def local_opt(node):
if type(node.op) in OP:
# This does not support nodes that have more than one output.
# either one of our inputs is on the gpu or
# all of our client are on the gpu
if (any([
i.owner and i.owner.op == host_from_gpu
for i in node.inputs
]) or all([
c != 'output' and c.op == gpu_from_host
for c, idx in node.outputs[0].clients
])):
new_op = maker(node)
# This is needed as sometimes new_op inherit from OP.
if new_op and new_op != node.op:
if isinstance(new_op, theano.Op):
return [
host_from_gpu(o)
for o in new_op(*node.inputs, return_list=True)
]
elif isinstance(new_op, (tuple, list)):
return [host_from_gpu(o) for o in new_op]
else: # suppose it is a variable on the GPU
return [host_from_gpu(new_op)]
return False
def test_hostfromgpu_shape_i():
"""
Test that the shape is lifted over hostfromgpu
"""
m = mode_with_gpu.including('local_dot_to_dot22',
'local_dot22_to_dot22scalar', 'specialize')
a = T.fmatrix('a')
ca = theano.sandbox.gpuarray.type.GpuArrayType('float32', (False, False))()
av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32')
cv = gpuarray.asarray(numpy.random.rand(5, 4), dtype='float32')
gpu_from_host = theano.sandbox.gpuarray.basic_ops.gpu_from_host
host_from_gpu = theano.sandbox.gpuarray.basic_ops.host_from_gpu
f = theano.function([a], gpu_from_host(a), mode=m)
assert gpu_from_host in [x.op for x in f.maker.fgraph.toposort()]
f = theano.function([a], gpu_from_host(a).shape, mode=m)
topo = f.maker.fgraph.toposort()
assert isinstance(topo[0].op, T.opt.Shape_i)
assert isinstance(topo[1].op, T.opt.Shape_i)
assert isinstance(topo[2].op, T.opt.MakeVector)
assert tuple(f(av)) == (5, 4)
f = theano.function([ca], host_from_gpu(ca), mode=m)
assert host_from_gpu in [x.op for x in f.maker.fgraph.toposort()]
f = theano.function([ca], host_from_gpu(ca).shape, mode=m)
topo = f.maker.fgraph.toposort()
assert isinstance(topo[0].op, theano.compile.Shape_i)
assert isinstance(topo[1].op, theano.compile.Shape_i)
assert isinstance(topo[2].op, theano.tensor.opt.MakeVector)
assert tuple(f(cv)) == (5, 4)
def test_hostfromgpu_shape_i():
"""
Test that the shape is lifted over hostfromgpu
"""
m = mode_with_gpu.including('local_dot_to_dot22',
'local_dot22_to_dot22scalar','specialize')
a = T.fmatrix('a')
ca = theano.sandbox.gpuarray.type.GpuArrayType('float32', (False, False))()
av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32')
cv = gpuarray.asarray(numpy.random.rand(5, 4),
dtype='float32')
gpu_from_host = theano.sandbox.gpuarray.basic_ops.gpu_from_host
host_from_gpu = theano.sandbox.gpuarray.basic_ops.host_from_gpu
f = theano.function([a], gpu_from_host(a), mode=m)
assert gpu_from_host in [x.op
for x in f.maker.fgraph.toposort()]
f = theano.function([a], gpu_from_host(a).shape, mode=m)
topo = f.maker.fgraph.toposort()
assert isinstance(topo[0].op, T.opt.Shape_i)
assert isinstance(topo[1].op, T.opt.Shape_i)
assert isinstance(topo[2].op, T.opt.MakeVector)
assert tuple(f(av)) == (5, 4)
f = theano.function([ca], host_from_gpu(ca), mode=m)
assert host_from_gpu in [x.op
for x in f.maker.fgraph.toposort()]
f = theano.function([ca], host_from_gpu(ca).shape, mode=m)
topo = f.maker.fgraph.toposort()
assert isinstance(topo[0].op, theano.compile.Shape_i)
assert isinstance(topo[1].op, theano.compile.Shape_i)
assert isinstance(topo[2].op, theano.tensor.opt.MakeVector)
assert tuple(f(cv)) == (5, 4)
def local_opt(node):
dev = theano.sandbox.gpuarray.init_dev.device
if cuda_only and not dev.startswith('cuda'):
return
if type(node.op) in OP:
# Either one of our inputs is on the gpu or
# all of our client are on the gpu
if (any([
i.owner and i.owner.op == host_from_gpu
for i in node.inputs
]) or all([
c != 'output' and c.op == gpu_from_host
for c, idx in node.outputs[0].clients
])):
new_op = maker(node)
