small fixes

theano/gof/opt.py

@@ -1369,7 +1369,7 @@ class LocalOptGroup(LocalOptimizer):
                 if isinstance(new_repl, (tuple, list)):
                     new_vars = new_repl
                 else:  # It must be a dict
-                    new_vars = new_repl.values()
+                    new_vars = list(new_repl.values())
                 if self.profile:
                     self.node_created[opt] += len(graph.ops(fgraph.variables, new_vars))
                     self.applied_true[opt] += 1

theano/gpuarray/sort.py

@@ -151,7 +151,7 @@ class GpuTopKOp(GpuKernelBase, TopKOp):
         fail = sub['fail']
         ctx = sub['params']
         k_dtype = node.inputs[1].type.dtype_specs()[1]
-        MAX_TPB = 1024  # max thread per block
+        MAX_TPB = 1024  # max threads per block
         WARP_SIZE = 32
 
         ndim = node.inputs[0].ndim
@@ -195,20 +195,19 @@ class GpuTopKOp(GpuKernelBase, TopKOp):
     for (int i=0; i<%(ndim)d; i++)
         odims[i] = dims[i];
 
-
     odims[%(axis)d] = k_>=0 ? k_ : -k_;
 
     if (0 == odims[%(axis)d]) {
         PyErr_SetString(
             PyExc_ValueError,
-            "topk: k must not be zero");
+            "topk: kth must not be zero");
         %(fail)s;
     } else if (dims[%(axis)d] < odims[%(axis)d]){
         PyErr_SetString(
             PyExc_ValueError,
-            "topk: k cannot larger than size on specified axis %(axis)d");
+            "topk: kth cannot larger than size on specified axis %(axis)d");
         %(fail)s;
-    } else if (dims[%(axis)d] > INT_MAX) {
+    } else if (dims[%(axis)d] >= (1u << 31)) {
         PyErr_SetString(
             PyExc_ValueError,
             "topk: on GPU, array size on specified axis cannot larger or equal than 2^31");

theano/tensor/opt.py

@@ -7566,12 +7566,8 @@ def local_useless_topk(node):
         return False
 
     x, k = node.inputs
-    ret_val = False
-    ret_idx = False
-    if op.return_values:
-        ret_val = bool(node.outputs[0].clients)
-    if op.return_indices:
-        ret_idx = bool(node.outputs[-1].clients)
+    ret_val = bool(node.outputs[0].clients)
+    ret_idx = bool(node.outputs[1].clients)
 
     if not (ret_val ^ ret_idx):
         # both true -> nothing to remove
@@ -7584,4 +7580,4 @@ def local_useless_topk(node):
         idx_dtype=op.idx_dtype,
         return_values=ret_val,
         return_indices=ret_idx)(x, k)
-    return {old_output:new_output}
+    return {old_output: new_output}

theano/tensor/sort.py

@@ -334,9 +334,7 @@ class TopKOp(theano.Op):
     # TODO c_code
     # TODO add opt, if k==1, use max/min reduce
     #      also if k is axis size, just copy input tensor
-    # TODO add opt to merge argtopk / topk, or split topk_and_argtopk when only
-    #      one result is needed
-
+    # TODO add opt, to merge argtopk / topk
     __props__ = ('axis', 'return_values', 'return_indices', 'idx_dtype')
 
     def __init__(
@@ -466,7 +464,8 @@ def topk(x, kth, axis=-1, sorted=True, idx_dtype='int64'):
 
     """
     if sorted:
-        raise NotImplementedError("sorted=True is not supported yet.")
+        raise NotImplementedError(
+            "We are still working on sorted topk. Use sorted=False for now.")
     if axis is None:
         x = theano.tensor.flatten(x)
         axis = 0
@@ -511,7 +510,8 @@ def argtopk(x, kth, axis=-1, sorted=True, idx_dtype='int64'):
 
     """
     if sorted:
-        raise NotImplementedError("sorted=True is not supported yet.")
+        raise NotImplementedError(
+            "We are still working on sorted topk. Use sorted=False for now.")
     if axis is None:
         x = theano.tensor.flatten(x)
         axis = 0
@@ -532,7 +532,8 @@ def topk_and_argtopk(x, kth, axis=-1, sorted=True, idx_dtype='int64'):
 
     """
     if sorted:
-        raise NotImplementedError("sorted=True is not supported yet.")
+        raise NotImplementedError(
+            "We are still working on sorted topk. Use sorted=False for now.")
     if axis is None:
         x = theano.tensor.flatten(x)
         axis = 0