def test_reshape():
a = tcn.CudaNdarrayType((False,))()
b = tcn.CudaNdarrayType((False,False))()
c = T.reshape(a, [2,3])
#basic
f = theano.function([a], c, mode=mode_without_gpu)
fv = f(cuda_ndarray.CudaNdarray(theano._asarray([0,1,2,3,4,5],dtype='float32')))
assert numpy.all(fv == numpy.asarray([[0,1,2], [3,4,5]]))
#test that it works without inplace operations
a_val = cuda_ndarray.CudaNdarray(theano._asarray([0,1,2,3,4,5],dtype='float32'))
a_val_copy = cuda_ndarray.CudaNdarray(theano._asarray([0,1,2,3,4,5],dtype='float32'))
b_val = cuda_ndarray.CudaNdarray(theano._asarray([[0,1,2],[3,4,5]],dtype='float32'))
f_sub = theano.function([a,b], c-b, mode=mode_without_gpu)
assert numpy.all(f_sub(a_val, b_val) == 0.0)
assert numpy.all(numpy.asarray(a_val) == numpy.asarray(a_val_copy))
#test that it works with inplace operations
a_val = theano._asarray([0,1,2,3,4,5], dtype='float32')
a_val_copy = theano._asarray([0,1,2,3,4,5], dtype='float32')
b_val = theano._asarray([[0,1,2],[3,4,5]], dtype='float32')
f_sub = theano.function([a,b], c-b, mode=mode_without_gpu)
assert numpy.all(f_sub(a_val, b_val) == 0.0)
assert numpy.all(numpy.asarray(a_val) == numpy.asarray(a_val_copy))
# verify gradient
def just_vals(v):
return T.Reshape(2)(v, theano._asarray([2,3], dtype='int32'))
utt.verify_grad(just_vals, [a_val])
def conv_grad(mode, bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsample, op):
ishape = (bs, ch, rImg1, rImg2)
kshape = (nf, ch, rFlt1, rFlt2)
npy_img = theano._asarray(numpy.random.rand(*ishape), dtype='float32')
npy_kern = theano._asarray(numpy.random.rand(*kshape), dtype='float32')
i = cuda.CudaNdarrayType(broadcastable=[sh == 1 for sh in npy_img.shape])()
k = cuda.CudaNdarrayType(broadcastable=[sh == 1
for sh in npy_kern.shape])()
# TODO: also test custom pad values
corr_op = op(mode, subsample)(i, k)
# try to compile reference implementation without shape,
# so we don't have to compile hundreds of versions
conv_op = tensor.nnet.conv2d(i,
k[:, :, ::-1, ::-1],
border_mode=mode,
subsample=subsample)
try:
conv_op_di = theano.grad(conv_op.sum(), i)
conv_op_dk = theano.grad(conv_op.sum(), k)
except Exception:
# compile with shape information only when needed
conv_op = tensor.nnet.conv2d(i, k[:, :, ::-1, ::-1], ishape, kshape,
mode, subsample)
conv_op_di = theano.grad(conv_op.sum(), i)
conv_op_dk = theano.grad(conv_op.sum(), k)
corr_op_di = theano.grad(corr_op.sum(), i)
corr_op_dk = theano.grad(corr_op.sum(), k)
outputs = [
corr_op, conv_op, corr_op_di, conv_op_di, corr_op_dk, conv_op_dk
]
try:
conv_op_dik = theano.grad(conv_op_di.sum(), k)
conv_op_dki = theano.grad(conv_op_dk.sum(), i)
corr_op_dik = theano.grad(corr_op_di.sum(), k)
corr_op_dki = theano.grad(corr_op_dk.sum(), i)
outputs.extend([corr_op_dik, conv_op_dik, corr_op_dki, conv_op_dki])
except Exception:
# skip if the reference implementation can't do it
pass
f = theano.function([i, k],
outputs,
mode=theano_mode.excluding('conv_dnn', 'conv_gemm'))
allvals = f(npy_img, npy_kern)
for a, b, oa, ob, p in zip(
allvals[::2], allvals[1::2], outputs[::2], outputs[1::2],
('top', 'dtop/dbottom', 'dtop/dweight', 'dtop/dbottom/dweight',
