several changes to tensor/blas.py

theano/tensor/blas.py

@@ -387,6 +387,12 @@ class GemmRelated(Op):
                 (long int)Ny[1], (long int)Nz[1]);
             %(fail)s;
         }
+        if (Nx[1] == 0)
+        {
+            PyErr_Format(PyExc_ValueError,
+                "Undefined semantics: x has 0 cols");
+            %(fail)s;
+        }
         """
 
     check_strides = """
@@ -497,6 +503,12 @@ class GemmRelated(Op):
                 int Nz0 = Nz[0], Nz1 = Nz[1], Nx1 = Nx[1];
                 //std::cerr << (unit/256) MOD 16 << (unit / 16) MOD 16 << unit MOD 16<< '\\n';
                 //double t0 = time_time();
+                //fprintf(stderr, "unit=%%x N= %%i %%i %%i S = %%i %%i %%i %%i %%i %%i\\n", unit,
+                //Nz1, Nz0, Nx1,
+                //sy_0, sy_1,
+                //sx_0, sx_1,
+                //sz_0, sz_1
+                //);
                 switch(unit)
                 {
                     case 0x000: dgemm_(&N, &N, &Nz1, &Nz0, &Nx1, &a, y, &sy_0, x, &sx_0, &b, z, &sz_0); break;
@@ -540,7 +552,7 @@ class GemmRelated(Op):
             self.end_switch_typenum), '')
 
     def build_gemm_version(self):
-        return (6,)
+        return (7,)
 
 class Gemm(GemmRelated):
     """In-place version of matrix-matrix multiplication (with accumulation):
@@ -818,13 +830,6 @@ def _beta_L_plus_alpha_M(beta, L, alpha, M, recurse_flip = True):
     #print 'BETA L + ALPHA M', beta, L, alpha, M, recurse_flip
     #EXPRESSION: (beta * L) + (alpha * M)
 
-    if M.type.broadcastable != L.type.broadcastable:
-        # GEMM cannot do the broadcasting that add used to be doing
-        # so abort.
-        return
-
-    assert L.type.dtype == M.type.dtype  # because of local_dot_to_dot22
-
     # we've already checked the client counts, now just make the type check.
     ####if res_is_a(M, _dot22, 1):
     if M.owner and M.owner.op == _dot22:
@@ -1017,20 +1022,23 @@ def _gemm_from_factored_list(lst):
     """
 
     # Make every pair in list have matching dtypes
-    lst = [(T.cast(si,Mi.type.dtype), Mi) for si,Mi in lst]
+    def is_pair(sM):
+        try:
+            s, M = sM
+            return True
+        except:
+            return False
+    lst = [(T.cast(sM[0],sM[1].type.dtype), sM[1])
+            for sM in lst if is_pair(sM)]
 
     # Try every pair in the sM_list, trying to turn it into a gemm operation
     for i in xrange(len(lst) - 1):
-        try:
         s_i,M_i = lst[i]
-        except:
-            continue
 
         for j in xrange(i+1, len(lst)):
-
-            try:
             s_j, M_j = lst[j]
-            except:
+
+            if M_i.type != M_j.type:
                 continue
 
             #print 'TRYING', (s_i, M_i, s_j, M_j)
@@ -1281,24 +1289,32 @@ class Dot22Scalar(GemmRelated):
     Also used to generate a gemm later.
     compute scalar*dot(x,y)
     """
-    def make_node(self, x, y, scalar):
-        if not _is_real_matrix(x):
-            raise TypeError(x)
-        if not _is_real_matrix(x):
-            raise TypeError(y)
-        if not _as_scalar(scalar):
-            raise TypeError(scalar)
-        if y.type.dtype != x.type.dtype and y.type.dtype != scalar.type.dtype:
-            raise TypeError('dtype mismatch to Dot22Scalar')
-        bz = [False, False]
+    def make_node(self, x, y, a):
+        if a.ndim != 0:
+            raise TypeError(Gemm.E_scalar, a)
+        if x.ndim != 2:
+            raise TypeError(Gemm.E_rank, x)
+        if y.ndim != 2:
+            raise TypeError(Gemm.E_rank, y)
+
+        if not (a.dtype == x.dtype == y.dtype):
+            raise TypeError('Dot22Scalar requires matching dtypes',
+                    (a.dtype, x.dtype, y.dtype))
+
+        if (not a.dtype.startswith('float')
+                and not a.dtype.startswith('complex')):
+            raise TypeError('Dot22Scalar requires float or complex args',
+                    a.dtype)
+
+        bz = [x.type.broadcastable[0], y.type.broadcastable[1]]
         outputs = [T.tensor(x.type.dtype, bz)]
-        return Apply(self, [x,y,scalar], outputs)
+        return Apply(self, [x,y,a], outputs)
 
