Fix and disable optimization that move Multinomial to the gpu as it is bugged.

theano/sandbox/multinomial.py

 import theano
 from theano import Op, Apply
 import theano.tensor as T
-from theano.tensor.opt import register_specialize
 from theano.gof import local_optimizer
 
-from theano.sandbox.cuda import cuda_available, cuda_enabled
+from theano.sandbox.cuda import cuda_available
 if cuda_available:
     from theano.sandbox.cuda import CudaNdarrayType
     from theano.sandbox.cuda.basic_ops import host_from_gpu, gpu_from_host
+    from theano.sandbox.cuda.opt import register_opt
 
 class MultinomialFromUniform(Op):
     '''Converts samples from a uniform into sample from a multinomial.'''
@@ -283,11 +283,15 @@ class GpuMultinomialFromUniform(MultinomialFromUniform):
 
 @local_optimizer()
 def use_gpu_multinomial(node):
-    if isinstance(node.op, MultinomialFromUniform):
+    if type(node.op) is MultinomialFromUniform:
         p, u = node.inputs
         m, = node.outputs
-        if p.dtype == u.dtype == m.dtype == 'float32':
-            gpu_op = GpuMultinomialFromUniform(op.odtype)
+        if (p.dtype == u.dtype == m.dtype == 'float32' and
+            any([i.owner and isinstance(i.owner.op, theano.sandbox.cuda.HostFromGpu)
+                 for i in node.inputs])):
+            gpu_op = GpuMultinomialFromUniform(node.op.odtype)
             return [host_from_gpu(gpu_op(*[gpu_from_host(i) for i in node.inputs]))]
-if cuda_enabled:#theano.config.device.startswith('gpu'):
-    register_specialize(use_gpu_multinomial)
+if cuda_available:
+    # Currently this it is bugged!
+    #register_opt()(use_gpu_multinomial)
+    pass