'dtop/dweight/dbottom')):
assert oa.type.broadcastable[:2] == ob.type.broadcastable[:2]
assert_allclose(a, b, rtol=1e-4)
def conv_grad(mode, bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsample, op):
ishape = (bs, ch, rImg1, rImg2)
kshape = (nf, ch, rFlt1, rFlt2)
npy_img = theano._asarray(numpy.random.rand(*ishape), dtype='float32')
npy_kern = theano._asarray(numpy.random.rand(*kshape), dtype='float32')
i = cuda.CudaNdarrayType(broadcastable=[sh == 1 for sh in npy_img.shape])()
k = cuda.CudaNdarrayType(broadcastable=[sh == 1
for sh in npy_kern.shape])()
# TODO: also test custom pad values
corr_op = op(mode, subsample)(i, k)
conv_op = tensor.nnet.conv2d(i,
k[:, :, ::-1, ::-1],
border_mode=mode,
subsample=subsample)
conv_op_di = theano.grad(conv_op.sum(), i)
conv_op_dk = theano.grad(conv_op.sum(), k)
corr_op_di = theano.grad(corr_op.sum(), i)
corr_op_dk = theano.grad(corr_op.sum(), k)
outputs = [
corr_op, conv_op, corr_op_di, conv_op_di, corr_op_dk, conv_op_dk
]
conv_op_dik = theano.grad(conv_op_di.sum(), k)
conv_op_dki = theano.grad(conv_op_dk.sum(), i)
corr_op_dik = theano.grad(corr_op_di.sum(), k)
corr_op_dki = theano.grad(corr_op_dk.sum(), i)
outputs.extend([corr_op_dik, conv_op_dik, corr_op_dki, conv_op_dki])
if not theano.config.blas.ldflags:
# Some of the operations are not transferred to the GPU,
# and withoug BLAS, the abstract Op will not be optimized
# to CorrMM either, so we have to accept the use of the
# slow Python convolution in that case.
mode = theano_mode.excluding('AbstractConvCheck')
else:
mode = theano_mode
f = theano.function([i, k], outputs, mode=mode)
allvals = f(npy_img, npy_kern)
for a, b, oa, ob, p in zip(
allvals[::2], allvals[1::2], outputs[::2], outputs[1::2],
('top', 'dtop/dbottom', 'dtop/dweight', 'dtop/dbottom/dweight',
'dtop/dweight/dbottom')):
assert oa.type.broadcastable[:2] == ob.type.broadcastable[:2]
assert_allclose(a, b, rtol=1e-4)
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 conv_grad(mode, bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsample, op):
ishape = (bs, ch, rImg1, rImg2)
kshape = (nf, ch, rFlt1, rFlt2)
npy_img = theano._asarray(numpy.random.rand(*ishape), dtype='float32')
npy_kern = theano._asarray(numpy.random.rand(*kshape), dtype='float32')
i = cuda.CudaNdarrayType(broadcastable=[sh == 1 for sh in npy_img.shape])()
k = cuda.CudaNdarrayType(broadcastable=[sh == 1
for sh in npy_kern.shape])()
# TODO: also test custom pad values
corr_op = op(mode, subsample)(i, k)
conv_op = tensor.nnet.conv2d(i,
k[:, :, ::-1, ::-1],
border_mode=mode,
subsample=subsample)
conv_op_di = theano.grad(conv_op.sum(), i)
conv_op_dk = theano.grad(conv_op.sum(), k)
corr_op_di = theano.grad(corr_op.sum(), i)
corr_op_dk = theano.grad(corr_op.sum(), k)
outputs = [
corr_op, conv_op, corr_op_di, conv_op_di, corr_op_dk, conv_op_dk
]
conv_op_dik = theano.grad(conv_op_di.sum(), k)
conv_op_dki = theano.grad(conv_op_dk.sum(), i)
corr_op_dik = theano.grad(corr_op_di.sum(), k)
corr_op_dki = theano.grad(corr_op_dk.sum(), i)
outputs.extend([corr_op_dik, conv_op_dik, corr_op_dki, conv_op_dki])
