def test_transfer_cuda_gpu():
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available is False:
raise SkipTest("Can't test interaction with cuda if cuda not present")
g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
c = cuda_ndarray.CudaNdarrayType((False, False))('c')
av = theano._asarray(rng.rand(5, 4), dtype='float32')
gv = gpuarray.array(av)
cv = cuda_ndarray.CudaNdarray(av)
gvs = gv[:, ::-2]
cvs = cv[:, ::-2]
f = theano.function([c], gpu_from_cuda(c))
fv = f(cv)
assert GpuArrayType.values_eq_approx(fv, gv)
fvs = f(cvs)
assert GpuArrayType.values_eq_approx(fvs, gvs)
f = theano.function([g], cuda_from_gpu(g))
fv = f(gv)
assert cuda_ndarray.CudaNdarrayType.values_eq_approx(fv, cv)
fvs = f(gvs)
assert cuda_ndarray.CudaNdarrayType.values_eq_approx(fvs, cvs)
def rand_gpuarray(*shape, **kwargs):
r = rng.rand(*shape) * 2 - 1
dtype = kwargs.pop('dtype', theano.config.floatX)
cls = kwargs.pop('cls', None)
if len(kwargs) != 0:
raise TypeError('Unexpected argument %s', kwargs.keys()[0])
return gpuarray.array(r, dtype=dtype, cls=cls)
def test_transfer_cuda_gpu():
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available is False:
raise SkipTest("Can't test interaction with cuda if cuda not present")
g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
c = cuda_ndarray.CudaNdarrayType((False, False))('c')
av = theano._asarray(rng.rand(5, 4), dtype='float32')
gv = gpuarray.array(av)
cv = cuda_ndarray.CudaNdarray(av)
gvs = gv[:, ::-2]
cvs = cv[:, ::-2]
f = theano.function([c], gpu_from_cuda(c))
fv = f(cv)
assert GpuArrayType.values_eq_approx(fv, gv)
fvs = f(cvs)
assert GpuArrayType.values_eq_approx(fvs, gvs)
f = theano.function([g], cuda_from_gpu(g))
fv = f(gv)
assert cuda_ndarray.CudaNdarrayType.values_eq_approx(fv, cv)
fvs = f(gvs)
assert cuda_ndarray.CudaNdarrayType.values_eq_approx(fvs, cvs)
def rand_gpuarray(*shape, **kwargs):
r = rng.rand(*shape) * 2 - 1
dtype = kwargs.pop('dtype', theano.config.floatX)
cls = kwargs.pop('cls', None)
if len(kwargs) != 0:
raise TypeError('Unexpected argument %s', kwargs.keys()[0])
return gpuarray.array(r, dtype=dtype, cls=cls)
def test_transfer_cpu_gpu():
a = T.fmatrix('a')
g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
av = numpy.asarray(rng.rand(5, 4), dtype='float32')
gv = gpuarray.array(av)
f = theano.function([a], gpu_from_host(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert numpy.all(fv == av)
def test_transfer_cpu_gpu():
a = T.fmatrix('a')
g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
av = numpy.asarray(rng.rand(5, 4), dtype='float32')
gv = gpuarray.array(av)
f = theano.function([a], gpu_from_host(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert numpy.all(fv == av)
def test_transfer_strided():
# This is just to ensure that it works in theano
# compyte has a much more comprehensive suit of tests to ensure correctness
a = T.fmatrix('a')
g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
av = numpy.asarray(rng.rand(5, 8), dtype='float32')
gv = gpuarray.array(av)
av = av[:, ::2]
gv = gv[:, ::2]
f = theano.function([a], gpu_from_host(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert numpy.all(fv == av)
def test_transfer_strided():
# This is just to ensure that it works in theano
# compyte has a much more comprehensive suit of tests to ensure correctness
a = T.fmatrix('a')
g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
av = numpy.asarray(rng.rand(5, 8), dtype='float32')
gv = gpuarray.array(av)
av = av[:,::2]
gv = gv[:,::2]
f = theano.function([a], gpu_from_host(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert numpy.all(fv == av)
