More memory optimizations

CHANGES.rst

@@ -34,6 +34,7 @@ Release history
 - Added ``nengo_dl.LeakyReLU`` and ``nengo_dl.SpikingLeakyReLU`` neuron models.
   (`#126`_)
 - Added support for leaky ReLU Keras layers to ``nengo_dl.Converter``. (`#126`_)
+- Added a new ``remove_reset_incs`` graph simplification step. (`#129`_)
 
 **Changed**
 
@@ -52,6 +53,10 @@ Release history
   (see `Nengo#1591`_). Note that this may change the number of trainable
   parameters in a network as the scalar default ``transform=1`` weights on
   non-Ensemble connections will no longer be present. (`#128`_)
+- Re-enabled the ``remove_constant_copies`` graph simplification by default. (`#129`_)
+- Reduced the amount of state that needs to be stored in the simulation. (`#129`_)
+- Added more information to the error message when loading saved parameters that
+  don't match the current model. (`#129`_)
 
 **Fixed**
 
@@ -68,6 +73,7 @@ Release history
 .. _#119: https://github.com/nengo/nengo-dl/pull/119
 .. _#126: https://github.com/nengo/nengo-dl/pull/126
 .. _#128: https://github.com/nengo/nengo-dl/pull/128
+.. _#129: https://github.com/nengo/nengo-dl/pull/129
 .. _#136: https://github.com/nengo/nengo-dl/pull/136
 .. _Nengo#1591: https://github.com/nengo/nengo/pull/1591
 

docs/config.rst

@@ -175,6 +175,8 @@ disable sorting via
     with nengo.Network() as net:
         nengo_dl.configure_settings(sorter=noop_order_signals)
 
+.. _config-simplifications:
+
 simplifications
 ---------------
 

docs/examples/spa-memory.ipynb

@@ -592,7 +592,7 @@
     "    # download pretrained parameters\n",
     "    urlretrieve(\n",
     "        \"https://drive.google.com/uc?export=download&\"\n",
-    "        \"id=182toR0aSWv1uExA7F5kn8t2lEU1SVrx1\",\n",
+    "        \"id=1aspi_dayS37Rx_IvDuRrXoybdIC_-tkn\",\n",
     "        \"mem_binding_params.npz\")"
    ]
   },

nengo_dl/benchmarks.py

@@ -649,7 +649,7 @@ def run_profile(
                     sim.minibatch_size * n_batches, n_steps, net.inp.size_out
                 )
             }
-        else:
+        elif hasattr(net, "inp_a"):
             x = {
                 net.inp_a: np.random.randn(
                     sim.minibatch_size * n_batches, n_steps, net.inp_a.size_out
@@ -658,6 +658,8 @@ def run_profile(
                     sim.minibatch_size * n_batches, n_steps, net.inp_b.size_out
                 ),
             }
+        else:
+            x = None
 
         if train:
             y = {
@@ -670,14 +672,14 @@ def run_profile(
 
             # run once to eliminate startup overhead
             start = timeit.default_timer()
-            sim.fit(x, y, epochs=1)
+            sim.fit(x, y, epochs=1, n_steps=n_steps)
             print("Warmup time:", timeit.default_timer() - start)
 
             for _ in range(reps):
                 if do_profile:
                     profiler.start()
                 start = timeit.default_timer()
-                sim.fit(x, y, epochs=1)
+                sim.fit(x, y, epochs=1, n_steps=n_steps)
                 exec_time = min(timeit.default_timer() - start, exec_time)
                 if do_profile:
                     profiler.save("profile", profiler.stop())

nengo_dl/config.py

@@ -41,7 +41,8 @@ def configure_settings(**kwargs):
     simplifications: list of graph simplification functions
         Pass a list of `graph simplification functions
         <https://www.nengo.ai/nengo-dl/reference.html#graph-optimization>`_ to change
-        the default simplifications applied.
+        the default simplifications applied.  The default list of simplifications
+        can be found in ``nengo_dl.graph_optimizer.default_simplifications``.
     inference_only : bool
         Set to True if the network will only be run in inference mode (i.e.,
         no calls to `.Simulator.fit`).  This may result in a small