add more error checks, fix minor errors

theano/tensor/basic.py

@@ -4711,10 +4711,15 @@ def tile(x, reps, ndim=None):
 
     See the docstring of `numpy.tile` for details.
 
-    Currently, x.ndim and len(reps) must be equal, and, if specified, 'ndim'
-    must be equal to both.
+    Currently, 'reps' can be constant integer (e.g. 3), constant vector(e.g. [2 3]),
+    symbolic scalar (e.g. tensor.iscalar()), symbolic vector (e.g. tensor.ivector())
+    or a list of symbolic scalar (e.g. [tensor.iscalar(), tensor.iscalar()]).
+    If reps is constant vector/list/symbolic vector, the length of such vector(list)
+    should be less or equal to x.ndim, and if specified, 'ndim' should be less or equal
+    to x.ndim.
 
-    TODO: expand this.
+    TODO: expand this so that it could support cases when the length of reps (or 'ndim')
+    is larger than x.ndim, which is supported by numpy.
 
     """
 
@@ -4722,53 +4727,49 @@ def tile(x, reps, ndim=None):
     if not isinstance(reps, (list, tuple)):
         reps_astensor = as_tensor_variable(reps)
         ndim_check = reps_astensor.ndim
+        if reps_astensor.dtype not in theano.tensor.discrete_dtypes:
+            raise ValueError("elements of reps must be integer dtype")
 
         # tensor.scalar/integer case
         if ndim_check == 0:
-            reps_ = []
-            reps_.append(reps)
-            reps = reps_
-        # tesor.vector case
+            reps = [reps]
+
+        # tensor.vector case
         elif ndim_check == 1:
-            if ndim == None:
+            if ndim is None:
                 raise ValueError("if reps is tensor.vector, you should specify "
                                  "the ndim")
             else:
+                if ndim > x.ndim:
+                    raise ValueError("ndim > x.ndim not currently supported")
+
                 offset = ndim-reps.shape[0]
+
                 # assert that reps.shape[0] does not exceed ndim
                 offset = theano.tensor.opt.assert_(offset, ge(offset, 0))
+
+                # if reps.ndim is less than x.ndim, we pad the reps with
+                # "1" so that reps will have the same ndim as x.
                 reps_ = [switch(i<offset, 1, reps[i-offset]) for i in range(ndim)]
                 reps = reps_
+
         #other raise error
         else:
             raise ValueError("the dimension of reps should not exceed 1")
     else:
-       ndim_check = None
+       if len(reps) > x.ndim:
+           raise ValueError("len(reps) > x.ndim not currently supported")
+       if not numpy.all([isinstance(r, (int, long)) or
+           (isinstance(r, TensorVariable) and
+            r.dtype in theano.tensor.discrete_dtypes) for r in reps]):
+            raise ValueError("elements of reps must be scalars of integer dtype")
 
     # if reps.ndim is less than x.ndim, we pad the reps with
     # "1" so that reps will have the same ndim as x.
+    reps = list(reps) 
     if len(reps) < x.ndim:
         reps = [1]*(x.ndim-len(reps)) + reps
 
-    try:
-        iter(reps)
-    except TypeError:
-        raise ValueError("reps must be iterable")
-
-    # if reps is tensor.vector, this check should not go through
-    if not ndim_check == 1:
-        if not numpy.all([isinstance(r, integer_types) or
-                          (isinstance(r, TensorVariable) and
-                          r.dtype in ["int8", "int16", "int32", "int64"])
-                          for r in reps]):
-            raise ValueError("elements of reps must be scalars of integer dtype")
-
-    if len(reps) != x.ndim:
-        raise ValueError("len(reps) != x.ndim not currently supported")
-    elif (ndim is not None) and ndim != x.ndim:
-        raise ValueError("if specified, ndim must be equal to both x.ndim and "
-                         "len(reps)")
-
     if ndim is None:
         ndim = len(reps)
     reps = list(reps)