Rename theano.gof.graph.variables to vars_between

tests/gof/test_graph.py

@@ -19,7 +19,7 @@ from theano.gof.graph import (
     is_in_ancestors,
     list_of_nodes,
     orphans,
-    variables,
+    vars_between,
     walk,
 )
 from theano.gof.op import Op
@@ -405,7 +405,7 @@ def test_variables_and_orphans():
     o2 = MyOp(r3, o1)
     o2.name = "o2"
 
-    vars_res = variables([r1, r2], [o2])
+    vars_res = vars_between([r1, r2], [o2])
     orphans_res = orphans([r1, r2], [o2])
 
     vars_res_list = list(vars_res)

theano/compile/function/types.py

@@ -1444,7 +1444,7 @@ class FunctionMaker:
                     ):
                         print("loop through outputs node for both graphs")
                         graph_old.variables = set(
-                            gof.graph.variables(graph_old.inputs, graph_old.outputs)
+                            gof.graph.vars_between(graph_old.inputs, graph_old.outputs)
                         )
 
                         # using clone allowed to avoid a lot of errors
@@ -1489,7 +1489,7 @@ class FunctionMaker:
                 # this is a brand new graph, optimize it, save it to graph_db
                 print("graph not found in graph_db, optimizing the graph")
                 self.fgraph.variables = set(
-                    gof.graph.variables(self.fgraph.inputs, self.fgraph.outputs)
+                    gof.graph.vars_between(self.fgraph.inputs, self.fgraph.outputs)
                 )
                 # check_integrity parameters was added to ignore
                 # "excess cached variables" errors. Works that way

theano/gof/fg.py

@@ -9,8 +9,7 @@ from theano.gof import toolbox, utils
 from theano.gof.graph import Apply, Constant, Variable, applys_between
 from theano.gof.graph import as_string as graph_as_string
 from theano.gof.graph import clone as clone_graph
-from theano.gof.graph import clone_get_equiv, io_toposort
-from theano.gof.graph import variables as variables_between
+from theano.gof.graph import clone_get_equiv, io_toposort, vars_between
 from theano.gof.utils import TestValueError, get_variable_trace_string
 from theano.misc.ordered_set import OrderedSet
 
@@ -725,7 +724,7 @@ class FunctionGraph(utils.MetaObject):
                     raise Exception(
                         f"Inconsistent clients list {(node, i)} in {clients}"
                     )
-        variables = set(variables_between(self.inputs, self.outputs))
+        variables = set(vars_between(self.inputs, self.outputs))
         if set(self.variables) != variables:
             missing = variables.difference(self.variables)
             excess = self.variables.difference(variables)

theano/gof/graph.py

@@ -781,7 +781,7 @@ def inputs(
     yield from (r for r in ancestors(graphs, blockers) if r.owner is None)
 
 
-def variables(
+def vars_between(
     ins: Collection[Variable], outs: Iterable[Variable]
 ) -> Generator[Variable, None, None]:
     """Extract the `Variable`s within the sub-graph between input and output nodes.
@@ -835,7 +835,7 @@ def orphans(
     [y]
 
     """
-    yield from (r for r in variables(ins, outs) if r.owner is None and r not in ins)
+    yield from (r for r in vars_between(ins, outs) if r.owner is None and r not in ins)
 
 
 def applys_between(
@@ -860,7 +860,7 @@ def applys_between(
 
     """
     yield from (
-        r.owner for r in variables(ins, outs) if r not in ins and r.owner is not None
+        r.owner for r in vars_between(ins, outs) if r not in ins and r.owner is not None
     )
 
 

theano/gof/toolbox.py

@@ -10,7 +10,7 @@ import numpy as np
 
 import theano
 from theano.configdefaults import config
-from theano.gof.graph import equal_computations, inputs, io_toposort, variables
+from theano.gof.graph import equal_computations, inputs, io_toposort, vars_between
 
 
 class AlreadyThere(Exception):
@@ -895,7 +895,7 @@ def is_same_graph(var1, var2, givens=None):
         # Compute the sets of all variables found in each computational graph.
         inputs_var = list(map(inputs, ([var1], [var2])))
         all_vars = [
-            set(variables(v_i, v_o))
+            set(vars_between(v_i, v_o))
             for v_i, v_o in ((inputs_var[0], [var1]), (inputs_var[1], [var2]))
         ]
 

theano/link/c/basic.py

@@ -14,8 +14,7 @@ import numpy as np
 from theano.compile.compilelock import lock_ctx
 from theano.configdefaults import config
 from theano.gof.callcache import CallCache
-from theano.gof.graph import Constant, NoParams, io_toposort
-from theano.gof.graph import variables as get_variables
+from theano.gof.graph import Constant, NoParams, io_toposort, vars_between
 from theano.gof.utils import MethodNotDefined
 from theano.link.basic import Container, Linker, LocalLinker, PerformLinker
 from theano.link.c.cmodule import (
@@ -637,7 +636,7 @@ class CLinker(Linker):
         # We need to include the unused inputs in our variables,
         # otherwise we can't pass them to the module.
         self.variables = [var for var in self.inputs if not len(fgraph.clients[var])]
-        self.variables += list(get_variables(self.inputs, self.outputs))
+        self.variables += list(vars_between(self.inputs, self.outputs))
 
         # This adds a hidden input which is the params for each node
         # that needs it

theano/tensor/nnet/bn.py

@@ -827,7 +827,7 @@ def local_abstract_batch_norm_train(fgraph, node):
         for (r, r_orig) in zip(results, node.outputs)
     ]
 
-    for var in theano.gof.graph.variables(node.inputs, results):
+    for var in theano.gof.graph.vars_between(node.inputs, results):
         if var not in node.inputs:
             copy_stack_trace(node.outputs[0], var)
     return results
@@ -866,7 +866,7 @@ def local_abstract_batch_norm_train_grad(fgraph, node):
         for (r, r_orig) in zip(results, node.outputs)
     ]
 
-    for var in theano.gof.graph.variables(node.inputs, results):
+    for var in theano.gof.graph.vars_between(node.inputs, results):
         if var not in node.inputs:
             copy_stack_trace(node.outputs[0], var)
     return results
@@ -898,7 +898,7 @@ def local_abstract_batch_norm_inference(fgraph, node):
     ) + bias
     result = tt.patternbroadcast(result, node.outputs[0].broadcastable)
 
-    for var in theano.gof.graph.variables(node.inputs, [result]):
+    for var in theano.gof.graph.vars_between(node.inputs, [result]):
         if var not in node.inputs:
             copy_stack_trace(node.outputs[0], var)
     return [result]