# This is needed as sometimes new_op inherit from OP.
if new_op and new_op != node.op:
if isinstance(new_op, theano.Op):
return [
safe_to_cpu(o)
for o in new_op(*node.inputs, return_list=True)
]
elif isinstance(new_op, (tuple, list)):
return [safe_to_cpu(o) for o in new_op]
else: # suppose it is a variable on the GPU
return [host_from_gpu(new_op)]
return False
def local_opt(node):
if type(node.op) is OP:
# This does not support nodes that have more than one output.
assert len(node.outputs) == 1
# either one of our inputs is on the gpu or
# all of our client are on the gpu
if (any([i.owner and i.owner.op == host_from_gpu
for i in node.inputs]) or
all([c != 'output' and c.op == gpu_from_host
for c, idx in node.outputs[0].clients])):
new_op = maker(node)
# This is needed as sometimes new_op inherit from OP.
if new_op and new_op != node.op:
if isinstance(new_op, theano.Op):
return [host_from_gpu(new_op(*node.inputs))]
else: # suppose it is a variable on the GPU
return [host_from_gpu(new_op)]
return False
def local_opt(node):
if type(node.op) in OP:
# Either one of our inputs is on the gpu or
# all of our client are on the gpu
if any([i.owner and i.owner.op == host_from_gpu for i in node.inputs]) or all(
[c != "output" and c.op == gpu_from_host for c, idx in node.outputs[0].clients]
):
new_op = maker(node)
# This is needed as sometimes new_op inherit from OP.
if new_op and new_op != node.op:
if isinstance(new_op, theano.Op):
return [host_from_gpu(o) for o in new_op(*node.inputs, return_list=True)]
elif isinstance(new_op, (tuple, list)):
return [host_from_gpu(o) for o in new_op]
else: # suppose it is a variable on the GPU
return [host_from_gpu(new_op)]
return False
def local_gpua_careduce(node):
if (isinstance(node.op.scalar_op, scalar.basic.Add) or
isinstance(node.op.scalar_op, scalar.basic.Mul)):
x, = node.inputs
greduce = GpuCAReduceCuda(node.op.scalar_op, axis=node.op.axis)
if x.dtype != "float32":
return
gvar = greduce(x)
#We need to have the make node called, otherwise the mask can
#be None
if gvar.owner.op.supports_c_code([gpu_from_host(x)]):
return greduce
else:
