Add a flag for turning off dropout, as in early dropout

Add a test that the early dropout is turning off all the dropouts in a model

stanza/models/constituency/parser_training.py

@@ -449,6 +449,11 @@ def iterate_training(args, trainer, train_trees, train_sequences, transitions, d
                     if watch_regex.search(n):
                         wandb.log({n: torch.linalg.norm(p)})
 
+        if args['early_dropout'] > 0 and trainer.epochs_trained >= args['early_dropout']:
+            trainer.model.word_dropout.p = 0
+            trainer.model.predict_dropout.p = 0
+            trainer.model.lstm_input_dropout.p = 0
+
         # recreate the optimizer and alter the model as needed if we hit a new multistage split
         if args['multistage'] and trainer.epochs_trained in multistage_splits:
             # we may be loading a save model from an earlier epoch if the scores stopped increasing

stanza/models/constituency_parser.py

@@ -562,6 +562,14 @@ def build_argparse():
     parser.add_argument('--loss', default='cross', help='cross, large_margin, or focal.  Focal requires `pip install focal_loss_torch`')
     parser.add_argument('--loss_focal_gamma', default=2, type=float, help='gamma value for a focal loss')
 
+    # turn off dropout for word_dropout, predict_dropout, and lstm_input_dropout
+    # this mechanism doesn't actually turn off lstm_layer_dropout (yet)
+    # but that is set to a default of 0 anyway
+    # this is reusing the idea presented in
+    # https://arxiv.org/pdf/2303.01500v2
+    # "Dropout Reduces Underfitting"
+    # Zhuang Liu, Zhiqiu Xu, Joseph Jin, Zhiqiang Shen, Trevor Darrell
+    parser.add_argument('--early_dropout', default=-1, type=int, help='When to turn off dropout')
     # When using word_dropout and predict_dropout in conjunction with relu, one particular experiment produced the following dev scores after 300 iterations:
     # 0.0: 0.9085
     # 0.2: 0.9165

stanza/tests/constituency/test_trainer.py

@@ -5,6 +5,7 @@
 
 import pytest
 import torch
+from torch import nn
 from torch import optim
 
 from stanza import Pipeline
@@ -253,6 +254,29 @@ def test_train(self, wordvec_pretrain_file):
         with tempfile.TemporaryDirectory(dir=TEST_WORKING_DIR) as tmpdirname:
             self.run_train_test(wordvec_pretrain_file, tmpdirname)
 
+    def test_early_dropout(self, wordvec_pretrain_file):
+        """
+        Test the whole thing for a few iterations on the fake data
+        """
+        with tempfile.TemporaryDirectory(dir=TEST_WORKING_DIR) as tmpdirname:
+            args = ['--early_dropout', '3']
+            _, model = self.run_train_test(wordvec_pretrain_file, tmpdirname, num_epochs=6, extra_args=args)
+            model = model.model
+            dropouts = [(name, module) for name, module in model.named_children() if isinstance(module, nn.Dropout)]
+            assert len(dropouts) > 0, "Didn't find any dropouts in the model!"
+            for name, module in dropouts:
+                assert module.p == 0.0, "Dropout module %s was not set to 0 with early_dropout"
+
+        with tempfile.TemporaryDirectory(dir=TEST_WORKING_DIR) as tmpdirname:
+            # test that when turned off, early_dropout doesn't happen
+            args = ['--early_dropout', '-1']
+            _, model = self.run_train_test(wordvec_pretrain_file, tmpdirname, num_epochs=6, extra_args=args)
+            model = model.model
+            dropouts = [(name, module) for name, module in model.named_children() if isinstance(module, nn.Dropout)]
+            assert len(dropouts) > 0, "Didn't find any dropouts in the model!"
+            if all(module.p == 0.0 for _, module in dropouts):
+                raise AssertionError("All dropouts were 0 after training even though early_dropout was set to -1")
+
     def test_train_silver(self, wordvec_pretrain_file):
         """
         Test the whole thing for a few iterations on the fake data