Rename theano.gof.graph.inputs to graph_inputs

tests/gof/test_graph.py

@@ -14,7 +14,7 @@ from theano.gof.graph import (
     clone,
     equal_computations,
     general_toposort,
-    inputs,
+    graph_inputs,
     io_toposort,
     is_in_ancestors,
     list_of_nodes,
@@ -132,8 +132,10 @@ class TestClone(X):
         _, new = clone([r1, r2, r5], node.outputs, False)
         new_node = new[0].owner
         new_node.inputs = [MyVariable(7), MyVariable(8)]
-        assert self.str(inputs(new_node.outputs), new_node.outputs) == ["MyOp(R7, R8)"]
-        assert self.str(inputs(node.outputs), node.outputs) == [
+        assert self.str(graph_inputs(new_node.outputs), new_node.outputs) == [
+            "MyOp(R7, R8)"
+        ]
+        assert self.str(graph_inputs(node.outputs), node.outputs) == [
             "MyOp(MyOp(R1, R2), R5)"
         ]
 
@@ -384,7 +386,7 @@ def test_ancestors():
     assert res_list == [o2, r3, o1]
 
 
-def test_inputs():
+def test_graph_inputs():
 
     r1, r2, r3 = MyVariable(1), MyVariable(2), MyVariable(3)
     o1 = MyOp(r1, r2)
@@ -392,7 +394,7 @@ def test_inputs():
     o2 = MyOp(r3, o1)
     o2.name = "o2"
 
-    res = inputs([o2], blockers=None)
+    res = graph_inputs([o2], blockers=None)
     res_list = list(res)
     assert res_list == [r3, r1, r2]
 

tests/scan/test_basic.py

@@ -1966,7 +1966,7 @@ class TestScan:
 
         f1 = z * (x + y) ** 2 + 5
         f2 = theano.clone(f1, replace=None, strict=True, share_inputs=True)
-        f2_inp = theano.gof.graph.inputs([f2])
+        f2_inp = theano.gof.graph.graph_inputs([f2])
 
         assert z in f2_inp
         assert x in f2_inp
@@ -1982,7 +1982,7 @@ class TestScan:
 
         f1 = z * (x + y) ** 2 + 5
         f2 = theano.clone(f1, replace=None, strict=True, share_inputs=False)
-        f2_inp = theano.gof.graph.inputs([f2])
+        f2_inp = theano.gof.graph.graph_inputs([f2])
 
         assert z not in f2_inp
         assert x not in f2_inp
@@ -2001,7 +2001,7 @@ class TestScan:
         f2 = theano.clone(
             f1, replace=OrderedDict([(y, y2)]), strict=True, share_inputs=True
         )
-        f2_inp = theano.gof.graph.inputs([f2])
+        f2_inp = theano.gof.graph.graph_inputs([f2])
         assert z in f2_inp
         assert x in f2_inp
         assert y2 in f2_inp
@@ -2019,7 +2019,7 @@ class TestScan:
         f2 = theano.clone(
             f1, replace=OrderedDict([(y, y2)]), strict=False, share_inputs=True
         )
-        f2_inp = theano.gof.graph.inputs([f2])
+        f2_inp = theano.gof.graph.graph_inputs([f2])
         assert z in f2_inp
         assert x in f2_inp
         assert y2 in f2_inp
@@ -2035,7 +2035,7 @@ class TestScan:
 
         f1 = z * (x + y) ** 2 + 5
         f2 = theano.clone(f1, replace=[(y, y2)], strict=True, share_inputs=False)
-        f2_inp = theano.gof.graph.inputs([f2])
+        f2_inp = theano.gof.graph.graph_inputs([f2])
         assert z not in f2_inp
         assert x not in f2_inp
         assert y2 not in f2_inp
@@ -2051,7 +2051,7 @@ class TestScan:
 
         f1 = z * (x + y) ** 2 + 5
         f2 = theano.clone(f1, replace=[(y, y2)], strict=False, share_inputs=False)
-        f2_inp = theano.gof.graph.inputs([f2])
+        f2_inp = theano.gof.graph.graph_inputs([f2])
         assert z not in f2_inp
         assert x not in f2_inp
         assert y2 not in f2_inp

