Do not return Gemm/Gemv candidates with the wrong dtype

theano/tensor/blas.py

@@ -822,9 +822,14 @@ def _factor_canonicalized(lst):
     # once i has touched a list element, it is permantent
     lst = list(lst)
     #print 'FACTOR', lst
-    #for (a,b) in lst:
-        #theano.printing.debugprint(a)
-        #theano.printing.debugprint(b)
+    #for t in lst:
+    #    if not isinstance(t, (list, tuple)):
+    #        t = (t,)
+    #    for e in t:
+    #        try:
+    #            theano.printing.debugprint(e)
+    #        except TypeError:
+    #            print e, type(e)
     i = 0
     while i < len(lst)-1:
         try:
@@ -904,9 +909,8 @@ def _gemm_from_node2(node):
         lst = _factor_canonicalized(lst)
         rval = _gemm_from_factored_list(lst)
         #print "RVAL", rval
-        if rval:
-            assert rval[0].type == node.outputs[0].type, (rval[0].type, node.outputs[0].type)
-        return rval
+        if rval and (rval[0].type == node.outputs[0].type):
+            return rval
 
 class GemmOptimizer(Optimizer):
     """Graph optimizer for inserting Gemm operations"""

theano/tensor/tests/test_blas.py

@@ -479,6 +479,35 @@ def test_gemm_factor():
     assert [(1.0, X), (1.0, Y)] == _factor_canonicalized([(1.0, X), (1.0, Y)])
     assert [(2.0, X)] == _factor_canonicalized([(1.0, X),(1.0, X)])
 
+def test_upcasting_scalar_nogemv():
+    # Test that the optimization does not crash when the scale has an incorrect
+    # dtype, and forces upcasting of the result
+    v = T.fvector('v')
+    w = T.fmatrix('w')
+    t = T.fvector('t')
+    alpha = T.dscalar('a')
+
+    rval = T.dot(w, v) * alpha + t
+
+    f = theano.function([w, v, t, alpha], rval)
+    t = f.maker.env.toposort()
+    assert numpy.sum([isinstance(n.op, Gemv) for n in t]) == 0
+    theano.printing.debugprint(f, print_type=True)
+
+def test_upcasting_scalar_nogemm():
+    # Test that the optimization does not crash when the scale has an incorrect
+    # dtype, and forces upcasting of the result
+    v = T.fmatrix('v')
+    w = T.fmatrix('w')
+    t = T.fmatrix('t')
+    alpha = T.dscalar('a')
+
+    rval = T.dot(w, v) * alpha + t
+
+    f = theano.function([w, v, t, alpha], rval)
+    t = f.maker.env.toposort()
+    assert numpy.sum([isinstance(n.op, Gemm) for n in t]) == 0
+    theano.printing.debugprint(f, print_type=True)
 
 def test_gemm_nested():
     X,Y,Z,a,b = T.dmatrix('X'), T.dmatrix('Y'), T.dmatrix('Z'), T.dscalar('a'), T.dscalar('b')