_field.copy()
self.cl_far_field_ref = cla.to_device(self.cl_queue,
self.far_field_ref.field,
allocator=self.cl_allocator)
def test_grad_outofplace(self):
gradx = np.zeros_like(self.gradin)
grady = np.zeros_like(self.gradin)
gradz = np.zeros_like(self.gradin)
gradx[..., :-1] = np.diff(self.gradin, axis=-1)
grady[..., :-1, :] = np.diff(self.gradin, axis=-2)
gradz[:, :-1, ...] = np.diff(self.gradin, axis=-3) * self.dz
grad = np.stack((gradx, grady, gradz), axis=-1)
inp = clarray.to_device(self.queue, self.gradin)
outp = self.grad.fwdoop(inp)
outp = outp.get()
np.testing.assert_allclose(outp[..., :-1], grad, rtol=RTOL, atol=ATOL)
def test_outoforderqueue_clmath(ctx_factory):
context = ctx_factory()
try:
queue = cl.CommandQueue(context,
properties=cl.command_queue_properties.
OUT_OF_ORDER_EXEC_MODE_ENABLE)
except Exception:
pytest.skip("out-of-order queue not available")
a = np.random.rand(10**6).astype(np.dtype('float32'))
a_gpu = cl_array.to_device(queue, a)
# testing that clmath functions wait for and create events
b_gpu = clmath.fabs(clmath.sin(a_gpu * 5))
queue.finish()
b1 = b_gpu.get()
b = np.abs(np.sin(a * 5))
assert np.abs(b1 - b).mean() < 1e-5
def test_identity_matrix_random_vector():
inp_layer = clsingle.random(inp_size)
matrix = [np.arange(out_size) == i for i in range(out_size)]
matrix = np.array(matrix).astype(np.float32)
matrix = pycl_array.to_device(clsingle.queue, matrix)
out_layer = clsingle.ones(out_size)
code.program.matrix_vector_mul(clsingle.queue, (out_size, TS), (WPT, TS),
inp_size, RESET_OUTPUT, inp_layer.data,
matrix.data, out_layer.data)
out_layer = out_layer.get()
inp_layer = inp_layer.get()
for i in range(out_size):
assert out_layer[i] == pytest.approx(inp_layer[i])