Support new optimizer Schedule free (kohya-ss#1250)

* init

* use no schedule

* fix typo

* update for eval()

* fix typo

fine_tune.py

@@ -255,18 +255,31 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet, text_encoder=text_encoder)
         else:
             ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet)
-        ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            ds_model, optimizer, train_dataloader, lr_scheduler
-        )
+        if args.optimizer_type.lower().endswith("schedulefree"):
+            ds_model, optimizer, train_dataloader = accelerator.prepare(
+                ds_model, optimizer, train_dataloader
+            )
+        else:
+            ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+                ds_model, optimizer, train_dataloader, lr_scheduler
+            )
         training_models = [ds_model]
     else:
         # acceleratorがなんかよろしくやってくれるらしい
         if args.train_text_encoder:
-            unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                unet, text_encoder, optimizer, train_dataloader, lr_scheduler
-            )
+            if args.optimizer_type.lower().endswith("schedulefree"):
+                unet, text_encoder, optimizer, train_dataloader  = accelerator.prepare(
+                    unet, text_encoder, optimizer, train_dataloader
+                )
+            else:
+                unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+                    unet, text_encoder, optimizer, train_dataloader, lr_scheduler
+                )
         else:
-            unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
+            if args.optimizer_type.lower().endswith("schedulefree"):
+                unet, optimizer, train_dataloader = accelerator.prepare(unet, optimizer, train_dataloader)
+            else:
+                unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
 
     # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
     if args.full_fp16:
@@ -328,6 +341,8 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             m.train()
 
         for step, batch in enumerate(train_dataloader):
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.train()
             current_step.value = global_step
             with accelerator.accumulate(*training_models):
                 with torch.no_grad():
@@ -400,9 +415,13 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
                     accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
                 optimizer.step()
-                lr_scheduler.step()
+                if not args.optimizer_type.lower().endswith("schedulefree"):
+                    lr_scheduler.step()
                 optimizer.zero_grad(set_to_none=True)
 
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.eval()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)

library/train_util.py

@@ -4248,6 +4248,21 @@ def get_optimizer(args, trainable_params):
         logger.info(f"use AdamW optimizer | {optimizer_kwargs}")
         optimizer_class = torch.optim.AdamW
         optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
+
+    elif optimizer_type.endswith("schedulefree".lower()):
+        try:
+            import schedulefree as sf
+        except ImportError:
+            raise ImportError("No schedulefree / schedulefreeがインストールされていないようです")
+        if optimizer_type == "AdamWScheduleFree".lower():
+                optimizer_class = sf.AdamWScheduleFree
+                logger.info(f"use AdamWScheduleFree optimizer | {optimizer_kwargs}")
+        elif optimizer_type == "SGDScheduleFree".lower():
+            optimizer_class = sf.SGDScheduleFree 
+            logger.info(f"use SGDScheduleFree optimizer | {optimizer_kwargs}")
+        else:
+            raise ValueError(f"Unknown optimizer type: {optimizer_type}")
+        optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
 
     if optimizer is None:
         # 任意のoptimizerを使う

sdxl_train.py

@@ -466,9 +466,14 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             text_encoder2=text_encoder2 if train_text_encoder2 else None,
         )
         # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007
-        ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            ds_model, optimizer, train_dataloader, lr_scheduler
-        )
+        if args.optimizer_type.lower().endswith("schedulefree"):
+            ds_model, optimizer, train_dataloader = accelerator.prepare(
+                ds_model, optimizer, train_dataloader
+            )
+        else:
+            ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+                ds_model, optimizer, train_dataloader, lr_scheduler
+            )
         training_models = [ds_model]
 
     else:
@@ -479,7 +484,10 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             text_encoder1 = accelerator.prepare(text_encoder1)
         if train_text_encoder2:
             text_encoder2 = accelerator.prepare(text_encoder2)
-        optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)
+        if args.optimizer_type.lower().endswith("schedulefree"):
+            optimizer, train_dataloader = accelerator.prepare(optimizer, train_dataloader)
+        else:
+            optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)
 
     if args.fused_backward_pass:
         # use fused optimizer for backward pass: other optimizers will be supported in the future
@@ -605,6 +613,8 @@ def optimizer_hook(parameter: torch.Tensor):
             m.train()
 
         for step, batch in enumerate(train_dataloader):
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.train()
             current_step.value = global_step
 
             if args.fused_optimizer_groups:
@@ -740,7 +750,8 @@ def optimizer_hook(parameter: torch.Tensor):
                         accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
                     optimizer.step()
-                    lr_scheduler.step()
+                    if not args.optimizer_type.lower().endswith("schedulefree"):
+                        lr_scheduler.step()
                     optimizer.zero_grad(set_to_none=True)
                 else:
                     # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook
@@ -749,6 +760,9 @@ def optimizer_hook(parameter: torch.Tensor):
                         for i in range(1, len(optimizers)):
                             lr_schedulers[i].step()
 
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.eval()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)

sdxl_train_control_net_lllite.py

@@ -287,11 +287,19 @@ def train(args):
     unet.to(weight_dtype)
 
     # acceleratorがなんかよろしくやってくれるらしい
-    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
+    if args.optimizer_type.lower().endswith("schedulefree"):
+        unet, optimizer, train_dataloader = accelerator.prepare(unet, optimizer, train_dataloader)
+    else:
+        unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
 
     if args.gradient_checkpointing:
+        if (args.optimizer_type.lower().endswith("schedulefree")):
+            optimizer.train()
         unet.train()  # according to TI example in Diffusers, train is required -> これオリジナルのU-Netしたので本当は外せる
+
     else:
+        if (args.optimizer_type.lower().endswith("schedulefree")):
+            optimizer.eval()
         unet.eval()
 
