added infer shape

theano/sparse/basic.py

@@ -1467,6 +1467,19 @@ class Dot(gof.op.Op):
     def __ne__(self, other):
         return not (self == other)
     
+    def infer_shape(self, node, shapes):
+        xshp, yshp = shapes
+        x, y = node.inputs
+        if x.ndim == 2 and y.ndim == 2:
+            return [(xshp[0], yshp[1])]
+        if x.ndim == 1 and y.ndim == 2:
+            return [(yshp[1],)]
+        if x.ndim == 2 and y.ndim == 1:
+            return [(xshp[0],)]
+        if x.ndim == 1 and y.ndim == 1:
+            return [()]
+        raise NotImplementedError()
+    
     def make_node(self, x, y):
         dtype_out = scalar.upcast(x.type.dtype, y.type.dtype)
         
@@ -1539,6 +1552,19 @@ class Usmm(gof.op.Op):
     def __str__(self):
         return 'Usmm{no_inplace}'
     
+    def infer_shape(self, node, shapes):
+        xshp, yshp = shapes
+        x, y = node.inputs
+        if x.ndim == 2 and y.ndim == 2:
+            return [(xshp[0], yshp[1])]
+        if x.ndim == 1 and y.ndim == 2:
+            return [(yshp[1],)]
+        if x.ndim == 2 and y.ndim == 1:
+            return [(xshp[0],)]
+        if x.ndim == 1 and y.ndim == 1:
+            return [()]
+        raise NotImplementedError()
+    
     def make_node(self, alpha, x, y, z):
         if not _is_sparse_variable(x) and not _is_sparse_variable(y):
             # If x and y are tensor, we don't want to use this class
@@ -1603,11 +1629,25 @@ class UsmmCscDense(gof.Op):
     def __eq__(self, other):
         return (type(self) == type(other)) and self.inplace == other.inplace
     def __hash__(self):
-        return hash(type(self))
+        return hash(type(self)) ^ self.inplace
+    def infer_shape(self, node, shapes):
+        xshp, yshp = shapes
+        x, y = node.inputs
+        if x.ndim == 2 and y.ndim == 2:
+            return [(xshp[0], yshp[1])]
+        if x.ndim == 1 and y.ndim == 2:
+            return [(yshp[1],)]
+        if x.ndim == 2 and y.ndim == 1:
+            return [(xshp[0],)]
+        if x.ndim == 1 and y.ndim == 1:
+            return [()]
+        raise NotImplementedError()
     def make_node(self, alpha, x_val, x_ind, x_ptr, x_nrows, y, z):
+        alpha = tensor.as_tensor_variable(alpha)
         x_val = tensor.as_tensor_variable(x_val)
         x_ind = tensor.as_tensor_variable(x_ind)
         x_ptr = tensor.as_tensor_variable(x_ptr)
+        x_nrows = tensor.as_tensor_variable(x_nrows)
         y = tensor.as_tensor_variable(y)
         z = tensor.as_tensor_variable(z)
         assert x_ind.dtype == 'int32'
@@ -1620,7 +1660,6 @@ class UsmmCscDense(gof.Op):
 
         dtype_out = scalar.upcast(alpha.type.dtype, x_val.type.dtype,
             y.type.dtype, z.type.dtype)
-        alpha = tensor.as_tensor_variable(alpha)
 
         if self.inplace:
             assert z.type.dtype == dtype_out
@@ -1631,10 +1670,14 @@ class UsmmCscDense(gof.Op):
         if dtype_out != x_val.type.dtype:
             x_val = tensor.cast(x_val, dtype_out)
         if dtype_out != y.type.dtype:
-            raise NotImplementedError("We need sparse cast to be implemented!")
+            y = tensor.cast(y, dtype_out)
         if dtype_out != z.type.dtype:
             z = tensor.cast(z, dtype_out)
-
+    
+        if node.inputs[1].type.dtype in ('complex64', 'complex128'):
+            raise NotImplementedError('Complex types are not supported for x_val')
+        if node.inputs[5].type.dtype in ('complex64', 'complex128'):
+            raise NotImplementedError('Complex types are not supported for y')
 
         r = gof.Apply(self, [alpha, x_val, x_ind, x_ptr, x_nrows, y, z], 
                 [tensor.tensor(dtype_out, (False, y.type.broadcastable[1]))])
@@ -1716,7 +1759,7 @@ class UsmmCscDense(gof.Op):
         {PyErr_SetString(PyExc_NotImplementedError, "x's number of columns doesn't match y's rows"); %(fail)s;}
         
         if (%(z)s->dimensions[0] != ((npy_int32 *)%(x_nrows)s->data)[0] || %(z)s->dimensions[1] != %(y)s->dimensions[1])
-        {PyErr_SetString(PyExc_NotImplementedError, "The dimension of z and the output must match"); %(fail)s;}
+        {PyErr_SetString(PyExc_NotImplementedError, "The dimension of the allocated output doesn't match the correct output size."); %(fail)s;}
         
         if (PyArray_SIZE(%(alpha)s) != 1)
         {PyErr_SetString(PyExc_NotImplementedError, "The number of element in alpha must be 1"); %(fail)s;}