def test_broadcast_0D(self):
with self.assertRaises(TypeError):
cl.broadcast(None, [1])
one = cl.from_host(ctx, c_int(1))
a = cl.broadcast(one, [10, 10])
self.assertEqual(a.shape, (10, 10))
self.assertEqual(a.strides, (0, 0))
queue = cl.Queue(ctx)
with a.map(queue) as view:
b = np.asarray(view)
self.assertEqual(b.shape, (10, 10))
self.assertEqual(b.strides, (0, 0))
def test_broadcast_0D(self):
with self.assertRaises(TypeError):
cl.broadcast(None, [1])
one = cl.from_host(ctx, c_int(1))
a = cl.broadcast(one, [10, 10])
self.assertEqual(a.shape, (10, 10))
self.assertEqual(a.strides, (0, 0))
queue = cl.Queue(ctx)
with a.map(queue) as view:
b = np.asarray(view)
self.assertEqual(b.shape, (10, 10))
self.assertEqual(b.strides, (0, 0))
def __call__(self, context, x, y, out=None, queue=None):
if queue is None:
if hasattr(x, 'queue'):
queue = x.queue
elif hasattr(y, 'queue'):
queue = y.queue
else:
queue = context.queue
if not isinstance(x, cl.DeviceMemoryView):
x = context.asarray(x)
if not isinstance(y, cl.DeviceMemoryView):
y = context.asarray(y)
if y.queue != queue:
queue.enqueue_wait_for_events(y.queue.marker())
if x.queue != queue:
queue.enqueue_wait_for_events(x.queue.marker())
new_shape = broadcast_shape(x.shape, y.shape)
a = cl.broadcast(x, new_shape)
b = cl.broadcast(y, new_shape)
if out is None:
out = context.empty(shape=new_shape, ctype=x.format, queue=queue)
# kernel_source = ufunc_kernel._compile(queue.context, function=self.device_func,
# a=cl.global_memory(a.format, flat=True),
# b=cl.global_memory(b.format, flat=True),
# out=cl.global_memory(out.format, flat=True), source_only=True)
kernel = ufunc_kernel.compile(context,
function=self.device_func,
a=cl.global_memory(a.format, flat=True),
b=cl.global_memory(b.format, flat=True),
out=cl.global_memory(out.format,
flat=True),
cly_meta=self.device_func.func_name)
kernel(queue, a, a.array_info, b, b.array_info, out, out.array_info)
array = CLArray._view_as_this(out)
array.__array_init__(context, queue)
return array
def __call__(self, context, x, y, out=None, queue=None):
if queue is None:
if hasattr(x,'queue'):
queue = x.queue
elif hasattr(y,'queue'):
queue = y.queue
else:
queue = context.queue
if not isinstance(x, cl.DeviceMemoryView):
x = context.asarray(x)
if not isinstance(y, cl.DeviceMemoryView):
y = context.asarray(y)
if y.queue != queue:
queue.enqueue_wait_for_events(y.queue.marker())
if x.queue != queue:
queue.enqueue_wait_for_events(x.queue.marker())
new_shape = broadcast_shape(x.shape, y.shape)
a = cl.broadcast(x, new_shape)
b = cl.broadcast(y, new_shape)
if out is None:
out = context.empty(shape=new_shape, ctype=x.format, queue=queue)
# kernel_source = ufunc_kernel._compile(queue.context, function=self.device_func,
# a=cl.global_memory(a.format, flat=True),
# b=cl.global_memory(b.format, flat=True),
# out=cl.global_memory(out.format, flat=True), source_only=True)
kernel = ufunc_kernel.compile(context, function=self.device_func,
a=cl.global_memory(a.format, flat=True),
b=cl.global_memory(b.format, flat=True),
out=cl.global_memory(out.format, flat=True),
cly_meta=self.device_func.func_name)
kernel(queue, a, a.array_info, b, b.array_info, out, out.array_info)
array = CLArray._view_as_this(out)
array.__array_init__(context, queue)
return array
def test_broadcast_2D(self):
with self.assertRaises(TypeError):
cl.broadcast(None, [1])
npa = np.arange(10, dtype=c_float)
z = np.zeros([10, 1])
ten = cl.from_host(ctx, npa)
a = cl.broadcast(ten, [10, 10])
self.assertEqual(a.shape, (10, 10))
self.assertEqual(a.strides, (0, sizeof(c_float)))
queue = cl.Queue(ctx)
with a.map(queue) as view:
b = np.asarray(view)
