Dropout and Max Norm Regularization for LoRA training (#545)

* Instantiate max_norm

* minor

* Move to end of step

* argparse

* metadata

* phrasing

* Sqrt ratio and logging

* fix logging

* Dropout test

* Dropout Args

* Dropout changed to affect LoRA only

---------

Co-authored-by: Kohya S <52813779+kohya-ss@users.noreply.github.com>

train_network.py

@@ -25,12 +25,16 @@ from library.config_util import (
 )
 import library.huggingface_util as huggingface_util
 import library.custom_train_functions as custom_train_functions
-from library.custom_train_functions import apply_snr_weight, get_weighted_text_embeddings, pyramid_noise_like, apply_noise_offset
+from library.custom_train_functions import apply_snr_weight, get_weighted_text_embeddings, pyramid_noise_like, apply_noise_offset, max_norm
 
 
 # TODO 他のスクリプトと共通化する
-def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
+def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler, keys_scaled=None, mean_norm=None, maximum_norm=None):
     logs = {"loss/current": current_loss, "loss/average": avr_loss}
+    if args.scale_weight_norms:
+      logs["keys_scaled"] = keys_scaled
+      logs["average_key_norm"] = mean_norm
+      logs["max_key_norm"] = maximum_norm
 
     lrs = lr_scheduler.get_last_lr()
 
@@ -196,13 +200,14 @@ def train(args):
     if args.dim_from_weights:
         network, _ = network_module.create_network_from_weights(1, args.network_weights, vae, text_encoder, unet, **net_kwargs)
     else:
-        network = network_module.create_network(1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, **net_kwargs)
+        network = network_module.create_network(1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, args.dropout, **net_kwargs)
     if network is None:
         return
 
     if hasattr(network, "prepare_network"):
         network.prepare_network(args)
 
+
     train_unet = not args.network_train_text_encoder_only
     train_text_encoder = not args.network_train_unet_only
     network.apply_to(text_encoder, unet, train_text_encoder, train_unet)
@@ -375,6 +380,8 @@ def train(args):
         "ss_face_crop_aug_range": args.face_crop_aug_range,
         "ss_prior_loss_weight": args.prior_loss_weight,
         "ss_min_snr_gamma": args.min_snr_gamma,
+        "ss_scale_weight_norms": args.scale_weight_norms,
+        "ss_dropout": args.dropout,
     }
 
     if use_user_config:
@@ -580,6 +587,7 @@ def train(args):
         network.on_epoch_start(text_encoder, unet)
 
         for step, batch in enumerate(train_dataloader):
+
             current_step.value = global_step
             with accelerator.accumulate(network):
                 on_step_start(text_encoder, unet)
@@ -651,6 +659,10 @@ def train(args):
                 optimizer.step()
                 lr_scheduler.step()
                 optimizer.zero_grad(set_to_none=True)
+            
+            if args.scale_weight_norms:
+              keys_scaled, mean_norm, maximum_norm = max_norm(network.state_dict(), args.scale_weight_norms)
+              max_mean_logs = {"Keys Scaled": keys_scaled, "Average key norm": mean_norm}  
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
@@ -686,9 +698,12 @@ def train(args):
             avr_loss = loss_total / len(loss_list)
             logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
             progress_bar.set_postfix(**logs)
+            if args.scale_weight_norms:
+              progress_bar.set_postfix(**max_mean_logs)
+
 
             if args.logging_dir is not None:
-                logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
+                logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler, keys_scaled, mean_norm, maximum_norm)
                 accelerator.log(logs, step=global_step)
 
             if global_step >= args.max_train_steps:
@@ -787,6 +802,18 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="automatically determine dim (rank) from network_weights / dim (rank)をnetwork_weightsで指定した重みから自動で決定する",
     )
+    parser.add_argument(
+        "--scale_weight_norms",
+        type=float,
+        default=None,
+        help="Scale the weight of each key pair to help prevent overtraing via exploding gradients. (1 is a good starting point)",
+    )
+    parser.add_argument(
+        "--dropout",
+        type=float,
+        default=None,
+        help="Drops neurons out of training every step (0 is default behavior, 1 would drop all neurons)",
+    )
     parser.add_argument(
         "--base_weights",
         type=str,