Fix alpha_merge and output_merge.

theano/sandbox/gpuarray/dnn.py

@@ -385,17 +385,17 @@ _zero = constant(numpy.asarray(0.0, dtype='float64'))
 _one = constant(numpy.asarray(1.0, dtype='float64'))
 
 
-def ensure_double(val, default, name):
+def ensure_dt(val, default, name, dtype):
     if val is None:
-        return default.clone()
+        val = default.clone()
     if not isinstance(val, Variable):
-        val = constant(val).astype('float64')
+        val = constant(val)
     if hasattr(val, 'ndim') and val.ndim == 0:
         val = as_scalar(val)
     if not isinstance(val.type, theano.scalar.Scalar):
         raise TypeError("%s: expected a scalar value" % (name,))
-    if not val.type.dtype == 'float64':
-        raise TypeError("%s: type is not float64" % (name,))
+    if not val.type.dtype == dtype:
+        val = val.astype(dtype)
     return val
 
 
@@ -456,8 +456,8 @@ class GpuDnnConv(DnnBase, COp):
                 or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
             raise TypeError('desc must be cudnnConvolutionDescriptor_t')
 
-        alpha = ensure_double(alpha, _one, 'alpha')
-        beta = ensure_double(beta, _zero, 'beta')
+        alpha = ensure_dt(alpha, _one, 'alpha', img.dtype)
+        beta = ensure_dt(beta, _zero, 'beta', img.dtype)
 
         return Apply(self, [img, kern, output, desc, alpha, beta],
                      [output.type()])
@@ -577,8 +577,8 @@ class GpuDnnConvGradW(DnnBase, COp):
                 or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
             raise TypeError('desc must be cudnnConvolutionDescriptor_t')
 
-        alpha = ensure_double(alpha, _one, 'alpha')
-        beta = ensure_double(beta, _zero, 'beta')
+        alpha = ensure_dt(alpha, _one, 'alpha', img.dtype)
+        beta = ensure_dt(beta, _zero, 'beta', img.dtype)
 
         return Apply(self, [img, topgrad, output, desc, alpha, beta],
                      [output.type()])
@@ -644,8 +644,8 @@ class GpuDnnConvGradI(DnnBase):
                 or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
             raise TypeError('desc must be cudnnConvolutionDescriptor_t')
 
-        alpha = ensure_double(alpha, _one, 'alpha')
-        beta = ensure_double(beta, _zero, 'beta')
+        alpha = ensure_dt(alpha, _one, 'alpha', kern.dtype)
+        beta = ensure_dt(beta, _zero, 'beta', kern.dtype)
 
         return Apply(self, [kern, topgrad, output, desc, alpha, beta],
                      [output.type()])

theano/sandbox/gpuarray/opt_util.py

@@ -7,10 +7,10 @@ from theano.gof import local_optimizer
 from theano.tensor import (DimShuffle, get_scalar_constant_value,
                            NotScalarConstantError)
 
-from .basic_ops import GpuFromHost, HostFromGpu, host_from_gpu
+from .basic_ops import GpuFromHost, HostFromGpu
 from .elemwise import GpuDimShuffle, GpuElemwise
 
-_one = scal.constant(numpy.asarray(1.0, dtype='float32'))
+_one = scal.constant(numpy.asarray(1.0, dtype='float64'))
 
 
 def grab_cpu_scalar(v, nd):
@@ -18,10 +18,10 @@ def grab_cpu_scalar(v, nd):
         n = v.owner
         if (isinstance(n.op, GpuDimShuffle) and
                 n.op.new_order == ('x',) * nd):
-            return host_from_gpu(n.inputs[0])
+            return grab_cpu_scalar(n.inputs[0])
         elif (isinstance(n.op, DimShuffle) and
               n.op.new_order == ('x',) * nd):
-            return n.inputs[0]
+            return grab_cpu_scalar(n.inputs[0])
         elif isinstance(n.op, GpuFromHost):
             return grab_cpu_scalar(n.inputs[0], nd=nd)
         else:
@@ -37,7 +37,7 @@ def find_node(v, cls, ignore_clients=False):
     # that has the op class specified. If ignore_clients is False (the
     # default) it will only dig through nodes that have a single
     # client.
-    if v.owner is not None and (ignore_clients or v.clients == 1):
+    if v.owner is not None and (ignore_clients or len(v.clients) == 1):
         if isinstance(v.owner.op, cls):
             return v.owner
         elif (isinstance(v.owner.op, GpuFromHost) and

theano/tensor/basic.py

@@ -593,7 +593,7 @@ def get_scalar_constant_value(orig_v, elemwise=True,
             # mess with the stabilization optimization and be too slow.
             # We put all the scalar Ops used by get_canonical_form_slice()
             # to allow it to determine the broadcast pattern correctly.
-            elif isinstance(v.owner.op, ScalarFromTensor):
+            elif isinstance(v.owner.op, (ScalarFromTensor, TensorFromScalar)):
                 return get_scalar_constant_value(v.owner.inputs[0])
             elif isinstance(v.owner.op, scal.ScalarOp):
                 if isinstance(v.owner.op, scal.Second):