def filter_variable(self, other, allow_convert=True):
from theano.gpuarray.basic_ops import gpu_from_host
if hasattr(other, '_as_GpuArrayVariable'):
other = other._as_GpuArrayVariable(self.context_name)
if not isinstance(other, Variable):
other = self.Constant(type=self, data=other)
if other.type == self:
return other
if not isinstance(other.type, tensor.TensorType):
raise TypeError('Incompatible type', (self, other.type))
if (other.type.dtype != self.dtype):
raise TypeError('Incompatible dtype', (self.dtype,
other.type.dtype))
if other.type.ndim != self.ndim:
raise TypeError('Incompatible number of dimensions.'
' Expected %d, got %d.' % (self.ndim, other.ndim))
if other.type.broadcastable != self.broadcastable:
if allow_convert:
type2 = other.type.clone(broadcastable=self.broadcastable)
other2 = type2.convert_variable(other)
else:
other2 = None
if other2 is None:
raise TypeError('Incompatible broadcastable dimensions.'
' Expected %s, got %s.' %
(str(other.type.broadcastable),
str(self.broadcastable)))
other = other2
return gpu_from_host(self.context_name)(other)
def filter_variable(self, other, allow_convert=True):
from theano.gpuarray.basic_ops import gpu_from_host
if hasattr(other, '_as_GpuArrayVariable'):
other = other._as_GpuArrayVariable(self.context_name)
if not isinstance(other, Variable):
other = self.Constant(type=self, data=other)
if other.type == self:
return other
if not isinstance(other.type, tensor.TensorType):
raise TypeError('Incompatible type', (self, other.type))
if (other.type.dtype != self.dtype):
raise TypeError('Incompatible dtype',
(self.dtype, other.type.dtype))
if other.type.ndim != self.ndim:
raise TypeError('Incompatible number of dimensions.'
' Expected %d, got %d.' % (self.ndim, other.ndim))
if other.type.broadcastable != self.broadcastable:
if allow_convert:
type2 = other.type.clone(broadcastable=self.broadcastable)
other2 = type2.convert_variable(other)
else:
other2 = None
if other2 is None:
raise TypeError(
'Incompatible broadcastable dimensions.'
' Expected %s, got %s.' %
(str(other.type.broadcastable), str(self.broadcastable)))
other = other2
return gpu_from_host(self.context_name)(other)
def traverse(out, x, x_copy, d, visited=None):
"""
Function used by scan to parse the tree and figure out which nodes
it needs to replace.
There are two options :
1) x and x_copy or on host, then you would replace x with x_copy
2) x is on gpu, x_copy on host, then you need to replace
host_from_gpu(x) with x_copy
This happens because initially shared variables are on GPU... which is
fine for the main computational graph but confuses things a bit for the
inner graph of scan.
"""
# ``visited`` is a set of nodes that are already known and don't need to be
# checked again, speeding up the traversal of multiply-connected graphs.
# if a ``visited`` set is given, it will be updated in-place so the callee
# knows which nodes we have seen.
if visited is None:
visited = set()
if out in visited:
return d
visited.add(out)
from theano.sandbox import cuda
from theano.gpuarray.basic_ops import gpu_from_host, host_from_gpu
from theano.gpuarray import pygpu_activated
from theano.gpuarray.type import GpuArrayType
if out == x:
if isinstance(x.type, cuda.CudaNdarrayType):
d[out] = cuda.gpu_from_host(x_copy)
else:
assert isinstance(x.type, GpuArrayType)
d[out] = gpu_from_host(x.type.context_name)(x_copy)
return d
elif out.owner is None:
return d
elif (cuda.cuda_available and
out.owner.op == cuda.host_from_gpu and
out.owner.inputs == [x]):
d[out] = tensor.as_tensor_variable(x_copy)
return d
elif (pygpu_activated and
out.owner.op == host_from_gpu and
out.owner.inputs == [x]):
d[out] = tensor.as_tensor_variable(x_copy)
return d
else:
for inp in out.owner.inputs:
d = traverse(inp, x, x_copy, d, visited)
return d
def traverse(out, x, x_copy, d, visited=None):
"""
Function used by scan to parse the tree and figure out which nodes
it needs to replace.
There are two options :
1) x and x_copy or on host, then you would replace x with x_copy
2) x is on gpu, x_copy on host, then you need to replace
host_from_gpu(x) with x_copy
This happens because initially shared variables are on GPU... which is
fine for the main computational graph but confuses things a bit for the
inner graph of scan.
"""
# ``visited`` is a set of nodes that are already known and don't need to be
# checked again, speeding up the traversal of multiply-connected graphs.
# if a ``visited`` set is given, it will be updated in-place so the callee
# knows which nodes we have seen.
if visited is None:
visited = set()
if out in visited:
return d
visited.add(out)
from theano.sandbox import cuda
from theano.gpuarray.basic_ops import gpu_from_host, host_from_gpu
from theano.gpuarray import pygpu_activated
from theano.gpuarray.type import GpuArrayType
if out == x:
if isinstance(x.type, cuda.CudaNdarrayType):
d[out] = cuda.gpu_from_host(x_copy)
else:
assert isinstance(x.type, GpuArrayType)
d[out] = gpu_from_host(x.type.context_name)(x_copy)
return d
elif out.owner is None:
return d
elif (cuda.cuda_available and out.owner.op == cuda.host_from_gpu
and out.owner.inputs == [x]):
d[out] = tensor.as_tensor_variable(x_copy)
return d
elif (pygpu_activated and out.owner.op == host_from_gpu
and out.owner.inputs == [x]):
d[out] = tensor.as_tensor_variable(x_copy)
return d
else:
for inp in out.owner.inputs:
d = traverse(inp, x, x_copy, d, visited)
return d