Moved get_constant_value to tensor.basic to use it in Alloc.make_node and/or infer_shape

theano/tensor/basic.py

@@ -304,6 +304,41 @@ def _allclose(a, b):
         rtol = float64_rtol
     return numpy.allclose(a,b, atol=atol, rtol=rtol)
 
+def get_constant_value(v):
+    """return the constant scalar(0-D) value underlying variable `v`
+
+    If v is the output of dimshuffles, fills, allocs, rebroadcasts,
+    this function digs through them.
+
+    If `v` is not some view of constant data, then raise a TypeError.
+
+    :note: There may be another function similar to this one in the code, but I'm not sure where it
+    is.
+    """
+
+    if isinstance(v, Constant):
+        #TODO: consider checking for arrays of the form e.g. [1,1,1,1] where
+        # it is not a constant, but in some cases it *could* be replaced with one.
+        # Note that this would have an effect on the broadcasting of inputs and so on
+        try:
+            complex(v.data) #works for all numeric scalars
+            return v.data
+        except:
+            raise TypeError(v)
+    if v.owner:
+        if isinstance(v.owner.op, Alloc):
+            return get_constant_value(v.owner.inputs[0])
+        if isinstance(v.owner.op, DimShuffle):
+            return get_constant_value(v.owner.inputs[0])
+        if isinstance(v.owner.op, Rebroadcast):
+            return get_constant_value(v.owner.inputs[0])
+        if v.owner.op == fill:
+            shape, val = v.owner.inputs
+            # fill(a,b) fills the shape of 'a' filled with 'b'
+            return get_constant_value(val)
+    raise TypeError(v)
+
+
 class TensorType(Type):
     """Symbolic `Type` representing a numpy.ndarray value."""
 
@@ -980,6 +1015,9 @@ class _tensor_py_operators:
     __array_priority__ = 1000
 
 
+    def get_constant_value(self):
+        return get_constant_value(self)
+    
 class TensorVariable(Variable, _tensor_py_operators):
     """Subclass to add the tensor operators to the basic `Variable` class."""
 TensorType.Variable = TensorVariable
@@ -1754,7 +1792,11 @@ class Alloc(gof.Op):
             if s.type.dtype[:3] not in ('int', 'uin'):
                 raise TypeError('Shape arguments must be integers', s)
             # if s is constant 1, then we're broadcastable in that dim
-            bcast.append(isinstance(s, TensorConstant) and (s.data == 1))
+            try:
+                const_shp = get_constant_value(s)
+            except TypeError:
+                const_shp = None
+            bcast.append(numpy.all(1 == const_shp))
         otype = TensorType(dtype=v.dtype, broadcastable=bcast)
         return gof.Apply(self, [v]+sh, [otype()])
 

theano/tensor/opt.py

@@ -25,6 +25,7 @@ from theano import compile  #to register the optimizer built by this file
 from theano.gof.python25 import any, all
 from theano.gof.opt import Optimizer
 from theano.gof import toolbox, DestroyHandler
+from basic import get_constant_value
 
 
 # Utilities
@@ -63,40 +64,6 @@ def encompasses_broadcastable(b1, b2):
 def merge_broadcastables(broadcastables):
     return [all(bcast) for bcast in zip(*broadcastables)]
 
-def get_constant_value(v):
-    """return the constant scalar(0-D) value underlying variable `v`
-
-    If v is the output of dimshuffles, fills, allocs, rebroadcasts,
-    this function digs through them.
-
-    If `v` is not some view of constant data, then raise a TypeError.
-
-    :note: There may be another function similar to this one in the code, but I'm not sure where it
-    is.
-    """
-
-    if isinstance(v, Constant):
-        #TODO: consider checking for arrays of the form e.g. [1,1,1,1] where
-        # it is not a constant, but in some cases it *could* be replaced with one.
-        # Note that this would have an effect on the broadcasting of inputs and so on
-        try:
-            complex(v.data) #works for all numeric scalars
-            return v.data
-        except:
-            raise TypeError(v)
-    if v.owner:
-        if isinstance(v.owner.op, T.Alloc):
-            return get_constant_value(v.owner.inputs[0])
-        if isinstance(v.owner.op, T.DimShuffle):
-            return get_constant_value(v.owner.inputs[0])
-        if isinstance(v.owner.op, T.Rebroadcast):
-            return get_constant_value(v.owner.inputs[0])
-        if v.owner.op == T.fill:
-            shape, val = v.owner.inputs
-            # fill(a,b) fills the shape of 'a' filled with 'b'
-            return get_constant_value(val)
-    raise TypeError(v)
-
 def scalarconsts_rest(inputs):
     """Partition a list of variables into two kinds:
     scalar constants, and the rest."""