Enable Travis tests for gated_linear_networks

* Restrict the number of training steps to 2k so that the test finishes faster
* General cleanup of flags

PiperOrigin-RevId: 368228930

.travis.yml

@@ -11,6 +11,7 @@ env:
     - PROJECT="adversarial_robustness"
     - PROJECT="avae"
     # - PROJECT="cs_gan"  # TODO(b/184845450): Fix and re-enable
+    - PROJECT="gated_linear_networks"
     - PROJECT="iodine"
     - PROJECT="kfac_ferminet_alpha"
     - PROJECT="learning_to_simulate"

gated_linear_networks/examples/bernoulli_mnist.py

@@ -25,21 +25,53 @@ import rlax
 from gated_linear_networks import bernoulli
 from gated_linear_networks.examples import utils
 
-FLAGS = flags.FLAGS
+MAX_TRAIN_STEPS = flags.DEFINE_integer(
+    name='max_train_steps',
+    default=None,
+    help='Maximum number of training steps to perform (None=no limit)',
+)
 
 # Small example network, achieves ~95% test set accuracy =======================
 # Network parameters.
-flags.DEFINE_integer('num_layers', 2, '')
+NUM_LAYERS = flags.DEFINE_integer(
-flags.DEFINE_integer('neurons_per_layer', 100, '')
+    name='num_layers',
-flags.DEFINE_integer('context_dim', 1, '')
+    default=2,
+    help='Number of network layers',
+)
+NEURONS_PER_LAYER = flags.DEFINE_integer(
+    name='neurons_per_layer',
+    default=100,
+    help='Number of neurons per layer',
+)
+CONTEXT_DIM = flags.DEFINE_integer(
+    name='context_dim',
+    default=1,
+    help='Context vector size',
+)
 
 # Learning rate schedule.
-flags.DEFINE_float('max_lr', 0.003, '')
+MAX_LR = flags.DEFINE_float(
-flags.DEFINE_float('lr_constant', 1.0, '')
+    name='max_lr',
-flags.DEFINE_float('lr_decay', 0.1, '')
+    default=0.003,
+    help='Maximum learning rate',
+)
+LR_CONSTANT = flags.DEFINE_float(
+    name='lr_constant',
+    default=1.0,
+    help='Learning rate constant parameter',
+)
+LR_DECAY = flags.DEFINE_float(
+    name='lr_decay',
+    default=0.1,
+    help='Learning rate decay parameter',
+)
 
 # Logging parameters.
-flags.DEFINE_integer('evaluate_every', 1000, '')
+EVALUATE_EVERY = flags.DEFINE_integer(
+    name='evaluate_every',
+    default=1000,
+    help='Number of training steps per evaluation epoch',
+)
 
 
 def main(unused_argv):
@@ -58,9 +90,9 @@ def main(unused_argv):
   def network_factory():
 
     def gln_factory():
-      output_sizes = [FLAGS.neurons_per_layer] * FLAGS.num_layers + [1]
+      output_sizes = [NEURONS_PER_LAYER.value] * NUM_LAYERS.value + [1]
       return bernoulli.GatedLinearNetwork(
-          output_sizes=output_sizes, context_dim=FLAGS.context_dim)
+          output_sizes=output_sizes, context_dim=CONTEXT_DIM.value)
 
     return bernoulli.LastNeuronAggregator(gln_factory)
 
@@ -104,7 +136,7 @@ def main(unused_argv):
 
     # Learning rate schedules.
     learning_rate = jnp.minimum(
-        FLAGS.max_lr, FLAGS.lr_constant / (1. + FLAGS.lr_decay * step))
+        MAX_LR.value, LR_CONSTANT.value / (1. + LR_DECAY.value * step))
 
     # Update weights and report log-loss.
     targets = hk.one_hot(jnp.asarray(label), num_classes)
@@ -128,7 +160,7 @@ def main(unused_argv):
     )
 
     # Evaluate on test split ===================================================
-    if not step % FLAGS.evaluate_every:
+    if not step % EVALUATE_EVERY.value:
       batch_accuracy = jax.vmap(accuracy, in_axes=(None, None, 0, 0))
       accuracies = batch_accuracy(params, state, test_images, test_labels)
       total_accuracy = float(jnp.mean(accuracies))
@@ -139,6 +171,9 @@ def main(unused_argv):
           'accuracy': float(total_accuracy),
       })
 
+    if MAX_TRAIN_STEPS.value is not None and step >= MAX_TRAIN_STEPS.value:
+      return
+
 
 if __name__ == '__main__':
   app.run(main)

gated_linear_networks/run.sh

@@ -14,13 +14,14 @@
 # limitations under the License.
 set -e
 
-python3 -m venv gln_venv
+python3 -m venv /tmp/gln_venv
-source gln_venv/bin/activate
+source /tmp/gln_venv/bin/activate
-pip3 install --upgrade setuptools wheel
+pip3 install --upgrade pip setuptools wheel
 pip3 install -r gated_linear_networks/requirements.txt
 
 # Run MNIST example with Bernoulli GLN
 python3 -m gated_linear_networks.examples.bernoulli_mnist \
   --num_layers=2 \
   --neurons_per_layer=100 \
-  --context_dim=1
+  --context_dim=1 \
+  --max_train_steps=2000