     def perform(self, node, inp, out):
         x, y, scalar = inp
         z, = out
         try:
-            z[0] = scalar * numpy.asarray(numpy.dot(x, y))
+            z[0] = numpy.asarray(scalar * numpy.dot(x, y))
         except ValueError, e:
             # The error raised by numpy has no shape information, we mean to add that
             e.args = e.args + (x.shape, y.shape)
@@ -1360,21 +1376,23 @@ def local_dot22_to_dot22scalar(node):
         return False
     i_dot22 = [x.owner and x.owner.op==_dot22 for x in node.inputs]
     if not any(i_dot22): return False # no dot22
-    if i_dot22.count(True)>1: return False #TODO fix
-    #we take the first _dot22 found. TODO check others!
+    if i_dot22.count(True)>1:
+        #TODO: try each of them.
+        pass
+        #return False #TODO fix
     dot22_idx = i_dot22.index(True)
     d = node.inputs[dot22_idx]
     i_scalar = [_as_scalar(x) for x in node.inputs]
-    if not any(i_scalar) and not any([x.owner and x.owner.op ==T.mul for x in node.inputs]):
+    if not any(i_scalar):
+        i_mul = [x.owner and x.owner.op ==T.mul for x in node.inputs]
+        if not any(i_mul):
             #no scalar in input and no multiplication
             #if their was a multiplication we couls reorder the graph by the associativity of the graph.
             return False
 
-    if not any(i_scalar):
         #maybe we can reorder the graph as this mul have a mul in input.
         #The canonizer should have merged those mul together.
         #We support only 1 additional level of mul.
-        i_mul = [x.owner and x.owner.op ==T.mul for x in node.inputs]
         mul_idx = i_mul.index(True)#we take the first mul!
         m = node.inputs[mul_idx]
 
@@ -1384,7 +1402,17 @@ def local_dot22_to_dot22scalar(node):
                 if _as_scalar(x):
                     scalar_idx=i
                     break
-            dot=_dot22scalar(d.owner.inputs[0], d.owner.inputs[1],m.owner.inputs[scalar_idx])
+
+            a = T.cast(_as_scalar(m.owner.inputs[scalar_idx]), d.type.dtype)
+            assert not a.type.ndim
+            dot=_dot22scalar(d.owner.inputs[0], d.owner.inputs[1], a)
+
+            # What about the other inputs to the original node that were
+            # neither part of the dot22 or this mul?
+            # I'm asserting there are no such inputs here:
+            assert dot22_idx != mul_idx
+            assert all((i in (dot22_idx, mul_idx))
+                    for i in range(len(node.inputs)))
 
             return [T.mul(m.owner.inputs[1-i],dot)]
         elif m.owner and m.owner.op == T.mul:
@@ -1397,7 +1425,9 @@ def local_dot22_to_dot22scalar(node):
 
     scalar_idx = -1
     for i,x in enumerate(node.inputs):
-        if i_scalar[i] and theano.scalar.upcast(x.type.dtype,d.type.dtype) == d.type.dtype:
+        if (i_scalar[i] is not None
+                and (theano.scalar.upcast(x.type.dtype,d.type.dtype)
+                    == d.type.dtype)):
             scalar_idx = i
             break
     if scalar_idx<0:
@@ -1405,15 +1435,18 @@ def local_dot22_to_dot22scalar(node):
                 'of the scalar cannot be upcasted to the matrix type',
                 node.inputs, [x.type for x in node.inputs])
         return False
-    assert scalar_idx<len(node.inputs)
+    assert scalar_idx < len(node.inputs)
     s = node.inputs[scalar_idx]
     o = copy.copy(node.inputs)
     o.remove(d)
     o.remove(s)
-    if len(o)==0:
-        return [_dot22scalar(d.owner.inputs[0], d.owner.inputs[1], s)]
+
+    a = T.cast(i_scalar[scalar_idx], d.type.dtype)
+    assert not a.type.ndim
+    if len(o) == 0:
+        return [_dot22scalar(d.owner.inputs[0], d.owner.inputs[1], a)]
     else:
-        return [T.mul(_dot22scalar(d.owner.inputs[0], d.owner.inputs[1], s), *o)]
+        return [T.mul(_dot22scalar(d.owner.inputs[0], d.owner.inputs[1], a), *o)]
 
 #must happen after gemm as the gemm optimizer don't understant dot22scalar and gemm give more speed up then dot22scalar
 blas_optdb.register('local_dot22_to_dot22scalar',