tests/tensor/random/test_basic.py

@@ -8,8 +8,7 @@ from pytest import fixture, importorskip, raises
 import theano.tensor as tt
 from theano import change_flags, config
 from theano.gof.fg import FunctionGraph
-from theano.gof.graph import Variable
-from theano.gof.graph import inputs as tt_inputs
+from theano.gof.graph import Variable, graph_inputs
 from theano.gof.op import get_test_value
 from theano.tensor.random.basic import (
     bernoulli,
@@ -145,7 +144,7 @@ def test_normal_ShapeFeature():
     d_rv.tag.test_value
 
     fg = FunctionGraph(
-        [i for i in tt_inputs([d_rv]) if not isinstance(i, tt.Constant)],
+        [i for i in graph_inputs([d_rv]) if not isinstance(i, tt.Constant)],
         [d_rv],
         clone=False,
         features=[tt.opt.ShapeFeature()],
@@ -296,7 +295,7 @@ def test_mvnormal_ShapeFeature():
     d_rv = multivariate_normal(tt.ones((M_tt,)), tt.eye(M_tt), size=2)
 
     fg = FunctionGraph(
-        [i for i in tt_inputs([d_rv]) if not isinstance(i, tt.Constant)],
+        [i for i in graph_inputs([d_rv]) if not isinstance(i, tt.Constant)],
         [d_rv],
         clone=False,
         features=[tt.opt.ShapeFeature()],
@@ -305,7 +304,7 @@ def test_mvnormal_ShapeFeature():
     s1, s2 = fg.shape_feature.shape_of[d_rv]
 
     assert get_test_value(s1) == 2
-    assert M_tt in tt_inputs([s2])
+    assert M_tt in graph_inputs([s2])
 
     # Test broadcasted shapes
     mean = tt.tensor(config.floatX, [True, False])
@@ -319,7 +318,7 @@ def test_mvnormal_ShapeFeature():
     d_rv = multivariate_normal(mean, cov, size=[2, 3])
 
     fg = FunctionGraph(
-        [i for i in tt_inputs([d_rv]) if not isinstance(i, tt.Constant)],
+        [i for i in graph_inputs([d_rv]) if not isinstance(i, tt.Constant)],
         [d_rv],
         clone=False,
         features=[tt.opt.ShapeFeature()],
@@ -392,7 +391,7 @@ def test_dirichlet_ShapeFeature():
     d_rv = dirichlet(tt.ones((M_tt, N_tt)), name="Gamma")
 
     fg = FunctionGraph(
-        [i for i in tt_inputs([d_rv]) if not isinstance(i, tt.Constant)],
+        [i for i in graph_inputs([d_rv]) if not isinstance(i, tt.Constant)],
         [d_rv],
         clone=False,
         features=[tt.opt.ShapeFeature()],
@@ -400,8 +399,8 @@ def test_dirichlet_ShapeFeature():
 
     s1, s2 = fg.shape_feature.shape_of[d_rv]
 
-    assert M_tt in tt_inputs([s1])
-    assert N_tt in tt_inputs([s2])
+    assert M_tt in graph_inputs([s1])
+    assert N_tt in graph_inputs([s2])
 
 
 def test_poisson_samples():

tests/test_gradient.py

@@ -22,7 +22,7 @@ def grad_sources_inputs(sources, inputs):
     the new interface so the tests don't need to be rewritten.
     """
     if inputs is None:
-        inputs = list(theano.gof.graph.inputs([source[0] for source in sources]))
+        inputs = list(theano.gof.graph.graph_inputs([source[0] for source in sources]))
     return dict(
         zip(
             inputs,

theano/compile/builders.py

@@ -339,7 +339,9 @@ class OpFromGraph(Op):
         # To correctly support shared variables the inner fct should
         # not see them. Otherwise there is a problem with the gradient.
         self.shared_inputs = [
-            var for var in gof.graph.inputs(outputs) if isinstance(var, SharedVariable)
+            var
+            for var in gof.graph.graph_inputs(outputs)
+            if isinstance(var, SharedVariable)
         ]
         shared_vars = [var.type() for var in self.shared_inputs]
 

theano/compile/debugmode.py

@@ -2416,7 +2416,7 @@ class _Maker(FunctionMaker):  # inheritance buys a few helper functions
         outputs = [self.wrap_out(o) for o in outputs]
 
         _inputs = list(
-            gof.graph.inputs(
+            gof.graph.graph_inputs(
                 [o.variable for o in outputs]
                 + [i.update for i in inputs if getattr(i, "update", False)]
             )

theano/compile/function/types.py

@@ -1206,7 +1206,7 @@ def insert_deepcopy(fgraph, wrapped_inputs, wrapped_outputs):
     }
 