# TODO: fix when the abstractconv tests can pass debug mode.
mode = theano_mode
if theano.config.mode == 'DEBUG_MODE':
mode = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
f = theano.function([i, k], outputs, mode=mode)
allvals = f(npy_img, npy_kern)
for a, b, oa, ob, p in zip(
allvals[::2], allvals[1::2], outputs[::2], outputs[1::2],
('top', 'dtop/dbottom', 'dtop/dweight', 'dtop/dbottom/dweight',
'dtop/dweight/dbottom')):
assert oa.type.broadcastable[:2] == ob.type.broadcastable[:2]
assert_allclose(a, b, rtol=1e-4)
def cpu_var_to_gpu_var(x):
from theano.sandbox import cuda
type = cuda.CudaNdarrayType(broadcastable=x.broadcastable)
name = 'gpu_%s' % x.name
name = None
gpu_var = cuda.CudaNdarrayVariable(type=type, name=name)
cpu_var = cuda.host_from_gpu(gpu_var)
return gpu_var, cpu_var
return cuda.host_from_gpu(cuda.CudaNdarrayVariable(type=type, name=name))
def SharedThunkLSTMFunc(rst, inpshape, oupshape, noot, backwards, MOMENTUM,
LEARN_RATE):
print "MAKE INNER FUNCTION", inpshape, oupshape, noot, backwards
cuda2d = cuda.CudaNdarrayType((False, False)) #T.fmatrix
isym = SymbolLayer(cuda2d(), (100, inpshape))
oval, l1f = BlockLSTMUnrollArrayToArray(rst,
isym,
oupshape,
noot=noot,
backwards=backwards)
oflag = cuda2d()
oupfunc = theano.function([isym.output],
cuda.basic_ops.as_cuda_ndarray_variable(
oval.output))
infshape = lambda x0: (x0[0][0], oupshape)
#GRADFUNC
g = T.sum(oval.output * oflag)
iglist = T.grad(g, [isym.output] + l1f.params)
olist = iglist[0]
glist = iglist[1:]
#Generate MOMENTUM
mom = []
for i in l1f.params:
init = np.zeros_like(i.get_value())
mom.append(theano.shared(init, name=i.name + '_momentum_ct'))
#Additive update
updates = []
for i, j in zip(glist, mom):
updates.append((j, j - i * LEARN_RATE))
momup = []
for i in mom:
momup.append((i, i * MOMENTUM))
print "MAIN UPDATES", updates
resetmom = theano.function([], [], updates=momup)
getgrad = theano.function([isym.output, oflag],
cuda.basic_ops.as_cuda_ndarray_variable(olist),
updates=updates)
sharedop = GPUSharedThunkOp(oupfunc, infshape, getgrad)
#Make sharedlayer
class SharedLayer(Layer, Param, CMomentum):
def get_momentums(self):
return []
def __init__(self, inp, paramroot=False):
if paramroot:
self.params = l1f.params
self.get_momentums = lambda: mom
else:
self.params = []
self.output = sharedop(inp.output)
self.output_shape = oval.output_shape
return SharedLayer, resetmom
def gemm_directly(bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsx, subsy,
direction):
ishape = (bs, ch, rImg1, rImg2)
kshape = (nf, ch, rFlt1, rFlt2)
subsample = (subsx, subsy)
npy_img = theano._asarray(numpy.random.rand(*ishape), dtype='float32')
npy_kern = theano._asarray(numpy.random.rand(*kshape), dtype='float32')
i = cuda.CudaNdarrayType(
broadcastable=[sh == 1 for sh in npy_img.shape])()
k = cuda.CudaNdarrayType(
broadcastable=[sh == 1 for sh in npy_kern.shape])()
if direction == 'fprop':
cpuval = py_conv(npy_img, npy_kern, 'valid', subsample)
op = theano.sandbox.cuda.blas.GpuCorrMM(border_mode='valid',
subsample=subsample)(i, k)
f = theano.function([i, k], op, mode=theano_mode)
gpuval = f(npy_img, npy_kern[:,:,::-1,::-1])
elif direction == 'bprop img':
cpuval = py_conv(npy_img, npy_kern, 'full', subsample)
op = theano.sandbox.cuda.blas.GpuCorrMM_gradInputs(
border_mode='valid', subsample=subsample)(i, k)
f = theano.function([i, k], op, mode=theano_mode)
gpuval = f(npy_kern.transpose(1, 0, 2, 3), npy_img)
elif direction == 'bprop kern':
cpuval = py_conv(npy_img, npy_kern, 'valid', subsample)
op = theano.sandbox.cuda.blas.GpuCorrMM_gradWeights(
border_mode='valid', subsample=subsample)(i, k)
f = theano.function([i, k], op, mode=theano_mode)
gpuval = numpy.array(f(
npy_img.transpose(1, 0, 2, 3),
npy_kern.transpose(1, 0, 2, 3)[:,:,::-1,::-1])).transpose(
1, 0, 2, 3)
assert_allclose(cpuval, gpuval, rtol=1e-4)
def make_node(self, img):
assert hasattr(self, '_props'), "Your version of theano is too old " \
"to support __props__."
