Ensure signal ordering is deterministic

CHANGES.rst

@@ -36,6 +36,9 @@ Release History
 **Fixed**
 
 - ``sim.data`` now implements the full ``collections.Mapping`` interface
+- Fixed bug where signal order was non-deterministic for Networks containing
+  objects with duplicate names
+  (`#9 <https://github.com/nengo/nengo_dl/issues/9>`_)
 
 0.5.1 (August 28, 2017)
 -----------------------

nengo_dl/graph_optimizer.py

@@ -620,9 +620,12 @@ def order_signals(plan, n_passes=10):
         of signals
     """
 
-    # get all the unique base signals
-    all_signals = sorted(set([s.base for ops in plan for op in ops
-                              for s in op.all_signals]), key=lambda s: s.name)
+    # get all the unique base signals (we use OrderedDict to drop the duplicate
+    # bases without changing their order, so that signal order will be
+    # deterministic for a given model)
+    all_signals = list(OrderedDict(
+        [(s.base, None) for ops in plan for op in ops
+         for s in op.all_signals]).keys())
 
     # figure out all the read blocks in the plan (in theory we would like each
     # block to become a contiguous chunk in the base array)

nengo_dl/tests/test_tensor_graph.py

@@ -1,4 +1,5 @@
 import nengo
+import numpy as np
 import pytest
 import tensorflow as tf
 
@@ -237,3 +238,21 @@ def test_planner_config(config_planner):
     tg = tensor_graph.TensorGraph(model, None, None, tf.float32, 1, None)
 
     assert len(tg.plan) == (2 if config_planner else 1)
+
+
+def test_signal_order_deterministic(Simulator, seed):
+    with nengo.Network(seed=seed) as net:
+        inp = nengo.Node([0])
+        ens0 = nengo.Ensemble(10, 1, label="ens")
+        ens1 = nengo.Ensemble(10, 1, label="ens")
+        nengo.Connection(inp, ens0, synapse=None)
+        nengo.Connection(inp, ens1, synapse=None)
+
+    with Simulator(net, seed=seed) as sim1:
+        pass
+
+    with Simulator(net, seed=seed) as sim2:
+        for (k, v), (k2, v2) in zip(
+                sim1.tensor_graph.base_arrays_init.items(),
+                sim2.tensor_graph.base_arrays_init.items()):
+            assert np.allclose(v[0], v2[0])