1 files changed, 23 insertions, 2 deletions

diff --git a/train_ti.py b/train_ti.py
index dd015f9..274a1ca 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -12,6 +12,7 @@ from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import LoggerType, set_seed
 from slugify import slugify
+import transformers
 
 from util.files import load_config, load_embeddings_from_dir
 from data.csv import VlpnDataModule, keyword_filter
@@ -75,6 +76,12 @@ def parse_args():
         help="A token to use as initializer word."
     )
     parser.add_argument(
+        "--initializer_noise",
+        type=float,
+        default=0,
+        help="Noise to apply to the initializer word"
+    )
+    parser.add_argument(
         "--alias_tokens",
         type=str,
         nargs='*',
@@ -323,7 +330,7 @@ def parse_args():
         "--optimizer",
         type=str,
         default="dadan",
-        help='Optimizer to use ["adam", "adam8bit", "dadam", "dadan"]'
+        help='Optimizer to use ["adam", "adam8bit", "dadam", "dadan", "adafactor"]'
     )
     parser.add_argument(
         "--dadaptation_d0",
@@ -659,6 +666,19 @@ def main():
             eps=args.adam_epsilon,
             amsgrad=args.adam_amsgrad,
         )
+    elif args.optimizer == 'adafactor':
+        create_optimizer = partial(
+            transformers.optimization.Adafactor,
+            beta1=args.adam_beta1,
+            weight_decay=args.adam_weight_decay,
+            scale_parameter=True,
+            relative_step=True,
+            warmup_init=True,
+        )
+
+        args.lr_scheduler = "adafactor"
+        args.lr_min_lr = args.learning_rate
+        args.learning_rate = None
     elif args.optimizer == 'dadam':
         try:
             import dadaptation
@@ -739,7 +759,8 @@ def main():
             embeddings=embeddings,
             placeholder_tokens=placeholder_tokens,
             initializer_tokens=initializer_tokens,
-            num_vectors=num_vectors
+            num_vectors=num_vectors,
+            initializer_noise=args.initializer_noise,
         )
 
         stats = list(zip(placeholder_tokens, placeholder_token_ids, initializer_tokens, initializer_token_ids))