Integration tests for NAS

examples/nas/classic_nas/mnist.py

@@ -149,7 +149,7 @@ def get_params():
     # Training settings
     parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
     parser.add_argument("--data_dir", type=str,
-                        default='/tmp/tensorflow/mnist/input_data', help="data directory")
+                        default='./data', help="data directory")
     parser.add_argument('--batch_size', type=int, default=64, metavar='N',
                         help='input batch size for training (default: 64)')
     parser.add_argument("--hidden_size", type=int, default=512, metavar='N',

examples/nas/darts/search.py

@@ -22,12 +22,13 @@
     parser.add_argument("--batch-size", default=64, type=int)
     parser.add_argument("--log-frequency", default=10, type=int)
     parser.add_argument("--epochs", default=50, type=int)
+    parser.add_argument("--channels", default=16, type=int)
     parser.add_argument("--unrolled", default=False, action="store_true")
     args = parser.parse_args()
 
     dataset_train, dataset_valid = datasets.get_dataset("cifar10")
 
-    model = CNN(32, 3, 16, 10, args.layers)
+    model = CNN(32, 3, args.channels, 10, args.layers)
     criterion = nn.CrossEntropyLoss()
 
     optim = torch.optim.SGD(model.parameters(), 0.025, momentum=0.9, weight_decay=3.0E-4)

examples/nas/enas/search.py

@@ -24,16 +24,17 @@
     parser.add_argument("--batch-size", default=128, type=int)
     parser.add_argument("--log-frequency", default=10, type=int)
     parser.add_argument("--search-for", choices=["macro", "micro"], default="macro")
+    parser.add_argument("--epochs", default=None, type=int, help="Number of epochs (default: macro 310, micro 150)")
     args = parser.parse_args()
 
     dataset_train, dataset_valid = datasets.get_dataset("cifar10")
     if args.search_for == "macro":
         model = GeneralNetwork()
-        num_epochs = 310
+        num_epochs = args.epochs or 310
         mutator = None
     elif args.search_for == "micro":
         model = MicroNetwork(num_layers=6, out_channels=20, num_nodes=5, dropout_rate=0.1, use_aux_heads=True)
-        num_epochs = 150
+        num_epochs = args.epochs or 150
         mutator = enas.EnasMutator(model, tanh_constant=1.1, cell_exit_extra_step=True)
     else:
         raise AssertionError

examples/nas/pdarts/search.py

@@ -25,23 +25,28 @@
 
 if __name__ == "__main__":
     parser = ArgumentParser("pdarts")
-    parser.add_argument('--add_layers', action='append',
-                        default=[0, 6, 12], help='add layers')
-    parser.add_argument('--dropped_ops', action='append',
-                        default=[3, 2, 1], help='drop ops')
+    parser.add_argument('--add_layers', action='append', type=int,
+                        help='add layers, default: [0, 6, 12]')
+    parser.add_argument('--dropped_ops', action='append', type=int,
+                        help='drop ops, default: [3, 2, 1]')
     parser.add_argument("--nodes", default=4, type=int)
     parser.add_argument("--init_layers", default=5, type=int)
+    parser.add_argument("--channels", default=16, type=int)
     parser.add_argument("--batch-size", default=64, type=int)
     parser.add_argument("--log-frequency", default=1, type=int)
     parser.add_argument("--epochs", default=50, type=int)
     parser.add_argument("--unrolled", default=False, action="store_true")
     args = parser.parse_args()
+    if args.add_layers is None:
+        args.add_layers = [0, 6, 12]
+    if args.dropped_ops is None:
+        args.dropped_ops = [3, 2, 1]
 
     logger.info("loading data")
     dataset_train, dataset_valid = datasets.get_dataset("cifar10")
 
     def model_creator(layers):
-        model = CNN(32, 3, 16, 10, layers, n_nodes=args.nodes)
+        model = CNN(32, 3, args.channels, 10, layers, n_nodes=args.nodes)
         criterion = nn.CrossEntropyLoss()
 
         optim = torch.optim.SGD(model.parameters(), 0.025, momentum=0.9, weight_decay=3.0E-4)

test/pipelines/pipelines-it-local.yml

@@ -26,6 +26,10 @@ jobs:
       cd test
       PATH=$HOME/.local/bin:$PATH python3 nni_test/nnitest/run_tests.py --config config/integration_tests.yml --ts local
     displayName: 'Integration test'
+  - script: |
+      cd test
+      PATH=$HOME/.local/bin:$PATH source scripts/nas.sh
+    displayName: 'NAS test'
   - script: |
       cd test
       source scripts/model_compression.sh

test/scripts/nas.sh

@@ -0,0 +1,37 @@
+#!/bin/bash
+set -e
+CWD=${PWD}
+
+echo ""
+echo "===========================Testing: NAS==========================="
+EXAMPLE_DIR=${CWD}/../examples/nas
+
+echo "testing classic nas..."
+cd $EXAMPLE_DIR/classic_nas
+SEARCH_SPACE_JSON=nni_auto_gen_search_space.json
+if [ -f $SEARCH_SPACE_JSON ]; then
+    rm $SEARCH_SPACE_JSON
+fi
+nnictl ss_gen -t "python3 mnist.py"
+if [ ! -f $SEARCH_SPACE_JSON ]; then
+    echo "Search space file not found!"
+    exit 1
+fi
+
+echo "testing darts..."
+cd $EXAMPLE_DIR/darts
+python3 search.py --epochs 1 --channels 2 --layers 4
+python3 retrain.py --arc-checkpoint ./checkpoints/epoch_0.json --layers 4 --epochs 1
+
+echo "testing enas..."
+cd $EXAMPLE_DIR/enas
+python3 search.py --search-for macro --epochs 1
+python3 search.py --search-for micro --epochs 1
+
+echo "testing naive..."
+cd $EXAMPLE_DIR/naive
+python3 train.py
+
+echo "testing pdarts..."
+cd $EXAMPLE_DIR/pdarts
+python3 search.py --epochs 1 --channels 4 --nodes 2 --log-frequency 10 --add_layers 0 --add_layers 1 --dropped_ops 3 --dropped_ops 3