# Try to make a simpler pattern based on reshaping
# The principle is that if two adjacent dimensions have
# the same value in the reduce_mask, then we can reshape
# to make them a single dimension, do the reduction, and
# then reshape to get them back.
if node.op.axis is None:
reduce_mask = [1] * x.type.ndim
else:
reduce_mask = [0] * x.type.ndim
for a in node.op.axis:
assert reduce_mask[a] == 0
reduce_mask[a] = 1
shape_of = node.fgraph.shape_feature.shape_of
x_shape = shape_of[x]
new_in_shp = [x_shape[0]]
new_mask = [reduce_mask[0]]
for i in xrange(1, x.type.ndim):
if reduce_mask[i] == reduce_mask[i - 1]:
new_in_shp[-1] *= x_shape[i]
else:
new_mask.append(reduce_mask[i])
new_in_shp.append(x_shape[i])
new_greduce = GpuCAReduceCuda(new_mask, scalar_op)
reshaped_x = x.reshape(tensor.stack(*new_in_shp))
gpu_reshaped_x = gpu_from_host(reshaped_x)
reshaped_gpu_inputs = [gpu_reshaped_x]
if new_greduce.supports_c_code(reshaped_gpu_inputs):
reduce_reshaped_x = host_from_gpu(
new_greduce(gpu_reshaped_x))
if reduce_reshaped_x.ndim != node.outputs[0].ndim:
unreshaped_reduce = reduce_reshaped_x.reshape(
tensor.stack(*shape_of[node.outputs[0]]))
else:
unreshaped_reduce = reduce_reshaped_x
return [unreshaped_reduce]
def local_gpuaalloc2(node):
"""
Join(axis, Alloc, Alloc, ...) -> Join(axis, GpuAlloc, Alloc, ...)
Moves an alloc that is an input to join to the gpu.
"""
if (isinstance(node.op, tensor.Alloc)
and all(c != 'output' and c.op == tensor.join and all(
i.owner and i.owner.op in [host_from_gpu, tensor.alloc]
for i in c.inputs[1:]) for c, idx in node.outputs[0].clients)):
return [host_from_gpu(gpu_alloc(*node.inputs))]
def local_gpua_careduce(node):
if (isinstance(node.op.scalar_op, scalar.basic.Add) or
isinstance(node.op.scalar_op, scalar.basic.Mul)):
x, = node.inputs
greduce = GpuCAReduceCuda(node.op.scalar_op, axis=node.op.axis)
if x.dtype != "float32":
return
gvar = greduce(x)
#We need to have the make node called, otherwise the mask can
#be None
if gvar.owner.op.supports_c_code([gpu_from_host(x)]):
return greduce
else:
# Try to make a simpler pattern based on reshaping
# The principle is that if two adjacent dimensions have
# the same value in the reduce_mask, then we can reshape
# to make them a single dimension, do the reduction, and
# then reshape to get them back.
if node.op.axis is None:
reduce_mask = [1] * x.type.ndim
else:
reduce_mask = [0] * x.type.ndim
for a in node.op.axis:
assert reduce_mask[a] == 0
reduce_mask[a] = 1
shape_of = node.fgraph.shape_feature.shape_of
x_shape = shape_of[x]
new_in_shp = [x_shape[0]]
new_mask = [reduce_mask[0]]
for i in xrange(1, x.type.ndim):
if reduce_mask[i] == reduce_mask[i - 1]:
new_in_shp[-1] *= x_shape[i]
else:
new_mask.append(reduce_mask[i])
new_in_shp.append(x_shape[i])
new_greduce = GpuCAReduceCuda(new_mask, scalar_op)
reshaped_x = x.reshape(tensor.stack(*new_in_shp))
gpu_reshaped_x = gpu_from_host(reshaped_x)
reshaped_gpu_inputs = [gpu_reshaped_x]
if new_greduce.supports_c_code(reshaped_gpu_inputs):
reduce_reshaped_x = host_from_gpu(
new_greduce(gpu_reshaped_x))
if reduce_reshaped_x.ndim != node.outputs[0].ndim:
unreshaped_reduce = reduce_reshaped_x.reshape(
tensor.stack(*shape_of[node.outputs[0]]))
else:
unreshaped_reduce = reduce_reshaped_x
return [unreshaped_reduce]
def local_gpuaalloc2(node):
"""
Join(axis, Alloc, Alloc, ...) -> Join(axis, GpuAlloc, Alloc, ...)
Moves an alloc that is an input to join to the gpu.
