Update

1 files changed, 22 insertions, 7 deletions

diff --git a/train_lora.py b/train_lora.py
index 39bf455..0b26965 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -242,6 +242,12 @@ def parse_args():
         help="Number of updates steps to accumulate before performing a backward/update pass.",
     )
     parser.add_argument(
+        "--pti_gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
         "--lora_r",
         type=int,
         default=8,
@@ -476,6 +482,12 @@ def parse_args():
         help="Batch size (per device) for the training dataloader."
     )
     parser.add_argument(
+        "--pti_batch_size",
+        type=int,
+        default=1,
+        help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument(
         "--sample_steps",
         type=int,
         default=10,
@@ -694,8 +706,8 @@ def main():
             args.train_batch_size * accelerator.num_processes
         )
         args.learning_rate_pti = (
-            args.learning_rate_pti * args.gradient_accumulation_steps *
-            args.train_batch_size * accelerator.num_processes
+            args.learning_rate_pti * args.pti_gradient_accumulation_steps *
+            args.pti_batch_size * accelerator.num_processes
         )
 
     if args.find_lr:
@@ -808,7 +820,6 @@ def main():
         guidance_scale=args.guidance_scale,
         prior_loss_weight=args.prior_loss_weight if args.num_class_images != 0 else 0,
         no_val=args.valid_set_size == 0,
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
         offset_noise_strength=args.offset_noise_strength,
         sample_scheduler=sample_scheduler,
         sample_batch_size=args.sample_batch_size,
@@ -820,7 +831,6 @@ def main():
     create_datamodule = partial(
         VlpnDataModule,
         data_file=args.train_data_file,
-        batch_size=args.train_batch_size,
         tokenizer=tokenizer,
         class_subdir=args.class_image_dir,
         with_guidance=args.guidance_scale != 0,
@@ -843,7 +853,6 @@ def main():
     create_lr_scheduler = partial(
         get_scheduler,
         args.lr_scheduler,
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
         min_lr=args.lr_min_lr,
         warmup_func=args.lr_warmup_func,
         annealing_func=args.lr_annealing_func,
@@ -863,6 +872,7 @@ def main():
     pti_sample_output_dir = pti_output_dir / "samples"
 
     pti_datamodule = create_datamodule(
+        batch_size=args.pti_batch_size,
         filter=partial(keyword_filter, args.placeholder_tokens, args.collection, args.exclude_collections),
     )
     pti_datamodule.setup()
@@ -872,7 +882,7 @@ def main():
     if num_pti_epochs is None:
         num_pti_epochs = math.ceil(
             args.num_pti_steps / len(pti_datamodule.train_dataset)
-        ) * args.gradient_accumulation_steps
+        ) * args.pti_gradient_accumulation_steps
         pti_sample_frequency = math.ceil(num_pti_epochs * (pti_sample_frequency / args.num_train_steps))
 
     pti_optimizer = create_optimizer(
@@ -886,6 +896,7 @@ def main():
     )
 
     pti_lr_scheduler = create_lr_scheduler(
+        gradient_accumulation_steps=args.pti_gradient_accumulation_steps,
         optimizer=pti_optimizer,
         num_training_steps_per_epoch=len(pti_datamodule.train_dataloader),
         train_epochs=num_pti_epochs,
@@ -893,12 +904,13 @@ def main():
 
     metrics = trainer(
         strategy=textual_inversion_strategy,
-        project="ti",
+        project="pti",
         train_dataloader=pti_datamodule.train_dataloader,
         val_dataloader=pti_datamodule.val_dataloader,
         optimizer=pti_optimizer,
         lr_scheduler=pti_lr_scheduler,
         num_train_epochs=num_pti_epochs,
+        gradient_accumulation_steps=args.pti_gradient_accumulation_steps,
         # --
         sample_output_dir=pti_sample_output_dir,
         checkpoint_output_dir=pti_checkpoint_output_dir,
@@ -920,6 +932,7 @@ def main():
     lora_sample_output_dir = lora_output_dir / "samples"
 
     lora_datamodule = create_datamodule(
+        batch_size=args.train_batch_size,
         filter=partial(keyword_filter, None, args.collection, args.exclude_collections),
     )
     lora_datamodule.setup()
@@ -954,6 +967,7 @@ def main():
     )
 
     lora_lr_scheduler = create_lr_scheduler(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
         optimizer=lora_optimizer,
         num_training_steps_per_epoch=len(lora_datamodule.train_dataloader),
         train_epochs=num_train_epochs,
@@ -967,6 +981,7 @@ def main():
         optimizer=lora_optimizer,
         lr_scheduler=lora_lr_scheduler,
         num_train_epochs=num_train_epochs,
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
         # --
         sample_output_dir=lora_sample_output_dir,
         checkpoint_output_dir=lora_checkpoint_output_dir,