     # We can't use fgraph.inputs as this don't include Constant Value.
-    all_graph_inputs = list(gof.graph.inputs(fgraph.outputs))
+    all_graph_inputs = list(gof.graph.graph_inputs(fgraph.outputs))
     has_destroyers_attr = hasattr(fgraph, "has_destroyers")
 
     for i in range(len(fgraph.outputs)):
@@ -1454,10 +1454,18 @@ class FunctionMaker:
                         t2 = f2.outputs[i]
 
                         givens = dict(
-                            zip(gof.graph.inputs([t1]), gof.graph.inputs([t2]))
+                            zip(
+                                gof.graph.graph_inputs([t1]),
+                                gof.graph.graph_inputs([t2]),
+                            )
                         )
 
-                        temp = dict(zip(gof.graph.inputs([t1]), gof.graph.inputs([t2])))
+                        temp = dict(
+                            zip(
+                                gof.graph.graph_inputs([t1]),
+                                gof.graph.graph_inputs([t2]),
+                            )
+                        )
 
                         # hack to remove inconstent entry in givens
                         # seems to work that but source of inconsistency
@@ -1554,7 +1562,7 @@ class FunctionMaker:
         inputs = [self.wrap_in(i) for i in inputs]
         outputs = [self.wrap_out(o) for o in outputs]
         _inputs = list(
-            gof.graph.inputs(
+            gof.graph.graph_inputs(
                 [o.variable for o in outputs]
                 + [i.update for i in inputs if getattr(i, "update", False)]
             )

theano/d3viz/formatting.py

@@ -11,7 +11,7 @@ import theano
 from theano.compile import Function, builders
 from theano.gof.fg import FunctionGraph
 from theano.gof.graph import Apply, Constant, Variable
-from theano.gof.graph import inputs as graph_inputs
+from theano.gof.graph import graph_inputs as graph_inputs
 from theano.printing import pydot_imported, pydot_imported_msg
 
 

theano/gof/graph.py

@@ -757,7 +757,7 @@ def ancestors(
     yield from walk(graphs, expand, False)
 
 
-def inputs(
+def graph_inputs(
     graphs: Iterable[Variable], blockers: Collection[Variable] = None
 ) -> Generator[Variable, None, None]:
     """Return the inputs required to compute the given Variables.

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, vars_between
+from theano.gof.graph import equal_computations, graph_inputs, io_toposort, vars_between
 
 
 class AlreadyThere(Exception):
@@ -807,10 +807,10 @@ def is_same_graph_with_merge(var1, var2, givens=None):
     vars = copied[0:2]
     givens = copied[2]
     # Create FunctionGraph.
-    graph_inputs = list(inputs(vars))
+    inputs = list(graph_inputs(vars))
     # The clone isn't needed as we did a deepcopy and we cloning will
     # break the mapping in givens.
-    fgraph = theano.gof.fg.FunctionGraph(graph_inputs, vars, clone=False)
+    fgraph = theano.gof.fg.FunctionGraph(inputs, vars, clone=False)
     # Perform Variable substitution.
     for to_replace, replace_by in givens.items():
         fgraph.replace(to_replace, replace_by)
@@ -893,7 +893,7 @@ def is_same_graph(var1, var2, givens=None):
         in_xs = []
         in_ys = []
         # Compute the sets of all variables found in each computational graph.
-        inputs_var = list(map(inputs, ([var1], [var2])))
+        inputs_var = list(map(graph_inputs, ([var1], [var2])))
         all_vars = [
             set(vars_between(v_i, v_o))
             for v_i, v_o in ((inputs_var[0], [var1]), (inputs_var[1], [var2]))

theano/printing.py

@@ -820,7 +820,7 @@ def pydotprint(
             fct = fct.outputs
         assert isinstance(fct, (list, tuple))
         assert all(isinstance(v, gof.Variable) for v in fct)
-        fct = gof.FunctionGraph(inputs=list(gof.graph.inputs(fct)), outputs=fct)
+        fct = gof.FunctionGraph(inputs=list(gof.graph.graph_inputs(fct)), outputs=fct)
         profile = None
         outputs = fct.outputs
         topo = fct.toposort()

theano/scan/basic.py

@@ -803,7 +803,7 @@ def scan(
             and not isinstance(x, SharedVariable)
             and not isinstance(x, gof.Constant)
         ),
-        gof.graph.inputs(fake_outputs),
+        gof.graph.graph_inputs(fake_outputs),
     )
     extra_inputs = [x for x in all_inputs if x not in args + fake_nonseqs]
     non_seqs += extra_inputs