# Theano's CudaNdArray support strides. But this require writing C
# code calling the functions of sandbox/cuda/cuda_ndarray.cuh
# and passing all the strides to the kernel to do the correct
# computation. Instead, enforce contiguous arrays.
cu_img = cuda.basic_ops.gpu_contiguous(
cuda.basic_ops.as_cuda_ndarray_variable(img))
assert cu_img.dtype == 'float32'
# N x nchannels x nbins
output = cuda.CudaNdarrayType(
dtype='float32',
broadcastable=[False, False, False])()
return theano.Apply(self, [cu_img], [output])
def make_node(self, img, kern):
img = cuda.basic_ops.gpu_contiguous(
cuda.basic_ops.as_cuda_ndarray_variable(img))
kern = cuda.basic_ops.gpu_contiguous(
cuda.basic_ops.as_cuda_ndarray_variable(kern))
if img.type.ndim != 5:
raise TypeError('img must be 5D tensor')
if kern.type.ndim != 5:
raise TypeError('kern must be 5D tensor')
broadcastable = [
kern.type.broadcastable[-1], False, False, False,
img.type.broadcastable[-1]
]
return theano.Apply(self, [img, kern],
[cuda.CudaNdarrayType(broadcastable)()])
def make_node(self, img, topgrad, shape):
img = cuda.basic_ops.as_cuda_ndarray_variable(img)
topgrad = cuda.basic_ops.as_cuda_ndarray_variable(topgrad)
if img.type.ndim != 5:
raise TypeError('img must be 5D tensor')
if topgrad.type.ndim != 5:
raise TypeError('topgrad must be 5D tensor')
depth_height_width = [shape[0], shape[1], shape[2]]
broadcastable = [
img.type.broadcastable[0], False, False, False,
topgrad.type.broadcastable[0]
]
return theano.Apply(self, [img, topgrad] + depth_height_width,
[cuda.CudaNdarrayType(broadcastable)()])
def test_elemwise_collapse6():
""" Test when all inputs have two broadcastable dimension at the
beginning"""
shape = (4, 5)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),
dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
a2 = tcn.shared_constructor(a, 'a')
a3 = a2.dimshuffle('x', 'x', 0, 1)
b = tcn.CudaNdarrayType((True, True, False, False))()
f = pfunc([b], [a3 + b], mode=mode_with_gpu)
v = theano._asarray(numpy.random.rand(1, 1, shape[0], shape[1]),
dtype='float32')
v = cuda_ndarray.CudaNdarray(v)
#let debugmode catch errors
out = f(v)[0]
assert numpy.allclose(out, a.reshape(1, 1, shape[0], shape[1]) + v)
def test_elemwise_collapse2():
""" Test when only one inputs have one broadcastable dimension """
shape = (4, 5, 9)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),
dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
a2 = tcn.shared_constructor(a, 'a')
a3 = a2.dimshuffle(0, 'x', 1, 2)
b = tcn.CudaNdarrayType((False, False, False, False))()
c = a3 + b
f = pfunc([b], [c], mode=mode_with_gpu)
v = theano._asarray(numpy.random.rand(shape[0], 5, *shape[1:]),
dtype='float32')
v = cuda_ndarray.CudaNdarray(v)
#let debugmode catch errors
out = f(v)[0]
assert numpy.allclose(out, a.reshape(shape[0], 1, *shape[1:]) + v)
def test_elemwise_collapse4():
""" Test when only one inputs have two broadcastable dimension at
each ends and we add a scalar"""
shape = (4, 5)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),
dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
a2 = tcn.shared_constructor(a, 'a')
a3 = a2.dimshuffle('x', 0, 1, 'x')
b = tcn.CudaNdarrayType((False, False, False, False))()
c = (a3 + b + 2)
f = pfunc([b], [c], mode=mode_with_gpu)
v = theano._asarray(numpy.random.rand(5, shape[0], shape[1], 4),
dtype='float32')
v = cuda_ndarray.CudaNdarray(v)
#let debugmode catch errors
out = f(v)[0]
assert numpy.allclose(out, a.reshape(1, shape[0], shape[1], 1) + v + 2)
def speed_elemwise_collapse():
""" used to time if the collapse of ccontiguous dims are useful """
shape = (30, 40, 50, 600)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),
dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
a2 = tcn.shared_constructor(a, 'a')
a3 = a2[:, ::2, :, :]
b = tcn.CudaNdarrayType((False, False, False, False))()
c = a3 + b * tensor.exp(1 + b ** a3)
f = pfunc([b], [c], mode=mode_with_gpu)
v = theano._asarray(numpy.random.rand(*shape), dtype='float32')
v = v[:, ::2, :, :]
v = cuda_ndarray.CudaNdarray(v)
t1 = time.time()
for i in range(100):
#let debugmode catch errors
f(v)
t2 = time.time()
def test_elemwise_collapse():
""" Test when all inputs have one(and the same) broadcastable dimension """
shape = (4,5,60)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
a2 = tcn.shared_constructor(a, 'a')
a3 = a2.dimshuffle(0,'x',1,2)
b = tcn.CudaNdarrayType((False, True, False, False))()
c = a3+b
f = pfunc([b], [c], mode=mode_with_gpu)
v = theano._asarray(numpy.random.rand(shape[0],1,*shape[1:]),dtype='float32')
v=cuda_ndarray.CudaNdarray(v)
if False:
for id,n in enumerate(f.maker.env.toposort()):
print id, n
#let debugmode catch errors
out=f(v)[0]
assert numpy.allclose(out,a.reshape(shape[0],1,*shape[1:])+v)
print "Expected collapse of all dimensions"
def test_elemwise_collapse5():
""" Test when only one inputs have two broadcastable dimension at the beginning and we add a scalar"""
shape = (4,5)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
a2 = tcn.shared_constructor(a, 'a')
a3 = a2.dimshuffle('x','x',0,1)
b = tcn.CudaNdarrayType((False, False, False, False))()
c = (a3+b+2)
f = pfunc([b], [c], mode=mode_with_gpu)
v = theano._asarray(numpy.random.rand(5,4,shape[0],shape[1]),dtype='float32')
v=cuda_ndarray.CudaNdarray(v)
if False:
for id,n in enumerate(f.maker.env.toposort()):
print id, n
#let debugmode catch errors
out=f(v)[0]
assert numpy.allclose(out,a.reshape(1,1,shape[0],shape[1])+v+2)
print "Expected collapse to 2 dimensions"
def speed_elemwise_collapse2():
""" used to test the speed up of the generalised collapse of ccontiguous dims"""
shape = (30,40,50,600)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
a2 = tcn.shared_constructor(a, 'a')
a3 = a2[:,:,:,::2]
b = tcn.CudaNdarrayType((False, False, False, False))()
c = a3+b * tensor.exp(1 + b**a3)
f = pfunc([b], [c], mode=mode_with_gpu)
v = theano._asarray(numpy.random.rand(*shape),dtype='float32')
v = v[:,:,:,::2]
v=cuda_ndarray.CudaNdarray(v)
for id,n in enumerate(f.maker.env.toposort()):
print id, n
t1=time.time()
for i in range(100):
#let debugmode catch errors
f(v)
t2=time.time()
#test with broadcast
for shape, pattern in [((5,),[0]),
((5,4),[0,1]),((5,4),[0]),
((5,4,3),[0]),((5,4,3),[0,1]),
((5,4,3),[2]),((5,4,3),[0,1,2]),
((5,4,3,2),[0,1,2,3]), ((5,4,3,2),[0,2,3])]:
op = careduce_op(scalar_op, axis=pattern)
pat = tensor_pattern_to_gpu_pattern(shape, pattern)
#GpuCAReduce{maximum} support only those patterns
if scalar_op is theano.scalar.maximum and pat not in [
(0, 1), (0, 1, 1), (0, 1, 1)]:
continue
shape = numpy.asarray(shape) * 2
a = tensor.TensorType('float32', (False,) * len(shape))()
a2 = tcn.CudaNdarrayType((False,) * len(shape))()
b = op(a)
b2 = op(a2)
val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
# val = numpy.ones(shape)
# val = numpy.arange(numpy.prod(shape)).reshape(shape)
val = theano._asarray(val, dtype='float32')
val2 = cuda.CudaNdarray(val)
if len(shape) == 1:
val = val[::2]
val2 = val2[::2]
elif len(shape) == 2:
val = val[::2, ::2]
val2 = val2[::2, ::2]
elif len(shape) == 3:
val = val[::2, ::2, ::2]
def _params_allgood(ishape,
kshape,
mode,
subsample=(1, 1),
img_stride=(1, 1),
kern_stride=(1, 1),
version=-1,
verbose=0,
random=True,
print_=None,
id=None,
rtol=1e-5,
atol=1e-8,
nb_iter=0,
ones=False,
compile_kshp=None,
theano_mode=None,
cls=None):