     # TextEncoderの出力をキャッシュするときにはCPUへ移動する
@@ -391,6 +399,8 @@ def remove_model(old_ckpt_name):
         current_epoch.value = epoch + 1
 
         for step, batch in enumerate(train_dataloader):
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.train()
             current_step.value = global_step
             with accelerator.accumulate(unet):
                 with torch.no_grad():
@@ -486,9 +496,13 @@ def remove_model(old_ckpt_name):
                     accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
                 optimizer.step()
-                lr_scheduler.step()
+                if not args.optimizer_type.lower().endswith("schedulefree"):
+                    lr_scheduler.step()
                 optimizer.zero_grad(set_to_none=True)
 
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.eval()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)

sdxl_train_control_net_lllite_old.py

@@ -254,15 +254,24 @@ def train(args):
         network.to(weight_dtype)
 
     # acceleratorがなんかよろしくやってくれるらしい
-    unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        unet, network, optimizer, train_dataloader, lr_scheduler
-    )
+    if args.optimizer_type.lower().endswith("schedulefree"):
+        unet, network, optimizer, train_dataloader = accelerator.prepare(
+            unet, network, optimizer, train_dataloader
+        )
+    else:
+        unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+            unet, network, optimizer, train_dataloader, lr_scheduler
+        )
     network: control_net_lllite.ControlNetLLLite
 
     if args.gradient_checkpointing:
         unet.train()  # according to TI example in Diffusers, train is required -> これオリジナルのU-Netしたので本当は外せる
+        if (args.optimizer_type.lower().endswith("schedulefree")):
+            optimizer.train()
     else:
         unet.eval()
+        if (args.optimizer_type.lower().endswith("schedulefree")):
+            optimizer.eval()
 
     network.prepare_grad_etc()
 
@@ -357,6 +366,8 @@ def remove_model(old_ckpt_name):
         network.on_epoch_start()  # train()
 
         for step, batch in enumerate(train_dataloader):
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.train()
             current_step.value = global_step
             with accelerator.accumulate(network):
                 with torch.no_grad():
@@ -449,9 +460,13 @@ def remove_model(old_ckpt_name):
                     accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
                 optimizer.step()
-                lr_scheduler.step()
+                if not args.optimizer_type.lower().endswith("schedulefree"):
+                    lr_scheduler.step()
                 optimizer.zero_grad(set_to_none=True)
 
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.eval()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)

train_controlnet.py

@@ -298,9 +298,14 @@ def __contains__(self, name):
         controlnet.to(weight_dtype)
 
     # acceleratorがなんかよろしくやってくれるらしい
-    controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        controlnet, optimizer, train_dataloader, lr_scheduler
-    )
+    if args.optimizer_type.lower().endswith("schedulefree"):
+        controlnet, optimizer, train_dataloader = accelerator.prepare(
+            controlnet, optimizer, train_dataloader
+        )
+    else:
+        controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+            controlnet, optimizer, train_dataloader, lr_scheduler
+        )
 
     unet.requires_grad_(False)
     text_encoder.requires_grad_(False)
@@ -417,6 +422,8 @@ def remove_model(old_ckpt_name):
         current_epoch.value = epoch + 1
 
         for step, batch in enumerate(train_dataloader):
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.train()
             current_step.value = global_step
             with accelerator.accumulate(controlnet):
                 with torch.no_grad():
@@ -500,6 +507,9 @@ def remove_model(old_ckpt_name):
                 lr_scheduler.step()
                 optimizer.zero_grad(set_to_none=True)
 
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.eval()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)

train_db.py

@@ -229,19 +229,32 @@ def train(args):
             ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet, text_encoder=text_encoder)
         else:
             ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet)
-        ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            ds_model, optimizer, train_dataloader, lr_scheduler
-        )
+        if args.optimizer_type.lower().endswith("schedulefree"):
+            ds_model, optimizer, train_dataloader = accelerator.prepare(
+                ds_model, optimizer, train_dataloader
+            )
+        else:
+            ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+                ds_model, optimizer, train_dataloader, lr_scheduler
+            )
         training_models = [ds_model]
 
     else:
         if train_text_encoder:
-            unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                unet, text_encoder, optimizer, train_dataloader, lr_scheduler
-            )
+            if args.optimizer_type.lower().endswith("schedulefree"):
+                unet, text_encoder, optimizer, train_dataloader  = accelerator.prepare(
+                    unet, text_encoder, optimizer, train_dataloader
+                )
+            else:
+                unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+                    unet, text_encoder, optimizer, train_dataloader, lr_scheduler
+                )
             training_models = [unet, text_encoder]
         else:
-            unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
+            if args.optimizer_type.lower().endswith("schedulefree"):
+                unet, optimizer, train_dataloader = accelerator.prepare(unet, optimizer, train_dataloader)
+            else:
+                unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
             training_models = [unet]
 
     if not train_text_encoder:
@@ -307,6 +320,8 @@ def train(args):
             text_encoder.train()
 
         for step, batch in enumerate(train_dataloader):
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.train()
             current_step.value = global_step
             # 指定したステップ数でText Encoderの学習を止める
             if global_step == args.stop_text_encoder_training:
@@ -384,9 +399,13 @@ def train(args):
                     accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
                 optimizer.step()
-                lr_scheduler.step()
+                if not args.optimizer_type.lower().endswith("schedulefree"):
+                    lr_scheduler.step()
                 optimizer.zero_grad(set_to_none=True)
 
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.eval()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)