"""
if isinstance(node.op, tensor.Alloc) and all(
c != "output"
and c.op == tensor.join
and all(i.owner and i.owner.op in [host_from_gpu, tensor.alloc] for i in c.inputs[1:])
for c, idx in node.outputs[0].clients
):
return [host_from_gpu(gpu_alloc(*node.inputs))]
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 apply(self, fgraph):
for input in fgraph.inputs:
if isinstance(input.type, GpuArrayType):
continue
if len(input.clients) == 1 and (input.clients[0][0] == "output" or input.clients[0][0].op == gpu_from_host):
continue
try:
new_input = host_from_gpu(gpu_from_host(input))
fgraph.replace_validate(input, new_input, "InputToGpuOptimizer")
except TypeError, e:
# This could fail if the inputs are not TensorTypes
pass
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 apply(self, fgraph):
for input in fgraph.inputs:
if isinstance(input.type, GpuArrayType):
continue
if (len(input.clients) == 1
and (input.clients[0][0] == 'output'
or input.clients[0][0].op == gpu_from_host)):
continue
try:
new_input = host_from_gpu(gpu_from_host(input))
fgraph.replace_validate(input, new_input,
"InputToGpuOptimizer")
except TypeError, e:
# This could fail if the inputs are not TensorTypes
pass
def local_gpua_subtensor(node):
x = node.inputs[0]
if (x.owner and isinstance(x.owner.op, HostFromGpu)):
gpu_x = x.owner.inputs[0]
if (gpu_x.owner and
isinstance(gpu_x.owner.op, GpuFromHost) and
# And it is a shared var or an input of the graph.
not gpu_x.owner.inputs[0].owner):
if len(x.clients) == 1:
if any([n == 'output' or any([isinstance(v.type, GpuArrayType)
for v in n.inputs + n.outputs])
for n,_ in node.outputs[0].clients]):
return
else:
return [host_from_gpu(gpu_from_host(node.outputs[0]))]
return GpuSubtensor(node.op.idx_list)
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)
def local_gpua_subtensor(node):
x = node.inputs[0]
if (x.owner and isinstance(x.owner.op, HostFromGpu)):
gpu_x = x.owner.inputs[0]
if (gpu_x.owner and isinstance(gpu_x.owner.op, GpuFromHost) and
# And it is a shared var or an input of the graph.
not gpu_x.owner.inputs[0].owner):
if len(x.clients) == 1:
if any([
n == 'output' or any([
isinstance(v.type, GpuArrayType)
for v in n.inputs + n.outputs
]) for n, _ in node.outputs[0].clients
]):
return
else:
return [host_from_gpu(gpu_from_host(node.outputs[0]))]
return GpuSubtensor(node.op.idx_list)
def local_opt(node):
dev = theano.sandbox.gpuarray.init_dev.device
if cuda_only and not dev.startswith('cuda'):
return
if type(node.op) in OP:
# Either one of our inputs is on the gpu or
# all of our client are on the gpu
if (any([i.owner and i.owner.op == host_from_gpu
for i in node.inputs]) or
all([c != 'output' and c.op == gpu_from_host
for c, idx in node.outputs[0].clients])):
new_op = maker(node)