#
# This function is the core of several of the big unit-test drivers,
# but it can also be used very directly on its own to test a specific
# kind of convolution.
#
# See `test_example` (above) for an example of how to use this directly.
#
# :param kshape: (4d)The shape of the kernel at run time.
# :param compile_kshp: (2d) hardcode the shape of the kernel in
# the generated code This is supposed to be
# faster, but we need to check That we raise
# an error if the input have the wrong shape.
#
if ones:
assert not random
npy_img = theano._asarray(numpy.ones(ishape), dtype='float32')
npy_kern = -theano._asarray(numpy.ones(kshape), dtype='float32')
elif random:
npy_img = theano._asarray(numpy.random.rand(*ishape) + 1,
dtype='float32')
npy_kern = theano._asarray(numpy.random.rand(*kshape) - 2,
dtype='float32')
else:
npy_img = theano._asarray(numpy.arange(
numpy.prod(ishape)).reshape(ishape),
dtype='float32') + 1
npy_kern = -(
theano._asarray(numpy.arange(numpy.prod(kshape)).reshape(kshape),
dtype='float32') + 1)
img = cuda_ndarray.CudaNdarray(npy_img)
kern = cuda_ndarray.CudaNdarray(npy_kern)
# we take the stride after the transfert as we make c_contiguous
# data on the GPU.
if img_stride != (1, 1):
img = img[:, :, ::img_stride[0], ::img_stride[1]]
npy_img = npy_img[:, :, ::img_stride[0], ::img_stride[1]]
if kern_stride != (1, 1):
kern = kern[:, :, ::kern_stride[0], ::kern_stride[1]]
npy_kern = npy_kern[:, :, ::kern_stride[0], ::kern_stride[1]]
i = cuda.CudaNdarrayType(broadcastable=[sh == 1 for sh in npy_img.shape])()
k = cuda.CudaNdarrayType(broadcastable=[sh == 1
for sh in npy_kern.shape])()
op = theano.sandbox.cuda.blas.GpuConv(border_mode=mode,
subsample=subsample,
version=version,
verbose=verbose,
kshp=compile_kshp)(i, k)
f = theano.function([i, k], op, mode=theano_mode)
if cls is not None:
assert any([
isinstance(node.op, cls) for node in f.maker.fgraph.toposort()
]), "Cannot find class %r in %r" % (cls, f.maker.fgraph.toposort())
t2 = time.time()
gpuval = f(img, kern)
t3 = time.time()
for i in range(nb_iter):
gpuval2 = f(img, kern)
assert (numpy.asarray(gpuval) == numpy.asarray(gpuval2)).all()
gpuval = numpy.asarray(gpuval)
# CPU val computed after GPU val to get the GPU errors.
t0 = time.time()
cpuval = py_conv(npy_img, npy_kern, mode, subsample)
t1 = time.time()
assert gpuval.shape == cpuval.shape, ("shape mismatch", gpuval.shape,
cpuval.shape)
assert_allclose(cpuval, gpuval, rtol=rtol, atol=atol)
assert numpy.all(numpy.isfinite(gpuval)), gpuval
assert [(sh == 1) is br
for sh, br in zip(cpuval.shape[:2], op.type.broadcastable[:2])]
if (t2 is not None):
if mode == 'valid':
approx_fp = cpuval.size * ishape[1] * kshape[2] * kshape[3] * 2
else:
approx_fp = (ishape[0] * kshape[0] * kshape[1] * kshape[2] *
kshape[3] * ishape[2] * ishape[3] * 2)
approx_fp /= 1e6
cpu_mflops = approx_fp / (t1 - t0)
gpu_mflops = approx_fp / (t3 - t2)
if verbose > 0:
print('%15s' % str(ishape),
'%15s' % str(kshape),
end=' ',
file=sys.stdout)
print('%12.5f %7.2f %7.2f %7.1f' %
(approx_fp, cpu_mflops, gpu_mflops, (t1 - t0) / (t2 - t1)),
file=sys.stdout)
def output_type(self, inp):
return cuda.CudaNdarrayType(broadcastable=[False] * (inp.type.ndim))
def output_type(self, inp):
return cuda.CudaNdarrayType(broadcastable=[False, False])
def cpu_to_gpu_var(x):
type = cuda.CudaNdarrayType(broadcastable=x.broadcastable)
name = gpu_name(x.name)
gpu_var = cuda.CudaNdarrayVariable(type=type, name=name)
cpu_var = cuda.host_from_gpu(gpu_var)
return gpu_var, cpu_var
from theano import tensor
from theano.gof.python25 import any
from theano.tests.unittest_tools import seed_rng
# Skip test if cuda_ndarray is not available.
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available == False:
raise SkipTest('Optional package cuda disabled')
#needed as the gpu conv don't have a perform implementation.