self.assertEqual(b.shape, (10, 10))
self.assertEqual(b.strides, (0, sizeof(c_float)))
self.assertTrue(np.all(b == z + npa))
def test_broadcast_2D(self):
with self.assertRaises(TypeError):
cl.broadcast(None, [1])
npa = np.arange(10, dtype=c_float)
z = np.zeros([10, 1])
ten = cl.from_host(ctx, npa)
a = cl.broadcast(ten, [10, 10])
self.assertEqual(a.shape, (10, 10))
self.assertEqual(a.strides, (0, sizeof(c_float)))
queue = cl.Queue(ctx)
with a.map(queue) as view:
b = np.asarray(view)
self.assertEqual(b.shape, (10, 10))
self.assertEqual(b.strides, (0, sizeof(c_float)))
self.assertTrue(np.all(b == z + npa))
def setslice(context, arr, value):
if not isinstance(value, cl.DeviceMemoryView):
value = context.asarray(value)
if value.queue != arr.queue:
arr.queue.enqueue_wait_for_events(value.queue.marker())
value = cl.broadcast(value, arr.shape)
kernel = setslice_kernel.compile(context, arr=cl.global_memory(arr.format, flat=True),
value=cl.global_memory(value.format, flat=True),
cly_meta='setslice')
return kernel(arr.queue, arr, arr.array_info, value, value.array_info)
def main():
ctx = cl.Context(device_type=cl.Device.GPU)
queue = cl.Queue(ctx)
npa = np.arange(1.0 * 12.0, dtype=c_float)
a = ca.arange(ctx, 12, ctype=c_float)
out = ca.empty_like(a[:])
output = cl.broadcast(out, a[:].shape)
ca.blitz(queue, lambda: a[:] + a[:] + 1, out=output)
print npa[1:] + npa[:-1]
with out.map() as view:
print view
def main():
ctx = cl.Context(device_type=cl.Device.GPU)
queue = cl.Queue(ctx)
npa = np.arange(1.0 * 12.0, dtype=c_float)
a = ca.arange(ctx, 12, ctype=c_float)
out = ca.empty_like(a[:])
output = cl.broadcast(out, a[:].shape)
ca.blitz(queue, lambda: a[:] + a[:] + 1, out=output)
print npa[1:] + npa[:-1]
with out.map() as view:
print view
def blitz(queue, func, out=None):
'''
lets get blitzed!
'''
func_ast = decompile_func(func)
func_globals = func.func_globals.copy()
if func.func_closure:
func_globals.update({
name: cell.cell_contents
for name, cell in zip(func.func_code.co_freevars,
func.func_closure)
})
blitzer = BlitzVisitor(func.func_code.co_filename, func_globals)
blitzed = ast.Expression(blitzer.visit(func_ast))
blitzed_code = compile(blitzed, func.func_code.co_filename, 'eval')
blitzed_func = eval(blitzed_code)
blitz_kernel = create_n_arg_kernel(sorted(blitzer.locls.keys()))
args = {}
for key, var in blitzer.locls.items():
if not isinstance(var, cl.DeviceMemoryView):
var = cl.from_host(queue.context, var)
args[key] = var
shape = broadcast_shapes([var.shape for var in args.values()])
print "shape", shape
for key, var in args.items():
args[key] = cl.broadcast(var, shape)
print "out, **args", out, args
blitz_kernel(queue, blitzed_func, out, **args)
def blitz(queue, func, out=None):
'''
lets get blitzed!
'''
func_ast = decompile_func(func)
func_globals = func.func_globals.copy()
if func.func_closure:
func_globals.update({name:cell.cell_contents for name, cell in zip(func.func_code.co_freevars, func.func_closure)})
blitzer = BlitzVisitor(func.func_code.co_filename, func_globals)
blitzed = ast.Expression(blitzer.visit(func_ast))
blitzed_code = compile(blitzed, func.func_code.co_filename, 'eval')
blitzed_func = eval(blitzed_code)
blitz_kernel = create_n_arg_kernel(sorted(blitzer.locls.keys()))
args = {}
for key, var in blitzer.locls.items():
if not isinstance(var, cl.DeviceMemoryView):
var = cl.from_host(queue.context, var)
args[key] = var
shape = broadcast_shapes([var.shape for var in args.values()])
print "shape", shape
for key, var in args.items():
args[key] = cl.broadcast(var, shape)
print "out, **args", out, args
blitz_kernel(queue, blitzed_func, out, **args)