# This is needed as sometimes new_op inherit from OP.
if new_op and new_op != node.op:
if isinstance(new_op, theano.Op):
return [safe_to_cpu(o) for o in
new_op(*node.inputs, return_list=True)]
elif isinstance(new_op, (tuple, list)):
return [safe_to_cpu(o) for o in new_op]
else: # suppose it is a variable on the GPU
return [host_from_gpu(new_op)]
return False
def safe_to_cpu(x):
if isinstance(x.type, GpuArrayType):
return host_from_gpu(x)
else:
return x
def local_gpu_conv(node):
"""
gpu_from_host(conv) -> gpu_conv(gpu_from_host)
conv(host_from_gpu) -> host_from_gpu(gpu_conv)
"""
def GpuConvOp_from_ConvOp(op):
logical_img_hw = None
if op.kshp_logical is not None and op.kshp_logical != op.kshp:
return None
# print op.kshp, op.imshp[1:3]
# print op.kshp_logical, logical_img_hw
ret = GpuConv(border_mode=op.out_mode,
subsample=(op.dx, op.dy),
logical_img_hw=logical_img_hw,
logical_kern_hw=op.kshp_logical,
logical_kern_align_top=op.kshp_logical_top_aligned,
kshp=op.kshp,
version=op.version,
verbose=op.verbose,
imshp=op.imshp,
)
if op.imshp_logical is not None:
logical_img_hw = op.imshp_logical[1:3]
if logical_img_hw != op.imshp[1:3]:
# this case is not implemented
# return None
rstride = int(numpy.ceil(op.imshp_logical[1] /
float(op.imshp[1])))
cstride = int(numpy.ceil(op.imshp_logical[2] /
float(op.imshp[2])))
def make_graph(img, kern):
buf = tensor.alloc(numpy.asarray(0, dtype=img.dtype),
img.shape[0], *op.imshp_logical)
img = tensor.set_subtensor(buf[:, :, ::rstride, ::cstride],
img)
img = gpu_from_host(img)
return ret(img, kern)
return make_graph
return ret
def values_eq_approx(a, b):
"""This fct is needed to don't have DebugMode raise useless
error due to ronding error.
This happen as We reduce on the two last dimensions, so this
can raise the absolute error if the number of element we
reduce on is significant.
"""
assert a.ndim == 4
atol = None
if a.shape[-1] * a.shape[-2] > 100:
# For float32 the default atol is 1e-5
atol = 3e-5
return GpuArrayType.values_eq_approx(a, b, atol=atol)
img, kern = node.inputs
gpu_conv = GpuConvOp_from_ConvOp(node.op)
if gpu_conv is None:
return
out = gpu_conv(gpu_from_host(img),
gpu_from_host(kern))
# in some case the ConvOp broadcast the last 2 dimensions
# differently then the gpu ConvOp
out = tensor.patternbroadcast(
host_from_gpu(out),
node.outputs[0].broadcastable)
# op_lifter want the output on the GPU.
out = gpu_from_host(out)
out.values_eq_approx = values_eq_approx
return [out]
def local_assert(node):
return [host_from_gpu(node.op(node.inputs[0].owner.inputs[0]))]
def local_gpua_careduce(node):
if isinstance(node.op.scalar_op, (scalar.Add, scalar.Mul,
scalar.Maximum, scalar.Minimum)):
dev = theano.sandbox.gpuarray.init_dev.device
if dev.startswith('opencl'):
op = GpuCAReduceCPY
if node.op.scalar_op not in [scalar.add, scalar.mul]:
# We don't support yet all reduction with cpy code.
return
else:
op = GpuCAReduceCuda
x, = node.inputs
greduce = op(
node.op.scalar_op, axis=node.op.axis,
dtype=getattr(node.op, 'dtype', None),
acc_dtype=getattr(node.op, 'acc_dtype', None))
gvar = greduce(x)
# We need to have the make node called, otherwise the mask can
# be None
if (op is GpuCAReduceCPY or
gvar.owner.op.supports_c_code([gpu_from_host(x)])):
return greduce
else:
# Try to make a simpler pattern based on reshaping
# The principle is that if two adjacent dimensions have
# the same value in the reduce_mask, then we can reshape
# to make them a single dimension, do the reduction, and
# then reshape to get them back.
if node.op.axis is None:
reduce_mask = [1] * x.type.ndim
else:
reduce_mask = [0] * x.type.ndim
for a in node.op.axis:
assert reduce_mask[a] == 0
reduce_mask[a] = 1
shape_of = node.fgraph.shape_feature.shape_of
x_shape = shape_of[x]
new_in_shp = [x_shape[0]]
new_mask = [reduce_mask[0]]
for i in xrange(1, x.type.ndim):
if reduce_mask[i] == reduce_mask[i - 1]:
new_in_shp[-1] *= x_shape[i]
else:
new_mask.append(reduce_mask[i])
new_in_shp.append(x_shape[i])
new_axis = []
for idx, m in enumerate(new_mask):
if m == 1:
new_axis.append(idx)
greduce = op(
node.op.scalar_op,
axis=new_axis, reduce_mask=new_mask,
dtype=getattr(node.op, 'dtype', None),
acc_dtype=getattr(node.op, 'acc_dtype', None))
reshaped_x = x.reshape(tensor.stack(*new_in_shp))
gpu_reshaped_x = gpu_from_host(reshaped_x)
gvar = greduce(gpu_reshaped_x)
# We need to have the make node called, otherwise the mask can
# be None
reshaped_gpu_inputs = [gpu_reshaped_x]
if greduce.supports_c_code(reshaped_gpu_inputs):
reduce_reshaped_x = host_from_gpu(
greduce(gpu_reshaped_x))
if reduce_reshaped_x.ndim != node.outputs[0].ndim:
unreshaped_reduce = reduce_reshaped_x.reshape(
tensor.stack(*shape_of[node.outputs[0]]))
else:
unreshaped_reduce = reduce_reshaped_x
return [unreshaped_reduce]
def local_gpu_conv(node):
"""
gpu_from_host(conv) -> gpu_conv(gpu_from_host)
conv(host_from_gpu) -> host_from_gpu(gpu_conv)
"""
def GpuConvOp_from_ConvOp(op):
logical_img_hw = None
if op.kshp_logical is not None and op.kshp_logical != op.kshp:
return None
# print op.kshp, op.imshp[1:3]
# print op.kshp_logical, logical_img_hw
ret = GpuConv(border_mode=op.out_mode,
subsample=(op.dx, op.dy),
logical_img_hw=logical_img_hw,
logical_kern_hw=op.kshp_logical,
logical_kern_align_top=op.kshp_logical_top_aligned,
kshp=op.kshp,
version=op.version,
verbose=op.verbose,
imshp=op.imshp,
)
if op.imshp_logical is not None:
logical_img_hw = op.imshp_logical[1:3]
if logical_img_hw != op.imshp[1:3]:
# this case is not implemented
# return None
rstride = int(numpy.ceil(op.imshp_logical[1] /
float(op.imshp[1])))
cstride = int(numpy.ceil(op.imshp_logical[2] /
float(op.imshp[2])))
def make_graph(img, kern):
buf = tensor.alloc(numpy.asarray(0, dtype=img.dtype),
img.shape[0], *op.imshp_logical)
img = tensor.set_subtensor(buf[:, :, ::rstride, ::cstride],
img)
img = gpu_from_host(img)
return ret(img, kern)
return make_graph
return ret
def values_eq_approx(a, b):
"""This fct is needed to don't have DebugMode raise useless
error due to ronding error.
This happen as We reduce on the two last dimensions, so this
can raise the absolute error if the number of element we
reduce on is significant.
"""
assert a.ndim == 4
atol = None
if a.shape[-1] * a.shape[-2] > 100:
# For float32 the default atol is 1e-5
atol = 3e-5
return GpuArrayType.values_eq_approx(a, b, atol=atol)
img, kern = node.inputs
gpu_conv = GpuConvOp_from_ConvOp(node.op)
if gpu_conv is None:
return
out = gpu_conv(gpu_from_host(img),
gpu_from_host(kern))
# in some case the ConvOp broadcast the last 2 dimensions
# differently then the gpu ConvOp
out = tensor.patternbroadcast(
host_from_gpu(out),
node.outputs[0].broadcastable)
# op_lifter want the output on the GPU.
out = gpu_from_host(out)
out.values_eq_approx = values_eq_approx
return [out]
def safe_to_cpu(x):
if isinstance(x.type, GpuArrayType):
return host_from_gpu(x)
else:
return x
def local_assert(node):
if (isinstance(node.op, theano.tensor.opt.Assert) and
node.inputs[0].owner and
isinstance(node.inputs[0].owner.op,
HostFromGpu)):
return [host_from_gpu(node.op(node.inputs[0].owner.inputs[0]))]