if theano.config.mode == 'FAST_COMPILE':
theano_mode = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
else:
theano_mode = theano.compile.mode.get_default_mode().including('gpu')
cuda_tensor4 = cuda_ndarray.CudaNdarrayType([False] * 4)
device_id = theano.sandbox.cuda.use.device_number
if device_id is None:
cuda_ndarray.shared_constructor(numpy.zeros(2, dtype='float32'))
device_id = theano.sandbox.cuda.use.device_number
if device_id is None:
cuda.use("gpu",
force=False,
default_to_move_computation_to_gpu=False,
move_shared_float32_to_gpu=False,
enable_cuda=False,
test_driver=True)
device_id = theano.sandbox.cuda.use.device_number
cuda_ndarray = theano.sandbox.cuda.cuda_ndarray.cuda_ndarray
device_prop = cuda_ndarray.device_properties(device_id)
def output_type(self, inp):
return cuda.CudaNdarrayType(
broadcastable=[False] *
(inp.type.ndim + 1)) # add one extra dim for real/imag
def output_type(self, inp):
return cuda.CudaNdarrayType(
broadcastable=[False] *
(inp.type.ndim - 1)) # remove extra real/imag dim
def test_sum():
"""
test sum pattern 1, 11, 10, 01, 100, 110, 011, 001, 111, 0011, 0101, 0111, 1011, 1111
test sum pattern implemented with reshape:
1000, 0100, 0010, 0001, 11111
others implemented by reshape that are not tested
0011,0101,0110,1001,1010,1100
1110,1101,1011
TODO: test with broadcast
"""
for shape, pattern in [((100,3,1300),[1]),
((0,),[0]),((5,),[0]),
((0,0),[0,1]),((1,0),[0,1]),((5,4),[0,1]),((33,31),[0,1]),((5,4),[1]),((5,4),[0]),#need something bigger then 32 for some opt test.
((5,4,3),[0]),((5,4,3),[1]),((5,4,3),[0,1]),((5,4,3),[2]),((5,4,3),[1,2]),((5,4,3),[0,1,2]),
((0,0,0,0),[0,1,2,3]),
((5,4,3,20),[2,3]), ((5,4,3,2),[0,1,2,3]), ((5,4,3,2),[0,2,3]),((5,4,3,2),[1,2,3]),
((5,4,3,10,11),[1,2]),
((5,4,3,20),[2,3]), ((5,4,3,2),[0,1,2,3]), ((5,4,3,2),[0,2,3]),((5,4,3,2),[1,2,3]),
#test shape bigger then 4096 on each dimension to make sure that we work correctly when we don't have enought thread/block in each dimensions
((4100,3),[0]),((3,4101),[0]),#10
((1024,33),[0]),((33,1024),[0]),#10
((1025,33),[0]),((33,1025),[0]),#10
((4100,3),[1]),((3,4101),[1]),#01
((1024,33),[1]),((33,1024),[1]),#01
((1025,33),[1]),((33,1025),[1]),#01
((4100,3),[0,1]),((3,4101),[0,1]),#11
((1024,33),[0,1]),((33,1024),[0,1]),#01
((1025,33),[0,1]),((33,1025),[0,1]),#01
((4100,4,3),[0]),((5,4100,3),[0]),((5,4,4100),[0]),#100
((4100,4,3),[1]),((5,4100,3),[1]),((5,4,4100),[1]),#010
((4100,4,3),[2]),((5,4100,3),[2]),((5,4,4100),[2]),#001
((4100,4,3),[0,1]),((5,4100,3),[0,1]),((5,4,4100),[0,1]),#110
((4100,4,3),[1,2]),((5,4100,3),[1,2]),((5,4,4100),[1,2]),#011
#((4100,4,3),[0,2]),((5,4100,3),[0,2]),((5,4,4100),[0,2]),#101 ##not implemented
((4100,4,3),[0,1,2]),((5,4100,3),[0,1,2]),((5,4,4100),[0,1,2]),#111
((4100,4,3,2),[2,3]),((4,4100,3,2),[2,3]),((4,3,4100,2),[2,3]),((4,3,2,4100),[2,3]),#0011
((4100,4,3,2),[1,3]),((4,4100,3,2),[1,3]),((4,3,4100,2),[1,3]),((4,3,2,4100),[1,3]),#0101
((4100,4,3,2),[0,2,3]),((4,4100,3,2),[0,2,3]),((4,3,4100,2),[0,2,3]),#((4,3,2,4100),[0,2,3]),#1011
((4100,4,3,2),[1,2,3]),((4,4100,3,2),[1,2,3]),((4,3,4100,2),[1,2,3]),((4,3,2,4100),[1,2,3]),#0111
((4100,2,3,4),[0,1,2,3]),((2,4100,3,4),[0,1,2,3]),((2,3,4100,4),[0,1,2,3]),((2,3,4,4100),[0,1,2,3]),#1111
#test pattern implemented by reshape
((4100,4,3,2),[0]),((4,4100,3,2),[0]),((4,3,4100,2),[0]),((4,3,2,4100),[0]),#1000
((4100,4,3,2),[1]),((4,4100,3,2),[1]),((4,3,4100,2),[1]),((4,3,2,4100),[1]),#0100