theano/scan/op.py

@@ -62,7 +62,7 @@ from theano.compile.profiling import ScanProfileStats, register_profiler_printer
 from theano.configdefaults import config
 from theano.gof.fg import MissingInputError
 from theano.gof.graph import Apply, Variable, equal_computations
-from theano.gof.graph import inputs as graph_inputs
+from theano.gof.graph import graph_inputs as graph_inputs
 from theano.gof.graph import io_connection_pattern
 from theano.gof.op import Op, ops_with_inner_function
 from theano.gof.toolbox import NoOutputFromInplace

theano/scan/opt.py

@@ -150,7 +150,7 @@ def remove_constants_and_unused_inputs_scan(fgraph, node):
     # Same for the outer graph, initialized w/ number of steps
     nw_outer = [node.inputs[0]]
 
-    all_ins = list(gof.graph.inputs(op_outs))
+    all_ins = list(gof.graph.graph_inputs(op_outs))
     for idx in range(op.n_seqs):
         node_inp = node.inputs[idx + 1]
         if (

theano/scan/utils.py

@@ -268,7 +268,7 @@ def map_variables(replacer, graphs, additional_inputs=None):
             return new_graph
 
     graphs = list(graphs)
-    inputs_ = list(set(list(gof.graph.inputs(graphs)) + list(additional_inputs)))
+    inputs_ = list(set(list(gof.graph.graph_inputs(graphs)) + list(additional_inputs)))
 
     # perform any desired replacement of input variables.  these
     # aren't replaced by the local optimizer approach because they are
@@ -280,7 +280,7 @@ def map_variables(replacer, graphs, additional_inputs=None):
         if new_input is not input_
     ]
     graphs = clone(graphs, share_inputs=True, replace=replacements)
-    inputs_ = list(set(list(gof.graph.inputs(graphs)) + list(additional_inputs)))
+    inputs_ = list(set(list(gof.graph.graph_inputs(graphs)) + list(additional_inputs)))
 
     fg = gof.fg.FunctionGraph(inputs_, graphs, clone=False)
 
@@ -330,7 +330,7 @@ def map_variables(replacer, graphs, additional_inputs=None):
             replacements = [wrapped_replacer(o) for o in node.outputs]
 
             # Add inputs to replacement graphs as inputs to this `fgraph`
-            for i in gof.graph.inputs(replacements):
+            for i in gof.graph.graph_inputs(replacements):
                 fgraph.add_input(i)
 
             return replacements
@@ -370,7 +370,7 @@ def _map_variables_inner(
 
         other_inputs = []
         constants = []
-        for input_ in gof.graph.inputs([new_graph]):
+        for input_ in gof.graph.graph_inputs([new_graph]):
             if isinstance(input_, gof.Variable):
                 if isinstance(input_, gof.Constant):
                     constants.append(input_)
@@ -714,7 +714,7 @@ def scan_can_remove_outs(op, out_idxs):
 
     """
     non_removable = [o for i, o in enumerate(op.outputs) if i not in out_idxs]
-    required_inputs = list(gof.graph.inputs(non_removable))
+    required_inputs = list(gof.graph.graph_inputs(non_removable))
 
     out_ins = []
     offset = op.n_seqs
@@ -734,7 +734,7 @@ def scan_can_remove_outs(op, out_idxs):
             if out_idxs_mask[pos] and any([x in required_inputs for x in out_ins[idx]]):
                 # This output is required ..
                 out_idxs_mask[pos] = 0
-                required_inputs += list(gof.graph.inputs([op.outputs[idx]]))
+                required_inputs += list(gof.graph.graph_inputs([op.outputs[idx]]))
                 added = True
 
     required_outs = [x for i, x in enumerate(out_idxs) if out_idxs_mask[i] == 0]
@@ -900,7 +900,7 @@ def reconstruct_graph(inputs, outputs, tag=None):
     givens = OrderedDict()
     for nw_x, x in zip(nw_inputs, inputs):
         givens[x] = nw_x
-    allinputs = list(theano.gof.graph.inputs(outputs))
+    allinputs = list(theano.gof.graph.graph_inputs(outputs))
     for inp in allinputs:
         if isinstance(inp, theano.Constant):
             givens[inp] = inp.clone()