((4100,4,3,2),[2]),((4,4100,3,2),[2]),((4,3,4100,2),[2]),((4,3,2,4100),[2]),#0010
((4100,4,3,2),[3]),((4,4100,3,2),[3]),((4,3,4100,2),[3]),((4,3,2,4100),[3]),#0001
((1100,2,3,4,5),[0,1,2,3,4]),((2,1100,3,4,5),[0,1,2,3,4]),((2,3,1100,4,5),[0,1,2,3,4]),((2,3,4,1100,5),[0,1,2,3,4]),((2,3,4,5,1100),[0,1,2,3,4]),#11111
]:
a = tensor.TensorType('float32',(False,)*len(shape))()
b = T.Sum(pattern)(a)
val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
# val = numpy.ones(shape)
# val = numpy.arange(numpy.prod(shape)).reshape(shape)
val = theano._asarray(val,dtype='float32')
f = theano.function([a],b, mode=mode_with_gpu)
f2 = theano.function([a],b, mode=mode_without_gpu)
assert tcn.GpuSum in [x.op.__class__ for x in f.maker.env.toposort()]
assert T.Sum in [x.op.__class__ for x in f2.maker.env.toposort()]
if val.size==0:
assert f2(val)==f(val), ('shape', shape, 'pattern', pattern)
else:
try:
#We raise the error threashold as we sum big matrix
#and this cause small rounding difference with some seed
#example in debug mode with unittests.rseed=9275
orig_rtol = theano.tensor.basic.float32_rtol
theano.tensor.basic.float32_rtol = 2e-5
assert _allclose(f2(val),f(val)), ('shape', shape, 'pattern', pattern, sum([shape[i] for i in pattern]))
finally:
theano.tensor.basic.float32_rtol = orig_rtol
#test with dimshuffle
#we shuffle the 2 outer dims.
for shape, pattern in [#((5,),[0]),
((5,4),[0,1]),((5,4),[0]),
((5,4,3),[0]),((5,4,3),[0,1]),((5,4,3),[2]),((5,4,3),[0,1,2]),
((5,4,3,2),[0,1,2,3]), ((5,4,3,2),[0,2,3])]:
a = tensor.TensorType('float32',(False,)*len(shape))()
dim_pattern = range(len(shape))
dim_pattern[0]=1
dim_pattern[1]=0
a = a.dimshuffle(dim_pattern)
b = T.Sum(pattern)(a)
val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
# val = numpy.ones(shape)
# val = numpy.arange(numpy.prod(shape)).reshape(shape)
val = theano._asarray(val,dtype='float32')
f = theano.function([a],b, mode=mode_with_gpu)
f2 = theano.function([a],b, mode=mode_without_gpu)
assert tcn.GpuSum in [x.op.__class__ for x in f.maker.env.toposort()]
assert T.Sum in [x.op.__class__ for x in f2.maker.env.toposort()]
assert _allclose(f2(val),f(val)), ('shape', shape, 'pattern', pattern, sum([shape[i] for i in pattern]))
#test with broadcast
for shape, pattern in [((5,),[0]),
((5,4),[0,1]),((5,4),[0]),
((5,4,3),[0]),((5,4,3),[0,1]),((5,4,3),[2]),((5,4,3),[0,1,2]),
((5,4,3,2),[0,1,2,3]), ((5,4,3,2),[0,2,3])]:
shape = numpy.asarray(shape)*2
a = tensor.TensorType('float32',(False,)*len(shape))()
a2 = tcn.CudaNdarrayType((False,)*len(shape))()
b = T.Sum(pattern)(a)
b2 = T.Sum(pattern)(a2)
val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
# val = numpy.ones(shape)
# val = numpy.arange(numpy.prod(shape)).reshape(shape)
val = theano._asarray(val,dtype='float32')
val2 = cuda.CudaNdarray(val)
if len(shape)==1:
val = val[::2]
val2 = val2[::2]
elif len(shape)==2:
val = val[::2,::2]
val2 = val2[::2,::2]
elif len(shape)==3:
val = val[::2,::2,::2]
val2 = val2[::2,::2,::2]
elif len(shape)==4:
val = val[::2,::2,::2,::2]
val2 = val2[::2,::2,::2,::2]
f = theano.function([a],b, mode=mode_without_gpu)
f2 = theano.function([a2],b2, mode=mode_with_gpu)
assert tcn.GpuSum in [x.op.__class__ for x in f2.maker.env.toposort()]
assert T.Sum in [x.op.__class__ for x in f.maker.env.toposort()]
assert _allclose(f2(val2),f(val)), ('shape', shape, 'pattern', pattern, sum([shape[i] for i in pattern]))
